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wandb: Syncing run MPE_5
wandb: ⭐️ View project at https://wandb.ai/804703098/Continue_Tag_Base_v1
wandb: 🚀 View run at https://wandb.ai/804703098/Continue_Tag_Base_v1/runs/sz4pky84
choose to use cpu...
idv policy and team policy use same initial params!

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 0/10000 episodes, total num timesteps 200/2000000, FPS 161.

team_policy eval average step individual rewards of agent0: -0.1024803146775837
team_policy eval average team episode rewards of agent0: 2.5
team_policy eval idv catch total num of agent0: 0
team_policy eval team catch total num: 1
team_policy eval average step individual rewards of agent1: 0.02996930370377422
team_policy eval average team episode rewards of agent1: 2.5
team_policy eval idv catch total num of agent1: 4
team_policy eval team catch total num: 1
team_policy eval average step individual rewards of agent2: -0.08944293265225359
team_policy eval average team episode rewards of agent2: 2.5
team_policy eval idv catch total num of agent2: 0
team_policy eval team catch total num: 1
team_policy eval average step individual rewards of agent3: 0.1319502867929988
team_policy eval average team episode rewards of agent3: 2.5
team_policy eval idv catch total num of agent3: 8
team_policy eval team catch total num: 1
team_policy eval average step individual rewards of agent4: -0.034531912437620244
team_policy eval average team episode rewards of agent4: 2.5
team_policy eval idv catch total num of agent4: 2
team_policy eval team catch total num: 1
idv_policy eval average step individual rewards of agent0: 0.015851966271251922
idv_policy eval average team episode rewards of agent0: 2.5
idv_policy eval idv catch total num of agent0: 4
idv_policy eval team catch total num: 1
idv_policy eval average step individual rewards of agent1: 0.04608961343162687
idv_policy eval average team episode rewards of agent1: 2.5
idv_policy eval idv catch total num of agent1: 6
idv_policy eval team catch total num: 1
idv_policy eval average step individual rewards of agent2: -0.02793136858017682
idv_policy eval average team episode rewards of agent2: 2.5
idv_policy eval idv catch total num of agent2: 2
idv_policy eval team catch total num: 1
idv_policy eval average step individual rewards of agent3: 0.11095113196103952
idv_policy eval average team episode rewards of agent3: 2.5
idv_policy eval idv catch total num of agent3: 8
idv_policy eval team catch total num: 1
idv_policy eval average step individual rewards of agent4: -0.03366517872563108
idv_policy eval average team episode rewards of agent4: 2.5
idv_policy eval idv catch total num of agent4: 2
idv_policy eval team catch total num: 1

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1/10000 episodes, total num timesteps 400/2000000, FPS 126.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2/10000 episodes, total num timesteps 600/2000000, FPS 143.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3/10000 episodes, total num timesteps 800/2000000, FPS 142.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4/10000 episodes, total num timesteps 1000/2000000, FPS 147.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5/10000 episodes, total num timesteps 1200/2000000, FPS 153.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6/10000 episodes, total num timesteps 1400/2000000, FPS 158.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7/10000 episodes, total num timesteps 1600/2000000, FPS 160.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8/10000 episodes, total num timesteps 1800/2000000, FPS 159.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9/10000 episodes, total num timesteps 2000/2000000, FPS 161.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 10/10000 episodes, total num timesteps 2200/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 11/10000 episodes, total num timesteps 2400/2000000, FPS 165.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 12/10000 episodes, total num timesteps 2600/2000000, FPS 165.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 13/10000 episodes, total num timesteps 2800/2000000, FPS 165.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 14/10000 episodes, total num timesteps 3000/2000000, FPS 164.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 15/10000 episodes, total num timesteps 3200/2000000, FPS 165.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 16/10000 episodes, total num timesteps 3400/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 17/10000 episodes, total num timesteps 3600/2000000, FPS 164.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 18/10000 episodes, total num timesteps 3800/2000000, FPS 164.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 19/10000 episodes, total num timesteps 4000/2000000, FPS 165.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 20/10000 episodes, total num timesteps 4200/2000000, FPS 164.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 21/10000 episodes, total num timesteps 4400/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 22/10000 episodes, total num timesteps 4600/2000000, FPS 164.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 23/10000 episodes, total num timesteps 4800/2000000, FPS 165.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 24/10000 episodes, total num timesteps 5000/2000000, FPS 165.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 25/10000 episodes, total num timesteps 5200/2000000, FPS 164.

team_policy eval average step individual rewards of agent0: -0.04687869448572396
team_policy eval average team episode rewards of agent0: 0.0
team_policy eval idv catch total num of agent0: 2
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent1: 0.053656379042741224
team_policy eval average team episode rewards of agent1: 0.0
team_policy eval idv catch total num of agent1: 6
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent2: -0.07702925269386435
team_policy eval average team episode rewards of agent2: 0.0
team_policy eval idv catch total num of agent2: 0
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent3: -0.020350255736542585
team_policy eval average team episode rewards of agent3: 0.0
team_policy eval idv catch total num of agent3: 3
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent4: -0.04300087461462703
team_policy eval average team episode rewards of agent4: 0.0
team_policy eval idv catch total num of agent4: 1
team_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent0: -0.10220902510880141
idv_policy eval average team episode rewards of agent0: 0.0
idv_policy eval idv catch total num of agent0: 1
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent1: -0.011364908431978758
idv_policy eval average team episode rewards of agent1: 0.0
idv_policy eval idv catch total num of agent1: 3
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent2: 0.052443777440991886
idv_policy eval average team episode rewards of agent2: 0.0
idv_policy eval idv catch total num of agent2: 5
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent3: -0.03556633228028298
idv_policy eval average team episode rewards of agent3: 0.0
idv_policy eval idv catch total num of agent3: 4
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent4: -0.06096363675357933
idv_policy eval average team episode rewards of agent4: 0.0
idv_policy eval idv catch total num of agent4: 2
idv_policy eval team catch total num: 0

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 26/10000 episodes, total num timesteps 5400/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 27/10000 episodes, total num timesteps 5600/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 28/10000 episodes, total num timesteps 5800/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 29/10000 episodes, total num timesteps 6000/2000000, FPS 162.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 30/10000 episodes, total num timesteps 6200/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 31/10000 episodes, total num timesteps 6400/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 32/10000 episodes, total num timesteps 6600/2000000, FPS 164.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 33/10000 episodes, total num timesteps 6800/2000000, FPS 164.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 34/10000 episodes, total num timesteps 7000/2000000, FPS 164.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 35/10000 episodes, total num timesteps 7200/2000000, FPS 164.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 36/10000 episodes, total num timesteps 7400/2000000, FPS 165.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 37/10000 episodes, total num timesteps 7600/2000000, FPS 166.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 38/10000 episodes, total num timesteps 7800/2000000, FPS 165.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 39/10000 episodes, total num timesteps 8000/2000000, FPS 165.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 40/10000 episodes, total num timesteps 8200/2000000, FPS 165.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 41/10000 episodes, total num timesteps 8400/2000000, FPS 165.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 42/10000 episodes, total num timesteps 8600/2000000, FPS 166.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 43/10000 episodes, total num timesteps 8800/2000000, FPS 166.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 44/10000 episodes, total num timesteps 9000/2000000, FPS 166.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 45/10000 episodes, total num timesteps 9200/2000000, FPS 166.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 46/10000 episodes, total num timesteps 9400/2000000, FPS 166.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 47/10000 episodes, total num timesteps 9600/2000000, FPS 166.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 48/10000 episodes, total num timesteps 9800/2000000, FPS 166.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 49/10000 episodes, total num timesteps 10000/2000000, FPS 167.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 50/10000 episodes, total num timesteps 10200/2000000, FPS 168.

team_policy eval average step individual rewards of agent0: -0.04566366629973907
team_policy eval average team episode rewards of agent0: 2.5
team_policy eval idv catch total num of agent0: 2
team_policy eval team catch total num: 1
team_policy eval average step individual rewards of agent1: -0.050129462792549616
team_policy eval average team episode rewards of agent1: 2.5
team_policy eval idv catch total num of agent1: 2
team_policy eval team catch total num: 1
team_policy eval average step individual rewards of agent2: -0.013864905072223764
team_policy eval average team episode rewards of agent2: 2.5
team_policy eval idv catch total num of agent2: 4
team_policy eval team catch total num: 1
team_policy eval average step individual rewards of agent3: -0.0697094705565367
team_policy eval average team episode rewards of agent3: 2.5
team_policy eval idv catch total num of agent3: 2
team_policy eval team catch total num: 1
team_policy eval average step individual rewards of agent4: -0.09521877466996415
team_policy eval average team episode rewards of agent4: 2.5
team_policy eval idv catch total num of agent4: 1
team_policy eval team catch total num: 1
idv_policy eval average step individual rewards of agent0: -0.07071355077733729
idv_policy eval average team episode rewards of agent0: 0.0
idv_policy eval idv catch total num of agent0: 1
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent1: -0.11077504005315068
idv_policy eval average team episode rewards of agent1: 0.0
idv_policy eval idv catch total num of agent1: 0
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent2: -0.11594371875471503
idv_policy eval average team episode rewards of agent2: 0.0
idv_policy eval idv catch total num of agent2: 0
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent3: -0.07165958794774109
idv_policy eval average team episode rewards of agent3: 0.0
idv_policy eval idv catch total num of agent3: 2
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent4: -0.07814136546777782
idv_policy eval average team episode rewards of agent4: 0.0
idv_policy eval idv catch total num of agent4: 2
idv_policy eval team catch total num: 0

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 51/10000 episodes, total num timesteps 10400/2000000, FPS 166.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 52/10000 episodes, total num timesteps 10600/2000000, FPS 165.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 53/10000 episodes, total num timesteps 10800/2000000, FPS 166.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 54/10000 episodes, total num timesteps 11000/2000000, FPS 166.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 55/10000 episodes, total num timesteps 11200/2000000, FPS 167.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 56/10000 episodes, total num timesteps 11400/2000000, FPS 167.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 57/10000 episodes, total num timesteps 11600/2000000, FPS 167.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 58/10000 episodes, total num timesteps 11800/2000000, FPS 167.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 59/10000 episodes, total num timesteps 12000/2000000, FPS 168.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 60/10000 episodes, total num timesteps 12200/2000000, FPS 168.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 61/10000 episodes, total num timesteps 12400/2000000, FPS 168.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 62/10000 episodes, total num timesteps 12600/2000000, FPS 168.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 63/10000 episodes, total num timesteps 12800/2000000, FPS 169.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 64/10000 episodes, total num timesteps 13000/2000000, FPS 168.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 65/10000 episodes, total num timesteps 13200/2000000, FPS 168.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 66/10000 episodes, total num timesteps 13400/2000000, FPS 168.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 67/10000 episodes, total num timesteps 13600/2000000, FPS 168.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 68/10000 episodes, total num timesteps 13800/2000000, FPS 168.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 69/10000 episodes, total num timesteps 14000/2000000, FPS 168.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 70/10000 episodes, total num timesteps 14200/2000000, FPS 169.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 71/10000 episodes, total num timesteps 14400/2000000, FPS 169.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 72/10000 episodes, total num timesteps 14600/2000000, FPS 169.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 73/10000 episodes, total num timesteps 14800/2000000, FPS 169.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 74/10000 episodes, total num timesteps 15000/2000000, FPS 169.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 75/10000 episodes, total num timesteps 15200/2000000, FPS 169.

team_policy eval average step individual rewards of agent0: -0.1424134386217382
team_policy eval average team episode rewards of agent0: 0.0
team_policy eval idv catch total num of agent0: 0
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent1: -0.06983409768922606
team_policy eval average team episode rewards of agent1: 0.0
team_policy eval idv catch total num of agent1: 2
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent2: -0.07116326401898893
team_policy eval average team episode rewards of agent2: 0.0
team_policy eval idv catch total num of agent2: 3
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent3: -0.10412015034180185
team_policy eval average team episode rewards of agent3: 0.0
team_policy eval idv catch total num of agent3: 1
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent4: -0.10027275731460965
team_policy eval average team episode rewards of agent4: 0.0
team_policy eval idv catch total num of agent4: 2
team_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent0: -0.09342648325788382
idv_policy eval average team episode rewards of agent0: 0.0
idv_policy eval idv catch total num of agent0: 2
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent1: -0.118114342553237
idv_policy eval average team episode rewards of agent1: 0.0
idv_policy eval idv catch total num of agent1: 1
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent2: -0.10902283796462636
idv_policy eval average team episode rewards of agent2: 0.0
idv_policy eval idv catch total num of agent2: 1
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent3: -0.17017967058614766
idv_policy eval average team episode rewards of agent3: 0.0
idv_policy eval idv catch total num of agent3: 0
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent4: -0.1458076903056346
idv_policy eval average team episode rewards of agent4: 0.0
idv_policy eval idv catch total num of agent4: 0
idv_policy eval team catch total num: 0

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 76/10000 episodes, total num timesteps 15400/2000000, FPS 168.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 77/10000 episodes, total num timesteps 15600/2000000, FPS 168.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 78/10000 episodes, total num timesteps 15800/2000000, FPS 168.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 79/10000 episodes, total num timesteps 16000/2000000, FPS 168.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 80/10000 episodes, total num timesteps 16200/2000000, FPS 168.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 81/10000 episodes, total num timesteps 16400/2000000, FPS 168.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 82/10000 episodes, total num timesteps 16600/2000000, FPS 168.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 83/10000 episodes, total num timesteps 16800/2000000, FPS 168.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 84/10000 episodes, total num timesteps 17000/2000000, FPS 168.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 85/10000 episodes, total num timesteps 17200/2000000, FPS 168.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 86/10000 episodes, total num timesteps 17400/2000000, FPS 168.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 87/10000 episodes, total num timesteps 17600/2000000, FPS 168.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 88/10000 episodes, total num timesteps 17800/2000000, FPS 169.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 89/10000 episodes, total num timesteps 18000/2000000, FPS 169.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 90/10000 episodes, total num timesteps 18200/2000000, FPS 169.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 91/10000 episodes, total num timesteps 18400/2000000, FPS 168.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 92/10000 episodes, total num timesteps 18600/2000000, FPS 169.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 93/10000 episodes, total num timesteps 18800/2000000, FPS 169.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 94/10000 episodes, total num timesteps 19000/2000000, FPS 169.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 95/10000 episodes, total num timesteps 19200/2000000, FPS 169.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 96/10000 episodes, total num timesteps 19400/2000000, FPS 169.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 97/10000 episodes, total num timesteps 19600/2000000, FPS 169.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 98/10000 episodes, total num timesteps 19800/2000000, FPS 169.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 99/10000 episodes, total num timesteps 20000/2000000, FPS 170.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 100/10000 episodes, total num timesteps 20200/2000000, FPS 170.

team_policy eval average step individual rewards of agent0: -0.062820258285847
team_policy eval average team episode rewards of agent0: 0.0
team_policy eval idv catch total num of agent0: 2
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent1: -0.10072061722059045
team_policy eval average team episode rewards of agent1: 0.0
team_policy eval idv catch total num of agent1: 2
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent2: -0.1262213636938472
team_policy eval average team episode rewards of agent2: 0.0
team_policy eval idv catch total num of agent2: 0
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent3: -0.11187942707676278
team_policy eval average team episode rewards of agent3: 0.0
team_policy eval idv catch total num of agent3: 0
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent4: -0.07968319975535933
team_policy eval average team episode rewards of agent4: 0.0
team_policy eval idv catch total num of agent4: 2
team_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent0: -0.012299091729321046
idv_policy eval average team episode rewards of agent0: 0.0
idv_policy eval idv catch total num of agent0: 4
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent1: -0.053617475358018286
idv_policy eval average team episode rewards of agent1: 0.0
idv_policy eval idv catch total num of agent1: 3
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent2: -0.0764936164826063
idv_policy eval average team episode rewards of agent2: 0.0
idv_policy eval idv catch total num of agent2: 2
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent3: -0.0863953977759204
idv_policy eval average team episode rewards of agent3: 0.0
idv_policy eval idv catch total num of agent3: 1
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent4: -0.15536071804189883
idv_policy eval average team episode rewards of agent4: 0.0
idv_policy eval idv catch total num of agent4: 0
idv_policy eval team catch total num: 0

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 101/10000 episodes, total num timesteps 20400/2000000, FPS 170.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 102/10000 episodes, total num timesteps 20600/2000000, FPS 170.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 103/10000 episodes, total num timesteps 20800/2000000, FPS 170.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 104/10000 episodes, total num timesteps 21000/2000000, FPS 170.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 105/10000 episodes, total num timesteps 21200/2000000, FPS 170.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 106/10000 episodes, total num timesteps 21400/2000000, FPS 170.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 107/10000 episodes, total num timesteps 21600/2000000, FPS 170.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 108/10000 episodes, total num timesteps 21800/2000000, FPS 170.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 109/10000 episodes, total num timesteps 22000/2000000, FPS 170.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 110/10000 episodes, total num timesteps 22200/2000000, FPS 170.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 111/10000 episodes, total num timesteps 22400/2000000, FPS 170.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 112/10000 episodes, total num timesteps 22600/2000000, FPS 171.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 113/10000 episodes, total num timesteps 22800/2000000, FPS 171.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 114/10000 episodes, total num timesteps 23000/2000000, FPS 171.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 115/10000 episodes, total num timesteps 23200/2000000, FPS 171.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 116/10000 episodes, total num timesteps 23400/2000000, FPS 171.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 117/10000 episodes, total num timesteps 23600/2000000, FPS 171.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 118/10000 episodes, total num timesteps 23800/2000000, FPS 171.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 119/10000 episodes, total num timesteps 24000/2000000, FPS 171.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 120/10000 episodes, total num timesteps 24200/2000000, FPS 171.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 121/10000 episodes, total num timesteps 24400/2000000, FPS 171.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 122/10000 episodes, total num timesteps 24600/2000000, FPS 170.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 123/10000 episodes, total num timesteps 24800/2000000, FPS 170.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 124/10000 episodes, total num timesteps 25000/2000000, FPS 170.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 125/10000 episodes, total num timesteps 25200/2000000, FPS 170.

team_policy eval average step individual rewards of agent0: -0.18066146496989416
team_policy eval average team episode rewards of agent0: 0.0
team_policy eval idv catch total num of agent0: 0
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent1: -0.15811023881704558
team_policy eval average team episode rewards of agent1: 0.0
team_policy eval idv catch total num of agent1: 0
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent2: -0.14733568423055235
team_policy eval average team episode rewards of agent2: 0.0
team_policy eval idv catch total num of agent2: 0
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent3: -0.14138850087616572
team_policy eval average team episode rewards of agent3: 0.0
team_policy eval idv catch total num of agent3: 0
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent4: -0.1518923345663646
team_policy eval average team episode rewards of agent4: 0.0
team_policy eval idv catch total num of agent4: 0
team_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent0: -0.12120820706579029
idv_policy eval average team episode rewards of agent0: 0.0
idv_policy eval idv catch total num of agent0: 1
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent1: -0.1285784003086963
idv_policy eval average team episode rewards of agent1: 0.0
idv_policy eval idv catch total num of agent1: 0
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent2: -0.06541872174640449
idv_policy eval average team episode rewards of agent2: 0.0
idv_policy eval idv catch total num of agent2: 3
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent3: -0.08400336729193363
idv_policy eval average team episode rewards of agent3: 0.0
idv_policy eval idv catch total num of agent3: 1
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent4: -0.023770785252691655
idv_policy eval average team episode rewards of agent4: 0.0
idv_policy eval idv catch total num of agent4: 4
idv_policy eval team catch total num: 0

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 126/10000 episodes, total num timesteps 25400/2000000, FPS 170.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 127/10000 episodes, total num timesteps 25600/2000000, FPS 170.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 128/10000 episodes, total num timesteps 25800/2000000, FPS 170.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 129/10000 episodes, total num timesteps 26000/2000000, FPS 170.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 130/10000 episodes, total num timesteps 26200/2000000, FPS 170.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 131/10000 episodes, total num timesteps 26400/2000000, FPS 170.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 132/10000 episodes, total num timesteps 26600/2000000, FPS 171.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 133/10000 episodes, total num timesteps 26800/2000000, FPS 171.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 134/10000 episodes, total num timesteps 27000/2000000, FPS 171.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 135/10000 episodes, total num timesteps 27200/2000000, FPS 171.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 136/10000 episodes, total num timesteps 27400/2000000, FPS 171.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 137/10000 episodes, total num timesteps 27600/2000000, FPS 172.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 138/10000 episodes, total num timesteps 27800/2000000, FPS 172.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 139/10000 episodes, total num timesteps 28000/2000000, FPS 172.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 140/10000 episodes, total num timesteps 28200/2000000, FPS 172.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 141/10000 episodes, total num timesteps 28400/2000000, FPS 172.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 142/10000 episodes, total num timesteps 28600/2000000, FPS 172.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 143/10000 episodes, total num timesteps 28800/2000000, FPS 172.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 144/10000 episodes, total num timesteps 29000/2000000, FPS 172.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 145/10000 episodes, total num timesteps 29200/2000000, FPS 172.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 146/10000 episodes, total num timesteps 29400/2000000, FPS 173.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 147/10000 episodes, total num timesteps 29600/2000000, FPS 173.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 148/10000 episodes, total num timesteps 29800/2000000, FPS 172.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 149/10000 episodes, total num timesteps 30000/2000000, FPS 172.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 150/10000 episodes, total num timesteps 30200/2000000, FPS 172.

team_policy eval average step individual rewards of agent0: -0.1402419071156335
team_policy eval average team episode rewards of agent0: 0.0
team_policy eval idv catch total num of agent0: 0
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent1: -0.052109689452595066
team_policy eval average team episode rewards of agent1: 0.0
team_policy eval idv catch total num of agent1: 2
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent2: -0.13194476022102467
team_policy eval average team episode rewards of agent2: 0.0
team_policy eval idv catch total num of agent2: 0
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent3: -0.1220477912644646
team_policy eval average team episode rewards of agent3: 0.0
team_policy eval idv catch total num of agent3: 1
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent4: -0.07718460021162717
team_policy eval average team episode rewards of agent4: 0.0
team_policy eval idv catch total num of agent4: 2
team_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent0: -0.10754625782867384
idv_policy eval average team episode rewards of agent0: 0.0
idv_policy eval idv catch total num of agent0: 2
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent1: -0.06051275280194043
idv_policy eval average team episode rewards of agent1: 0.0
idv_policy eval idv catch total num of agent1: 2
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent2: -0.07710686529793827
idv_policy eval average team episode rewards of agent2: 0.0
idv_policy eval idv catch total num of agent2: 2
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent3: -0.11567673858035078
idv_policy eval average team episode rewards of agent3: 0.0
idv_policy eval idv catch total num of agent3: 1
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent4: -0.08926564215381722
idv_policy eval average team episode rewards of agent4: 0.0
idv_policy eval idv catch total num of agent4: 1
idv_policy eval team catch total num: 0

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 151/10000 episodes, total num timesteps 30400/2000000, FPS 172.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 152/10000 episodes, total num timesteps 30600/2000000, FPS 172.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 153/10000 episodes, total num timesteps 30800/2000000, FPS 172.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 154/10000 episodes, total num timesteps 31000/2000000, FPS 172.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 155/10000 episodes, total num timesteps 31200/2000000, FPS 172.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 156/10000 episodes, total num timesteps 31400/2000000, FPS 172.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 157/10000 episodes, total num timesteps 31600/2000000, FPS 172.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 158/10000 episodes, total num timesteps 31800/2000000, FPS 172.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 159/10000 episodes, total num timesteps 32000/2000000, FPS 172.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 160/10000 episodes, total num timesteps 32200/2000000, FPS 172.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 161/10000 episodes, total num timesteps 32400/2000000, FPS 172.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 162/10000 episodes, total num timesteps 32600/2000000, FPS 172.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 163/10000 episodes, total num timesteps 32800/2000000, FPS 173.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 164/10000 episodes, total num timesteps 33000/2000000, FPS 173.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 165/10000 episodes, total num timesteps 33200/2000000, FPS 173.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 166/10000 episodes, total num timesteps 33400/2000000, FPS 173.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 167/10000 episodes, total num timesteps 33600/2000000, FPS 173.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 168/10000 episodes, total num timesteps 33800/2000000, FPS 173.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 169/10000 episodes, total num timesteps 34000/2000000, FPS 173.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 170/10000 episodes, total num timesteps 34200/2000000, FPS 172.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 171/10000 episodes, total num timesteps 34400/2000000, FPS 172.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 172/10000 episodes, total num timesteps 34600/2000000, FPS 172.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 173/10000 episodes, total num timesteps 34800/2000000, FPS 172.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 174/10000 episodes, total num timesteps 35000/2000000, FPS 172.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 175/10000 episodes, total num timesteps 35200/2000000, FPS 172.

team_policy eval average step individual rewards of agent0: 0.021109114820349006
team_policy eval average team episode rewards of agent0: 0.0
team_policy eval idv catch total num of agent0: 6
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent1: -0.0730134385188682
team_policy eval average team episode rewards of agent1: 0.0
team_policy eval idv catch total num of agent1: 2
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent2: -0.16357615802641098
team_policy eval average team episode rewards of agent2: 0.0
team_policy eval idv catch total num of agent2: 0
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent3: -0.04175386548312528
team_policy eval average team episode rewards of agent3: 0.0
team_policy eval idv catch total num of agent3: 3
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent4: -0.17034922512003767
team_policy eval average team episode rewards of agent4: 0.0
team_policy eval idv catch total num of agent4: 0
team_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent0: -0.12953515363981086
idv_policy eval average team episode rewards of agent0: 0.0
idv_policy eval idv catch total num of agent0: 0
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent1: -0.08375412983902976
idv_policy eval average team episode rewards of agent1: 0.0
idv_policy eval idv catch total num of agent1: 2
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent2: -0.13107353423799478
idv_policy eval average team episode rewards of agent2: 0.0
idv_policy eval idv catch total num of agent2: 1
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent3: -0.13504552177933393
idv_policy eval average team episode rewards of agent3: 0.0
idv_policy eval idv catch total num of agent3: 1
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent4: -0.14541046568125807
idv_policy eval average team episode rewards of agent4: 0.0
idv_policy eval idv catch total num of agent4: 0
idv_policy eval team catch total num: 0

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 176/10000 episodes, total num timesteps 35400/2000000, FPS 172.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 177/10000 episodes, total num timesteps 35600/2000000, FPS 172.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 178/10000 episodes, total num timesteps 35800/2000000, FPS 172.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 179/10000 episodes, total num timesteps 36000/2000000, FPS 172.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 180/10000 episodes, total num timesteps 36200/2000000, FPS 171.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 181/10000 episodes, total num timesteps 36400/2000000, FPS 171.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 182/10000 episodes, total num timesteps 36600/2000000, FPS 171.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 183/10000 episodes, total num timesteps 36800/2000000, FPS 171.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 184/10000 episodes, total num timesteps 37000/2000000, FPS 171.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 185/10000 episodes, total num timesteps 37200/2000000, FPS 171.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 186/10000 episodes, total num timesteps 37400/2000000, FPS 170.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 187/10000 episodes, total num timesteps 37600/2000000, FPS 170.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 188/10000 episodes, total num timesteps 37800/2000000, FPS 171.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 189/10000 episodes, total num timesteps 38000/2000000, FPS 171.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 190/10000 episodes, total num timesteps 38200/2000000, FPS 170.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 191/10000 episodes, total num timesteps 38400/2000000, FPS 170.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 192/10000 episodes, total num timesteps 38600/2000000, FPS 170.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 193/10000 episodes, total num timesteps 38800/2000000, FPS 170.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 194/10000 episodes, total num timesteps 39000/2000000, FPS 170.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 195/10000 episodes, total num timesteps 39200/2000000, FPS 170.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 196/10000 episodes, total num timesteps 39400/2000000, FPS 170.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 197/10000 episodes, total num timesteps 39600/2000000, FPS 170.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 198/10000 episodes, total num timesteps 39800/2000000, FPS 170.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 199/10000 episodes, total num timesteps 40000/2000000, FPS 170.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 200/10000 episodes, total num timesteps 40200/2000000, FPS 170.

team_policy eval average step individual rewards of agent0: -0.1131645878721768
team_policy eval average team episode rewards of agent0: 0.0
team_policy eval idv catch total num of agent0: 0
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent1: -0.0014412878650100769
team_policy eval average team episode rewards of agent1: 0.0
team_policy eval idv catch total num of agent1: 4
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent2: -0.1540462803743459
team_policy eval average team episode rewards of agent2: 0.0
team_policy eval idv catch total num of agent2: 0
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent3: -0.015573443104409375
team_policy eval average team episode rewards of agent3: 0.0
team_policy eval idv catch total num of agent3: 5
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent4: -0.12255204123818071
team_policy eval average team episode rewards of agent4: 0.0
team_policy eval idv catch total num of agent4: 0
team_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent0: -0.14559362867196493
idv_policy eval average team episode rewards of agent0: 0.0
idv_policy eval idv catch total num of agent0: 0
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent1: -0.1881830478554082
idv_policy eval average team episode rewards of agent1: 0.0
idv_policy eval idv catch total num of agent1: 0
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent2: -0.17308728996389605
idv_policy eval average team episode rewards of agent2: 0.0
idv_policy eval idv catch total num of agent2: 0
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent3: -0.16530818238932585
idv_policy eval average team episode rewards of agent3: 0.0
idv_policy eval idv catch total num of agent3: 0
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent4: -0.1591251620498881
idv_policy eval average team episode rewards of agent4: 0.0
idv_policy eval idv catch total num of agent4: 1
idv_policy eval team catch total num: 0

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 201/10000 episodes, total num timesteps 40400/2000000, FPS 170.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 202/10000 episodes, total num timesteps 40600/2000000, FPS 170.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 203/10000 episodes, total num timesteps 40800/2000000, FPS 170.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 204/10000 episodes, total num timesteps 41000/2000000, FPS 170.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 205/10000 episodes, total num timesteps 41200/2000000, FPS 170.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 206/10000 episodes, total num timesteps 41400/2000000, FPS 169.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 207/10000 episodes, total num timesteps 41600/2000000, FPS 169.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 208/10000 episodes, total num timesteps 41800/2000000, FPS 169.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 209/10000 episodes, total num timesteps 42000/2000000, FPS 169.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 210/10000 episodes, total num timesteps 42200/2000000, FPS 169.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 211/10000 episodes, total num timesteps 42400/2000000, FPS 169.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 212/10000 episodes, total num timesteps 42600/2000000, FPS 168.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 213/10000 episodes, total num timesteps 42800/2000000, FPS 168.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 214/10000 episodes, total num timesteps 43000/2000000, FPS 168.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 215/10000 episodes, total num timesteps 43200/2000000, FPS 168.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 216/10000 episodes, total num timesteps 43400/2000000, FPS 168.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 217/10000 episodes, total num timesteps 43600/2000000, FPS 168.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 218/10000 episodes, total num timesteps 43800/2000000, FPS 168.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 219/10000 episodes, total num timesteps 44000/2000000, FPS 168.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 220/10000 episodes, total num timesteps 44200/2000000, FPS 167.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 221/10000 episodes, total num timesteps 44400/2000000, FPS 167.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 222/10000 episodes, total num timesteps 44600/2000000, FPS 167.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 223/10000 episodes, total num timesteps 44800/2000000, FPS 167.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 224/10000 episodes, total num timesteps 45000/2000000, FPS 167.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 225/10000 episodes, total num timesteps 45200/2000000, FPS 167.

team_policy eval average step individual rewards of agent0: -0.14156185567352303
team_policy eval average team episode rewards of agent0: 0.0
team_policy eval idv catch total num of agent0: 0
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent1: -0.1029346140958808
team_policy eval average team episode rewards of agent1: 0.0
team_policy eval idv catch total num of agent1: 1
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent2: -0.13113324275722418
team_policy eval average team episode rewards of agent2: 0.0
team_policy eval idv catch total num of agent2: 0
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent3: -0.10639627481424262
team_policy eval average team episode rewards of agent3: 0.0
team_policy eval idv catch total num of agent3: 1
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent4: -0.01756839063211703
team_policy eval average team episode rewards of agent4: 0.0
team_policy eval idv catch total num of agent4: 3
team_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent0: 0.020485177990502902
idv_policy eval average team episode rewards of agent0: 2.5
idv_policy eval idv catch total num of agent0: 5
idv_policy eval team catch total num: 1
idv_policy eval average step individual rewards of agent1: -0.0470061155460696
idv_policy eval average team episode rewards of agent1: 2.5
idv_policy eval idv catch total num of agent1: 3
idv_policy eval team catch total num: 1
idv_policy eval average step individual rewards of agent2: -0.03070118232214644
idv_policy eval average team episode rewards of agent2: 2.5
idv_policy eval idv catch total num of agent2: 3
idv_policy eval team catch total num: 1
idv_policy eval average step individual rewards of agent3: 0.02084640465487892
idv_policy eval average team episode rewards of agent3: 2.5
idv_policy eval idv catch total num of agent3: 5
idv_policy eval team catch total num: 1
idv_policy eval average step individual rewards of agent4: 0.0962278351490349
idv_policy eval average team episode rewards of agent4: 2.5
idv_policy eval idv catch total num of agent4: 8
idv_policy eval team catch total num: 1

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 226/10000 episodes, total num timesteps 45400/2000000, FPS 167.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 227/10000 episodes, total num timesteps 45600/2000000, FPS 166.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 228/10000 episodes, total num timesteps 45800/2000000, FPS 166.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 229/10000 episodes, total num timesteps 46000/2000000, FPS 166.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 230/10000 episodes, total num timesteps 46200/2000000, FPS 166.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 231/10000 episodes, total num timesteps 46400/2000000, FPS 166.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 232/10000 episodes, total num timesteps 46600/2000000, FPS 166.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 233/10000 episodes, total num timesteps 46800/2000000, FPS 166.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 234/10000 episodes, total num timesteps 47000/2000000, FPS 166.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 235/10000 episodes, total num timesteps 47200/2000000, FPS 166.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 236/10000 episodes, total num timesteps 47400/2000000, FPS 166.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 237/10000 episodes, total num timesteps 47600/2000000, FPS 166.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 238/10000 episodes, total num timesteps 47800/2000000, FPS 165.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 239/10000 episodes, total num timesteps 48000/2000000, FPS 165.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 240/10000 episodes, total num timesteps 48200/2000000, FPS 165.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 241/10000 episodes, total num timesteps 48400/2000000, FPS 165.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 242/10000 episodes, total num timesteps 48600/2000000, FPS 165.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 243/10000 episodes, total num timesteps 48800/2000000, FPS 165.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 244/10000 episodes, total num timesteps 49000/2000000, FPS 165.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 245/10000 episodes, total num timesteps 49200/2000000, FPS 165.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 246/10000 episodes, total num timesteps 49400/2000000, FPS 165.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 247/10000 episodes, total num timesteps 49600/2000000, FPS 164.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 248/10000 episodes, total num timesteps 49800/2000000, FPS 164.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 249/10000 episodes, total num timesteps 50000/2000000, FPS 164.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 250/10000 episodes, total num timesteps 50200/2000000, FPS 164.

team_policy eval average step individual rewards of agent0: -0.14674144039964493
team_policy eval average team episode rewards of agent0: 0.0
team_policy eval idv catch total num of agent0: 1
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent1: -0.1764996866941168
team_policy eval average team episode rewards of agent1: 0.0
team_policy eval idv catch total num of agent1: 0
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent2: -0.14774151036108044
team_policy eval average team episode rewards of agent2: 0.0
team_policy eval idv catch total num of agent2: 1
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent3: -0.09903806944234285
team_policy eval average team episode rewards of agent3: 0.0
team_policy eval idv catch total num of agent3: 1
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent4: -0.08691820896514246
team_policy eval average team episode rewards of agent4: 0.0
team_policy eval idv catch total num of agent4: 2
team_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent0: -0.00633519961837985
idv_policy eval average team episode rewards of agent0: 0.0
idv_policy eval idv catch total num of agent0: 5
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent1: -0.1325554184545573
idv_policy eval average team episode rewards of agent1: 0.0
idv_policy eval idv catch total num of agent1: 1
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent2: -0.10823694783973872
idv_policy eval average team episode rewards of agent2: 0.0
idv_policy eval idv catch total num of agent2: 1
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent3: -0.10629851124644185
idv_policy eval average team episode rewards of agent3: 0.0
idv_policy eval idv catch total num of agent3: 1
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent4: -0.09756484961973394
idv_policy eval average team episode rewards of agent4: 0.0
idv_policy eval idv catch total num of agent4: 2
idv_policy eval team catch total num: 0

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 251/10000 episodes, total num timesteps 50400/2000000, FPS 164.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 252/10000 episodes, total num timesteps 50600/2000000, FPS 164.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 253/10000 episodes, total num timesteps 50800/2000000, FPS 164.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 254/10000 episodes, total num timesteps 51000/2000000, FPS 164.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 255/10000 episodes, total num timesteps 51200/2000000, FPS 164.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 256/10000 episodes, total num timesteps 51400/2000000, FPS 164.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 257/10000 episodes, total num timesteps 51600/2000000, FPS 164.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 258/10000 episodes, total num timesteps 51800/2000000, FPS 164.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 259/10000 episodes, total num timesteps 52000/2000000, FPS 164.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 260/10000 episodes, total num timesteps 52200/2000000, FPS 164.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 261/10000 episodes, total num timesteps 52400/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 262/10000 episodes, total num timesteps 52600/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 263/10000 episodes, total num timesteps 52800/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 264/10000 episodes, total num timesteps 53000/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 265/10000 episodes, total num timesteps 53200/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 266/10000 episodes, total num timesteps 53400/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 267/10000 episodes, total num timesteps 53600/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 268/10000 episodes, total num timesteps 53800/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 269/10000 episodes, total num timesteps 54000/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 270/10000 episodes, total num timesteps 54200/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 271/10000 episodes, total num timesteps 54400/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 272/10000 episodes, total num timesteps 54600/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 273/10000 episodes, total num timesteps 54800/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 274/10000 episodes, total num timesteps 55000/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 275/10000 episodes, total num timesteps 55200/2000000, FPS 163.

team_policy eval average step individual rewards of agent0: -0.12173006210479383
team_policy eval average team episode rewards of agent0: 0.0
team_policy eval idv catch total num of agent0: 0
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent1: -0.11579056743351082
team_policy eval average team episode rewards of agent1: 0.0
team_policy eval idv catch total num of agent1: 0
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent2: -0.0735825007161652
team_policy eval average team episode rewards of agent2: 0.0
team_policy eval idv catch total num of agent2: 2
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent3: -0.06844710636241516
team_policy eval average team episode rewards of agent3: 0.0
team_policy eval idv catch total num of agent3: 2
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent4: 0.007020767944121636
team_policy eval average team episode rewards of agent4: 0.0
team_policy eval idv catch total num of agent4: 4
team_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent0: -0.11946048349516898
idv_policy eval average team episode rewards of agent0: 0.0
idv_policy eval idv catch total num of agent0: 0
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent1: 0.03994424954342646
idv_policy eval average team episode rewards of agent1: 0.0
idv_policy eval idv catch total num of agent1: 6
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent2: -0.14705223204857243
idv_policy eval average team episode rewards of agent2: 0.0
idv_policy eval idv catch total num of agent2: 0
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent3: -0.08942188254702332
idv_policy eval average team episode rewards of agent3: 0.0
idv_policy eval idv catch total num of agent3: 2
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent4: -0.10225280436755722
idv_policy eval average team episode rewards of agent4: 0.0
idv_policy eval idv catch total num of agent4: 1
idv_policy eval team catch total num: 0

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 276/10000 episodes, total num timesteps 55400/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 277/10000 episodes, total num timesteps 55600/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 278/10000 episodes, total num timesteps 55800/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 279/10000 episodes, total num timesteps 56000/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 280/10000 episodes, total num timesteps 56200/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 281/10000 episodes, total num timesteps 56400/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 282/10000 episodes, total num timesteps 56600/2000000, FPS 162.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 283/10000 episodes, total num timesteps 56800/2000000, FPS 162.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 284/10000 episodes, total num timesteps 57000/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 285/10000 episodes, total num timesteps 57200/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 286/10000 episodes, total num timesteps 57400/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 287/10000 episodes, total num timesteps 57600/2000000, FPS 162.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 288/10000 episodes, total num timesteps 57800/2000000, FPS 162.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 289/10000 episodes, total num timesteps 58000/2000000, FPS 162.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 290/10000 episodes, total num timesteps 58200/2000000, FPS 162.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 291/10000 episodes, total num timesteps 58400/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 292/10000 episodes, total num timesteps 58600/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 293/10000 episodes, total num timesteps 58800/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 294/10000 episodes, total num timesteps 59000/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 295/10000 episodes, total num timesteps 59200/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 296/10000 episodes, total num timesteps 59400/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 297/10000 episodes, total num timesteps 59600/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 298/10000 episodes, total num timesteps 59800/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 299/10000 episodes, total num timesteps 60000/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 300/10000 episodes, total num timesteps 60200/2000000, FPS 163.

team_policy eval average step individual rewards of agent0: -0.1235428974864032
team_policy eval average team episode rewards of agent0: 0.0
team_policy eval idv catch total num of agent0: 1
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent1: -0.12816866009989566
team_policy eval average team episode rewards of agent1: 0.0
team_policy eval idv catch total num of agent1: 1
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent2: -0.13180123954537937
team_policy eval average team episode rewards of agent2: 0.0
team_policy eval idv catch total num of agent2: 1
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent3: -0.1515394277588604
team_policy eval average team episode rewards of agent3: 0.0
team_policy eval idv catch total num of agent3: 1
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent4: -0.14529294784003263
team_policy eval average team episode rewards of agent4: 0.0
team_policy eval idv catch total num of agent4: 0
team_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent0: -0.08057733080910617
idv_policy eval average team episode rewards of agent0: 0.0
idv_policy eval idv catch total num of agent0: 3
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent1: -0.030304074486848034
idv_policy eval average team episode rewards of agent1: 0.0
idv_policy eval idv catch total num of agent1: 4
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent2: -0.16893976595439064
idv_policy eval average team episode rewards of agent2: 0.0
idv_policy eval idv catch total num of agent2: 0
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent3: -0.14205806484018643
idv_policy eval average team episode rewards of agent3: 0.0
idv_policy eval idv catch total num of agent3: 0
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent4: -0.19603932246291442
idv_policy eval average team episode rewards of agent4: 0.0
idv_policy eval idv catch total num of agent4: 0
idv_policy eval team catch total num: 0

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 301/10000 episodes, total num timesteps 60400/2000000, FPS 162.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 302/10000 episodes, total num timesteps 60600/2000000, FPS 162.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 303/10000 episodes, total num timesteps 60800/2000000, FPS 162.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 304/10000 episodes, total num timesteps 61000/2000000, FPS 162.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 305/10000 episodes, total num timesteps 61200/2000000, FPS 162.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 306/10000 episodes, total num timesteps 61400/2000000, FPS 162.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 307/10000 episodes, total num timesteps 61600/2000000, FPS 162.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 308/10000 episodes, total num timesteps 61800/2000000, FPS 162.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 309/10000 episodes, total num timesteps 62000/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 310/10000 episodes, total num timesteps 62200/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 311/10000 episodes, total num timesteps 62400/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 312/10000 episodes, total num timesteps 62600/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 313/10000 episodes, total num timesteps 62800/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 314/10000 episodes, total num timesteps 63000/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 315/10000 episodes, total num timesteps 63200/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 316/10000 episodes, total num timesteps 63400/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 317/10000 episodes, total num timesteps 63600/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 318/10000 episodes, total num timesteps 63800/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 319/10000 episodes, total num timesteps 64000/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 320/10000 episodes, total num timesteps 64200/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 321/10000 episodes, total num timesteps 64400/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 322/10000 episodes, total num timesteps 64600/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 323/10000 episodes, total num timesteps 64800/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 324/10000 episodes, total num timesteps 65000/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 325/10000 episodes, total num timesteps 65200/2000000, FPS 163.

team_policy eval average step individual rewards of agent0: -0.082491753500811
team_policy eval average team episode rewards of agent0: 0.0
team_policy eval idv catch total num of agent0: 2
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent1: -0.03921105049525452
team_policy eval average team episode rewards of agent1: 0.0
team_policy eval idv catch total num of agent1: 3
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent2: -0.1358385102382939
team_policy eval average team episode rewards of agent2: 0.0
team_policy eval idv catch total num of agent2: 0
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent3: -0.0608248100082976
team_policy eval average team episode rewards of agent3: 0.0
team_policy eval idv catch total num of agent3: 2
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent4: -0.07794136354490515
team_policy eval average team episode rewards of agent4: 0.0
team_policy eval idv catch total num of agent4: 2
team_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent0: -0.030109536124109697
idv_policy eval average team episode rewards of agent0: 2.5
idv_policy eval idv catch total num of agent0: 3
idv_policy eval team catch total num: 1
idv_policy eval average step individual rewards of agent1: 0.06954665860820793
idv_policy eval average team episode rewards of agent1: 2.5
idv_policy eval idv catch total num of agent1: 7
idv_policy eval team catch total num: 1
idv_policy eval average step individual rewards of agent2: -0.025627314574270397
idv_policy eval average team episode rewards of agent2: 2.5
idv_policy eval idv catch total num of agent2: 3
idv_policy eval team catch total num: 1
idv_policy eval average step individual rewards of agent3: -0.12979260725035732
idv_policy eval average team episode rewards of agent3: 2.5
idv_policy eval idv catch total num of agent3: 0
idv_policy eval team catch total num: 1
idv_policy eval average step individual rewards of agent4: -0.10129092826974782
idv_policy eval average team episode rewards of agent4: 2.5
idv_policy eval idv catch total num of agent4: 0
idv_policy eval team catch total num: 1

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 326/10000 episodes, total num timesteps 65400/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 327/10000 episodes, total num timesteps 65600/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 328/10000 episodes, total num timesteps 65800/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 329/10000 episodes, total num timesteps 66000/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 330/10000 episodes, total num timesteps 66200/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 331/10000 episodes, total num timesteps 66400/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 332/10000 episodes, total num timesteps 66600/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 333/10000 episodes, total num timesteps 66800/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 334/10000 episodes, total num timesteps 67000/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 335/10000 episodes, total num timesteps 67200/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 336/10000 episodes, total num timesteps 67400/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 337/10000 episodes, total num timesteps 67600/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 338/10000 episodes, total num timesteps 67800/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 339/10000 episodes, total num timesteps 68000/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 340/10000 episodes, total num timesteps 68200/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 341/10000 episodes, total num timesteps 68400/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 342/10000 episodes, total num timesteps 68600/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 343/10000 episodes, total num timesteps 68800/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 344/10000 episodes, total num timesteps 69000/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 345/10000 episodes, total num timesteps 69200/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 346/10000 episodes, total num timesteps 69400/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 347/10000 episodes, total num timesteps 69600/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 348/10000 episodes, total num timesteps 69800/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 349/10000 episodes, total num timesteps 70000/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 350/10000 episodes, total num timesteps 70200/2000000, FPS 163.

team_policy eval average step individual rewards of agent0: -0.04183775916593389
team_policy eval average team episode rewards of agent0: 0.0
team_policy eval idv catch total num of agent0: 3
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent1: -0.061546869655041615
team_policy eval average team episode rewards of agent1: 0.0
team_policy eval idv catch total num of agent1: 2
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent2: -0.036124168759435256
team_policy eval average team episode rewards of agent2: 0.0
team_policy eval idv catch total num of agent2: 4
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent3: -0.0551832245591995
team_policy eval average team episode rewards of agent3: 0.0
team_policy eval idv catch total num of agent3: 2
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent4: -0.12506066318165918
team_policy eval average team episode rewards of agent4: 0.0
team_policy eval idv catch total num of agent4: 0
team_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent0: -0.11992006281181372
idv_policy eval average team episode rewards of agent0: 0.0
idv_policy eval idv catch total num of agent0: 0
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent1: -0.10177970398037693
idv_policy eval average team episode rewards of agent1: 0.0
idv_policy eval idv catch total num of agent1: 1
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent2: -0.12001089905751193
idv_policy eval average team episode rewards of agent2: 0.0
idv_policy eval idv catch total num of agent2: 0
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent3: -0.1164536675979207
idv_policy eval average team episode rewards of agent3: 0.0
idv_policy eval idv catch total num of agent3: 0
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent4: -0.010360643720611478
idv_policy eval average team episode rewards of agent4: 0.0
idv_policy eval idv catch total num of agent4: 4
idv_policy eval team catch total num: 0

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 351/10000 episodes, total num timesteps 70400/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 352/10000 episodes, total num timesteps 70600/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 353/10000 episodes, total num timesteps 70800/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 354/10000 episodes, total num timesteps 71000/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 355/10000 episodes, total num timesteps 71200/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 356/10000 episodes, total num timesteps 71400/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 357/10000 episodes, total num timesteps 71600/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 358/10000 episodes, total num timesteps 71800/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 359/10000 episodes, total num timesteps 72000/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 360/10000 episodes, total num timesteps 72200/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 361/10000 episodes, total num timesteps 72400/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 362/10000 episodes, total num timesteps 72600/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 363/10000 episodes, total num timesteps 72800/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 364/10000 episodes, total num timesteps 73000/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 365/10000 episodes, total num timesteps 73200/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 366/10000 episodes, total num timesteps 73400/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 367/10000 episodes, total num timesteps 73600/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 368/10000 episodes, total num timesteps 73800/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 369/10000 episodes, total num timesteps 74000/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 370/10000 episodes, total num timesteps 74200/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 371/10000 episodes, total num timesteps 74400/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 372/10000 episodes, total num timesteps 74600/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 373/10000 episodes, total num timesteps 74800/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 374/10000 episodes, total num timesteps 75000/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 375/10000 episodes, total num timesteps 75200/2000000, FPS 163.

team_policy eval average step individual rewards of agent0: -0.06012233929637099
team_policy eval average team episode rewards of agent0: 0.0
team_policy eval idv catch total num of agent0: 3
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent1: -0.12477382853820337
team_policy eval average team episode rewards of agent1: 0.0
team_policy eval idv catch total num of agent1: 0
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent2: -0.09818677636440405
team_policy eval average team episode rewards of agent2: 0.0
team_policy eval idv catch total num of agent2: 1
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent3: -0.09789414414844888
team_policy eval average team episode rewards of agent3: 0.0
team_policy eval idv catch total num of agent3: 1
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent4: -0.12612355713662374
team_policy eval average team episode rewards of agent4: 0.0
team_policy eval idv catch total num of agent4: 0
team_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent0: -0.01874713374505955
idv_policy eval average team episode rewards of agent0: 5.0
idv_policy eval idv catch total num of agent0: 4
idv_policy eval team catch total num: 2
idv_policy eval average step individual rewards of agent1: -0.012171079864237787
idv_policy eval average team episode rewards of agent1: 5.0
idv_policy eval idv catch total num of agent1: 4
idv_policy eval team catch total num: 2
idv_policy eval average step individual rewards of agent2: -0.08057875032982566
idv_policy eval average team episode rewards of agent2: 5.0
idv_policy eval idv catch total num of agent2: 2
idv_policy eval team catch total num: 2
idv_policy eval average step individual rewards of agent3: -0.12074975032558732
idv_policy eval average team episode rewards of agent3: 5.0
idv_policy eval idv catch total num of agent3: 0
idv_policy eval team catch total num: 2
idv_policy eval average step individual rewards of agent4: -0.042989210681083134
idv_policy eval average team episode rewards of agent4: 5.0
idv_policy eval idv catch total num of agent4: 3
idv_policy eval team catch total num: 2

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 376/10000 episodes, total num timesteps 75400/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 377/10000 episodes, total num timesteps 75600/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 378/10000 episodes, total num timesteps 75800/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 379/10000 episodes, total num timesteps 76000/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 380/10000 episodes, total num timesteps 76200/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 381/10000 episodes, total num timesteps 76400/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 382/10000 episodes, total num timesteps 76600/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 383/10000 episodes, total num timesteps 76800/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 384/10000 episodes, total num timesteps 77000/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 385/10000 episodes, total num timesteps 77200/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 386/10000 episodes, total num timesteps 77400/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 387/10000 episodes, total num timesteps 77600/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 388/10000 episodes, total num timesteps 77800/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 389/10000 episodes, total num timesteps 78000/2000000, FPS 163.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 390/10000 episodes, total num timesteps 78200/2000000, FPS 164.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 391/10000 episodes, total num timesteps 78400/2000000, FPS 164.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 392/10000 episodes, total num timesteps 78600/2000000, FPS 164.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 393/10000 episodes, total num timesteps 78800/2000000, FPS 164.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 394/10000 episodes, total num timesteps 79000/2000000, FPS 164.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 395/10000 episodes, total num timesteps 79200/2000000, FPS 164.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 396/10000 episodes, total num timesteps 79400/2000000, FPS 164.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 397/10000 episodes, total num timesteps 79600/2000000, FPS 164.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 398/10000 episodes, total num timesteps 79800/2000000, FPS 164.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 399/10000 episodes, total num timesteps 80000/2000000, FPS 164.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 400/10000 episodes, total num timesteps 80200/2000000, FPS 164.

team_policy eval average step individual rewards of agent0: -0.042530998585100795
team_policy eval average team episode rewards of agent0: 5.0
team_policy eval idv catch total num of agent0: 2
team_policy eval team catch total num: 2
team_policy eval average step individual rewards of agent1: 0.22174588841293655
team_policy eval average team episode rewards of agent1: 5.0
team_policy eval idv catch total num of agent1: 12
team_policy eval team catch total num: 2
team_policy eval average step individual rewards of agent2: 0.09967194477477309
team_policy eval average team episode rewards of agent2: 5.0
team_policy eval idv catch total num of agent2: 7
team_policy eval team catch total num: 2
team_policy eval average step individual rewards of agent3: 0.022044807649918764
team_policy eval average team episode rewards of agent3: 5.0
team_policy eval idv catch total num of agent3: 4
team_policy eval team catch total num: 2
team_policy eval average step individual rewards of agent4: 0.01767572618004828
team_policy eval average team episode rewards of agent4: 5.0
team_policy eval idv catch total num of agent4: 4
team_policy eval team catch total num: 2
idv_policy eval average step individual rewards of agent0: -0.06851888818371439
idv_policy eval average team episode rewards of agent0: 0.0
idv_policy eval idv catch total num of agent0: 2
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent1: -0.08525029126486235
idv_policy eval average team episode rewards of agent1: 0.0
idv_policy eval idv catch total num of agent1: 1
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent2: -0.11820981608102621
idv_policy eval average team episode rewards of agent2: 0.0
idv_policy eval idv catch total num of agent2: 0
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent3: -0.10186484061353035
idv_policy eval average team episode rewards of agent3: 0.0
idv_policy eval idv catch total num of agent3: 0
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent4: -0.1194807863096581
idv_policy eval average team episode rewards of agent4: 0.0
idv_policy eval idv catch total num of agent4: 0
idv_policy eval team catch total num: 0

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 401/10000 episodes, total num timesteps 80400/2000000, FPS 164.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 402/10000 episodes, total num timesteps 80600/2000000, FPS 164.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 403/10000 episodes, total num timesteps 80800/2000000, FPS 164.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 404/10000 episodes, total num timesteps 81000/2000000, FPS 164.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 405/10000 episodes, total num timesteps 81200/2000000, FPS 164.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 406/10000 episodes, total num timesteps 81400/2000000, FPS 165.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 407/10000 episodes, total num timesteps 81600/2000000, FPS 165.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 408/10000 episodes, total num timesteps 81800/2000000, FPS 165.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 409/10000 episodes, total num timesteps 82000/2000000, FPS 165.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 410/10000 episodes, total num timesteps 82200/2000000, FPS 165.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 411/10000 episodes, total num timesteps 82400/2000000, FPS 165.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 412/10000 episodes, total num timesteps 82600/2000000, FPS 165.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 413/10000 episodes, total num timesteps 82800/2000000, FPS 165.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 414/10000 episodes, total num timesteps 83000/2000000, FPS 165.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 415/10000 episodes, total num timesteps 83200/2000000, FPS 165.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 416/10000 episodes, total num timesteps 83400/2000000, FPS 165.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 417/10000 episodes, total num timesteps 83600/2000000, FPS 165.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 418/10000 episodes, total num timesteps 83800/2000000, FPS 165.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 419/10000 episodes, total num timesteps 84000/2000000, FPS 166.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 420/10000 episodes, total num timesteps 84200/2000000, FPS 166.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 421/10000 episodes, total num timesteps 84400/2000000, FPS 166.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 422/10000 episodes, total num timesteps 84600/2000000, FPS 166.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 423/10000 episodes, total num timesteps 84800/2000000, FPS 166.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 424/10000 episodes, total num timesteps 85000/2000000, FPS 166.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 425/10000 episodes, total num timesteps 85200/2000000, FPS 166.

team_policy eval average step individual rewards of agent0: -0.048192726227824355
team_policy eval average team episode rewards of agent0: 0.0
team_policy eval idv catch total num of agent0: 3
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent1: -0.09884946822148645
team_policy eval average team episode rewards of agent1: 0.0
team_policy eval idv catch total num of agent1: 2
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent2: -0.03924890328133261
team_policy eval average team episode rewards of agent2: 0.0
team_policy eval idv catch total num of agent2: 3
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent3: -0.12683673665781872
team_policy eval average team episode rewards of agent3: 0.0
team_policy eval idv catch total num of agent3: 0
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent4: -0.09455437889822528
team_policy eval average team episode rewards of agent4: 0.0
team_policy eval idv catch total num of agent4: 1
team_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent0: -0.05872229997842966
idv_policy eval average team episode rewards of agent0: 2.5
idv_policy eval idv catch total num of agent0: 2
idv_policy eval team catch total num: 1
idv_policy eval average step individual rewards of agent1: -0.0308908164537673
idv_policy eval average team episode rewards of agent1: 2.5
idv_policy eval idv catch total num of agent1: 4
idv_policy eval team catch total num: 1
idv_policy eval average step individual rewards of agent2: -0.08298409467799825
idv_policy eval average team episode rewards of agent2: 2.5
idv_policy eval idv catch total num of agent2: 1
idv_policy eval team catch total num: 1
idv_policy eval average step individual rewards of agent3: -0.07411238666875429
idv_policy eval average team episode rewards of agent3: 2.5
idv_policy eval idv catch total num of agent3: 1
idv_policy eval team catch total num: 1
idv_policy eval average step individual rewards of agent4: 0.023077862423264955
idv_policy eval average team episode rewards of agent4: 2.5
idv_policy eval idv catch total num of agent4: 5
idv_policy eval team catch total num: 1

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 426/10000 episodes, total num timesteps 85400/2000000, FPS 166.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 427/10000 episodes, total num timesteps 85600/2000000, FPS 166.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 428/10000 episodes, total num timesteps 85800/2000000, FPS 166.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 429/10000 episodes, total num timesteps 86000/2000000, FPS 166.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 430/10000 episodes, total num timesteps 86200/2000000, FPS 166.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 431/10000 episodes, total num timesteps 86400/2000000, FPS 166.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 432/10000 episodes, total num timesteps 86600/2000000, FPS 166.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 433/10000 episodes, total num timesteps 86800/2000000, FPS 166.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 434/10000 episodes, total num timesteps 87000/2000000, FPS 167.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 435/10000 episodes, total num timesteps 87200/2000000, FPS 167.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 436/10000 episodes, total num timesteps 87400/2000000, FPS 167.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 437/10000 episodes, total num timesteps 87600/2000000, FPS 167.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 438/10000 episodes, total num timesteps 87800/2000000, FPS 167.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 439/10000 episodes, total num timesteps 88000/2000000, FPS 167.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 440/10000 episodes, total num timesteps 88200/2000000, FPS 167.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 441/10000 episodes, total num timesteps 88400/2000000, FPS 167.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 442/10000 episodes, total num timesteps 88600/2000000, FPS 167.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 443/10000 episodes, total num timesteps 88800/2000000, FPS 167.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 444/10000 episodes, total num timesteps 89000/2000000, FPS 167.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 445/10000 episodes, total num timesteps 89200/2000000, FPS 167.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 446/10000 episodes, total num timesteps 89400/2000000, FPS 167.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 447/10000 episodes, total num timesteps 89600/2000000, FPS 167.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 448/10000 episodes, total num timesteps 89800/2000000, FPS 167.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 449/10000 episodes, total num timesteps 90000/2000000, FPS 167.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 450/10000 episodes, total num timesteps 90200/2000000, FPS 167.

team_policy eval average step individual rewards of agent0: -0.053464893728396135
team_policy eval average team episode rewards of agent0: 0.0
team_policy eval idv catch total num of agent0: 2
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent1: 0.009400901670046978
team_policy eval average team episode rewards of agent1: 0.0
team_policy eval idv catch total num of agent1: 5
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent2: -0.11179878600923748
team_policy eval average team episode rewards of agent2: 0.0
team_policy eval idv catch total num of agent2: 0
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent3: 0.06454856264353989
team_policy eval average team episode rewards of agent3: 0.0
team_policy eval idv catch total num of agent3: 7
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent4: 0.024090008083936985
team_policy eval average team episode rewards of agent4: 0.0
team_policy eval idv catch total num of agent4: 5
team_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent0: -0.03723359723984784
idv_policy eval average team episode rewards of agent0: 2.5
idv_policy eval idv catch total num of agent0: 2
idv_policy eval team catch total num: 1
idv_policy eval average step individual rewards of agent1: -0.03758783306264982
idv_policy eval average team episode rewards of agent1: 2.5
idv_policy eval idv catch total num of agent1: 3
idv_policy eval team catch total num: 1
idv_policy eval average step individual rewards of agent2: 0.020428065879612677
idv_policy eval average team episode rewards of agent2: 2.5
idv_policy eval idv catch total num of agent2: 5
idv_policy eval team catch total num: 1
idv_policy eval average step individual rewards of agent3: -0.0010957472932897883
idv_policy eval average team episode rewards of agent3: 2.5
idv_policy eval idv catch total num of agent3: 4
idv_policy eval team catch total num: 1
idv_policy eval average step individual rewards of agent4: 0.033578204083966195
idv_policy eval average team episode rewards of agent4: 2.5
idv_policy eval idv catch total num of agent4: 5
idv_policy eval team catch total num: 1

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 451/10000 episodes, total num timesteps 90400/2000000, FPS 167.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 452/10000 episodes, total num timesteps 90600/2000000, FPS 167.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 453/10000 episodes, total num timesteps 90800/2000000, FPS 167.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 454/10000 episodes, total num timesteps 91000/2000000, FPS 167.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 455/10000 episodes, total num timesteps 91200/2000000, FPS 167.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 456/10000 episodes, total num timesteps 91400/2000000, FPS 167.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 457/10000 episodes, total num timesteps 91600/2000000, FPS 167.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 458/10000 episodes, total num timesteps 91800/2000000, FPS 167.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 459/10000 episodes, total num timesteps 92000/2000000, FPS 167.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 460/10000 episodes, total num timesteps 92200/2000000, FPS 167.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 461/10000 episodes, total num timesteps 92400/2000000, FPS 168.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 462/10000 episodes, total num timesteps 92600/2000000, FPS 168.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 463/10000 episodes, total num timesteps 92800/2000000, FPS 168.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 464/10000 episodes, total num timesteps 93000/2000000, FPS 168.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 465/10000 episodes, total num timesteps 93200/2000000, FPS 168.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 466/10000 episodes, total num timesteps 93400/2000000, FPS 168.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 467/10000 episodes, total num timesteps 93600/2000000, FPS 168.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 468/10000 episodes, total num timesteps 93800/2000000, FPS 168.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 469/10000 episodes, total num timesteps 94000/2000000, FPS 168.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 470/10000 episodes, total num timesteps 94200/2000000, FPS 168.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 471/10000 episodes, total num timesteps 94400/2000000, FPS 168.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 472/10000 episodes, total num timesteps 94600/2000000, FPS 168.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 473/10000 episodes, total num timesteps 94800/2000000, FPS 168.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 474/10000 episodes, total num timesteps 95000/2000000, FPS 168.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 475/10000 episodes, total num timesteps 95200/2000000, FPS 168.

team_policy eval average step individual rewards of agent0: -0.048697933840941676
team_policy eval average team episode rewards of agent0: 2.5
team_policy eval idv catch total num of agent0: 2
team_policy eval team catch total num: 1
team_policy eval average step individual rewards of agent1: -0.05472771678595359
team_policy eval average team episode rewards of agent1: 2.5
team_policy eval idv catch total num of agent1: 2
team_policy eval team catch total num: 1
team_policy eval average step individual rewards of agent2: 0.08127892333651492
team_policy eval average team episode rewards of agent2: 2.5
team_policy eval idv catch total num of agent2: 7
team_policy eval team catch total num: 1
team_policy eval average step individual rewards of agent3: 0.06915413299716551
team_policy eval average team episode rewards of agent3: 2.5
team_policy eval idv catch total num of agent3: 6
team_policy eval team catch total num: 1
team_policy eval average step individual rewards of agent4: -0.03953956907070655
team_policy eval average team episode rewards of agent4: 2.5
team_policy eval idv catch total num of agent4: 2
team_policy eval team catch total num: 1
idv_policy eval average step individual rewards of agent0: 0.017645926190312907
idv_policy eval average team episode rewards of agent0: 0.0
idv_policy eval idv catch total num of agent0: 4
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent1: -0.0313159121386853
idv_policy eval average team episode rewards of agent1: 0.0
idv_policy eval idv catch total num of agent1: 3
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent2: 0.06802671678627678
idv_policy eval average team episode rewards of agent2: 0.0
idv_policy eval idv catch total num of agent2: 6
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent3: 0.02133699403892158
idv_policy eval average team episode rewards of agent3: 0.0
idv_policy eval idv catch total num of agent3: 4
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent4: -0.10281613381820094
idv_policy eval average team episode rewards of agent4: 0.0
idv_policy eval idv catch total num of agent4: 0
idv_policy eval team catch total num: 0

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 476/10000 episodes, total num timesteps 95400/2000000, FPS 168.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 477/10000 episodes, total num timesteps 95600/2000000, FPS 168.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 478/10000 episodes, total num timesteps 95800/2000000, FPS 168.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 479/10000 episodes, total num timesteps 96000/2000000, FPS 168.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 480/10000 episodes, total num timesteps 96200/2000000, FPS 169.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 481/10000 episodes, total num timesteps 96400/2000000, FPS 169.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 482/10000 episodes, total num timesteps 96600/2000000, FPS 169.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 483/10000 episodes, total num timesteps 96800/2000000, FPS 169.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 484/10000 episodes, total num timesteps 97000/2000000, FPS 169.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 485/10000 episodes, total num timesteps 97200/2000000, FPS 169.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 486/10000 episodes, total num timesteps 97400/2000000, FPS 169.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 487/10000 episodes, total num timesteps 97600/2000000, FPS 169.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 488/10000 episodes, total num timesteps 97800/2000000, FPS 169.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 489/10000 episodes, total num timesteps 98000/2000000, FPS 169.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 490/10000 episodes, total num timesteps 98200/2000000, FPS 169.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 491/10000 episodes, total num timesteps 98400/2000000, FPS 169.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 492/10000 episodes, total num timesteps 98600/2000000, FPS 169.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 493/10000 episodes, total num timesteps 98800/2000000, FPS 169.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 494/10000 episodes, total num timesteps 99000/2000000, FPS 169.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 495/10000 episodes, total num timesteps 99200/2000000, FPS 169.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 496/10000 episodes, total num timesteps 99400/2000000, FPS 169.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 497/10000 episodes, total num timesteps 99600/2000000, FPS 169.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 498/10000 episodes, total num timesteps 99800/2000000, FPS 169.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 499/10000 episodes, total num timesteps 100000/2000000, FPS 169.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 500/10000 episodes, total num timesteps 100200/2000000, FPS 169.

team_policy eval average step individual rewards of agent0: -0.010079957517716105
team_policy eval average team episode rewards of agent0: 0.0
team_policy eval idv catch total num of agent0: 3
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent1: -0.014143102309639319
team_policy eval average team episode rewards of agent1: 0.0
team_policy eval idv catch total num of agent1: 4
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent2: -0.10352657321239478
team_policy eval average team episode rewards of agent2: 0.0
team_policy eval idv catch total num of agent2: 0
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent3: 0.011349234691041517
team_policy eval average team episode rewards of agent3: 0.0
team_policy eval idv catch total num of agent3: 4
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent4: -0.012871124896253567
team_policy eval average team episode rewards of agent4: 0.0
team_policy eval idv catch total num of agent4: 3
team_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent0: 0.04179623990540791
idv_policy eval average team episode rewards of agent0: 0.0
idv_policy eval idv catch total num of agent0: 5
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent1: -0.07251374753641208
idv_policy eval average team episode rewards of agent1: 0.0
idv_policy eval idv catch total num of agent1: 1
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent2: -0.04555779222763587
idv_policy eval average team episode rewards of agent2: 0.0
idv_policy eval idv catch total num of agent2: 2
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent3: -0.07588563314254912
idv_policy eval average team episode rewards of agent3: 0.0
idv_policy eval idv catch total num of agent3: 1
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent4: -0.07415618296219467
idv_policy eval average team episode rewards of agent4: 0.0
idv_policy eval idv catch total num of agent4: 1
idv_policy eval team catch total num: 0

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 501/10000 episodes, total num timesteps 100400/2000000, FPS 169.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 502/10000 episodes, total num timesteps 100600/2000000, FPS 169.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 503/10000 episodes, total num timesteps 100800/2000000, FPS 169.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 504/10000 episodes, total num timesteps 101000/2000000, FPS 169.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 505/10000 episodes, total num timesteps 101200/2000000, FPS 169.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 506/10000 episodes, total num timesteps 101400/2000000, FPS 170.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 507/10000 episodes, total num timesteps 101600/2000000, FPS 170.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 508/10000 episodes, total num timesteps 101800/2000000, FPS 170.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 509/10000 episodes, total num timesteps 102000/2000000, FPS 170.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 510/10000 episodes, total num timesteps 102200/2000000, FPS 170.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 511/10000 episodes, total num timesteps 102400/2000000, FPS 170.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 512/10000 episodes, total num timesteps 102600/2000000, FPS 170.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 513/10000 episodes, total num timesteps 102800/2000000, FPS 170.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 514/10000 episodes, total num timesteps 103000/2000000, FPS 170.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 515/10000 episodes, total num timesteps 103200/2000000, FPS 170.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 516/10000 episodes, total num timesteps 103400/2000000, FPS 170.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 517/10000 episodes, total num timesteps 103600/2000000, FPS 170.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 518/10000 episodes, total num timesteps 103800/2000000, FPS 170.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 519/10000 episodes, total num timesteps 104000/2000000, FPS 170.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 520/10000 episodes, total num timesteps 104200/2000000, FPS 170.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 521/10000 episodes, total num timesteps 104400/2000000, FPS 170.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 522/10000 episodes, total num timesteps 104600/2000000, FPS 170.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 523/10000 episodes, total num timesteps 104800/2000000, FPS 170.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 524/10000 episodes, total num timesteps 105000/2000000, FPS 171.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 525/10000 episodes, total num timesteps 105200/2000000, FPS 171.

team_policy eval average step individual rewards of agent0: 0.2692235854705075
team_policy eval average team episode rewards of agent0: 22.5
team_policy eval idv catch total num of agent0: 13
team_policy eval team catch total num: 9
team_policy eval average step individual rewards of agent1: 0.0703489877956041
team_policy eval average team episode rewards of agent1: 22.5
team_policy eval idv catch total num of agent1: 5
team_policy eval team catch total num: 9
team_policy eval average step individual rewards of agent2: -0.029417673075707487
team_policy eval average team episode rewards of agent2: 22.5
team_policy eval idv catch total num of agent2: 1
team_policy eval team catch total num: 9
team_policy eval average step individual rewards of agent3: 0.5075693490277299
team_policy eval average team episode rewards of agent3: 22.5
team_policy eval idv catch total num of agent3: 22
team_policy eval team catch total num: 9
team_policy eval average step individual rewards of agent4: 0.046940701149018924
team_policy eval average team episode rewards of agent4: 22.5
team_policy eval idv catch total num of agent4: 4
team_policy eval team catch total num: 9
idv_policy eval average step individual rewards of agent0: -0.047737197554012595
idv_policy eval average team episode rewards of agent0: 0.0
idv_policy eval idv catch total num of agent0: 2
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent1: -0.07986620111452249
idv_policy eval average team episode rewards of agent1: 0.0
idv_policy eval idv catch total num of agent1: 0
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent2: -0.026871373779161725
idv_policy eval average team episode rewards of agent2: 0.0
idv_policy eval idv catch total num of agent2: 2
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent3: -0.02194196385507346
idv_policy eval average team episode rewards of agent3: 0.0
idv_policy eval idv catch total num of agent3: 2
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent4: -0.07478705952931562
idv_policy eval average team episode rewards of agent4: 0.0
idv_policy eval idv catch total num of agent4: 0
idv_policy eval team catch total num: 0

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 526/10000 episodes, total num timesteps 105400/2000000, FPS 170.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 527/10000 episodes, total num timesteps 105600/2000000, FPS 170.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 528/10000 episodes, total num timesteps 105800/2000000, FPS 171.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 529/10000 episodes, total num timesteps 106000/2000000, FPS 171.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 530/10000 episodes, total num timesteps 106200/2000000, FPS 170.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 531/10000 episodes, total num timesteps 106400/2000000, FPS 170.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 532/10000 episodes, total num timesteps 106600/2000000, FPS 171.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 533/10000 episodes, total num timesteps 106800/2000000, FPS 171.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 534/10000 episodes, total num timesteps 107000/2000000, FPS 171.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 535/10000 episodes, total num timesteps 107200/2000000, FPS 171.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 536/10000 episodes, total num timesteps 107400/2000000, FPS 171.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 537/10000 episodes, total num timesteps 107600/2000000, FPS 171.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 538/10000 episodes, total num timesteps 107800/2000000, FPS 171.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 539/10000 episodes, total num timesteps 108000/2000000, FPS 171.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 540/10000 episodes, total num timesteps 108200/2000000, FPS 171.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 541/10000 episodes, total num timesteps 108400/2000000, FPS 171.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 542/10000 episodes, total num timesteps 108600/2000000, FPS 171.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 543/10000 episodes, total num timesteps 108800/2000000, FPS 171.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 544/10000 episodes, total num timesteps 109000/2000000, FPS 171.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 545/10000 episodes, total num timesteps 109200/2000000, FPS 171.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 546/10000 episodes, total num timesteps 109400/2000000, FPS 171.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 547/10000 episodes, total num timesteps 109600/2000000, FPS 171.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 548/10000 episodes, total num timesteps 109800/2000000, FPS 171.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 549/10000 episodes, total num timesteps 110000/2000000, FPS 171.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 550/10000 episodes, total num timesteps 110200/2000000, FPS 171.

team_policy eval average step individual rewards of agent0: 0.09535736654305965
team_policy eval average team episode rewards of agent0: 2.5
team_policy eval idv catch total num of agent0: 7
team_policy eval team catch total num: 1
team_policy eval average step individual rewards of agent1: 0.02882617719199759
team_policy eval average team episode rewards of agent1: 2.5
team_policy eval idv catch total num of agent1: 4
team_policy eval team catch total num: 1
team_policy eval average step individual rewards of agent2: -0.08200600550859269
team_policy eval average team episode rewards of agent2: 2.5
team_policy eval idv catch total num of agent2: 0
team_policy eval team catch total num: 1
team_policy eval average step individual rewards of agent3: 0.026665288997750548
team_policy eval average team episode rewards of agent3: 2.5
team_policy eval idv catch total num of agent3: 4
team_policy eval team catch total num: 1
team_policy eval average step individual rewards of agent4: -0.0013987519681885185
team_policy eval average team episode rewards of agent4: 2.5
team_policy eval idv catch total num of agent4: 3
team_policy eval team catch total num: 1
idv_policy eval average step individual rewards of agent0: -0.02623999025042691
idv_policy eval average team episode rewards of agent0: 2.5
idv_policy eval idv catch total num of agent0: 2
idv_policy eval team catch total num: 1
idv_policy eval average step individual rewards of agent1: 0.08020398704457772
idv_policy eval average team episode rewards of agent1: 2.5
idv_policy eval idv catch total num of agent1: 6
idv_policy eval team catch total num: 1
idv_policy eval average step individual rewards of agent2: -0.030007837797873825
idv_policy eval average team episode rewards of agent2: 2.5
idv_policy eval idv catch total num of agent2: 2
idv_policy eval team catch total num: 1
idv_policy eval average step individual rewards of agent3: 0.047383431141959115
idv_policy eval average team episode rewards of agent3: 2.5
idv_policy eval idv catch total num of agent3: 5
idv_policy eval team catch total num: 1
idv_policy eval average step individual rewards of agent4: 0.004193132594521689
idv_policy eval average team episode rewards of agent4: 2.5
idv_policy eval idv catch total num of agent4: 3
idv_policy eval team catch total num: 1

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 551/10000 episodes, total num timesteps 110400/2000000, FPS 171.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 552/10000 episodes, total num timesteps 110600/2000000, FPS 172.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 553/10000 episodes, total num timesteps 110800/2000000, FPS 172.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 554/10000 episodes, total num timesteps 111000/2000000, FPS 172.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 555/10000 episodes, total num timesteps 111200/2000000, FPS 172.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 556/10000 episodes, total num timesteps 111400/2000000, FPS 172.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 557/10000 episodes, total num timesteps 111600/2000000, FPS 172.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 558/10000 episodes, total num timesteps 111800/2000000, FPS 172.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 559/10000 episodes, total num timesteps 112000/2000000, FPS 172.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 560/10000 episodes, total num timesteps 112200/2000000, FPS 172.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 561/10000 episodes, total num timesteps 112400/2000000, FPS 172.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 562/10000 episodes, total num timesteps 112600/2000000, FPS 172.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 563/10000 episodes, total num timesteps 112800/2000000, FPS 172.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 564/10000 episodes, total num timesteps 113000/2000000, FPS 172.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 565/10000 episodes, total num timesteps 113200/2000000, FPS 172.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 566/10000 episodes, total num timesteps 113400/2000000, FPS 172.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 567/10000 episodes, total num timesteps 113600/2000000, FPS 173.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 568/10000 episodes, total num timesteps 113800/2000000, FPS 173.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 569/10000 episodes, total num timesteps 114000/2000000, FPS 173.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 570/10000 episodes, total num timesteps 114200/2000000, FPS 173.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 571/10000 episodes, total num timesteps 114400/2000000, FPS 173.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 572/10000 episodes, total num timesteps 114600/2000000, FPS 173.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 573/10000 episodes, total num timesteps 114800/2000000, FPS 173.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 574/10000 episodes, total num timesteps 115000/2000000, FPS 173.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 575/10000 episodes, total num timesteps 115200/2000000, FPS 173.

team_policy eval average step individual rewards of agent0: 0.03788235402873713
team_policy eval average team episode rewards of agent0: 17.5
team_policy eval idv catch total num of agent0: 4
team_policy eval team catch total num: 7
team_policy eval average step individual rewards of agent1: 0.1164588676939923
team_policy eval average team episode rewards of agent1: 17.5
team_policy eval idv catch total num of agent1: 7
team_policy eval team catch total num: 7
team_policy eval average step individual rewards of agent2: 0.0669506391008758
team_policy eval average team episode rewards of agent2: 17.5
team_policy eval idv catch total num of agent2: 5
team_policy eval team catch total num: 7
team_policy eval average step individual rewards of agent3: 0.29874884487584885
team_policy eval average team episode rewards of agent3: 17.5
team_policy eval idv catch total num of agent3: 14
team_policy eval team catch total num: 7
team_policy eval average step individual rewards of agent4: 0.06640490987470855
team_policy eval average team episode rewards of agent4: 17.5
team_policy eval idv catch total num of agent4: 5
team_policy eval team catch total num: 7
idv_policy eval average step individual rewards of agent0: 0.035602441747292754
idv_policy eval average team episode rewards of agent0: 22.5
idv_policy eval idv catch total num of agent0: 4
idv_policy eval team catch total num: 9
idv_policy eval average step individual rewards of agent1: 0.1471266652113624
idv_policy eval average team episode rewards of agent1: 22.5
idv_policy eval idv catch total num of agent1: 8
idv_policy eval team catch total num: 9
idv_policy eval average step individual rewards of agent2: 0.2209721278432331
idv_policy eval average team episode rewards of agent2: 22.5
idv_policy eval idv catch total num of agent2: 11
idv_policy eval team catch total num: 9
idv_policy eval average step individual rewards of agent3: 0.11201077810923643
idv_policy eval average team episode rewards of agent3: 22.5
idv_policy eval idv catch total num of agent3: 7
idv_policy eval team catch total num: 9
idv_policy eval average step individual rewards of agent4: 0.0878777012733613
idv_policy eval average team episode rewards of agent4: 22.5
idv_policy eval idv catch total num of agent4: 6
idv_policy eval team catch total num: 9

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 576/10000 episodes, total num timesteps 115400/2000000, FPS 173.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 577/10000 episodes, total num timesteps 115600/2000000, FPS 173.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 578/10000 episodes, total num timesteps 115800/2000000, FPS 173.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 579/10000 episodes, total num timesteps 116000/2000000, FPS 173.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 580/10000 episodes, total num timesteps 116200/2000000, FPS 173.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 581/10000 episodes, total num timesteps 116400/2000000, FPS 173.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 582/10000 episodes, total num timesteps 116600/2000000, FPS 173.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 583/10000 episodes, total num timesteps 116800/2000000, FPS 173.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 584/10000 episodes, total num timesteps 117000/2000000, FPS 173.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 585/10000 episodes, total num timesteps 117200/2000000, FPS 174.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 586/10000 episodes, total num timesteps 117400/2000000, FPS 174.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 587/10000 episodes, total num timesteps 117600/2000000, FPS 174.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 588/10000 episodes, total num timesteps 117800/2000000, FPS 174.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 589/10000 episodes, total num timesteps 118000/2000000, FPS 174.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 590/10000 episodes, total num timesteps 118200/2000000, FPS 174.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 591/10000 episodes, total num timesteps 118400/2000000, FPS 174.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 592/10000 episodes, total num timesteps 118600/2000000, FPS 174.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 593/10000 episodes, total num timesteps 118800/2000000, FPS 174.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 594/10000 episodes, total num timesteps 119000/2000000, FPS 174.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 595/10000 episodes, total num timesteps 119200/2000000, FPS 174.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 596/10000 episodes, total num timesteps 119400/2000000, FPS 174.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 597/10000 episodes, total num timesteps 119600/2000000, FPS 174.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 598/10000 episodes, total num timesteps 119800/2000000, FPS 174.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 599/10000 episodes, total num timesteps 120000/2000000, FPS 174.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 600/10000 episodes, total num timesteps 120200/2000000, FPS 174.

team_policy eval average step individual rewards of agent0: 0.07967434831277513
team_policy eval average team episode rewards of agent0: 20.0
team_policy eval idv catch total num of agent0: 5
team_policy eval team catch total num: 8
team_policy eval average step individual rewards of agent1: 0.300183444049335
team_policy eval average team episode rewards of agent1: 20.0
team_policy eval idv catch total num of agent1: 14
team_policy eval team catch total num: 8
team_policy eval average step individual rewards of agent2: 0.12677832698726085
team_policy eval average team episode rewards of agent2: 20.0
team_policy eval idv catch total num of agent2: 7
team_policy eval team catch total num: 8
team_policy eval average step individual rewards of agent3: 0.28046574931539653
team_policy eval average team episode rewards of agent3: 20.0
team_policy eval idv catch total num of agent3: 13
team_policy eval team catch total num: 8
team_policy eval average step individual rewards of agent4: 0.046809050042045816
team_policy eval average team episode rewards of agent4: 20.0
team_policy eval idv catch total num of agent4: 4
team_policy eval team catch total num: 8
idv_policy eval average step individual rewards of agent0: 0.16495313981377227
idv_policy eval average team episode rewards of agent0: 20.0
idv_policy eval idv catch total num of agent0: 9
idv_policy eval team catch total num: 8
idv_policy eval average step individual rewards of agent1: 0.21379732641912055
idv_policy eval average team episode rewards of agent1: 20.0
idv_policy eval idv catch total num of agent1: 11
idv_policy eval team catch total num: 8
idv_policy eval average step individual rewards of agent2: 0.12032283387792132
idv_policy eval average team episode rewards of agent2: 20.0
idv_policy eval idv catch total num of agent2: 7
idv_policy eval team catch total num: 8
idv_policy eval average step individual rewards of agent3: 0.11390487859337231
idv_policy eval average team episode rewards of agent3: 20.0
idv_policy eval idv catch total num of agent3: 7
idv_policy eval team catch total num: 8
idv_policy eval average step individual rewards of agent4: 0.24543893166233757
idv_policy eval average team episode rewards of agent4: 20.0
idv_policy eval idv catch total num of agent4: 12
idv_policy eval team catch total num: 8

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 601/10000 episodes, total num timesteps 120400/2000000, FPS 174.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 602/10000 episodes, total num timesteps 120600/2000000, FPS 174.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 603/10000 episodes, total num timesteps 120800/2000000, FPS 175.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 604/10000 episodes, total num timesteps 121000/2000000, FPS 174.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 605/10000 episodes, total num timesteps 121200/2000000, FPS 175.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 606/10000 episodes, total num timesteps 121400/2000000, FPS 175.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 607/10000 episodes, total num timesteps 121600/2000000, FPS 175.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 608/10000 episodes, total num timesteps 121800/2000000, FPS 175.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 609/10000 episodes, total num timesteps 122000/2000000, FPS 175.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 610/10000 episodes, total num timesteps 122200/2000000, FPS 175.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 611/10000 episodes, total num timesteps 122400/2000000, FPS 175.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 612/10000 episodes, total num timesteps 122600/2000000, FPS 175.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 613/10000 episodes, total num timesteps 122800/2000000, FPS 175.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 614/10000 episodes, total num timesteps 123000/2000000, FPS 175.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 615/10000 episodes, total num timesteps 123200/2000000, FPS 175.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 616/10000 episodes, total num timesteps 123400/2000000, FPS 175.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 617/10000 episodes, total num timesteps 123600/2000000, FPS 175.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 618/10000 episodes, total num timesteps 123800/2000000, FPS 175.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 619/10000 episodes, total num timesteps 124000/2000000, FPS 175.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 620/10000 episodes, total num timesteps 124200/2000000, FPS 175.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 621/10000 episodes, total num timesteps 124400/2000000, FPS 175.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 622/10000 episodes, total num timesteps 124600/2000000, FPS 175.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 623/10000 episodes, total num timesteps 124800/2000000, FPS 175.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 624/10000 episodes, total num timesteps 125000/2000000, FPS 176.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 625/10000 episodes, total num timesteps 125200/2000000, FPS 176.

team_policy eval average step individual rewards of agent0: 0.184399847325728
team_policy eval average team episode rewards of agent0: 30.0
team_policy eval idv catch total num of agent0: 10
team_policy eval team catch total num: 12
team_policy eval average step individual rewards of agent1: 0.1444590132963074
team_policy eval average team episode rewards of agent1: 30.0
team_policy eval idv catch total num of agent1: 8
team_policy eval team catch total num: 12
team_policy eval average step individual rewards of agent2: 0.24211050519340596
team_policy eval average team episode rewards of agent2: 30.0
team_policy eval idv catch total num of agent2: 12
team_policy eval team catch total num: 12
team_policy eval average step individual rewards of agent3: 0.09405060123711294
team_policy eval average team episode rewards of agent3: 30.0
team_policy eval idv catch total num of agent3: 6
team_policy eval team catch total num: 12
team_policy eval average step individual rewards of agent4: 0.27202596015750463
team_policy eval average team episode rewards of agent4: 30.0
team_policy eval idv catch total num of agent4: 13
team_policy eval team catch total num: 12
idv_policy eval average step individual rewards of agent0: 0.015856480622821377
idv_policy eval average team episode rewards of agent0: 12.5
idv_policy eval idv catch total num of agent0: 3
idv_policy eval team catch total num: 5
idv_policy eval average step individual rewards of agent1: 0.21724195524874917
idv_policy eval average team episode rewards of agent1: 12.5
idv_policy eval idv catch total num of agent1: 11
idv_policy eval team catch total num: 5
idv_policy eval average step individual rewards of agent2: 0.12092989583656895
idv_policy eval average team episode rewards of agent2: 12.5
idv_policy eval idv catch total num of agent2: 7
idv_policy eval team catch total num: 5
idv_policy eval average step individual rewards of agent3: 0.043497392214504424
idv_policy eval average team episode rewards of agent3: 12.5
idv_policy eval idv catch total num of agent3: 4
idv_policy eval team catch total num: 5
idv_policy eval average step individual rewards of agent4: 0.09582729339758395
idv_policy eval average team episode rewards of agent4: 12.5
idv_policy eval idv catch total num of agent4: 6
idv_policy eval team catch total num: 5

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 626/10000 episodes, total num timesteps 125400/2000000, FPS 176.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 627/10000 episodes, total num timesteps 125600/2000000, FPS 176.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 628/10000 episodes, total num timesteps 125800/2000000, FPS 176.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 629/10000 episodes, total num timesteps 126000/2000000, FPS 176.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 630/10000 episodes, total num timesteps 126200/2000000, FPS 176.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 631/10000 episodes, total num timesteps 126400/2000000, FPS 176.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 632/10000 episodes, total num timesteps 126600/2000000, FPS 176.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 633/10000 episodes, total num timesteps 126800/2000000, FPS 176.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 634/10000 episodes, total num timesteps 127000/2000000, FPS 176.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 635/10000 episodes, total num timesteps 127200/2000000, FPS 176.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 636/10000 episodes, total num timesteps 127400/2000000, FPS 176.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 637/10000 episodes, total num timesteps 127600/2000000, FPS 176.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 638/10000 episodes, total num timesteps 127800/2000000, FPS 176.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 639/10000 episodes, total num timesteps 128000/2000000, FPS 176.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 640/10000 episodes, total num timesteps 128200/2000000, FPS 176.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 641/10000 episodes, total num timesteps 128400/2000000, FPS 176.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 642/10000 episodes, total num timesteps 128600/2000000, FPS 176.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 643/10000 episodes, total num timesteps 128800/2000000, FPS 176.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 644/10000 episodes, total num timesteps 129000/2000000, FPS 177.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 645/10000 episodes, total num timesteps 129200/2000000, FPS 177.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 646/10000 episodes, total num timesteps 129400/2000000, FPS 177.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 647/10000 episodes, total num timesteps 129600/2000000, FPS 177.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 648/10000 episodes, total num timesteps 129800/2000000, FPS 177.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 649/10000 episodes, total num timesteps 130000/2000000, FPS 177.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 650/10000 episodes, total num timesteps 130200/2000000, FPS 177.

team_policy eval average step individual rewards of agent0: -0.06886900353648227
team_policy eval average team episode rewards of agent0: 0.0
team_policy eval idv catch total num of agent0: 0
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent1: -0.06434185281377496
team_policy eval average team episode rewards of agent1: 0.0
team_policy eval idv catch total num of agent1: 0
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent2: -0.062484484107528396
team_policy eval average team episode rewards of agent2: 0.0
team_policy eval idv catch total num of agent2: 0
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent3: 0.033015993765208335
team_policy eval average team episode rewards of agent3: 0.0
team_policy eval idv catch total num of agent3: 4
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent4: -0.04070575963479852
team_policy eval average team episode rewards of agent4: 0.0
team_policy eval idv catch total num of agent4: 1
team_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent0: 0.03728053090658166
idv_policy eval average team episode rewards of agent0: 10.0
idv_policy eval idv catch total num of agent0: 4
idv_policy eval team catch total num: 4
idv_policy eval average step individual rewards of agent1: 0.2208689189051155
idv_policy eval average team episode rewards of agent1: 10.0
idv_policy eval idv catch total num of agent1: 11
idv_policy eval team catch total num: 4
idv_policy eval average step individual rewards of agent2: 0.044310442377143426
idv_policy eval average team episode rewards of agent2: 10.0
idv_policy eval idv catch total num of agent2: 4
idv_policy eval team catch total num: 4
idv_policy eval average step individual rewards of agent3: 0.12429271904294645
idv_policy eval average team episode rewards of agent3: 10.0
idv_policy eval idv catch total num of agent3: 7
idv_policy eval team catch total num: 4
idv_policy eval average step individual rewards of agent4: 0.03777615358808891
idv_policy eval average team episode rewards of agent4: 10.0
idv_policy eval idv catch total num of agent4: 4
idv_policy eval team catch total num: 4

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 651/10000 episodes, total num timesteps 130400/2000000, FPS 177.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 652/10000 episodes, total num timesteps 130600/2000000, FPS 177.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 653/10000 episodes, total num timesteps 130800/2000000, FPS 177.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 654/10000 episodes, total num timesteps 131000/2000000, FPS 177.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 655/10000 episodes, total num timesteps 131200/2000000, FPS 177.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 656/10000 episodes, total num timesteps 131400/2000000, FPS 177.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 657/10000 episodes, total num timesteps 131600/2000000, FPS 177.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 658/10000 episodes, total num timesteps 131800/2000000, FPS 177.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 659/10000 episodes, total num timesteps 132000/2000000, FPS 177.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 660/10000 episodes, total num timesteps 132200/2000000, FPS 177.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 661/10000 episodes, total num timesteps 132400/2000000, FPS 177.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 662/10000 episodes, total num timesteps 132600/2000000, FPS 177.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 663/10000 episodes, total num timesteps 132800/2000000, FPS 178.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 664/10000 episodes, total num timesteps 133000/2000000, FPS 178.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 665/10000 episodes, total num timesteps 133200/2000000, FPS 178.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 666/10000 episodes, total num timesteps 133400/2000000, FPS 178.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 667/10000 episodes, total num timesteps 133600/2000000, FPS 178.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 668/10000 episodes, total num timesteps 133800/2000000, FPS 178.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 669/10000 episodes, total num timesteps 134000/2000000, FPS 178.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 670/10000 episodes, total num timesteps 134200/2000000, FPS 178.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 671/10000 episodes, total num timesteps 134400/2000000, FPS 178.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 672/10000 episodes, total num timesteps 134600/2000000, FPS 178.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 673/10000 episodes, total num timesteps 134800/2000000, FPS 178.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 674/10000 episodes, total num timesteps 135000/2000000, FPS 178.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 675/10000 episodes, total num timesteps 135200/2000000, FPS 178.

team_policy eval average step individual rewards of agent0: 0.11344121931037535
team_policy eval average team episode rewards of agent0: 27.5
team_policy eval idv catch total num of agent0: 7
team_policy eval team catch total num: 11
team_policy eval average step individual rewards of agent1: 0.24739344226119855
team_policy eval average team episode rewards of agent1: 27.5
team_policy eval idv catch total num of agent1: 12
team_policy eval team catch total num: 11
team_policy eval average step individual rewards of agent2: 0.19351323170725085
team_policy eval average team episode rewards of agent2: 27.5
team_policy eval idv catch total num of agent2: 10
team_policy eval team catch total num: 11
team_policy eval average step individual rewards of agent3: 0.33109111111341216
team_policy eval average team episode rewards of agent3: 27.5
team_policy eval idv catch total num of agent3: 16
team_policy eval team catch total num: 11
team_policy eval average step individual rewards of agent4: 0.15933398536403293
team_policy eval average team episode rewards of agent4: 27.5
team_policy eval idv catch total num of agent4: 9
team_policy eval team catch total num: 11
idv_policy eval average step individual rewards of agent0: 0.02922632964567965
idv_policy eval average team episode rewards of agent0: 17.5
idv_policy eval idv catch total num of agent0: 3
idv_policy eval team catch total num: 7
idv_policy eval average step individual rewards of agent1: 0.2730697125328896
idv_policy eval average team episode rewards of agent1: 17.5
idv_policy eval idv catch total num of agent1: 13
idv_policy eval team catch total num: 7
idv_policy eval average step individual rewards of agent2: 0.12608039107284089
idv_policy eval average team episode rewards of agent2: 17.5
idv_policy eval idv catch total num of agent2: 7
idv_policy eval team catch total num: 7
idv_policy eval average step individual rewards of agent3: 0.07609831283182936
idv_policy eval average team episode rewards of agent3: 17.5
idv_policy eval idv catch total num of agent3: 5
idv_policy eval team catch total num: 7
idv_policy eval average step individual rewards of agent4: 0.14985030033869418
idv_policy eval average team episode rewards of agent4: 17.5
idv_policy eval idv catch total num of agent4: 8
idv_policy eval team catch total num: 7

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 676/10000 episodes, total num timesteps 135400/2000000, FPS 178.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 677/10000 episodes, total num timesteps 135600/2000000, FPS 178.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 678/10000 episodes, total num timesteps 135800/2000000, FPS 178.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 679/10000 episodes, total num timesteps 136000/2000000, FPS 178.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 680/10000 episodes, total num timesteps 136200/2000000, FPS 178.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 681/10000 episodes, total num timesteps 136400/2000000, FPS 178.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 682/10000 episodes, total num timesteps 136600/2000000, FPS 179.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 683/10000 episodes, total num timesteps 136800/2000000, FPS 179.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 684/10000 episodes, total num timesteps 137000/2000000, FPS 179.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 685/10000 episodes, total num timesteps 137200/2000000, FPS 179.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 686/10000 episodes, total num timesteps 137400/2000000, FPS 179.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 687/10000 episodes, total num timesteps 137600/2000000, FPS 179.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 688/10000 episodes, total num timesteps 137800/2000000, FPS 179.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 689/10000 episodes, total num timesteps 138000/2000000, FPS 179.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 690/10000 episodes, total num timesteps 138200/2000000, FPS 179.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 691/10000 episodes, total num timesteps 138400/2000000, FPS 179.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 692/10000 episodes, total num timesteps 138600/2000000, FPS 179.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 693/10000 episodes, total num timesteps 138800/2000000, FPS 179.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 694/10000 episodes, total num timesteps 139000/2000000, FPS 179.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 695/10000 episodes, total num timesteps 139200/2000000, FPS 179.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 696/10000 episodes, total num timesteps 139400/2000000, FPS 179.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 697/10000 episodes, total num timesteps 139600/2000000, FPS 179.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 698/10000 episodes, total num timesteps 139800/2000000, FPS 179.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 699/10000 episodes, total num timesteps 140000/2000000, FPS 179.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 700/10000 episodes, total num timesteps 140200/2000000, FPS 179.

team_policy eval average step individual rewards of agent0: 0.17477127816871105
team_policy eval average team episode rewards of agent0: 17.5
team_policy eval idv catch total num of agent0: 9
team_policy eval team catch total num: 7
team_policy eval average step individual rewards of agent1: 0.048073251523988336
team_policy eval average team episode rewards of agent1: 17.5
team_policy eval idv catch total num of agent1: 4
team_policy eval team catch total num: 7
team_policy eval average step individual rewards of agent2: 0.1475902137033434
team_policy eval average team episode rewards of agent2: 17.5
team_policy eval idv catch total num of agent2: 8
team_policy eval team catch total num: 7
team_policy eval average step individual rewards of agent3: 0.04597716780848056
team_policy eval average team episode rewards of agent3: 17.5
team_policy eval idv catch total num of agent3: 4
team_policy eval team catch total num: 7
team_policy eval average step individual rewards of agent4: 0.12049624505184588
team_policy eval average team episode rewards of agent4: 17.5
team_policy eval idv catch total num of agent4: 7
team_policy eval team catch total num: 7
idv_policy eval average step individual rewards of agent0: 0.11208340290523182
idv_policy eval average team episode rewards of agent0: 20.0
idv_policy eval idv catch total num of agent0: 7
idv_policy eval team catch total num: 8
idv_policy eval average step individual rewards of agent1: 0.007535611238235864
idv_policy eval average team episode rewards of agent1: 20.0
idv_policy eval idv catch total num of agent1: 3
idv_policy eval team catch total num: 8
idv_policy eval average step individual rewards of agent2: 0.10840082917647789
idv_policy eval average team episode rewards of agent2: 20.0
idv_policy eval idv catch total num of agent2: 7
idv_policy eval team catch total num: 8
idv_policy eval average step individual rewards of agent3: 0.15589518982955966
idv_policy eval average team episode rewards of agent3: 20.0
idv_policy eval idv catch total num of agent3: 9
idv_policy eval team catch total num: 8
idv_policy eval average step individual rewards of agent4: 0.07130987936327046
idv_policy eval average team episode rewards of agent4: 20.0
idv_policy eval idv catch total num of agent4: 6
idv_policy eval team catch total num: 8

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 701/10000 episodes, total num timesteps 140400/2000000, FPS 179.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 702/10000 episodes, total num timesteps 140600/2000000, FPS 179.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 703/10000 episodes, total num timesteps 140800/2000000, FPS 179.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 704/10000 episodes, total num timesteps 141000/2000000, FPS 179.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 705/10000 episodes, total num timesteps 141200/2000000, FPS 179.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 706/10000 episodes, total num timesteps 141400/2000000, FPS 179.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 707/10000 episodes, total num timesteps 141600/2000000, FPS 179.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 708/10000 episodes, total num timesteps 141800/2000000, FPS 179.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 709/10000 episodes, total num timesteps 142000/2000000, FPS 180.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 710/10000 episodes, total num timesteps 142200/2000000, FPS 180.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 711/10000 episodes, total num timesteps 142400/2000000, FPS 180.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 712/10000 episodes, total num timesteps 142600/2000000, FPS 180.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 713/10000 episodes, total num timesteps 142800/2000000, FPS 180.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 714/10000 episodes, total num timesteps 143000/2000000, FPS 180.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 715/10000 episodes, total num timesteps 143200/2000000, FPS 180.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 716/10000 episodes, total num timesteps 143400/2000000, FPS 180.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 717/10000 episodes, total num timesteps 143600/2000000, FPS 180.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 718/10000 episodes, total num timesteps 143800/2000000, FPS 180.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 719/10000 episodes, total num timesteps 144000/2000000, FPS 180.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 720/10000 episodes, total num timesteps 144200/2000000, FPS 180.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 721/10000 episodes, total num timesteps 144400/2000000, FPS 180.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 722/10000 episodes, total num timesteps 144600/2000000, FPS 180.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 723/10000 episodes, total num timesteps 144800/2000000, FPS 180.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 724/10000 episodes, total num timesteps 145000/2000000, FPS 180.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 725/10000 episodes, total num timesteps 145200/2000000, FPS 180.

team_policy eval average step individual rewards of agent0: 0.23138144175407416
team_policy eval average team episode rewards of agent0: 30.0
team_policy eval idv catch total num of agent0: 11
team_policy eval team catch total num: 12
team_policy eval average step individual rewards of agent1: 0.3011615819796593
team_policy eval average team episode rewards of agent1: 30.0
team_policy eval idv catch total num of agent1: 14
team_policy eval team catch total num: 12
team_policy eval average step individual rewards of agent2: 0.14975655869095608
team_policy eval average team episode rewards of agent2: 30.0
team_policy eval idv catch total num of agent2: 8
team_policy eval team catch total num: 12
team_policy eval average step individual rewards of agent3: -0.004214788510722092
team_policy eval average team episode rewards of agent3: 30.0
team_policy eval idv catch total num of agent3: 2
team_policy eval team catch total num: 12
team_policy eval average step individual rewards of agent4: 0.225690710881178
team_policy eval average team episode rewards of agent4: 30.0
team_policy eval idv catch total num of agent4: 11
team_policy eval team catch total num: 12
idv_policy eval average step individual rewards of agent0: 0.2748207646457375
idv_policy eval average team episode rewards of agent0: 32.5
idv_policy eval idv catch total num of agent0: 13
idv_policy eval team catch total num: 13
idv_policy eval average step individual rewards of agent1: 0.27404313756511944
idv_policy eval average team episode rewards of agent1: 32.5
idv_policy eval idv catch total num of agent1: 13
idv_policy eval team catch total num: 13
idv_policy eval average step individual rewards of agent2: 0.20486964243774544
idv_policy eval average team episode rewards of agent2: 32.5
idv_policy eval idv catch total num of agent2: 10
idv_policy eval team catch total num: 13
idv_policy eval average step individual rewards of agent3: 0.1979178869801804
idv_policy eval average team episode rewards of agent3: 32.5
idv_policy eval idv catch total num of agent3: 10
idv_policy eval team catch total num: 13
idv_policy eval average step individual rewards of agent4: 0.17589420773331213
idv_policy eval average team episode rewards of agent4: 32.5
idv_policy eval idv catch total num of agent4: 9
idv_policy eval team catch total num: 13

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 726/10000 episodes, total num timesteps 145400/2000000, FPS 180.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 727/10000 episodes, total num timesteps 145600/2000000, FPS 180.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 728/10000 episodes, total num timesteps 145800/2000000, FPS 180.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 729/10000 episodes, total num timesteps 146000/2000000, FPS 180.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 730/10000 episodes, total num timesteps 146200/2000000, FPS 181.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 731/10000 episodes, total num timesteps 146400/2000000, FPS 181.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 732/10000 episodes, total num timesteps 146600/2000000, FPS 181.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 733/10000 episodes, total num timesteps 146800/2000000, FPS 181.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 734/10000 episodes, total num timesteps 147000/2000000, FPS 181.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 735/10000 episodes, total num timesteps 147200/2000000, FPS 181.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 736/10000 episodes, total num timesteps 147400/2000000, FPS 181.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 737/10000 episodes, total num timesteps 147600/2000000, FPS 181.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 738/10000 episodes, total num timesteps 147800/2000000, FPS 181.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 739/10000 episodes, total num timesteps 148000/2000000, FPS 181.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 740/10000 episodes, total num timesteps 148200/2000000, FPS 181.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 741/10000 episodes, total num timesteps 148400/2000000, FPS 181.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 742/10000 episodes, total num timesteps 148600/2000000, FPS 181.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 743/10000 episodes, total num timesteps 148800/2000000, FPS 181.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 744/10000 episodes, total num timesteps 149000/2000000, FPS 181.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 745/10000 episodes, total num timesteps 149200/2000000, FPS 181.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 746/10000 episodes, total num timesteps 149400/2000000, FPS 181.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 747/10000 episodes, total num timesteps 149600/2000000, FPS 181.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 748/10000 episodes, total num timesteps 149800/2000000, FPS 181.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 749/10000 episodes, total num timesteps 150000/2000000, FPS 181.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 750/10000 episodes, total num timesteps 150200/2000000, FPS 181.

team_policy eval average step individual rewards of agent0: 0.06637243796669651
team_policy eval average team episode rewards of agent0: 20.0
team_policy eval idv catch total num of agent0: 5
team_policy eval team catch total num: 8
team_policy eval average step individual rewards of agent1: 0.09968640686639431
team_policy eval average team episode rewards of agent1: 20.0
team_policy eval idv catch total num of agent1: 6
team_policy eval team catch total num: 8
team_policy eval average step individual rewards of agent2: 0.14959168445592688
team_policy eval average team episode rewards of agent2: 20.0
team_policy eval idv catch total num of agent2: 8
team_policy eval team catch total num: 8
team_policy eval average step individual rewards of agent3: 0.06444390110577335
team_policy eval average team episode rewards of agent3: 20.0
team_policy eval idv catch total num of agent3: 5
team_policy eval team catch total num: 8
team_policy eval average step individual rewards of agent4: 0.14265129686536926
team_policy eval average team episode rewards of agent4: 20.0
team_policy eval idv catch total num of agent4: 8
team_policy eval team catch total num: 8
idv_policy eval average step individual rewards of agent0: -0.026212408208142923
idv_policy eval average team episode rewards of agent0: 15.0
idv_policy eval idv catch total num of agent0: 1
idv_policy eval team catch total num: 6
idv_policy eval average step individual rewards of agent1: 0.13176839354501596
idv_policy eval average team episode rewards of agent1: 15.0
idv_policy eval idv catch total num of agent1: 7
idv_policy eval team catch total num: 6
idv_policy eval average step individual rewards of agent2: 0.058486183041507184
idv_policy eval average team episode rewards of agent2: 15.0
idv_policy eval idv catch total num of agent2: 4
idv_policy eval team catch total num: 6
idv_policy eval average step individual rewards of agent3: 0.18138672424271207
idv_policy eval average team episode rewards of agent3: 15.0
idv_policy eval idv catch total num of agent3: 9
idv_policy eval team catch total num: 6
idv_policy eval average step individual rewards of agent4: 0.17985070219216895
idv_policy eval average team episode rewards of agent4: 15.0
idv_policy eval idv catch total num of agent4: 9
idv_policy eval team catch total num: 6

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 751/10000 episodes, total num timesteps 150400/2000000, FPS 181.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 752/10000 episodes, total num timesteps 150600/2000000, FPS 181.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 753/10000 episodes, total num timesteps 150800/2000000, FPS 181.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 754/10000 episodes, total num timesteps 151000/2000000, FPS 181.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 755/10000 episodes, total num timesteps 151200/2000000, FPS 181.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 756/10000 episodes, total num timesteps 151400/2000000, FPS 181.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 757/10000 episodes, total num timesteps 151600/2000000, FPS 181.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 758/10000 episodes, total num timesteps 151800/2000000, FPS 181.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 759/10000 episodes, total num timesteps 152000/2000000, FPS 181.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 760/10000 episodes, total num timesteps 152200/2000000, FPS 181.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 761/10000 episodes, total num timesteps 152400/2000000, FPS 181.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 762/10000 episodes, total num timesteps 152600/2000000, FPS 181.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 763/10000 episodes, total num timesteps 152800/2000000, FPS 182.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 764/10000 episodes, total num timesteps 153000/2000000, FPS 181.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 765/10000 episodes, total num timesteps 153200/2000000, FPS 182.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 766/10000 episodes, total num timesteps 153400/2000000, FPS 182.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 767/10000 episodes, total num timesteps 153600/2000000, FPS 182.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 768/10000 episodes, total num timesteps 153800/2000000, FPS 182.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 769/10000 episodes, total num timesteps 154000/2000000, FPS 182.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 770/10000 episodes, total num timesteps 154200/2000000, FPS 182.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 771/10000 episodes, total num timesteps 154400/2000000, FPS 182.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 772/10000 episodes, total num timesteps 154600/2000000, FPS 182.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 773/10000 episodes, total num timesteps 154800/2000000, FPS 182.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 774/10000 episodes, total num timesteps 155000/2000000, FPS 182.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 775/10000 episodes, total num timesteps 155200/2000000, FPS 182.

team_policy eval average step individual rewards of agent0: 0.015489735957585744
team_policy eval average team episode rewards of agent0: 42.5
team_policy eval idv catch total num of agent0: 3
team_policy eval team catch total num: 17
team_policy eval average step individual rewards of agent1: 0.3295136519645878
team_policy eval average team episode rewards of agent1: 42.5
team_policy eval idv catch total num of agent1: 15
team_policy eval team catch total num: 17
team_policy eval average step individual rewards of agent2: 0.43187223266928626
team_policy eval average team episode rewards of agent2: 42.5
team_policy eval idv catch total num of agent2: 19
team_policy eval team catch total num: 17
team_policy eval average step individual rewards of agent3: 0.2828615779123277
team_policy eval average team episode rewards of agent3: 42.5
team_policy eval idv catch total num of agent3: 13
team_policy eval team catch total num: 17
team_policy eval average step individual rewards of agent4: 0.25588395116417706
team_policy eval average team episode rewards of agent4: 42.5
team_policy eval idv catch total num of agent4: 12
team_policy eval team catch total num: 17
idv_policy eval average step individual rewards of agent0: 0.025903297135550202
idv_policy eval average team episode rewards of agent0: 27.5
idv_policy eval idv catch total num of agent0: 3
idv_policy eval team catch total num: 11
idv_policy eval average step individual rewards of agent1: 0.1535323612849607
idv_policy eval average team episode rewards of agent1: 27.5
idv_policy eval idv catch total num of agent1: 8
idv_policy eval team catch total num: 11
idv_policy eval average step individual rewards of agent2: 0.22691557444025953
idv_policy eval average team episode rewards of agent2: 27.5
idv_policy eval idv catch total num of agent2: 11
idv_policy eval team catch total num: 11
idv_policy eval average step individual rewards of agent3: 0.22944275587857363
idv_policy eval average team episode rewards of agent3: 27.5
idv_policy eval idv catch total num of agent3: 11
idv_policy eval team catch total num: 11
idv_policy eval average step individual rewards of agent4: 0.15047838704610872
idv_policy eval average team episode rewards of agent4: 27.5
idv_policy eval idv catch total num of agent4: 8
idv_policy eval team catch total num: 11

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 776/10000 episodes, total num timesteps 155400/2000000, FPS 182.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 777/10000 episodes, total num timesteps 155600/2000000, FPS 182.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 778/10000 episodes, total num timesteps 155800/2000000, FPS 182.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 779/10000 episodes, total num timesteps 156000/2000000, FPS 182.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 780/10000 episodes, total num timesteps 156200/2000000, FPS 182.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 781/10000 episodes, total num timesteps 156400/2000000, FPS 182.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 782/10000 episodes, total num timesteps 156600/2000000, FPS 182.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 783/10000 episodes, total num timesteps 156800/2000000, FPS 182.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 784/10000 episodes, total num timesteps 157000/2000000, FPS 182.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 785/10000 episodes, total num timesteps 157200/2000000, FPS 182.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 786/10000 episodes, total num timesteps 157400/2000000, FPS 182.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 787/10000 episodes, total num timesteps 157600/2000000, FPS 182.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 788/10000 episodes, total num timesteps 157800/2000000, FPS 182.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 789/10000 episodes, total num timesteps 158000/2000000, FPS 182.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 790/10000 episodes, total num timesteps 158200/2000000, FPS 182.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 791/10000 episodes, total num timesteps 158400/2000000, FPS 182.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 792/10000 episodes, total num timesteps 158600/2000000, FPS 183.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 793/10000 episodes, total num timesteps 158800/2000000, FPS 183.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 794/10000 episodes, total num timesteps 159000/2000000, FPS 183.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 795/10000 episodes, total num timesteps 159200/2000000, FPS 183.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 796/10000 episodes, total num timesteps 159400/2000000, FPS 183.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 797/10000 episodes, total num timesteps 159600/2000000, FPS 183.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 798/10000 episodes, total num timesteps 159800/2000000, FPS 183.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 799/10000 episodes, total num timesteps 160000/2000000, FPS 183.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 800/10000 episodes, total num timesteps 160200/2000000, FPS 183.

team_policy eval average step individual rewards of agent0: 0.1766581382299675
team_policy eval average team episode rewards of agent0: 32.5
team_policy eval idv catch total num of agent0: 9
team_policy eval team catch total num: 13
team_policy eval average step individual rewards of agent1: 0.30396143713646007
team_policy eval average team episode rewards of agent1: 32.5
team_policy eval idv catch total num of agent1: 14
team_policy eval team catch total num: 13
team_policy eval average step individual rewards of agent2: 0.3566603307199604
team_policy eval average team episode rewards of agent2: 32.5
team_policy eval idv catch total num of agent2: 16
team_policy eval team catch total num: 13
team_policy eval average step individual rewards of agent3: 0.07119797870250441
team_policy eval average team episode rewards of agent3: 32.5
team_policy eval idv catch total num of agent3: 5
team_policy eval team catch total num: 13
team_policy eval average step individual rewards of agent4: 0.3859036969513597
team_policy eval average team episode rewards of agent4: 32.5
team_policy eval idv catch total num of agent4: 17
team_policy eval team catch total num: 13
idv_policy eval average step individual rewards of agent0: 0.18535451632040995
idv_policy eval average team episode rewards of agent0: 12.5
idv_policy eval idv catch total num of agent0: 9
idv_policy eval team catch total num: 5
idv_policy eval average step individual rewards of agent1: 0.2554919602316935
idv_policy eval average team episode rewards of agent1: 12.5
idv_policy eval idv catch total num of agent1: 12
idv_policy eval team catch total num: 5
idv_policy eval average step individual rewards of agent2: 0.15069672708724796
idv_policy eval average team episode rewards of agent2: 12.5
idv_policy eval idv catch total num of agent2: 8
idv_policy eval team catch total num: 5
idv_policy eval average step individual rewards of agent3: 0.16128853050961778
idv_policy eval average team episode rewards of agent3: 12.5
idv_policy eval idv catch total num of agent3: 8
idv_policy eval team catch total num: 5
idv_policy eval average step individual rewards of agent4: 0.004288410626572985
idv_policy eval average team episode rewards of agent4: 12.5
idv_policy eval idv catch total num of agent4: 2
idv_policy eval team catch total num: 5

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 801/10000 episodes, total num timesteps 160400/2000000, FPS 183.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 802/10000 episodes, total num timesteps 160600/2000000, FPS 183.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 803/10000 episodes, total num timesteps 160800/2000000, FPS 183.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 804/10000 episodes, total num timesteps 161000/2000000, FPS 183.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 805/10000 episodes, total num timesteps 161200/2000000, FPS 183.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 806/10000 episodes, total num timesteps 161400/2000000, FPS 183.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 807/10000 episodes, total num timesteps 161600/2000000, FPS 183.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 808/10000 episodes, total num timesteps 161800/2000000, FPS 183.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 809/10000 episodes, total num timesteps 162000/2000000, FPS 183.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 810/10000 episodes, total num timesteps 162200/2000000, FPS 183.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 811/10000 episodes, total num timesteps 162400/2000000, FPS 183.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 812/10000 episodes, total num timesteps 162600/2000000, FPS 183.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 813/10000 episodes, total num timesteps 162800/2000000, FPS 183.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 814/10000 episodes, total num timesteps 163000/2000000, FPS 183.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 815/10000 episodes, total num timesteps 163200/2000000, FPS 183.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 816/10000 episodes, total num timesteps 163400/2000000, FPS 183.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 817/10000 episodes, total num timesteps 163600/2000000, FPS 183.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 818/10000 episodes, total num timesteps 163800/2000000, FPS 183.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 819/10000 episodes, total num timesteps 164000/2000000, FPS 183.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 820/10000 episodes, total num timesteps 164200/2000000, FPS 183.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 821/10000 episodes, total num timesteps 164400/2000000, FPS 183.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 822/10000 episodes, total num timesteps 164600/2000000, FPS 183.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 823/10000 episodes, total num timesteps 164800/2000000, FPS 184.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 824/10000 episodes, total num timesteps 165000/2000000, FPS 184.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 825/10000 episodes, total num timesteps 165200/2000000, FPS 184.

team_policy eval average step individual rewards of agent0: 0.2612545106729792
team_policy eval average team episode rewards of agent0: 7.5
team_policy eval idv catch total num of agent0: 12
team_policy eval team catch total num: 3
team_policy eval average step individual rewards of agent1: -0.02254894623296021
team_policy eval average team episode rewards of agent1: 7.5
team_policy eval idv catch total num of agent1: 1
team_policy eval team catch total num: 3
team_policy eval average step individual rewards of agent2: 0.05508035237545619
team_policy eval average team episode rewards of agent2: 7.5
team_policy eval idv catch total num of agent2: 4
team_policy eval team catch total num: 3
team_policy eval average step individual rewards of agent3: 0.004116447960982552
team_policy eval average team episode rewards of agent3: 7.5
team_policy eval idv catch total num of agent3: 2
team_policy eval team catch total num: 3
team_policy eval average step individual rewards of agent4: 0.2573721111179826
team_policy eval average team episode rewards of agent4: 7.5
team_policy eval idv catch total num of agent4: 12
team_policy eval team catch total num: 3
idv_policy eval average step individual rewards of agent0: 0.08468659195532627
idv_policy eval average team episode rewards of agent0: 17.5
idv_policy eval idv catch total num of agent0: 6
idv_policy eval team catch total num: 7
idv_policy eval average step individual rewards of agent1: -0.0038464743405754232
idv_policy eval average team episode rewards of agent1: 17.5
idv_policy eval idv catch total num of agent1: 2
idv_policy eval team catch total num: 7
idv_policy eval average step individual rewards of agent2: 0.1463302230993299
idv_policy eval average team episode rewards of agent2: 17.5
idv_policy eval idv catch total num of agent2: 8
idv_policy eval team catch total num: 7
idv_policy eval average step individual rewards of agent3: 0.14042082529912825
idv_policy eval average team episode rewards of agent3: 17.5
idv_policy eval idv catch total num of agent3: 8
idv_policy eval team catch total num: 7
idv_policy eval average step individual rewards of agent4: 0.23450647415418394
idv_policy eval average team episode rewards of agent4: 17.5
idv_policy eval idv catch total num of agent4: 12
idv_policy eval team catch total num: 7

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 826/10000 episodes, total num timesteps 165400/2000000, FPS 184.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 827/10000 episodes, total num timesteps 165600/2000000, FPS 184.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 828/10000 episodes, total num timesteps 165800/2000000, FPS 184.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 829/10000 episodes, total num timesteps 166000/2000000, FPS 184.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 830/10000 episodes, total num timesteps 166200/2000000, FPS 184.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 831/10000 episodes, total num timesteps 166400/2000000, FPS 184.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 832/10000 episodes, total num timesteps 166600/2000000, FPS 184.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 833/10000 episodes, total num timesteps 166800/2000000, FPS 184.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 834/10000 episodes, total num timesteps 167000/2000000, FPS 184.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 835/10000 episodes, total num timesteps 167200/2000000, FPS 184.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 836/10000 episodes, total num timesteps 167400/2000000, FPS 184.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 837/10000 episodes, total num timesteps 167600/2000000, FPS 184.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 838/10000 episodes, total num timesteps 167800/2000000, FPS 184.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 839/10000 episodes, total num timesteps 168000/2000000, FPS 184.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 840/10000 episodes, total num timesteps 168200/2000000, FPS 184.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 841/10000 episodes, total num timesteps 168400/2000000, FPS 184.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 842/10000 episodes, total num timesteps 168600/2000000, FPS 184.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 843/10000 episodes, total num timesteps 168800/2000000, FPS 184.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 844/10000 episodes, total num timesteps 169000/2000000, FPS 184.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 845/10000 episodes, total num timesteps 169200/2000000, FPS 184.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 846/10000 episodes, total num timesteps 169400/2000000, FPS 184.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 847/10000 episodes, total num timesteps 169600/2000000, FPS 184.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 848/10000 episodes, total num timesteps 169800/2000000, FPS 184.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 849/10000 episodes, total num timesteps 170000/2000000, FPS 184.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 850/10000 episodes, total num timesteps 170200/2000000, FPS 184.

team_policy eval average step individual rewards of agent0: 0.30450596060832846
team_policy eval average team episode rewards of agent0: 40.0
team_policy eval idv catch total num of agent0: 14
team_policy eval team catch total num: 16
team_policy eval average step individual rewards of agent1: 0.14864114611411045
team_policy eval average team episode rewards of agent1: 40.0
team_policy eval idv catch total num of agent1: 8
team_policy eval team catch total num: 16
team_policy eval average step individual rewards of agent2: 0.24281974562526518
team_policy eval average team episode rewards of agent2: 40.0
team_policy eval idv catch total num of agent2: 12
team_policy eval team catch total num: 16
team_policy eval average step individual rewards of agent3: 0.42832549016554394
team_policy eval average team episode rewards of agent3: 40.0
team_policy eval idv catch total num of agent3: 19
team_policy eval team catch total num: 16
team_policy eval average step individual rewards of agent4: 0.3193397583807977
team_policy eval average team episode rewards of agent4: 40.0
team_policy eval idv catch total num of agent4: 15
team_policy eval team catch total num: 16
idv_policy eval average step individual rewards of agent0: 0.3260153464575555
idv_policy eval average team episode rewards of agent0: 50.0
idv_policy eval idv catch total num of agent0: 15
idv_policy eval team catch total num: 20
idv_policy eval average step individual rewards of agent1: 0.3263341974485452
idv_policy eval average team episode rewards of agent1: 50.0
idv_policy eval idv catch total num of agent1: 15
idv_policy eval team catch total num: 20
idv_policy eval average step individual rewards of agent2: 0.22944374586898653
idv_policy eval average team episode rewards of agent2: 50.0
idv_policy eval idv catch total num of agent2: 11
idv_policy eval team catch total num: 20
idv_policy eval average step individual rewards of agent3: 0.3091761309024202
idv_policy eval average team episode rewards of agent3: 50.0
idv_policy eval idv catch total num of agent3: 14
idv_policy eval team catch total num: 20
idv_policy eval average step individual rewards of agent4: 0.3775462117056674
idv_policy eval average team episode rewards of agent4: 50.0
idv_policy eval idv catch total num of agent4: 17
idv_policy eval team catch total num: 20

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 851/10000 episodes, total num timesteps 170400/2000000, FPS 184.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 852/10000 episodes, total num timesteps 170600/2000000, FPS 184.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 853/10000 episodes, total num timesteps 170800/2000000, FPS 184.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 854/10000 episodes, total num timesteps 171000/2000000, FPS 184.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 855/10000 episodes, total num timesteps 171200/2000000, FPS 184.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 856/10000 episodes, total num timesteps 171400/2000000, FPS 184.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 857/10000 episodes, total num timesteps 171600/2000000, FPS 185.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 858/10000 episodes, total num timesteps 171800/2000000, FPS 185.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 859/10000 episodes, total num timesteps 172000/2000000, FPS 185.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 860/10000 episodes, total num timesteps 172200/2000000, FPS 185.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 861/10000 episodes, total num timesteps 172400/2000000, FPS 185.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 862/10000 episodes, total num timesteps 172600/2000000, FPS 185.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 863/10000 episodes, total num timesteps 172800/2000000, FPS 185.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 864/10000 episodes, total num timesteps 173000/2000000, FPS 185.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 865/10000 episodes, total num timesteps 173200/2000000, FPS 185.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 866/10000 episodes, total num timesteps 173400/2000000, FPS 185.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 867/10000 episodes, total num timesteps 173600/2000000, FPS 185.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 868/10000 episodes, total num timesteps 173800/2000000, FPS 185.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 869/10000 episodes, total num timesteps 174000/2000000, FPS 185.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 870/10000 episodes, total num timesteps 174200/2000000, FPS 185.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 871/10000 episodes, total num timesteps 174400/2000000, FPS 185.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 872/10000 episodes, total num timesteps 174600/2000000, FPS 185.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 873/10000 episodes, total num timesteps 174800/2000000, FPS 185.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 874/10000 episodes, total num timesteps 175000/2000000, FPS 185.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 875/10000 episodes, total num timesteps 175200/2000000, FPS 185.

team_policy eval average step individual rewards of agent0: 0.16571378211165566
team_policy eval average team episode rewards of agent0: 10.0
team_policy eval idv catch total num of agent0: 9
team_policy eval team catch total num: 4
team_policy eval average step individual rewards of agent1: 0.2784257872618569
team_policy eval average team episode rewards of agent1: 10.0
team_policy eval idv catch total num of agent1: 13
team_policy eval team catch total num: 4
team_policy eval average step individual rewards of agent2: -0.013250079622434328
team_policy eval average team episode rewards of agent2: 10.0
team_policy eval idv catch total num of agent2: 2
team_policy eval team catch total num: 4
team_policy eval average step individual rewards of agent3: 0.11425834609943854
team_policy eval average team episode rewards of agent3: 10.0
team_policy eval idv catch total num of agent3: 7
team_policy eval team catch total num: 4
team_policy eval average step individual rewards of agent4: -0.0754015972832091
team_policy eval average team episode rewards of agent4: 10.0
team_policy eval idv catch total num of agent4: 0
team_policy eval team catch total num: 4
idv_policy eval average step individual rewards of agent0: 0.31904842572640674
idv_policy eval average team episode rewards of agent0: 32.5
idv_policy eval idv catch total num of agent0: 15
idv_policy eval team catch total num: 13
idv_policy eval average step individual rewards of agent1: 0.16926567654231983
idv_policy eval average team episode rewards of agent1: 32.5
idv_policy eval idv catch total num of agent1: 9
idv_policy eval team catch total num: 13
idv_policy eval average step individual rewards of agent2: 0.1976230404834161
idv_policy eval average team episode rewards of agent2: 32.5
idv_policy eval idv catch total num of agent2: 10
idv_policy eval team catch total num: 13
idv_policy eval average step individual rewards of agent3: 0.22968055406097007
idv_policy eval average team episode rewards of agent3: 32.5
idv_policy eval idv catch total num of agent3: 11
idv_policy eval team catch total num: 13
idv_policy eval average step individual rewards of agent4: 0.1532462210900076
idv_policy eval average team episode rewards of agent4: 32.5
idv_policy eval idv catch total num of agent4: 8
idv_policy eval team catch total num: 13

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 876/10000 episodes, total num timesteps 175400/2000000, FPS 185.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 877/10000 episodes, total num timesteps 175600/2000000, FPS 185.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 878/10000 episodes, total num timesteps 175800/2000000, FPS 185.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 879/10000 episodes, total num timesteps 176000/2000000, FPS 185.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 880/10000 episodes, total num timesteps 176200/2000000, FPS 185.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 881/10000 episodes, total num timesteps 176400/2000000, FPS 185.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 882/10000 episodes, total num timesteps 176600/2000000, FPS 185.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 883/10000 episodes, total num timesteps 176800/2000000, FPS 185.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 884/10000 episodes, total num timesteps 177000/2000000, FPS 185.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 885/10000 episodes, total num timesteps 177200/2000000, FPS 185.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 886/10000 episodes, total num timesteps 177400/2000000, FPS 185.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 887/10000 episodes, total num timesteps 177600/2000000, FPS 185.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 888/10000 episodes, total num timesteps 177800/2000000, FPS 185.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 889/10000 episodes, total num timesteps 178000/2000000, FPS 185.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 890/10000 episodes, total num timesteps 178200/2000000, FPS 185.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 891/10000 episodes, total num timesteps 178400/2000000, FPS 186.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 892/10000 episodes, total num timesteps 178600/2000000, FPS 186.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 893/10000 episodes, total num timesteps 178800/2000000, FPS 186.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 894/10000 episodes, total num timesteps 179000/2000000, FPS 186.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 895/10000 episodes, total num timesteps 179200/2000000, FPS 186.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 896/10000 episodes, total num timesteps 179400/2000000, FPS 186.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 897/10000 episodes, total num timesteps 179600/2000000, FPS 186.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 898/10000 episodes, total num timesteps 179800/2000000, FPS 186.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 899/10000 episodes, total num timesteps 180000/2000000, FPS 186.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 900/10000 episodes, total num timesteps 180200/2000000, FPS 186.

team_policy eval average step individual rewards of agent0: 0.039188940454777145
team_policy eval average team episode rewards of agent0: 22.5
team_policy eval idv catch total num of agent0: 4
team_policy eval team catch total num: 9
team_policy eval average step individual rewards of agent1: 0.0743172063406161
team_policy eval average team episode rewards of agent1: 22.5
team_policy eval idv catch total num of agent1: 5
team_policy eval team catch total num: 9
team_policy eval average step individual rewards of agent2: 0.12755112243152134
team_policy eval average team episode rewards of agent2: 22.5
team_policy eval idv catch total num of agent2: 7
team_policy eval team catch total num: 9
team_policy eval average step individual rewards of agent3: 0.17779026431986591
team_policy eval average team episode rewards of agent3: 22.5
team_policy eval idv catch total num of agent3: 9
team_policy eval team catch total num: 9
team_policy eval average step individual rewards of agent4: 0.1997418272053404
team_policy eval average team episode rewards of agent4: 22.5
team_policy eval idv catch total num of agent4: 10
team_policy eval team catch total num: 9
idv_policy eval average step individual rewards of agent0: 0.45234349853636346
idv_policy eval average team episode rewards of agent0: 57.5
idv_policy eval idv catch total num of agent0: 20
idv_policy eval team catch total num: 23
idv_policy eval average step individual rewards of agent1: 0.19513622950062412
idv_policy eval average team episode rewards of agent1: 57.5
idv_policy eval idv catch total num of agent1: 10
idv_policy eval team catch total num: 23
idv_policy eval average step individual rewards of agent2: 0.34625702208614423
idv_policy eval average team episode rewards of agent2: 57.5
idv_policy eval idv catch total num of agent2: 16
idv_policy eval team catch total num: 23
idv_policy eval average step individual rewards of agent3: 0.31497725545468314
idv_policy eval average team episode rewards of agent3: 57.5
idv_policy eval idv catch total num of agent3: 15
idv_policy eval team catch total num: 23
idv_policy eval average step individual rewards of agent4: 0.4008974333213499
idv_policy eval average team episode rewards of agent4: 57.5
idv_policy eval idv catch total num of agent4: 18
idv_policy eval team catch total num: 23

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 901/10000 episodes, total num timesteps 180400/2000000, FPS 186.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 902/10000 episodes, total num timesteps 180600/2000000, FPS 186.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 903/10000 episodes, total num timesteps 180800/2000000, FPS 186.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 904/10000 episodes, total num timesteps 181000/2000000, FPS 186.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 905/10000 episodes, total num timesteps 181200/2000000, FPS 186.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 906/10000 episodes, total num timesteps 181400/2000000, FPS 186.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 907/10000 episodes, total num timesteps 181600/2000000, FPS 186.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 908/10000 episodes, total num timesteps 181800/2000000, FPS 186.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 909/10000 episodes, total num timesteps 182000/2000000, FPS 186.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 910/10000 episodes, total num timesteps 182200/2000000, FPS 186.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 911/10000 episodes, total num timesteps 182400/2000000, FPS 186.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 912/10000 episodes, total num timesteps 182600/2000000, FPS 186.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 913/10000 episodes, total num timesteps 182800/2000000, FPS 186.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 914/10000 episodes, total num timesteps 183000/2000000, FPS 186.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 915/10000 episodes, total num timesteps 183200/2000000, FPS 186.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 916/10000 episodes, total num timesteps 183400/2000000, FPS 186.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 917/10000 episodes, total num timesteps 183600/2000000, FPS 186.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 918/10000 episodes, total num timesteps 183800/2000000, FPS 186.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 919/10000 episodes, total num timesteps 184000/2000000, FPS 186.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 920/10000 episodes, total num timesteps 184200/2000000, FPS 186.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 921/10000 episodes, total num timesteps 184400/2000000, FPS 186.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 922/10000 episodes, total num timesteps 184600/2000000, FPS 186.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 923/10000 episodes, total num timesteps 184800/2000000, FPS 186.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 924/10000 episodes, total num timesteps 185000/2000000, FPS 187.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 925/10000 episodes, total num timesteps 185200/2000000, FPS 187.

team_policy eval average step individual rewards of agent0: 0.22003780219965285
team_policy eval average team episode rewards of agent0: 40.0
team_policy eval idv catch total num of agent0: 11
team_policy eval team catch total num: 16
team_policy eval average step individual rewards of agent1: 0.2445985643443683
team_policy eval average team episode rewards of agent1: 40.0
team_policy eval idv catch total num of agent1: 12
team_policy eval team catch total num: 16
team_policy eval average step individual rewards of agent2: 0.3611708623748424
team_policy eval average team episode rewards of agent2: 40.0
team_policy eval idv catch total num of agent2: 16
team_policy eval team catch total num: 16
team_policy eval average step individual rewards of agent3: 0.1981672202627118
team_policy eval average team episode rewards of agent3: 40.0
team_policy eval idv catch total num of agent3: 10
team_policy eval team catch total num: 16
team_policy eval average step individual rewards of agent4: 0.20162028156887196
team_policy eval average team episode rewards of agent4: 40.0
team_policy eval idv catch total num of agent4: 10
team_policy eval team catch total num: 16
idv_policy eval average step individual rewards of agent0: 0.11916707842457885
idv_policy eval average team episode rewards of agent0: 30.0
idv_policy eval idv catch total num of agent0: 7
idv_policy eval team catch total num: 12
idv_policy eval average step individual rewards of agent1: 0.3768356635961645
idv_policy eval average team episode rewards of agent1: 30.0
idv_policy eval idv catch total num of agent1: 17
idv_policy eval team catch total num: 12
idv_policy eval average step individual rewards of agent2: 0.3009339437886552
idv_policy eval average team episode rewards of agent2: 30.0
idv_policy eval idv catch total num of agent2: 14
idv_policy eval team catch total num: 12
idv_policy eval average step individual rewards of agent3: 0.18141228719379376
idv_policy eval average team episode rewards of agent3: 30.0
idv_policy eval idv catch total num of agent3: 9
idv_policy eval team catch total num: 12
idv_policy eval average step individual rewards of agent4: 0.20003405416408776
idv_policy eval average team episode rewards of agent4: 30.0
idv_policy eval idv catch total num of agent4: 10
idv_policy eval team catch total num: 12

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 926/10000 episodes, total num timesteps 185400/2000000, FPS 187.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 927/10000 episodes, total num timesteps 185600/2000000, FPS 186.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 928/10000 episodes, total num timesteps 185800/2000000, FPS 187.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 929/10000 episodes, total num timesteps 186000/2000000, FPS 187.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 930/10000 episodes, total num timesteps 186200/2000000, FPS 187.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 931/10000 episodes, total num timesteps 186400/2000000, FPS 187.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 932/10000 episodes, total num timesteps 186600/2000000, FPS 187.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 933/10000 episodes, total num timesteps 186800/2000000, FPS 187.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 934/10000 episodes, total num timesteps 187000/2000000, FPS 187.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 935/10000 episodes, total num timesteps 187200/2000000, FPS 187.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 936/10000 episodes, total num timesteps 187400/2000000, FPS 187.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 937/10000 episodes, total num timesteps 187600/2000000, FPS 187.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 938/10000 episodes, total num timesteps 187800/2000000, FPS 187.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 939/10000 episodes, total num timesteps 188000/2000000, FPS 187.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 940/10000 episodes, total num timesteps 188200/2000000, FPS 187.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 941/10000 episodes, total num timesteps 188400/2000000, FPS 187.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 942/10000 episodes, total num timesteps 188600/2000000, FPS 187.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 943/10000 episodes, total num timesteps 188800/2000000, FPS 187.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 944/10000 episodes, total num timesteps 189000/2000000, FPS 187.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 945/10000 episodes, total num timesteps 189200/2000000, FPS 187.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 946/10000 episodes, total num timesteps 189400/2000000, FPS 187.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 947/10000 episodes, total num timesteps 189600/2000000, FPS 187.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 948/10000 episodes, total num timesteps 189800/2000000, FPS 187.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 949/10000 episodes, total num timesteps 190000/2000000, FPS 187.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 950/10000 episodes, total num timesteps 190200/2000000, FPS 187.

team_policy eval average step individual rewards of agent0: 0.333028087112042
team_policy eval average team episode rewards of agent0: 57.5
team_policy eval idv catch total num of agent0: 15
team_policy eval team catch total num: 23
team_policy eval average step individual rewards of agent1: 0.33017063683413844
team_policy eval average team episode rewards of agent1: 57.5
team_policy eval idv catch total num of agent1: 15
team_policy eval team catch total num: 23
team_policy eval average step individual rewards of agent2: 0.4871066799855825
team_policy eval average team episode rewards of agent2: 57.5
team_policy eval idv catch total num of agent2: 21
team_policy eval team catch total num: 23
team_policy eval average step individual rewards of agent3: 0.41434632920668607
team_policy eval average team episode rewards of agent3: 57.5
team_policy eval idv catch total num of agent3: 18
team_policy eval team catch total num: 23
team_policy eval average step individual rewards of agent4: 0.3828881244973203
team_policy eval average team episode rewards of agent4: 57.5
team_policy eval idv catch total num of agent4: 17
team_policy eval team catch total num: 23
idv_policy eval average step individual rewards of agent0: 0.507587379278969
idv_policy eval average team episode rewards of agent0: 67.5
idv_policy eval idv catch total num of agent0: 22
idv_policy eval team catch total num: 27
idv_policy eval average step individual rewards of agent1: 0.3389761416011175
idv_policy eval average team episode rewards of agent1: 67.5
idv_policy eval idv catch total num of agent1: 15
idv_policy eval team catch total num: 27
idv_policy eval average step individual rewards of agent2: 0.49023633909835523
idv_policy eval average team episode rewards of agent2: 67.5
idv_policy eval idv catch total num of agent2: 21
idv_policy eval team catch total num: 27
idv_policy eval average step individual rewards of agent3: 0.540202826356782
idv_policy eval average team episode rewards of agent3: 67.5
idv_policy eval idv catch total num of agent3: 23
idv_policy eval team catch total num: 27
idv_policy eval average step individual rewards of agent4: 0.6106811327282285
idv_policy eval average team episode rewards of agent4: 67.5
idv_policy eval idv catch total num of agent4: 26
idv_policy eval team catch total num: 27

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 951/10000 episodes, total num timesteps 190400/2000000, FPS 187.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 952/10000 episodes, total num timesteps 190600/2000000, FPS 187.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 953/10000 episodes, total num timesteps 190800/2000000, FPS 187.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 954/10000 episodes, total num timesteps 191000/2000000, FPS 187.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 955/10000 episodes, total num timesteps 191200/2000000, FPS 187.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 956/10000 episodes, total num timesteps 191400/2000000, FPS 188.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 957/10000 episodes, total num timesteps 191600/2000000, FPS 188.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 958/10000 episodes, total num timesteps 191800/2000000, FPS 188.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 959/10000 episodes, total num timesteps 192000/2000000, FPS 188.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 960/10000 episodes, total num timesteps 192200/2000000, FPS 188.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 961/10000 episodes, total num timesteps 192400/2000000, FPS 188.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 962/10000 episodes, total num timesteps 192600/2000000, FPS 188.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 963/10000 episodes, total num timesteps 192800/2000000, FPS 188.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 964/10000 episodes, total num timesteps 193000/2000000, FPS 188.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 965/10000 episodes, total num timesteps 193200/2000000, FPS 188.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 966/10000 episodes, total num timesteps 193400/2000000, FPS 188.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 967/10000 episodes, total num timesteps 193600/2000000, FPS 188.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 968/10000 episodes, total num timesteps 193800/2000000, FPS 188.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 969/10000 episodes, total num timesteps 194000/2000000, FPS 188.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 970/10000 episodes, total num timesteps 194200/2000000, FPS 188.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 971/10000 episodes, total num timesteps 194400/2000000, FPS 188.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 972/10000 episodes, total num timesteps 194600/2000000, FPS 188.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 973/10000 episodes, total num timesteps 194800/2000000, FPS 188.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 974/10000 episodes, total num timesteps 195000/2000000, FPS 188.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 975/10000 episodes, total num timesteps 195200/2000000, FPS 188.

team_policy eval average step individual rewards of agent0: 0.8125663280930208
team_policy eval average team episode rewards of agent0: 80.0
team_policy eval idv catch total num of agent0: 34
team_policy eval team catch total num: 32
team_policy eval average step individual rewards of agent1: 0.6904427044578566
team_policy eval average team episode rewards of agent1: 80.0
team_policy eval idv catch total num of agent1: 29
team_policy eval team catch total num: 32
team_policy eval average step individual rewards of agent2: 0.6202648221048149
team_policy eval average team episode rewards of agent2: 80.0
team_policy eval idv catch total num of agent2: 26
team_policy eval team catch total num: 32
team_policy eval average step individual rewards of agent3: 0.5100910842520345
team_policy eval average team episode rewards of agent3: 80.0
team_policy eval idv catch total num of agent3: 22
team_policy eval team catch total num: 32
team_policy eval average step individual rewards of agent4: 0.28454685962731296
team_policy eval average team episode rewards of agent4: 80.0
team_policy eval idv catch total num of agent4: 13
team_policy eval team catch total num: 32
idv_policy eval average step individual rewards of agent0: 0.3582173788991067
idv_policy eval average team episode rewards of agent0: 45.0
idv_policy eval idv catch total num of agent0: 16
idv_policy eval team catch total num: 18
idv_policy eval average step individual rewards of agent1: 0.4517682308841608
idv_policy eval average team episode rewards of agent1: 45.0
idv_policy eval idv catch total num of agent1: 20
idv_policy eval team catch total num: 18
idv_policy eval average step individual rewards of agent2: 0.42814897208743324
idv_policy eval average team episode rewards of agent2: 45.0
idv_policy eval idv catch total num of agent2: 19
idv_policy eval team catch total num: 18
idv_policy eval average step individual rewards of agent3: 0.16404827645894046
idv_policy eval average team episode rewards of agent3: 45.0
idv_policy eval idv catch total num of agent3: 9
idv_policy eval team catch total num: 18
idv_policy eval average step individual rewards of agent4: 0.24403012082604655
idv_policy eval average team episode rewards of agent4: 45.0
idv_policy eval idv catch total num of agent4: 12
idv_policy eval team catch total num: 18

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 976/10000 episodes, total num timesteps 195400/2000000, FPS 188.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 977/10000 episodes, total num timesteps 195600/2000000, FPS 188.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 978/10000 episodes, total num timesteps 195800/2000000, FPS 188.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 979/10000 episodes, total num timesteps 196000/2000000, FPS 188.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 980/10000 episodes, total num timesteps 196200/2000000, FPS 188.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 981/10000 episodes, total num timesteps 196400/2000000, FPS 188.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 982/10000 episodes, total num timesteps 196600/2000000, FPS 188.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 983/10000 episodes, total num timesteps 196800/2000000, FPS 188.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 984/10000 episodes, total num timesteps 197000/2000000, FPS 188.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 985/10000 episodes, total num timesteps 197200/2000000, FPS 188.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 986/10000 episodes, total num timesteps 197400/2000000, FPS 188.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 987/10000 episodes, total num timesteps 197600/2000000, FPS 188.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 988/10000 episodes, total num timesteps 197800/2000000, FPS 188.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 989/10000 episodes, total num timesteps 198000/2000000, FPS 189.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 990/10000 episodes, total num timesteps 198200/2000000, FPS 189.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 991/10000 episodes, total num timesteps 198400/2000000, FPS 189.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 992/10000 episodes, total num timesteps 198600/2000000, FPS 189.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 993/10000 episodes, total num timesteps 198800/2000000, FPS 189.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 994/10000 episodes, total num timesteps 199000/2000000, FPS 189.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 995/10000 episodes, total num timesteps 199200/2000000, FPS 189.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 996/10000 episodes, total num timesteps 199400/2000000, FPS 189.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 997/10000 episodes, total num timesteps 199600/2000000, FPS 189.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 998/10000 episodes, total num timesteps 199800/2000000, FPS 189.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 999/10000 episodes, total num timesteps 200000/2000000, FPS 189.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1000/10000 episodes, total num timesteps 200200/2000000, FPS 189.

team_policy eval average step individual rewards of agent0: 0.43479411336489965
team_policy eval average team episode rewards of agent0: 57.5
team_policy eval idv catch total num of agent0: 19
team_policy eval team catch total num: 23
team_policy eval average step individual rewards of agent1: 0.5343348308225772
team_policy eval average team episode rewards of agent1: 57.5
team_policy eval idv catch total num of agent1: 23
team_policy eval team catch total num: 23
team_policy eval average step individual rewards of agent2: 0.1535544430395589
team_policy eval average team episode rewards of agent2: 57.5
team_policy eval idv catch total num of agent2: 8
team_policy eval team catch total num: 23
team_policy eval average step individual rewards of agent3: 0.4093782085856864
team_policy eval average team episode rewards of agent3: 57.5
team_policy eval idv catch total num of agent3: 18
team_policy eval team catch total num: 23
team_policy eval average step individual rewards of agent4: 0.3345009969986975
team_policy eval average team episode rewards of agent4: 57.5
team_policy eval idv catch total num of agent4: 15
team_policy eval team catch total num: 23
idv_policy eval average step individual rewards of agent0: 0.3588351254558475
idv_policy eval average team episode rewards of agent0: 57.5
idv_policy eval idv catch total num of agent0: 16
idv_policy eval team catch total num: 23
idv_policy eval average step individual rewards of agent1: 0.455742468367675
idv_policy eval average team episode rewards of agent1: 57.5
idv_policy eval idv catch total num of agent1: 20
idv_policy eval team catch total num: 23
idv_policy eval average step individual rewards of agent2: 0.4796963610122188
idv_policy eval average team episode rewards of agent2: 57.5
idv_policy eval idv catch total num of agent2: 21
idv_policy eval team catch total num: 23
idv_policy eval average step individual rewards of agent3: 0.23680100762959974
idv_policy eval average team episode rewards of agent3: 57.5
idv_policy eval idv catch total num of agent3: 11
idv_policy eval team catch total num: 23
idv_policy eval average step individual rewards of agent4: 0.35889391095582435
idv_policy eval average team episode rewards of agent4: 57.5
idv_policy eval idv catch total num of agent4: 16
idv_policy eval team catch total num: 23

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1001/10000 episodes, total num timesteps 200400/2000000, FPS 189.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1002/10000 episodes, total num timesteps 200600/2000000, FPS 189.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1003/10000 episodes, total num timesteps 200800/2000000, FPS 189.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1004/10000 episodes, total num timesteps 201000/2000000, FPS 189.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1005/10000 episodes, total num timesteps 201200/2000000, FPS 189.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1006/10000 episodes, total num timesteps 201400/2000000, FPS 189.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1007/10000 episodes, total num timesteps 201600/2000000, FPS 189.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1008/10000 episodes, total num timesteps 201800/2000000, FPS 189.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1009/10000 episodes, total num timesteps 202000/2000000, FPS 189.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1010/10000 episodes, total num timesteps 202200/2000000, FPS 189.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1011/10000 episodes, total num timesteps 202400/2000000, FPS 189.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1012/10000 episodes, total num timesteps 202600/2000000, FPS 189.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1013/10000 episodes, total num timesteps 202800/2000000, FPS 189.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1014/10000 episodes, total num timesteps 203000/2000000, FPS 189.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1015/10000 episodes, total num timesteps 203200/2000000, FPS 189.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1016/10000 episodes, total num timesteps 203400/2000000, FPS 189.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1017/10000 episodes, total num timesteps 203600/2000000, FPS 189.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1018/10000 episodes, total num timesteps 203800/2000000, FPS 189.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1019/10000 episodes, total num timesteps 204000/2000000, FPS 189.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1020/10000 episodes, total num timesteps 204200/2000000, FPS 189.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1021/10000 episodes, total num timesteps 204400/2000000, FPS 189.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1022/10000 episodes, total num timesteps 204600/2000000, FPS 189.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1023/10000 episodes, total num timesteps 204800/2000000, FPS 190.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1024/10000 episodes, total num timesteps 205000/2000000, FPS 190.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1025/10000 episodes, total num timesteps 205200/2000000, FPS 190.

team_policy eval average step individual rewards of agent0: 0.20379863683013158
team_policy eval average team episode rewards of agent0: 40.0
team_policy eval idv catch total num of agent0: 10
team_policy eval team catch total num: 16
team_policy eval average step individual rewards of agent1: 0.1820352720194499
team_policy eval average team episode rewards of agent1: 40.0
team_policy eval idv catch total num of agent1: 9
team_policy eval team catch total num: 16
team_policy eval average step individual rewards of agent2: 0.2062805328839602
team_policy eval average team episode rewards of agent2: 40.0
team_policy eval idv catch total num of agent2: 10
team_policy eval team catch total num: 16
team_policy eval average step individual rewards of agent3: 0.330666746207206
team_policy eval average team episode rewards of agent3: 40.0
team_policy eval idv catch total num of agent3: 15
team_policy eval team catch total num: 16
team_policy eval average step individual rewards of agent4: 0.2573793031593261
team_policy eval average team episode rewards of agent4: 40.0
team_policy eval idv catch total num of agent4: 12
team_policy eval team catch total num: 16
idv_policy eval average step individual rewards of agent0: 0.3302176991148132
idv_policy eval average team episode rewards of agent0: 85.0
idv_policy eval idv catch total num of agent0: 15
idv_policy eval team catch total num: 34
idv_policy eval average step individual rewards of agent1: 0.7317840404571784
idv_policy eval average team episode rewards of agent1: 85.0
idv_policy eval idv catch total num of agent1: 31
idv_policy eval team catch total num: 34
idv_policy eval average step individual rewards of agent2: 0.7155501959712858
idv_policy eval average team episode rewards of agent2: 85.0
idv_policy eval idv catch total num of agent2: 30
idv_policy eval team catch total num: 34
idv_policy eval average step individual rewards of agent3: 0.48874155403120156
idv_policy eval average team episode rewards of agent3: 85.0
idv_policy eval idv catch total num of agent3: 21
idv_policy eval team catch total num: 34
idv_policy eval average step individual rewards of agent4: 0.5108196182629113
idv_policy eval average team episode rewards of agent4: 85.0
idv_policy eval idv catch total num of agent4: 22
idv_policy eval team catch total num: 34

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1026/10000 episodes, total num timesteps 205400/2000000, FPS 190.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1027/10000 episodes, total num timesteps 205600/2000000, FPS 190.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1028/10000 episodes, total num timesteps 205800/2000000, FPS 190.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1029/10000 episodes, total num timesteps 206000/2000000, FPS 190.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1030/10000 episodes, total num timesteps 206200/2000000, FPS 190.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1031/10000 episodes, total num timesteps 206400/2000000, FPS 190.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1032/10000 episodes, total num timesteps 206600/2000000, FPS 190.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1033/10000 episodes, total num timesteps 206800/2000000, FPS 190.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1034/10000 episodes, total num timesteps 207000/2000000, FPS 190.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1035/10000 episodes, total num timesteps 207200/2000000, FPS 190.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1036/10000 episodes, total num timesteps 207400/2000000, FPS 190.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1037/10000 episodes, total num timesteps 207600/2000000, FPS 190.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1038/10000 episodes, total num timesteps 207800/2000000, FPS 190.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1039/10000 episodes, total num timesteps 208000/2000000, FPS 190.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1040/10000 episodes, total num timesteps 208200/2000000, FPS 190.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1041/10000 episodes, total num timesteps 208400/2000000, FPS 190.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1042/10000 episodes, total num timesteps 208600/2000000, FPS 190.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1043/10000 episodes, total num timesteps 208800/2000000, FPS 190.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1044/10000 episodes, total num timesteps 209000/2000000, FPS 190.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1045/10000 episodes, total num timesteps 209200/2000000, FPS 190.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1046/10000 episodes, total num timesteps 209400/2000000, FPS 190.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1047/10000 episodes, total num timesteps 209600/2000000, FPS 190.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1048/10000 episodes, total num timesteps 209800/2000000, FPS 190.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1049/10000 episodes, total num timesteps 210000/2000000, FPS 190.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1050/10000 episodes, total num timesteps 210200/2000000, FPS 190.

team_policy eval average step individual rewards of agent0: 0.2585664520568918
team_policy eval average team episode rewards of agent0: 37.5
team_policy eval idv catch total num of agent0: 12
team_policy eval team catch total num: 15
team_policy eval average step individual rewards of agent1: 0.16349876083603976
team_policy eval average team episode rewards of agent1: 37.5
team_policy eval idv catch total num of agent1: 8
team_policy eval team catch total num: 15
team_policy eval average step individual rewards of agent2: 0.3612736687538651
team_policy eval average team episode rewards of agent2: 37.5
team_policy eval idv catch total num of agent2: 16
team_policy eval team catch total num: 15
team_policy eval average step individual rewards of agent3: 0.3275869922172412
team_policy eval average team episode rewards of agent3: 37.5
team_policy eval idv catch total num of agent3: 15
team_policy eval team catch total num: 15
team_policy eval average step individual rewards of agent4: 0.4621752769034007
team_policy eval average team episode rewards of agent4: 37.5
team_policy eval idv catch total num of agent4: 20
team_policy eval team catch total num: 15
idv_policy eval average step individual rewards of agent0: 0.6167770815821926
idv_policy eval average team episode rewards of agent0: 97.5
idv_policy eval idv catch total num of agent0: 26
idv_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent1: 0.6947295584409892
idv_policy eval average team episode rewards of agent1: 97.5
idv_policy eval idv catch total num of agent1: 29
idv_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent2: 0.4559439268124179
idv_policy eval average team episode rewards of agent2: 97.5
idv_policy eval idv catch total num of agent2: 20
idv_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent3: 0.5646369076652286
idv_policy eval average team episode rewards of agent3: 97.5
idv_policy eval idv catch total num of agent3: 24
idv_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent4: 0.7321102280792365
idv_policy eval average team episode rewards of agent4: 97.5
idv_policy eval idv catch total num of agent4: 31
idv_policy eval team catch total num: 39

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1051/10000 episodes, total num timesteps 210400/2000000, FPS 190.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1052/10000 episodes, total num timesteps 210600/2000000, FPS 190.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1053/10000 episodes, total num timesteps 210800/2000000, FPS 190.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1054/10000 episodes, total num timesteps 211000/2000000, FPS 190.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1055/10000 episodes, total num timesteps 211200/2000000, FPS 190.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1056/10000 episodes, total num timesteps 211400/2000000, FPS 190.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1057/10000 episodes, total num timesteps 211600/2000000, FPS 191.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1058/10000 episodes, total num timesteps 211800/2000000, FPS 191.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1059/10000 episodes, total num timesteps 212000/2000000, FPS 191.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1060/10000 episodes, total num timesteps 212200/2000000, FPS 191.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1061/10000 episodes, total num timesteps 212400/2000000, FPS 191.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1062/10000 episodes, total num timesteps 212600/2000000, FPS 191.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1063/10000 episodes, total num timesteps 212800/2000000, FPS 191.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1064/10000 episodes, total num timesteps 213000/2000000, FPS 191.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1065/10000 episodes, total num timesteps 213200/2000000, FPS 191.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1066/10000 episodes, total num timesteps 213400/2000000, FPS 191.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1067/10000 episodes, total num timesteps 213600/2000000, FPS 191.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1068/10000 episodes, total num timesteps 213800/2000000, FPS 191.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1069/10000 episodes, total num timesteps 214000/2000000, FPS 191.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1070/10000 episodes, total num timesteps 214200/2000000, FPS 191.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1071/10000 episodes, total num timesteps 214400/2000000, FPS 191.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1072/10000 episodes, total num timesteps 214600/2000000, FPS 191.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1073/10000 episodes, total num timesteps 214800/2000000, FPS 191.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1074/10000 episodes, total num timesteps 215000/2000000, FPS 191.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1075/10000 episodes, total num timesteps 215200/2000000, FPS 191.

team_policy eval average step individual rewards of agent0: 0.7736412382816847
team_policy eval average team episode rewards of agent0: 100.0
team_policy eval idv catch total num of agent0: 32
team_policy eval team catch total num: 40
team_policy eval average step individual rewards of agent1: 0.6480819192578653
team_policy eval average team episode rewards of agent1: 100.0
team_policy eval idv catch total num of agent1: 27
team_policy eval team catch total num: 40
team_policy eval average step individual rewards of agent2: 0.7678504908152997
team_policy eval average team episode rewards of agent2: 100.0
team_policy eval idv catch total num of agent2: 32
team_policy eval team catch total num: 40
team_policy eval average step individual rewards of agent3: 0.8941745428437148
team_policy eval average team episode rewards of agent3: 100.0
team_policy eval idv catch total num of agent3: 37
team_policy eval team catch total num: 40
team_policy eval average step individual rewards of agent4: 0.3815773357374391
team_policy eval average team episode rewards of agent4: 100.0
team_policy eval idv catch total num of agent4: 17
team_policy eval team catch total num: 40
idv_policy eval average step individual rewards of agent0: 0.10281131212598654
idv_policy eval average team episode rewards of agent0: 57.5
idv_policy eval idv catch total num of agent0: 6
idv_policy eval team catch total num: 23
idv_policy eval average step individual rewards of agent1: 0.5315984942620834
idv_policy eval average team episode rewards of agent1: 57.5
idv_policy eval idv catch total num of agent1: 23
idv_policy eval team catch total num: 23
idv_policy eval average step individual rewards of agent2: 0.4536623823998333
idv_policy eval average team episode rewards of agent2: 57.5
idv_policy eval idv catch total num of agent2: 20
idv_policy eval team catch total num: 23
idv_policy eval average step individual rewards of agent3: 0.3587786990053987
idv_policy eval average team episode rewards of agent3: 57.5
idv_policy eval idv catch total num of agent3: 16
idv_policy eval team catch total num: 23
idv_policy eval average step individual rewards of agent4: 0.6589166410660616
idv_policy eval average team episode rewards of agent4: 57.5
idv_policy eval idv catch total num of agent4: 28
idv_policy eval team catch total num: 23

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1076/10000 episodes, total num timesteps 215400/2000000, FPS 191.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1077/10000 episodes, total num timesteps 215600/2000000, FPS 191.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1078/10000 episodes, total num timesteps 215800/2000000, FPS 191.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1079/10000 episodes, total num timesteps 216000/2000000, FPS 191.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1080/10000 episodes, total num timesteps 216200/2000000, FPS 191.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1081/10000 episodes, total num timesteps 216400/2000000, FPS 191.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1082/10000 episodes, total num timesteps 216600/2000000, FPS 191.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1083/10000 episodes, total num timesteps 216800/2000000, FPS 191.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1084/10000 episodes, total num timesteps 217000/2000000, FPS 191.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1085/10000 episodes, total num timesteps 217200/2000000, FPS 191.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1086/10000 episodes, total num timesteps 217400/2000000, FPS 191.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1087/10000 episodes, total num timesteps 217600/2000000, FPS 191.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1088/10000 episodes, total num timesteps 217800/2000000, FPS 191.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1089/10000 episodes, total num timesteps 218000/2000000, FPS 191.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1090/10000 episodes, total num timesteps 218200/2000000, FPS 191.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1091/10000 episodes, total num timesteps 218400/2000000, FPS 191.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1092/10000 episodes, total num timesteps 218600/2000000, FPS 191.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1093/10000 episodes, total num timesteps 218800/2000000, FPS 192.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1094/10000 episodes, total num timesteps 219000/2000000, FPS 191.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1095/10000 episodes, total num timesteps 219200/2000000, FPS 191.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1096/10000 episodes, total num timesteps 219400/2000000, FPS 192.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1097/10000 episodes, total num timesteps 219600/2000000, FPS 192.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1098/10000 episodes, total num timesteps 219800/2000000, FPS 192.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1099/10000 episodes, total num timesteps 220000/2000000, FPS 192.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1100/10000 episodes, total num timesteps 220200/2000000, FPS 192.

team_policy eval average step individual rewards of agent0: 0.4895015446539911
team_policy eval average team episode rewards of agent0: 62.5
team_policy eval idv catch total num of agent0: 21
team_policy eval team catch total num: 25
team_policy eval average step individual rewards of agent1: 0.4017466650986201
team_policy eval average team episode rewards of agent1: 62.5
team_policy eval idv catch total num of agent1: 18
team_policy eval team catch total num: 25
team_policy eval average step individual rewards of agent2: 0.40425451044792626
team_policy eval average team episode rewards of agent2: 62.5
team_policy eval idv catch total num of agent2: 18
team_policy eval team catch total num: 25
team_policy eval average step individual rewards of agent3: 0.6350972299608065
team_policy eval average team episode rewards of agent3: 62.5
team_policy eval idv catch total num of agent3: 27
team_policy eval team catch total num: 25
team_policy eval average step individual rewards of agent4: 0.47957791512504094
team_policy eval average team episode rewards of agent4: 62.5
team_policy eval idv catch total num of agent4: 21
team_policy eval team catch total num: 25
idv_policy eval average step individual rewards of agent0: 0.8227024002194321
idv_policy eval average team episode rewards of agent0: 92.5
idv_policy eval idv catch total num of agent0: 34
idv_policy eval team catch total num: 37
idv_policy eval average step individual rewards of agent1: 0.7236175086199963
idv_policy eval average team episode rewards of agent1: 92.5
idv_policy eval idv catch total num of agent1: 30
idv_policy eval team catch total num: 37
idv_policy eval average step individual rewards of agent2: 0.5934078845063961
idv_policy eval average team episode rewards of agent2: 92.5
idv_policy eval idv catch total num of agent2: 25
idv_policy eval team catch total num: 37
idv_policy eval average step individual rewards of agent3: 0.49564241274783194
idv_policy eval average team episode rewards of agent3: 92.5
idv_policy eval idv catch total num of agent3: 21
idv_policy eval team catch total num: 37
idv_policy eval average step individual rewards of agent4: 0.5624302822414439
idv_policy eval average team episode rewards of agent4: 92.5
idv_policy eval idv catch total num of agent4: 24
idv_policy eval team catch total num: 37

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1101/10000 episodes, total num timesteps 220400/2000000, FPS 192.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1102/10000 episodes, total num timesteps 220600/2000000, FPS 192.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1103/10000 episodes, total num timesteps 220800/2000000, FPS 192.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1104/10000 episodes, total num timesteps 221000/2000000, FPS 192.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1105/10000 episodes, total num timesteps 221200/2000000, FPS 192.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1106/10000 episodes, total num timesteps 221400/2000000, FPS 192.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1107/10000 episodes, total num timesteps 221600/2000000, FPS 192.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1108/10000 episodes, total num timesteps 221800/2000000, FPS 192.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1109/10000 episodes, total num timesteps 222000/2000000, FPS 192.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1110/10000 episodes, total num timesteps 222200/2000000, FPS 192.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1111/10000 episodes, total num timesteps 222400/2000000, FPS 192.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1112/10000 episodes, total num timesteps 222600/2000000, FPS 192.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1113/10000 episodes, total num timesteps 222800/2000000, FPS 192.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1114/10000 episodes, total num timesteps 223000/2000000, FPS 192.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1115/10000 episodes, total num timesteps 223200/2000000, FPS 192.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1116/10000 episodes, total num timesteps 223400/2000000, FPS 192.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1117/10000 episodes, total num timesteps 223600/2000000, FPS 192.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1118/10000 episodes, total num timesteps 223800/2000000, FPS 192.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1119/10000 episodes, total num timesteps 224000/2000000, FPS 192.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1120/10000 episodes, total num timesteps 224200/2000000, FPS 192.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1121/10000 episodes, total num timesteps 224400/2000000, FPS 192.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1122/10000 episodes, total num timesteps 224600/2000000, FPS 192.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1123/10000 episodes, total num timesteps 224800/2000000, FPS 192.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1124/10000 episodes, total num timesteps 225000/2000000, FPS 192.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1125/10000 episodes, total num timesteps 225200/2000000, FPS 192.

team_policy eval average step individual rewards of agent0: 0.9426832596195288
team_policy eval average team episode rewards of agent0: 100.0
team_policy eval idv catch total num of agent0: 39
team_policy eval team catch total num: 40
team_policy eval average step individual rewards of agent1: 1.012919539062709
team_policy eval average team episode rewards of agent1: 100.0
team_policy eval idv catch total num of agent1: 42
team_policy eval team catch total num: 40
team_policy eval average step individual rewards of agent2: 0.7891958936480694
team_policy eval average team episode rewards of agent2: 100.0
team_policy eval idv catch total num of agent2: 33
team_policy eval team catch total num: 40
team_policy eval average step individual rewards of agent3: 0.3467280470248999
team_policy eval average team episode rewards of agent3: 100.0
team_policy eval idv catch total num of agent3: 16
team_policy eval team catch total num: 40
team_policy eval average step individual rewards of agent4: 0.5931718600699696
team_policy eval average team episode rewards of agent4: 100.0
team_policy eval idv catch total num of agent4: 25
team_policy eval team catch total num: 40
idv_policy eval average step individual rewards of agent0: 0.6694125497965547
idv_policy eval average team episode rewards of agent0: 87.5
idv_policy eval idv catch total num of agent0: 28
idv_policy eval team catch total num: 35
idv_policy eval average step individual rewards of agent1: 0.4388784915984179
idv_policy eval average team episode rewards of agent1: 87.5
idv_policy eval idv catch total num of agent1: 19
idv_policy eval team catch total num: 35
idv_policy eval average step individual rewards of agent2: 0.9159251524905734
idv_policy eval average team episode rewards of agent2: 87.5
idv_policy eval idv catch total num of agent2: 38
idv_policy eval team catch total num: 35
idv_policy eval average step individual rewards of agent3: 0.7132594408789535
idv_policy eval average team episode rewards of agent3: 87.5
idv_policy eval idv catch total num of agent3: 30
idv_policy eval team catch total num: 35
idv_policy eval average step individual rewards of agent4: 0.6103109600538769
idv_policy eval average team episode rewards of agent4: 87.5
idv_policy eval idv catch total num of agent4: 26
idv_policy eval team catch total num: 35

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1126/10000 episodes, total num timesteps 225400/2000000, FPS 192.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1127/10000 episodes, total num timesteps 225600/2000000, FPS 192.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1128/10000 episodes, total num timesteps 225800/2000000, FPS 192.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1129/10000 episodes, total num timesteps 226000/2000000, FPS 192.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1130/10000 episodes, total num timesteps 226200/2000000, FPS 192.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1131/10000 episodes, total num timesteps 226400/2000000, FPS 192.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1132/10000 episodes, total num timesteps 226600/2000000, FPS 192.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1133/10000 episodes, total num timesteps 226800/2000000, FPS 192.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1134/10000 episodes, total num timesteps 227000/2000000, FPS 192.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1135/10000 episodes, total num timesteps 227200/2000000, FPS 192.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1136/10000 episodes, total num timesteps 227400/2000000, FPS 192.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1137/10000 episodes, total num timesteps 227600/2000000, FPS 192.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1138/10000 episodes, total num timesteps 227800/2000000, FPS 193.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1139/10000 episodes, total num timesteps 228000/2000000, FPS 193.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1140/10000 episodes, total num timesteps 228200/2000000, FPS 193.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1141/10000 episodes, total num timesteps 228400/2000000, FPS 193.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1142/10000 episodes, total num timesteps 228600/2000000, FPS 193.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1143/10000 episodes, total num timesteps 228800/2000000, FPS 193.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1144/10000 episodes, total num timesteps 229000/2000000, FPS 193.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1145/10000 episodes, total num timesteps 229200/2000000, FPS 193.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1146/10000 episodes, total num timesteps 229400/2000000, FPS 193.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1147/10000 episodes, total num timesteps 229600/2000000, FPS 193.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1148/10000 episodes, total num timesteps 229800/2000000, FPS 193.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1149/10000 episodes, total num timesteps 230000/2000000, FPS 193.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1150/10000 episodes, total num timesteps 230200/2000000, FPS 193.

team_policy eval average step individual rewards of agent0: 0.5198123261469427
team_policy eval average team episode rewards of agent0: 90.0
team_policy eval idv catch total num of agent0: 22
team_policy eval team catch total num: 36
team_policy eval average step individual rewards of agent1: 0.7404402594735424
team_policy eval average team episode rewards of agent1: 90.0
team_policy eval idv catch total num of agent1: 31
team_policy eval team catch total num: 36
team_policy eval average step individual rewards of agent2: 0.8397772929284159
team_policy eval average team episode rewards of agent2: 90.0
team_policy eval idv catch total num of agent2: 35
team_policy eval team catch total num: 36
team_policy eval average step individual rewards of agent3: 0.9193564969952436
team_policy eval average team episode rewards of agent3: 90.0
team_policy eval idv catch total num of agent3: 38
team_policy eval team catch total num: 36
team_policy eval average step individual rewards of agent4: 0.3566578527485922
team_policy eval average team episode rewards of agent4: 90.0
team_policy eval idv catch total num of agent4: 16
team_policy eval team catch total num: 36
idv_policy eval average step individual rewards of agent0: 0.6934828691628993
idv_policy eval average team episode rewards of agent0: 120.0
idv_policy eval idv catch total num of agent0: 29
idv_policy eval team catch total num: 48
idv_policy eval average step individual rewards of agent1: 0.7372110844206299
idv_policy eval average team episode rewards of agent1: 120.0
idv_policy eval idv catch total num of agent1: 31
idv_policy eval team catch total num: 48
idv_policy eval average step individual rewards of agent2: 0.7176329705812405
idv_policy eval average team episode rewards of agent2: 120.0
idv_policy eval idv catch total num of agent2: 30
idv_policy eval team catch total num: 48
idv_policy eval average step individual rewards of agent3: 0.6877759737380836
idv_policy eval average team episode rewards of agent3: 120.0
idv_policy eval idv catch total num of agent3: 29
idv_policy eval team catch total num: 48
idv_policy eval average step individual rewards of agent4: 0.873552381117056
idv_policy eval average team episode rewards of agent4: 120.0
idv_policy eval idv catch total num of agent4: 36
idv_policy eval team catch total num: 48

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1151/10000 episodes, total num timesteps 230400/2000000, FPS 193.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1152/10000 episodes, total num timesteps 230600/2000000, FPS 193.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1153/10000 episodes, total num timesteps 230800/2000000, FPS 193.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1154/10000 episodes, total num timesteps 231000/2000000, FPS 193.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1155/10000 episodes, total num timesteps 231200/2000000, FPS 193.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1156/10000 episodes, total num timesteps 231400/2000000, FPS 193.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1157/10000 episodes, total num timesteps 231600/2000000, FPS 193.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1158/10000 episodes, total num timesteps 231800/2000000, FPS 193.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1159/10000 episodes, total num timesteps 232000/2000000, FPS 193.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1160/10000 episodes, total num timesteps 232200/2000000, FPS 193.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1161/10000 episodes, total num timesteps 232400/2000000, FPS 193.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1162/10000 episodes, total num timesteps 232600/2000000, FPS 193.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1163/10000 episodes, total num timesteps 232800/2000000, FPS 193.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1164/10000 episodes, total num timesteps 233000/2000000, FPS 193.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1165/10000 episodes, total num timesteps 233200/2000000, FPS 193.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1166/10000 episodes, total num timesteps 233400/2000000, FPS 193.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1167/10000 episodes, total num timesteps 233600/2000000, FPS 193.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1168/10000 episodes, total num timesteps 233800/2000000, FPS 193.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1169/10000 episodes, total num timesteps 234000/2000000, FPS 193.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1170/10000 episodes, total num timesteps 234200/2000000, FPS 193.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1171/10000 episodes, total num timesteps 234400/2000000, FPS 193.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1172/10000 episodes, total num timesteps 234600/2000000, FPS 193.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1173/10000 episodes, total num timesteps 234800/2000000, FPS 193.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1174/10000 episodes, total num timesteps 235000/2000000, FPS 193.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1175/10000 episodes, total num timesteps 235200/2000000, FPS 193.

team_policy eval average step individual rewards of agent0: 0.3319114396156999
team_policy eval average team episode rewards of agent0: 72.5
team_policy eval idv catch total num of agent0: 15
team_policy eval team catch total num: 29
team_policy eval average step individual rewards of agent1: 0.6104174701939672
team_policy eval average team episode rewards of agent1: 72.5
team_policy eval idv catch total num of agent1: 26
team_policy eval team catch total num: 29
team_policy eval average step individual rewards of agent2: 0.7057413418758587
team_policy eval average team episode rewards of agent2: 72.5
team_policy eval idv catch total num of agent2: 30
team_policy eval team catch total num: 29
team_policy eval average step individual rewards of agent3: 0.25074113550463406
team_policy eval average team episode rewards of agent3: 72.5
team_policy eval idv catch total num of agent3: 12
team_policy eval team catch total num: 29
team_policy eval average step individual rewards of agent4: 0.2476204132463506
team_policy eval average team episode rewards of agent4: 72.5
team_policy eval idv catch total num of agent4: 12
team_policy eval team catch total num: 29
idv_policy eval average step individual rewards of agent0: 0.44582959608395456
idv_policy eval average team episode rewards of agent0: 72.5
idv_policy eval idv catch total num of agent0: 20
idv_policy eval team catch total num: 29
idv_policy eval average step individual rewards of agent1: 0.7497758833708695
idv_policy eval average team episode rewards of agent1: 72.5
idv_policy eval idv catch total num of agent1: 32
idv_policy eval team catch total num: 29
idv_policy eval average step individual rewards of agent2: 0.5349879727098374
idv_policy eval average team episode rewards of agent2: 72.5
idv_policy eval idv catch total num of agent2: 23
idv_policy eval team catch total num: 29
idv_policy eval average step individual rewards of agent3: 0.7127712714805764
idv_policy eval average team episode rewards of agent3: 72.5
idv_policy eval idv catch total num of agent3: 30
idv_policy eval team catch total num: 29
idv_policy eval average step individual rewards of agent4: 0.593495300051513
idv_policy eval average team episode rewards of agent4: 72.5
idv_policy eval idv catch total num of agent4: 25
idv_policy eval team catch total num: 29

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1176/10000 episodes, total num timesteps 235400/2000000, FPS 193.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1177/10000 episodes, total num timesteps 235600/2000000, FPS 194.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1178/10000 episodes, total num timesteps 235800/2000000, FPS 194.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1179/10000 episodes, total num timesteps 236000/2000000, FPS 194.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1180/10000 episodes, total num timesteps 236200/2000000, FPS 194.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1181/10000 episodes, total num timesteps 236400/2000000, FPS 194.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1182/10000 episodes, total num timesteps 236600/2000000, FPS 194.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1183/10000 episodes, total num timesteps 236800/2000000, FPS 194.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1184/10000 episodes, total num timesteps 237000/2000000, FPS 194.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1185/10000 episodes, total num timesteps 237200/2000000, FPS 194.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1186/10000 episodes, total num timesteps 237400/2000000, FPS 194.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1187/10000 episodes, total num timesteps 237600/2000000, FPS 194.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1188/10000 episodes, total num timesteps 237800/2000000, FPS 194.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1189/10000 episodes, total num timesteps 238000/2000000, FPS 194.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1190/10000 episodes, total num timesteps 238200/2000000, FPS 194.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1191/10000 episodes, total num timesteps 238400/2000000, FPS 194.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1192/10000 episodes, total num timesteps 238600/2000000, FPS 194.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1193/10000 episodes, total num timesteps 238800/2000000, FPS 194.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1194/10000 episodes, total num timesteps 239000/2000000, FPS 194.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1195/10000 episodes, total num timesteps 239200/2000000, FPS 194.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1196/10000 episodes, total num timesteps 239400/2000000, FPS 194.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1197/10000 episodes, total num timesteps 239600/2000000, FPS 194.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1198/10000 episodes, total num timesteps 239800/2000000, FPS 194.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1199/10000 episodes, total num timesteps 240000/2000000, FPS 194.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1200/10000 episodes, total num timesteps 240200/2000000, FPS 194.

team_policy eval average step individual rewards of agent0: 0.51628073526116
team_policy eval average team episode rewards of agent0: 60.0
team_policy eval idv catch total num of agent0: 22
team_policy eval team catch total num: 24
team_policy eval average step individual rewards of agent1: 0.43900756943811237
team_policy eval average team episode rewards of agent1: 60.0
team_policy eval idv catch total num of agent1: 19
team_policy eval team catch total num: 24
team_policy eval average step individual rewards of agent2: 0.36225564592996323
team_policy eval average team episode rewards of agent2: 60.0
team_policy eval idv catch total num of agent2: 16
team_policy eval team catch total num: 24
team_policy eval average step individual rewards of agent3: 0.5337792043722024
team_policy eval average team episode rewards of agent3: 60.0
team_policy eval idv catch total num of agent3: 23
team_policy eval team catch total num: 24
team_policy eval average step individual rewards of agent4: 0.5820578668712516
team_policy eval average team episode rewards of agent4: 60.0
team_policy eval idv catch total num of agent4: 25
team_policy eval team catch total num: 24
idv_policy eval average step individual rewards of agent0: 0.5921026862798605
idv_policy eval average team episode rewards of agent0: 70.0
idv_policy eval idv catch total num of agent0: 26
idv_policy eval team catch total num: 28
idv_policy eval average step individual rewards of agent1: 0.4317387389843011
idv_policy eval average team episode rewards of agent1: 70.0
idv_policy eval idv catch total num of agent1: 19
idv_policy eval team catch total num: 28
idv_policy eval average step individual rewards of agent2: 0.6026798132212436
idv_policy eval average team episode rewards of agent2: 70.0
idv_policy eval idv catch total num of agent2: 26
idv_policy eval team catch total num: 28
idv_policy eval average step individual rewards of agent3: 0.48442309819913837
idv_policy eval average team episode rewards of agent3: 70.0
idv_policy eval idv catch total num of agent3: 21
idv_policy eval team catch total num: 28
idv_policy eval average step individual rewards of agent4: 0.19969849522643623
idv_policy eval average team episode rewards of agent4: 70.0
idv_policy eval idv catch total num of agent4: 10
idv_policy eval team catch total num: 28

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1201/10000 episodes, total num timesteps 240400/2000000, FPS 194.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1202/10000 episodes, total num timesteps 240600/2000000, FPS 194.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1203/10000 episodes, total num timesteps 240800/2000000, FPS 194.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1204/10000 episodes, total num timesteps 241000/2000000, FPS 194.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1205/10000 episodes, total num timesteps 241200/2000000, FPS 194.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1206/10000 episodes, total num timesteps 241400/2000000, FPS 194.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1207/10000 episodes, total num timesteps 241600/2000000, FPS 194.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1208/10000 episodes, total num timesteps 241800/2000000, FPS 194.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1209/10000 episodes, total num timesteps 242000/2000000, FPS 194.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1210/10000 episodes, total num timesteps 242200/2000000, FPS 194.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1211/10000 episodes, total num timesteps 242400/2000000, FPS 194.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1212/10000 episodes, total num timesteps 242600/2000000, FPS 194.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1213/10000 episodes, total num timesteps 242800/2000000, FPS 194.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1214/10000 episodes, total num timesteps 243000/2000000, FPS 194.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1215/10000 episodes, total num timesteps 243200/2000000, FPS 194.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1216/10000 episodes, total num timesteps 243400/2000000, FPS 194.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1217/10000 episodes, total num timesteps 243600/2000000, FPS 194.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1218/10000 episodes, total num timesteps 243800/2000000, FPS 194.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1219/10000 episodes, total num timesteps 244000/2000000, FPS 194.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1220/10000 episodes, total num timesteps 244200/2000000, FPS 194.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1221/10000 episodes, total num timesteps 244400/2000000, FPS 194.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1222/10000 episodes, total num timesteps 244600/2000000, FPS 194.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1223/10000 episodes, total num timesteps 244800/2000000, FPS 194.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1224/10000 episodes, total num timesteps 245000/2000000, FPS 194.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1225/10000 episodes, total num timesteps 245200/2000000, FPS 194.

team_policy eval average step individual rewards of agent0: 0.3057747004375681
team_policy eval average team episode rewards of agent0: 37.5
team_policy eval idv catch total num of agent0: 14
team_policy eval team catch total num: 15
team_policy eval average step individual rewards of agent1: 0.12885358256086663
team_policy eval average team episode rewards of agent1: 37.5
team_policy eval idv catch total num of agent1: 7
team_policy eval team catch total num: 15
team_policy eval average step individual rewards of agent2: 0.3844137001848322
team_policy eval average team episode rewards of agent2: 37.5
team_policy eval idv catch total num of agent2: 17
team_policy eval team catch total num: 15
team_policy eval average step individual rewards of agent3: 0.5835402252573912
team_policy eval average team episode rewards of agent3: 37.5
team_policy eval idv catch total num of agent3: 25
team_policy eval team catch total num: 15
team_policy eval average step individual rewards of agent4: 0.30598645747771075
team_policy eval average team episode rewards of agent4: 37.5
team_policy eval idv catch total num of agent4: 14
team_policy eval team catch total num: 15
idv_policy eval average step individual rewards of agent0: 0.6622953989165974
idv_policy eval average team episode rewards of agent0: 75.0
idv_policy eval idv catch total num of agent0: 28
idv_policy eval team catch total num: 30
idv_policy eval average step individual rewards of agent1: 0.5364992050918524
idv_policy eval average team episode rewards of agent1: 75.0
idv_policy eval idv catch total num of agent1: 23
idv_policy eval team catch total num: 30
idv_policy eval average step individual rewards of agent2: 0.8198704405304426
idv_policy eval average team episode rewards of agent2: 75.0
idv_policy eval idv catch total num of agent2: 34
idv_policy eval team catch total num: 30
idv_policy eval average step individual rewards of agent3: 0.6583301227061356
idv_policy eval average team episode rewards of agent3: 75.0
idv_policy eval idv catch total num of agent3: 28
idv_policy eval team catch total num: 30
idv_policy eval average step individual rewards of agent4: 0.5099039723390532
idv_policy eval average team episode rewards of agent4: 75.0
idv_policy eval idv catch total num of agent4: 22
idv_policy eval team catch total num: 30

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1226/10000 episodes, total num timesteps 245400/2000000, FPS 194.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1227/10000 episodes, total num timesteps 245600/2000000, FPS 195.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1228/10000 episodes, total num timesteps 245800/2000000, FPS 195.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1229/10000 episodes, total num timesteps 246000/2000000, FPS 195.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1230/10000 episodes, total num timesteps 246200/2000000, FPS 195.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1231/10000 episodes, total num timesteps 246400/2000000, FPS 195.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1232/10000 episodes, total num timesteps 246600/2000000, FPS 195.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1233/10000 episodes, total num timesteps 246800/2000000, FPS 195.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1234/10000 episodes, total num timesteps 247000/2000000, FPS 195.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1235/10000 episodes, total num timesteps 247200/2000000, FPS 195.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1236/10000 episodes, total num timesteps 247400/2000000, FPS 195.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1237/10000 episodes, total num timesteps 247600/2000000, FPS 195.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1238/10000 episodes, total num timesteps 247800/2000000, FPS 195.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1239/10000 episodes, total num timesteps 248000/2000000, FPS 195.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1240/10000 episodes, total num timesteps 248200/2000000, FPS 195.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1241/10000 episodes, total num timesteps 248400/2000000, FPS 195.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1242/10000 episodes, total num timesteps 248600/2000000, FPS 195.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1243/10000 episodes, total num timesteps 248800/2000000, FPS 195.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1244/10000 episodes, total num timesteps 249000/2000000, FPS 195.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1245/10000 episodes, total num timesteps 249200/2000000, FPS 195.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1246/10000 episodes, total num timesteps 249400/2000000, FPS 195.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1247/10000 episodes, total num timesteps 249600/2000000, FPS 195.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1248/10000 episodes, total num timesteps 249800/2000000, FPS 195.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1249/10000 episodes, total num timesteps 250000/2000000, FPS 195.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1250/10000 episodes, total num timesteps 250200/2000000, FPS 195.

team_policy eval average step individual rewards of agent0: 0.9144568142634829
team_policy eval average team episode rewards of agent0: 72.5
team_policy eval idv catch total num of agent0: 38
team_policy eval team catch total num: 29
team_policy eval average step individual rewards of agent1: 0.35679996679975146
team_policy eval average team episode rewards of agent1: 72.5
team_policy eval idv catch total num of agent1: 16
team_policy eval team catch total num: 29
team_policy eval average step individual rewards of agent2: 0.41227136395754277
team_policy eval average team episode rewards of agent2: 72.5
team_policy eval idv catch total num of agent2: 19
team_policy eval team catch total num: 29
team_policy eval average step individual rewards of agent3: 0.7124504597638193
team_policy eval average team episode rewards of agent3: 72.5
team_policy eval idv catch total num of agent3: 30
team_policy eval team catch total num: 29
team_policy eval average step individual rewards of agent4: 0.27625908410440564
team_policy eval average team episode rewards of agent4: 72.5
team_policy eval idv catch total num of agent4: 13
team_policy eval team catch total num: 29
idv_policy eval average step individual rewards of agent0: 0.6576394380658368
idv_policy eval average team episode rewards of agent0: 75.0
idv_policy eval idv catch total num of agent0: 28
idv_policy eval team catch total num: 30
idv_policy eval average step individual rewards of agent1: 0.6396572730515213
idv_policy eval average team episode rewards of agent1: 75.0
idv_policy eval idv catch total num of agent1: 27
idv_policy eval team catch total num: 30
idv_policy eval average step individual rewards of agent2: 0.5187651727472854
idv_policy eval average team episode rewards of agent2: 75.0
idv_policy eval idv catch total num of agent2: 22
idv_policy eval team catch total num: 30
idv_policy eval average step individual rewards of agent3: 0.27748479402223647
idv_policy eval average team episode rewards of agent3: 75.0
idv_policy eval idv catch total num of agent3: 13
idv_policy eval team catch total num: 30
idv_policy eval average step individual rewards of agent4: 0.5866280295475306
idv_policy eval average team episode rewards of agent4: 75.0
idv_policy eval idv catch total num of agent4: 25
idv_policy eval team catch total num: 30

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1251/10000 episodes, total num timesteps 250400/2000000, FPS 195.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1252/10000 episodes, total num timesteps 250600/2000000, FPS 195.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1253/10000 episodes, total num timesteps 250800/2000000, FPS 195.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1254/10000 episodes, total num timesteps 251000/2000000, FPS 195.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1255/10000 episodes, total num timesteps 251200/2000000, FPS 195.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1256/10000 episodes, total num timesteps 251400/2000000, FPS 195.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1257/10000 episodes, total num timesteps 251600/2000000, FPS 195.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1258/10000 episodes, total num timesteps 251800/2000000, FPS 195.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1259/10000 episodes, total num timesteps 252000/2000000, FPS 195.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1260/10000 episodes, total num timesteps 252200/2000000, FPS 195.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1261/10000 episodes, total num timesteps 252400/2000000, FPS 195.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1262/10000 episodes, total num timesteps 252600/2000000, FPS 195.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1263/10000 episodes, total num timesteps 252800/2000000, FPS 195.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1264/10000 episodes, total num timesteps 253000/2000000, FPS 195.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1265/10000 episodes, total num timesteps 253200/2000000, FPS 195.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1266/10000 episodes, total num timesteps 253400/2000000, FPS 195.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1267/10000 episodes, total num timesteps 253600/2000000, FPS 195.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1268/10000 episodes, total num timesteps 253800/2000000, FPS 195.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1269/10000 episodes, total num timesteps 254000/2000000, FPS 195.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1270/10000 episodes, total num timesteps 254200/2000000, FPS 195.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1271/10000 episodes, total num timesteps 254400/2000000, FPS 195.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1272/10000 episodes, total num timesteps 254600/2000000, FPS 195.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1273/10000 episodes, total num timesteps 254800/2000000, FPS 196.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1274/10000 episodes, total num timesteps 255000/2000000, FPS 196.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1275/10000 episodes, total num timesteps 255200/2000000, FPS 196.

team_policy eval average step individual rewards of agent0: 0.37358397833424634
team_policy eval average team episode rewards of agent0: 85.0
team_policy eval idv catch total num of agent0: 17
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent1: 0.48188587691967855
team_policy eval average team episode rewards of agent1: 85.0
team_policy eval idv catch total num of agent1: 21
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent2: 0.749425656141454
team_policy eval average team episode rewards of agent2: 85.0
team_policy eval idv catch total num of agent2: 32
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent3: 0.7601160109975161
team_policy eval average team episode rewards of agent3: 85.0
team_policy eval idv catch total num of agent3: 32
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent4: 0.60402556262716
team_policy eval average team episode rewards of agent4: 85.0
team_policy eval idv catch total num of agent4: 26
team_policy eval team catch total num: 34
idv_policy eval average step individual rewards of agent0: 0.40485483247407644
idv_policy eval average team episode rewards of agent0: 35.0
idv_policy eval idv catch total num of agent0: 18
idv_policy eval team catch total num: 14
idv_policy eval average step individual rewards of agent1: 0.2840538421235365
idv_policy eval average team episode rewards of agent1: 35.0
idv_policy eval idv catch total num of agent1: 14
idv_policy eval team catch total num: 14
idv_policy eval average step individual rewards of agent2: 0.35338044353234865
idv_policy eval average team episode rewards of agent2: 35.0
idv_policy eval idv catch total num of agent2: 16
idv_policy eval team catch total num: 14
idv_policy eval average step individual rewards of agent3: 0.22902188283167585
idv_policy eval average team episode rewards of agent3: 35.0
idv_policy eval idv catch total num of agent3: 11
idv_policy eval team catch total num: 14
idv_policy eval average step individual rewards of agent4: 0.3567453494045859
idv_policy eval average team episode rewards of agent4: 35.0
idv_policy eval idv catch total num of agent4: 16
idv_policy eval team catch total num: 14

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1276/10000 episodes, total num timesteps 255400/2000000, FPS 196.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1277/10000 episodes, total num timesteps 255600/2000000, FPS 196.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1278/10000 episodes, total num timesteps 255800/2000000, FPS 196.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1279/10000 episodes, total num timesteps 256000/2000000, FPS 196.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1280/10000 episodes, total num timesteps 256200/2000000, FPS 196.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1281/10000 episodes, total num timesteps 256400/2000000, FPS 196.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1282/10000 episodes, total num timesteps 256600/2000000, FPS 196.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1283/10000 episodes, total num timesteps 256800/2000000, FPS 196.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1284/10000 episodes, total num timesteps 257000/2000000, FPS 196.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1285/10000 episodes, total num timesteps 257200/2000000, FPS 196.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1286/10000 episodes, total num timesteps 257400/2000000, FPS 196.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1287/10000 episodes, total num timesteps 257600/2000000, FPS 196.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1288/10000 episodes, total num timesteps 257800/2000000, FPS 196.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1289/10000 episodes, total num timesteps 258000/2000000, FPS 196.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1290/10000 episodes, total num timesteps 258200/2000000, FPS 196.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1291/10000 episodes, total num timesteps 258400/2000000, FPS 196.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1292/10000 episodes, total num timesteps 258600/2000000, FPS 196.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1293/10000 episodes, total num timesteps 258800/2000000, FPS 196.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1294/10000 episodes, total num timesteps 259000/2000000, FPS 196.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1295/10000 episodes, total num timesteps 259200/2000000, FPS 196.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1296/10000 episodes, total num timesteps 259400/2000000, FPS 196.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1297/10000 episodes, total num timesteps 259600/2000000, FPS 196.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1298/10000 episodes, total num timesteps 259800/2000000, FPS 196.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1299/10000 episodes, total num timesteps 260000/2000000, FPS 196.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1300/10000 episodes, total num timesteps 260200/2000000, FPS 196.

team_policy eval average step individual rewards of agent0: 0.1871917557860522
team_policy eval average team episode rewards of agent0: 67.5
team_policy eval idv catch total num of agent0: 9
team_policy eval team catch total num: 27
team_policy eval average step individual rewards of agent1: 0.6829799818102263
team_policy eval average team episode rewards of agent1: 67.5
team_policy eval idv catch total num of agent1: 29
team_policy eval team catch total num: 27
team_policy eval average step individual rewards of agent2: 0.5357626558880153
team_policy eval average team episode rewards of agent2: 67.5
team_policy eval idv catch total num of agent2: 23
team_policy eval team catch total num: 27
team_policy eval average step individual rewards of agent3: 0.3314926849420351
team_policy eval average team episode rewards of agent3: 67.5
team_policy eval idv catch total num of agent3: 15
team_policy eval team catch total num: 27
team_policy eval average step individual rewards of agent4: 0.763374520404668
team_policy eval average team episode rewards of agent4: 67.5
team_policy eval idv catch total num of agent4: 32
team_policy eval team catch total num: 27
idv_policy eval average step individual rewards of agent0: 0.1727996961019971
idv_policy eval average team episode rewards of agent0: 40.0
idv_policy eval idv catch total num of agent0: 9
idv_policy eval team catch total num: 16
idv_policy eval average step individual rewards of agent1: 0.24328495588501553
idv_policy eval average team episode rewards of agent1: 40.0
idv_policy eval idv catch total num of agent1: 12
idv_policy eval team catch total num: 16
idv_policy eval average step individual rewards of agent2: 0.3307759856427056
idv_policy eval average team episode rewards of agent2: 40.0
idv_policy eval idv catch total num of agent2: 15
idv_policy eval team catch total num: 16
idv_policy eval average step individual rewards of agent3: 0.39722165422262606
idv_policy eval average team episode rewards of agent3: 40.0
idv_policy eval idv catch total num of agent3: 18
idv_policy eval team catch total num: 16
idv_policy eval average step individual rewards of agent4: 0.16450697524039615
idv_policy eval average team episode rewards of agent4: 40.0
idv_policy eval idv catch total num of agent4: 9
idv_policy eval team catch total num: 16

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1301/10000 episodes, total num timesteps 260400/2000000, FPS 196.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1302/10000 episodes, total num timesteps 260600/2000000, FPS 196.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1303/10000 episodes, total num timesteps 260800/2000000, FPS 196.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1304/10000 episodes, total num timesteps 261000/2000000, FPS 196.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1305/10000 episodes, total num timesteps 261200/2000000, FPS 196.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1306/10000 episodes, total num timesteps 261400/2000000, FPS 196.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1307/10000 episodes, total num timesteps 261600/2000000, FPS 196.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1308/10000 episodes, total num timesteps 261800/2000000, FPS 196.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1309/10000 episodes, total num timesteps 262000/2000000, FPS 196.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1310/10000 episodes, total num timesteps 262200/2000000, FPS 196.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1311/10000 episodes, total num timesteps 262400/2000000, FPS 196.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1312/10000 episodes, total num timesteps 262600/2000000, FPS 196.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1313/10000 episodes, total num timesteps 262800/2000000, FPS 196.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1314/10000 episodes, total num timesteps 263000/2000000, FPS 196.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1315/10000 episodes, total num timesteps 263200/2000000, FPS 196.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1316/10000 episodes, total num timesteps 263400/2000000, FPS 196.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1317/10000 episodes, total num timesteps 263600/2000000, FPS 196.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1318/10000 episodes, total num timesteps 263800/2000000, FPS 196.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1319/10000 episodes, total num timesteps 264000/2000000, FPS 196.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1320/10000 episodes, total num timesteps 264200/2000000, FPS 196.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1321/10000 episodes, total num timesteps 264400/2000000, FPS 196.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1322/10000 episodes, total num timesteps 264600/2000000, FPS 196.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1323/10000 episodes, total num timesteps 264800/2000000, FPS 196.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1324/10000 episodes, total num timesteps 265000/2000000, FPS 196.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1325/10000 episodes, total num timesteps 265200/2000000, FPS 196.

team_policy eval average step individual rewards of agent0: 0.9411626103749319
team_policy eval average team episode rewards of agent0: 147.5
team_policy eval idv catch total num of agent0: 39
team_policy eval team catch total num: 59
team_policy eval average step individual rewards of agent1: 1.0907330744705606
team_policy eval average team episode rewards of agent1: 147.5
team_policy eval idv catch total num of agent1: 45
team_policy eval team catch total num: 59
team_policy eval average step individual rewards of agent2: 0.8176130818097121
team_policy eval average team episode rewards of agent2: 147.5
team_policy eval idv catch total num of agent2: 34
team_policy eval team catch total num: 59
team_policy eval average step individual rewards of agent3: 0.935174884494862
team_policy eval average team episode rewards of agent3: 147.5
team_policy eval idv catch total num of agent3: 39
team_policy eval team catch total num: 59
team_policy eval average step individual rewards of agent4: 0.8725436636388832
team_policy eval average team episode rewards of agent4: 147.5
team_policy eval idv catch total num of agent4: 36
team_policy eval team catch total num: 59
idv_policy eval average step individual rewards of agent0: 0.5403758903587288
idv_policy eval average team episode rewards of agent0: 90.0
idv_policy eval idv catch total num of agent0: 23
idv_policy eval team catch total num: 36
idv_policy eval average step individual rewards of agent1: 0.6984467237564655
idv_policy eval average team episode rewards of agent1: 90.0
idv_policy eval idv catch total num of agent1: 29
idv_policy eval team catch total num: 36
idv_policy eval average step individual rewards of agent2: 0.6640608195828185
idv_policy eval average team episode rewards of agent2: 90.0
idv_policy eval idv catch total num of agent2: 28
idv_policy eval team catch total num: 36
idv_policy eval average step individual rewards of agent3: 0.5849325696468398
idv_policy eval average team episode rewards of agent3: 90.0
idv_policy eval idv catch total num of agent3: 25
idv_policy eval team catch total num: 36
idv_policy eval average step individual rewards of agent4: 0.7370944402422316
idv_policy eval average team episode rewards of agent4: 90.0
idv_policy eval idv catch total num of agent4: 31
idv_policy eval team catch total num: 36

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1326/10000 episodes, total num timesteps 265400/2000000, FPS 196.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1327/10000 episodes, total num timesteps 265600/2000000, FPS 196.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1328/10000 episodes, total num timesteps 265800/2000000, FPS 197.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1329/10000 episodes, total num timesteps 266000/2000000, FPS 197.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1330/10000 episodes, total num timesteps 266200/2000000, FPS 197.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1331/10000 episodes, total num timesteps 266400/2000000, FPS 197.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1332/10000 episodes, total num timesteps 266600/2000000, FPS 197.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1333/10000 episodes, total num timesteps 266800/2000000, FPS 197.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1334/10000 episodes, total num timesteps 267000/2000000, FPS 197.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1335/10000 episodes, total num timesteps 267200/2000000, FPS 197.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1336/10000 episodes, total num timesteps 267400/2000000, FPS 197.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1337/10000 episodes, total num timesteps 267600/2000000, FPS 197.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1338/10000 episodes, total num timesteps 267800/2000000, FPS 197.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1339/10000 episodes, total num timesteps 268000/2000000, FPS 197.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1340/10000 episodes, total num timesteps 268200/2000000, FPS 197.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1341/10000 episodes, total num timesteps 268400/2000000, FPS 197.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1342/10000 episodes, total num timesteps 268600/2000000, FPS 197.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1343/10000 episodes, total num timesteps 268800/2000000, FPS 197.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1344/10000 episodes, total num timesteps 269000/2000000, FPS 197.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1345/10000 episodes, total num timesteps 269200/2000000, FPS 197.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1346/10000 episodes, total num timesteps 269400/2000000, FPS 197.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1347/10000 episodes, total num timesteps 269600/2000000, FPS 197.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1348/10000 episodes, total num timesteps 269800/2000000, FPS 197.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1349/10000 episodes, total num timesteps 270000/2000000, FPS 197.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1350/10000 episodes, total num timesteps 270200/2000000, FPS 197.

team_policy eval average step individual rewards of agent0: 0.6203122481451796
team_policy eval average team episode rewards of agent0: 75.0
team_policy eval idv catch total num of agent0: 26
team_policy eval team catch total num: 30
team_policy eval average step individual rewards of agent1: 0.30557230361248344
team_policy eval average team episode rewards of agent1: 75.0
team_policy eval idv catch total num of agent1: 14
team_policy eval team catch total num: 30
team_policy eval average step individual rewards of agent2: 0.7686476979277095
team_policy eval average team episode rewards of agent2: 75.0
team_policy eval idv catch total num of agent2: 32
team_policy eval team catch total num: 30
team_policy eval average step individual rewards of agent3: 0.5062784117351893
team_policy eval average team episode rewards of agent3: 75.0
team_policy eval idv catch total num of agent3: 22
team_policy eval team catch total num: 30
team_policy eval average step individual rewards of agent4: 0.33467229641038704
team_policy eval average team episode rewards of agent4: 75.0
team_policy eval idv catch total num of agent4: 15
team_policy eval team catch total num: 30
idv_policy eval average step individual rewards of agent0: 0.6123641045895575
idv_policy eval average team episode rewards of agent0: 105.0
idv_policy eval idv catch total num of agent0: 26
idv_policy eval team catch total num: 42
idv_policy eval average step individual rewards of agent1: 0.667093735959507
idv_policy eval average team episode rewards of agent1: 105.0
idv_policy eval idv catch total num of agent1: 28
idv_policy eval team catch total num: 42
idv_policy eval average step individual rewards of agent2: 0.6894295849423421
idv_policy eval average team episode rewards of agent2: 105.0
idv_policy eval idv catch total num of agent2: 29
idv_policy eval team catch total num: 42
idv_policy eval average step individual rewards of agent3: 0.6938843394168975
idv_policy eval average team episode rewards of agent3: 105.0
idv_policy eval idv catch total num of agent3: 29
idv_policy eval team catch total num: 42
idv_policy eval average step individual rewards of agent4: 0.6938190208211834
idv_policy eval average team episode rewards of agent4: 105.0
idv_policy eval idv catch total num of agent4: 29
idv_policy eval team catch total num: 42

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1351/10000 episodes, total num timesteps 270400/2000000, FPS 197.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1352/10000 episodes, total num timesteps 270600/2000000, FPS 197.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1353/10000 episodes, total num timesteps 270800/2000000, FPS 197.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1354/10000 episodes, total num timesteps 271000/2000000, FPS 197.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1355/10000 episodes, total num timesteps 271200/2000000, FPS 197.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1356/10000 episodes, total num timesteps 271400/2000000, FPS 197.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1357/10000 episodes, total num timesteps 271600/2000000, FPS 197.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1358/10000 episodes, total num timesteps 271800/2000000, FPS 197.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1359/10000 episodes, total num timesteps 272000/2000000, FPS 197.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1360/10000 episodes, total num timesteps 272200/2000000, FPS 197.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1361/10000 episodes, total num timesteps 272400/2000000, FPS 197.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1362/10000 episodes, total num timesteps 272600/2000000, FPS 197.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1363/10000 episodes, total num timesteps 272800/2000000, FPS 197.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1364/10000 episodes, total num timesteps 273000/2000000, FPS 197.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1365/10000 episodes, total num timesteps 273200/2000000, FPS 197.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1366/10000 episodes, total num timesteps 273400/2000000, FPS 197.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1367/10000 episodes, total num timesteps 273600/2000000, FPS 197.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1368/10000 episodes, total num timesteps 273800/2000000, FPS 197.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1369/10000 episodes, total num timesteps 274000/2000000, FPS 197.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1370/10000 episodes, total num timesteps 274200/2000000, FPS 197.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1371/10000 episodes, total num timesteps 274400/2000000, FPS 197.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1372/10000 episodes, total num timesteps 274600/2000000, FPS 197.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1373/10000 episodes, total num timesteps 274800/2000000, FPS 197.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1374/10000 episodes, total num timesteps 275000/2000000, FPS 197.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1375/10000 episodes, total num timesteps 275200/2000000, FPS 197.

team_policy eval average step individual rewards of agent0: 0.3754382852768756
team_policy eval average team episode rewards of agent0: 80.0
team_policy eval idv catch total num of agent0: 17
team_policy eval team catch total num: 32
team_policy eval average step individual rewards of agent1: 0.7095473340702668
team_policy eval average team episode rewards of agent1: 80.0
team_policy eval idv catch total num of agent1: 30
team_policy eval team catch total num: 32
team_policy eval average step individual rewards of agent2: 0.8624233546867792
team_policy eval average team episode rewards of agent2: 80.0
team_policy eval idv catch total num of agent2: 36
team_policy eval team catch total num: 32
team_policy eval average step individual rewards of agent3: 0.5916670432248469
team_policy eval average team episode rewards of agent3: 80.0
team_policy eval idv catch total num of agent3: 25
team_policy eval team catch total num: 32
team_policy eval average step individual rewards of agent4: 0.51864470862394
team_policy eval average team episode rewards of agent4: 80.0
team_policy eval idv catch total num of agent4: 22
team_policy eval team catch total num: 32
idv_policy eval average step individual rewards of agent0: 1.1942394851552978
idv_policy eval average team episode rewards of agent0: 130.0
idv_policy eval idv catch total num of agent0: 49
idv_policy eval team catch total num: 52
idv_policy eval average step individual rewards of agent1: 0.9871309075803225
idv_policy eval average team episode rewards of agent1: 130.0
idv_policy eval idv catch total num of agent1: 41
idv_policy eval team catch total num: 52
idv_policy eval average step individual rewards of agent2: 1.2004052699931116
idv_policy eval average team episode rewards of agent2: 130.0
idv_policy eval idv catch total num of agent2: 49
idv_policy eval team catch total num: 52
idv_policy eval average step individual rewards of agent3: 0.6332386768215826
idv_policy eval average team episode rewards of agent3: 130.0
idv_policy eval idv catch total num of agent3: 27
idv_policy eval team catch total num: 52
idv_policy eval average step individual rewards of agent4: 0.8175325931238153
idv_policy eval average team episode rewards of agent4: 130.0
idv_policy eval idv catch total num of agent4: 34
idv_policy eval team catch total num: 52

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1376/10000 episodes, total num timesteps 275400/2000000, FPS 197.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1377/10000 episodes, total num timesteps 275600/2000000, FPS 197.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1378/10000 episodes, total num timesteps 275800/2000000, FPS 197.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1379/10000 episodes, total num timesteps 276000/2000000, FPS 198.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1380/10000 episodes, total num timesteps 276200/2000000, FPS 198.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1381/10000 episodes, total num timesteps 276400/2000000, FPS 198.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1382/10000 episodes, total num timesteps 276600/2000000, FPS 198.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1383/10000 episodes, total num timesteps 276800/2000000, FPS 198.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1384/10000 episodes, total num timesteps 277000/2000000, FPS 198.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1385/10000 episodes, total num timesteps 277200/2000000, FPS 198.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1386/10000 episodes, total num timesteps 277400/2000000, FPS 198.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1387/10000 episodes, total num timesteps 277600/2000000, FPS 198.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1388/10000 episodes, total num timesteps 277800/2000000, FPS 198.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1389/10000 episodes, total num timesteps 278000/2000000, FPS 198.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1390/10000 episodes, total num timesteps 278200/2000000, FPS 198.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1391/10000 episodes, total num timesteps 278400/2000000, FPS 198.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1392/10000 episodes, total num timesteps 278600/2000000, FPS 198.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1393/10000 episodes, total num timesteps 278800/2000000, FPS 198.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1394/10000 episodes, total num timesteps 279000/2000000, FPS 198.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1395/10000 episodes, total num timesteps 279200/2000000, FPS 198.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1396/10000 episodes, total num timesteps 279400/2000000, FPS 198.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1397/10000 episodes, total num timesteps 279600/2000000, FPS 198.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1398/10000 episodes, total num timesteps 279800/2000000, FPS 198.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1399/10000 episodes, total num timesteps 280000/2000000, FPS 198.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1400/10000 episodes, total num timesteps 280200/2000000, FPS 198.

team_policy eval average step individual rewards of agent0: 0.8677683676262353
team_policy eval average team episode rewards of agent0: 77.5
team_policy eval idv catch total num of agent0: 36
team_policy eval team catch total num: 31
team_policy eval average step individual rewards of agent1: 0.5892840413997174
team_policy eval average team episode rewards of agent1: 77.5
team_policy eval idv catch total num of agent1: 25
team_policy eval team catch total num: 31
team_policy eval average step individual rewards of agent2: 0.5342891549721418
team_policy eval average team episode rewards of agent2: 77.5
team_policy eval idv catch total num of agent2: 23
team_policy eval team catch total num: 31
team_policy eval average step individual rewards of agent3: 0.48915876160012833
team_policy eval average team episode rewards of agent3: 77.5
team_policy eval idv catch total num of agent3: 21
team_policy eval team catch total num: 31
team_policy eval average step individual rewards of agent4: 0.5785285584228234
team_policy eval average team episode rewards of agent4: 77.5
team_policy eval idv catch total num of agent4: 25
team_policy eval team catch total num: 31
idv_policy eval average step individual rewards of agent0: 0.4582812767176344
idv_policy eval average team episode rewards of agent0: 82.5
idv_policy eval idv catch total num of agent0: 20
idv_policy eval team catch total num: 33
idv_policy eval average step individual rewards of agent1: 0.6895451340170109
idv_policy eval average team episode rewards of agent1: 82.5
idv_policy eval idv catch total num of agent1: 29
idv_policy eval team catch total num: 33
idv_policy eval average step individual rewards of agent2: 0.3362119425199372
idv_policy eval average team episode rewards of agent2: 82.5
idv_policy eval idv catch total num of agent2: 15
idv_policy eval team catch total num: 33
idv_policy eval average step individual rewards of agent3: 0.48632574168154924
idv_policy eval average team episode rewards of agent3: 82.5
idv_policy eval idv catch total num of agent3: 21
idv_policy eval team catch total num: 33
idv_policy eval average step individual rewards of agent4: 1.325198247501562
idv_policy eval average team episode rewards of agent4: 82.5
idv_policy eval idv catch total num of agent4: 54
idv_policy eval team catch total num: 33

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1401/10000 episodes, total num timesteps 280400/2000000, FPS 198.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1402/10000 episodes, total num timesteps 280600/2000000, FPS 198.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1403/10000 episodes, total num timesteps 280800/2000000, FPS 198.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1404/10000 episodes, total num timesteps 281000/2000000, FPS 198.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1405/10000 episodes, total num timesteps 281200/2000000, FPS 198.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1406/10000 episodes, total num timesteps 281400/2000000, FPS 198.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1407/10000 episodes, total num timesteps 281600/2000000, FPS 198.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1408/10000 episodes, total num timesteps 281800/2000000, FPS 198.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1409/10000 episodes, total num timesteps 282000/2000000, FPS 198.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1410/10000 episodes, total num timesteps 282200/2000000, FPS 198.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1411/10000 episodes, total num timesteps 282400/2000000, FPS 198.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1412/10000 episodes, total num timesteps 282600/2000000, FPS 198.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1413/10000 episodes, total num timesteps 282800/2000000, FPS 198.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1414/10000 episodes, total num timesteps 283000/2000000, FPS 198.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1415/10000 episodes, total num timesteps 283200/2000000, FPS 198.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1416/10000 episodes, total num timesteps 283400/2000000, FPS 198.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1417/10000 episodes, total num timesteps 283600/2000000, FPS 198.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1418/10000 episodes, total num timesteps 283800/2000000, FPS 198.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1419/10000 episodes, total num timesteps 284000/2000000, FPS 198.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1420/10000 episodes, total num timesteps 284200/2000000, FPS 198.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1421/10000 episodes, total num timesteps 284400/2000000, FPS 198.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1422/10000 episodes, total num timesteps 284600/2000000, FPS 198.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1423/10000 episodes, total num timesteps 284800/2000000, FPS 198.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1424/10000 episodes, total num timesteps 285000/2000000, FPS 198.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1425/10000 episodes, total num timesteps 285200/2000000, FPS 198.

team_policy eval average step individual rewards of agent0: 0.43401292440725364
team_policy eval average team episode rewards of agent0: 95.0
team_policy eval idv catch total num of agent0: 19
team_policy eval team catch total num: 38
team_policy eval average step individual rewards of agent1: 1.0660580258828818
team_policy eval average team episode rewards of agent1: 95.0
team_policy eval idv catch total num of agent1: 44
team_policy eval team catch total num: 38
team_policy eval average step individual rewards of agent2: 0.9361313224556672
team_policy eval average team episode rewards of agent2: 95.0
team_policy eval idv catch total num of agent2: 39
team_policy eval team catch total num: 38
team_policy eval average step individual rewards of agent3: 0.406020207327561
team_policy eval average team episode rewards of agent3: 95.0
team_policy eval idv catch total num of agent3: 18
team_policy eval team catch total num: 38
team_policy eval average step individual rewards of agent4: 0.5860421746528164
team_policy eval average team episode rewards of agent4: 95.0
team_policy eval idv catch total num of agent4: 25
team_policy eval team catch total num: 38
idv_policy eval average step individual rewards of agent0: 0.7096729483707017
idv_policy eval average team episode rewards of agent0: 95.0
idv_policy eval idv catch total num of agent0: 30
idv_policy eval team catch total num: 38
idv_policy eval average step individual rewards of agent1: 0.6389376141984284
idv_policy eval average team episode rewards of agent1: 95.0
idv_policy eval idv catch total num of agent1: 27
idv_policy eval team catch total num: 38
idv_policy eval average step individual rewards of agent2: 0.8093735415747656
idv_policy eval average team episode rewards of agent2: 95.0
idv_policy eval idv catch total num of agent2: 34
idv_policy eval team catch total num: 38
idv_policy eval average step individual rewards of agent3: 0.868344626936522
idv_policy eval average team episode rewards of agent3: 95.0
idv_policy eval idv catch total num of agent3: 36
idv_policy eval team catch total num: 38
idv_policy eval average step individual rewards of agent4: 0.5343927530317956
idv_policy eval average team episode rewards of agent4: 95.0
idv_policy eval idv catch total num of agent4: 23
idv_policy eval team catch total num: 38

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1426/10000 episodes, total num timesteps 285400/2000000, FPS 198.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1427/10000 episodes, total num timesteps 285600/2000000, FPS 198.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1428/10000 episodes, total num timesteps 285800/2000000, FPS 198.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1429/10000 episodes, total num timesteps 286000/2000000, FPS 198.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1430/10000 episodes, total num timesteps 286200/2000000, FPS 198.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1431/10000 episodes, total num timesteps 286400/2000000, FPS 198.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1432/10000 episodes, total num timesteps 286600/2000000, FPS 198.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1433/10000 episodes, total num timesteps 286800/2000000, FPS 198.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1434/10000 episodes, total num timesteps 287000/2000000, FPS 198.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1435/10000 episodes, total num timesteps 287200/2000000, FPS 198.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1436/10000 episodes, total num timesteps 287400/2000000, FPS 198.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1437/10000 episodes, total num timesteps 287600/2000000, FPS 198.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1438/10000 episodes, total num timesteps 287800/2000000, FPS 198.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1439/10000 episodes, total num timesteps 288000/2000000, FPS 198.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1440/10000 episodes, total num timesteps 288200/2000000, FPS 198.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1441/10000 episodes, total num timesteps 288400/2000000, FPS 198.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1442/10000 episodes, total num timesteps 288600/2000000, FPS 198.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1443/10000 episodes, total num timesteps 288800/2000000, FPS 199.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1444/10000 episodes, total num timesteps 289000/2000000, FPS 199.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1445/10000 episodes, total num timesteps 289200/2000000, FPS 199.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1446/10000 episodes, total num timesteps 289400/2000000, FPS 199.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1447/10000 episodes, total num timesteps 289600/2000000, FPS 199.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1448/10000 episodes, total num timesteps 289800/2000000, FPS 199.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1449/10000 episodes, total num timesteps 290000/2000000, FPS 199.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1450/10000 episodes, total num timesteps 290200/2000000, FPS 199.

team_policy eval average step individual rewards of agent0: 0.40184482382061687
team_policy eval average team episode rewards of agent0: 52.5
team_policy eval idv catch total num of agent0: 18
team_policy eval team catch total num: 21
team_policy eval average step individual rewards of agent1: 0.7154161074719819
team_policy eval average team episode rewards of agent1: 52.5
team_policy eval idv catch total num of agent1: 30
team_policy eval team catch total num: 21
team_policy eval average step individual rewards of agent2: 0.6835671423625039
team_policy eval average team episode rewards of agent2: 52.5
team_policy eval idv catch total num of agent2: 29
team_policy eval team catch total num: 21
team_policy eval average step individual rewards of agent3: 0.3385148405753665
team_policy eval average team episode rewards of agent3: 52.5
team_policy eval idv catch total num of agent3: 15
team_policy eval team catch total num: 21
team_policy eval average step individual rewards of agent4: 0.3397420631023504
team_policy eval average team episode rewards of agent4: 52.5
team_policy eval idv catch total num of agent4: 15
team_policy eval team catch total num: 21
idv_policy eval average step individual rewards of agent0: 0.8071105881886738
idv_policy eval average team episode rewards of agent0: 105.0
idv_policy eval idv catch total num of agent0: 34
idv_policy eval team catch total num: 42
idv_policy eval average step individual rewards of agent1: 0.5107019125217129
idv_policy eval average team episode rewards of agent1: 105.0
idv_policy eval idv catch total num of agent1: 22
idv_policy eval team catch total num: 42
idv_policy eval average step individual rewards of agent2: 1.0460268969394506
idv_policy eval average team episode rewards of agent2: 105.0
idv_policy eval idv catch total num of agent2: 43
idv_policy eval team catch total num: 42
idv_policy eval average step individual rewards of agent3: 0.7043102854763248
idv_policy eval average team episode rewards of agent3: 105.0
idv_policy eval idv catch total num of agent3: 30
idv_policy eval team catch total num: 42
idv_policy eval average step individual rewards of agent4: 0.7825974771086345
idv_policy eval average team episode rewards of agent4: 105.0
idv_policy eval idv catch total num of agent4: 33
idv_policy eval team catch total num: 42

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1451/10000 episodes, total num timesteps 290400/2000000, FPS 199.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1452/10000 episodes, total num timesteps 290600/2000000, FPS 199.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1453/10000 episodes, total num timesteps 290800/2000000, FPS 199.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1454/10000 episodes, total num timesteps 291000/2000000, FPS 199.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1455/10000 episodes, total num timesteps 291200/2000000, FPS 199.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1456/10000 episodes, total num timesteps 291400/2000000, FPS 199.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1457/10000 episodes, total num timesteps 291600/2000000, FPS 199.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1458/10000 episodes, total num timesteps 291800/2000000, FPS 199.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1459/10000 episodes, total num timesteps 292000/2000000, FPS 199.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1460/10000 episodes, total num timesteps 292200/2000000, FPS 199.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1461/10000 episodes, total num timesteps 292400/2000000, FPS 199.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1462/10000 episodes, total num timesteps 292600/2000000, FPS 199.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1463/10000 episodes, total num timesteps 292800/2000000, FPS 199.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1464/10000 episodes, total num timesteps 293000/2000000, FPS 199.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1465/10000 episodes, total num timesteps 293200/2000000, FPS 199.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1466/10000 episodes, total num timesteps 293400/2000000, FPS 199.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1467/10000 episodes, total num timesteps 293600/2000000, FPS 199.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1468/10000 episodes, total num timesteps 293800/2000000, FPS 199.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1469/10000 episodes, total num timesteps 294000/2000000, FPS 199.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1470/10000 episodes, total num timesteps 294200/2000000, FPS 199.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1471/10000 episodes, total num timesteps 294400/2000000, FPS 199.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1472/10000 episodes, total num timesteps 294600/2000000, FPS 199.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1473/10000 episodes, total num timesteps 294800/2000000, FPS 199.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1474/10000 episodes, total num timesteps 295000/2000000, FPS 199.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1475/10000 episodes, total num timesteps 295200/2000000, FPS 199.

team_policy eval average step individual rewards of agent0: 0.7803521836932404
team_policy eval average team episode rewards of agent0: 102.5
team_policy eval idv catch total num of agent0: 33
team_policy eval team catch total num: 41
team_policy eval average step individual rewards of agent1: 0.9873582232762871
team_policy eval average team episode rewards of agent1: 102.5
team_policy eval idv catch total num of agent1: 41
team_policy eval team catch total num: 41
team_policy eval average step individual rewards of agent2: 0.5529694417958068
team_policy eval average team episode rewards of agent2: 102.5
team_policy eval idv catch total num of agent2: 24
team_policy eval team catch total num: 41
team_policy eval average step individual rewards of agent3: 0.8852427860662405
team_policy eval average team episode rewards of agent3: 102.5
team_policy eval idv catch total num of agent3: 37
team_policy eval team catch total num: 41
team_policy eval average step individual rewards of agent4: 0.8394465879492915
team_policy eval average team episode rewards of agent4: 102.5
team_policy eval idv catch total num of agent4: 35
team_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent0: 0.45390299332798706
idv_policy eval average team episode rewards of agent0: 82.5
idv_policy eval idv catch total num of agent0: 20
idv_policy eval team catch total num: 33
idv_policy eval average step individual rewards of agent1: 0.5061357062235877
idv_policy eval average team episode rewards of agent1: 82.5
idv_policy eval idv catch total num of agent1: 22
idv_policy eval team catch total num: 33
idv_policy eval average step individual rewards of agent2: 0.9413148775814293
idv_policy eval average team episode rewards of agent2: 82.5
idv_policy eval idv catch total num of agent2: 39
idv_policy eval team catch total num: 33
idv_policy eval average step individual rewards of agent3: 0.7041536657124243
idv_policy eval average team episode rewards of agent3: 82.5
idv_policy eval idv catch total num of agent3: 30
idv_policy eval team catch total num: 33
idv_policy eval average step individual rewards of agent4: 0.4764266222125908
idv_policy eval average team episode rewards of agent4: 82.5
idv_policy eval idv catch total num of agent4: 21
idv_policy eval team catch total num: 33

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1476/10000 episodes, total num timesteps 295400/2000000, FPS 199.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1477/10000 episodes, total num timesteps 295600/2000000, FPS 199.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1478/10000 episodes, total num timesteps 295800/2000000, FPS 199.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1479/10000 episodes, total num timesteps 296000/2000000, FPS 199.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1480/10000 episodes, total num timesteps 296200/2000000, FPS 199.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1481/10000 episodes, total num timesteps 296400/2000000, FPS 199.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1482/10000 episodes, total num timesteps 296600/2000000, FPS 199.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1483/10000 episodes, total num timesteps 296800/2000000, FPS 199.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1484/10000 episodes, total num timesteps 297000/2000000, FPS 199.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1485/10000 episodes, total num timesteps 297200/2000000, FPS 199.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1486/10000 episodes, total num timesteps 297400/2000000, FPS 199.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1487/10000 episodes, total num timesteps 297600/2000000, FPS 199.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1488/10000 episodes, total num timesteps 297800/2000000, FPS 199.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1489/10000 episodes, total num timesteps 298000/2000000, FPS 199.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1490/10000 episodes, total num timesteps 298200/2000000, FPS 199.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1491/10000 episodes, total num timesteps 298400/2000000, FPS 199.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1492/10000 episodes, total num timesteps 298600/2000000, FPS 199.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1493/10000 episodes, total num timesteps 298800/2000000, FPS 199.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1494/10000 episodes, total num timesteps 299000/2000000, FPS 199.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1495/10000 episodes, total num timesteps 299200/2000000, FPS 199.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1496/10000 episodes, total num timesteps 299400/2000000, FPS 199.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1497/10000 episodes, total num timesteps 299600/2000000, FPS 199.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1498/10000 episodes, total num timesteps 299800/2000000, FPS 199.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1499/10000 episodes, total num timesteps 300000/2000000, FPS 199.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1500/10000 episodes, total num timesteps 300200/2000000, FPS 199.

team_policy eval average step individual rewards of agent0: 0.4666979200507298
team_policy eval average team episode rewards of agent0: 92.5
team_policy eval idv catch total num of agent0: 20
team_policy eval team catch total num: 37
team_policy eval average step individual rewards of agent1: 0.8114064842714345
team_policy eval average team episode rewards of agent1: 92.5
team_policy eval idv catch total num of agent1: 34
team_policy eval team catch total num: 37
team_policy eval average step individual rewards of agent2: 0.7654681442061428
team_policy eval average team episode rewards of agent2: 92.5
team_policy eval idv catch total num of agent2: 32
team_policy eval team catch total num: 37
team_policy eval average step individual rewards of agent3: 0.6092751028098512
team_policy eval average team episode rewards of agent3: 92.5
team_policy eval idv catch total num of agent3: 26
team_policy eval team catch total num: 37
team_policy eval average step individual rewards of agent4: 0.5009101914088958
team_policy eval average team episode rewards of agent4: 92.5
team_policy eval idv catch total num of agent4: 22
team_policy eval team catch total num: 37
idv_policy eval average step individual rewards of agent0: 0.5146038306695929
idv_policy eval average team episode rewards of agent0: 100.0
idv_policy eval idv catch total num of agent0: 22
idv_policy eval team catch total num: 40
idv_policy eval average step individual rewards of agent1: 0.7398411100999948
idv_policy eval average team episode rewards of agent1: 100.0
idv_policy eval idv catch total num of agent1: 31
idv_policy eval team catch total num: 40
idv_policy eval average step individual rewards of agent2: 0.48349793404490754
idv_policy eval average team episode rewards of agent2: 100.0
idv_policy eval idv catch total num of agent2: 21
idv_policy eval team catch total num: 40
idv_policy eval average step individual rewards of agent3: 1.113661708676745
idv_policy eval average team episode rewards of agent3: 100.0
idv_policy eval idv catch total num of agent3: 46
idv_policy eval team catch total num: 40
idv_policy eval average step individual rewards of agent4: 0.6341046560093913
idv_policy eval average team episode rewards of agent4: 100.0
idv_policy eval idv catch total num of agent4: 27
idv_policy eval team catch total num: 40

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1501/10000 episodes, total num timesteps 300400/2000000, FPS 199.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1502/10000 episodes, total num timesteps 300600/2000000, FPS 199.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1503/10000 episodes, total num timesteps 300800/2000000, FPS 199.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1504/10000 episodes, total num timesteps 301000/2000000, FPS 199.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1505/10000 episodes, total num timesteps 301200/2000000, FPS 199.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1506/10000 episodes, total num timesteps 301400/2000000, FPS 199.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1507/10000 episodes, total num timesteps 301600/2000000, FPS 199.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1508/10000 episodes, total num timesteps 301800/2000000, FPS 199.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1509/10000 episodes, total num timesteps 302000/2000000, FPS 200.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1510/10000 episodes, total num timesteps 302200/2000000, FPS 200.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1511/10000 episodes, total num timesteps 302400/2000000, FPS 200.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1512/10000 episodes, total num timesteps 302600/2000000, FPS 200.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1513/10000 episodes, total num timesteps 302800/2000000, FPS 200.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1514/10000 episodes, total num timesteps 303000/2000000, FPS 200.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1515/10000 episodes, total num timesteps 303200/2000000, FPS 200.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1516/10000 episodes, total num timesteps 303400/2000000, FPS 200.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1517/10000 episodes, total num timesteps 303600/2000000, FPS 200.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1518/10000 episodes, total num timesteps 303800/2000000, FPS 200.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1519/10000 episodes, total num timesteps 304000/2000000, FPS 200.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1520/10000 episodes, total num timesteps 304200/2000000, FPS 200.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1521/10000 episodes, total num timesteps 304400/2000000, FPS 200.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1522/10000 episodes, total num timesteps 304600/2000000, FPS 200.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1523/10000 episodes, total num timesteps 304800/2000000, FPS 200.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1524/10000 episodes, total num timesteps 305000/2000000, FPS 200.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1525/10000 episodes, total num timesteps 305200/2000000, FPS 200.

team_policy eval average step individual rewards of agent0: 0.7926353002980693
team_policy eval average team episode rewards of agent0: 82.5
team_policy eval idv catch total num of agent0: 33
team_policy eval team catch total num: 33
team_policy eval average step individual rewards of agent1: 0.3589507887231157
team_policy eval average team episode rewards of agent1: 82.5
team_policy eval idv catch total num of agent1: 16
team_policy eval team catch total num: 33
team_policy eval average step individual rewards of agent2: 0.3516155705405856
team_policy eval average team episode rewards of agent2: 82.5
team_policy eval idv catch total num of agent2: 16
team_policy eval team catch total num: 33
team_policy eval average step individual rewards of agent3: 0.7153112624458012
team_policy eval average team episode rewards of agent3: 82.5
team_policy eval idv catch total num of agent3: 30
team_policy eval team catch total num: 33
team_policy eval average step individual rewards of agent4: 0.7091907984997331
team_policy eval average team episode rewards of agent4: 82.5
team_policy eval idv catch total num of agent4: 30
team_policy eval team catch total num: 33
idv_policy eval average step individual rewards of agent0: 0.6360361786149784
idv_policy eval average team episode rewards of agent0: 132.5
idv_policy eval idv catch total num of agent0: 27
idv_policy eval team catch total num: 53
idv_policy eval average step individual rewards of agent1: 0.7119430000813892
idv_policy eval average team episode rewards of agent1: 132.5
idv_policy eval idv catch total num of agent1: 30
idv_policy eval team catch total num: 53
idv_policy eval average step individual rewards of agent2: 0.9895007795440298
idv_policy eval average team episode rewards of agent2: 132.5
idv_policy eval idv catch total num of agent2: 41
idv_policy eval team catch total num: 53
idv_policy eval average step individual rewards of agent3: 1.4666954832421815
idv_policy eval average team episode rewards of agent3: 132.5
idv_policy eval idv catch total num of agent3: 60
idv_policy eval team catch total num: 53
idv_policy eval average step individual rewards of agent4: 1.148652962224303
idv_policy eval average team episode rewards of agent4: 132.5
idv_policy eval idv catch total num of agent4: 47
idv_policy eval team catch total num: 53

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1526/10000 episodes, total num timesteps 305400/2000000, FPS 200.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1527/10000 episodes, total num timesteps 305600/2000000, FPS 200.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1528/10000 episodes, total num timesteps 305800/2000000, FPS 200.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1529/10000 episodes, total num timesteps 306000/2000000, FPS 200.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1530/10000 episodes, total num timesteps 306200/2000000, FPS 200.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1531/10000 episodes, total num timesteps 306400/2000000, FPS 200.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1532/10000 episodes, total num timesteps 306600/2000000, FPS 200.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1533/10000 episodes, total num timesteps 306800/2000000, FPS 200.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1534/10000 episodes, total num timesteps 307000/2000000, FPS 200.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1535/10000 episodes, total num timesteps 307200/2000000, FPS 200.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1536/10000 episodes, total num timesteps 307400/2000000, FPS 200.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1537/10000 episodes, total num timesteps 307600/2000000, FPS 200.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1538/10000 episodes, total num timesteps 307800/2000000, FPS 200.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1539/10000 episodes, total num timesteps 308000/2000000, FPS 200.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1540/10000 episodes, total num timesteps 308200/2000000, FPS 200.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1541/10000 episodes, total num timesteps 308400/2000000, FPS 200.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1542/10000 episodes, total num timesteps 308600/2000000, FPS 200.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1543/10000 episodes, total num timesteps 308800/2000000, FPS 200.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1544/10000 episodes, total num timesteps 309000/2000000, FPS 200.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1545/10000 episodes, total num timesteps 309200/2000000, FPS 200.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1546/10000 episodes, total num timesteps 309400/2000000, FPS 200.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1547/10000 episodes, total num timesteps 309600/2000000, FPS 200.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1548/10000 episodes, total num timesteps 309800/2000000, FPS 200.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1549/10000 episodes, total num timesteps 310000/2000000, FPS 200.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1550/10000 episodes, total num timesteps 310200/2000000, FPS 200.

team_policy eval average step individual rewards of agent0: 0.5117507746483646
team_policy eval average team episode rewards of agent0: 70.0
team_policy eval idv catch total num of agent0: 22
team_policy eval team catch total num: 28
team_policy eval average step individual rewards of agent1: 0.8579712526702025
team_policy eval average team episode rewards of agent1: 70.0
team_policy eval idv catch total num of agent1: 36
team_policy eval team catch total num: 28
team_policy eval average step individual rewards of agent2: 0.5050716061258786
team_policy eval average team episode rewards of agent2: 70.0
team_policy eval idv catch total num of agent2: 22
team_policy eval team catch total num: 28
team_policy eval average step individual rewards of agent3: 0.45650429653481805
team_policy eval average team episode rewards of agent3: 70.0
team_policy eval idv catch total num of agent3: 20
team_policy eval team catch total num: 28
team_policy eval average step individual rewards of agent4: 0.42707954420381333
team_policy eval average team episode rewards of agent4: 70.0
team_policy eval idv catch total num of agent4: 19
team_policy eval team catch total num: 28
idv_policy eval average step individual rewards of agent0: 0.385486688911482
idv_policy eval average team episode rewards of agent0: 57.5
idv_policy eval idv catch total num of agent0: 17
idv_policy eval team catch total num: 23
idv_policy eval average step individual rewards of agent1: 0.10080213492887051
idv_policy eval average team episode rewards of agent1: 57.5
idv_policy eval idv catch total num of agent1: 6
idv_policy eval team catch total num: 23
idv_policy eval average step individual rewards of agent2: 0.3414608130529374
idv_policy eval average team episode rewards of agent2: 57.5
idv_policy eval idv catch total num of agent2: 16
idv_policy eval team catch total num: 23
idv_policy eval average step individual rewards of agent3: 0.6073077066473341
idv_policy eval average team episode rewards of agent3: 57.5
idv_policy eval idv catch total num of agent3: 26
idv_policy eval team catch total num: 23
idv_policy eval average step individual rewards of agent4: 0.6520018064864391
idv_policy eval average team episode rewards of agent4: 57.5
idv_policy eval idv catch total num of agent4: 28
idv_policy eval team catch total num: 23

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1551/10000 episodes, total num timesteps 310400/2000000, FPS 200.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1552/10000 episodes, total num timesteps 310600/2000000, FPS 200.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1553/10000 episodes, total num timesteps 310800/2000000, FPS 200.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1554/10000 episodes, total num timesteps 311000/2000000, FPS 200.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1555/10000 episodes, total num timesteps 311200/2000000, FPS 200.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1556/10000 episodes, total num timesteps 311400/2000000, FPS 200.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1557/10000 episodes, total num timesteps 311600/2000000, FPS 200.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1558/10000 episodes, total num timesteps 311800/2000000, FPS 200.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1559/10000 episodes, total num timesteps 312000/2000000, FPS 200.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1560/10000 episodes, total num timesteps 312200/2000000, FPS 200.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1561/10000 episodes, total num timesteps 312400/2000000, FPS 200.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1562/10000 episodes, total num timesteps 312600/2000000, FPS 200.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1563/10000 episodes, total num timesteps 312800/2000000, FPS 200.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1564/10000 episodes, total num timesteps 313000/2000000, FPS 200.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1565/10000 episodes, total num timesteps 313200/2000000, FPS 200.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1566/10000 episodes, total num timesteps 313400/2000000, FPS 200.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1567/10000 episodes, total num timesteps 313600/2000000, FPS 200.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1568/10000 episodes, total num timesteps 313800/2000000, FPS 200.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1569/10000 episodes, total num timesteps 314000/2000000, FPS 200.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1570/10000 episodes, total num timesteps 314200/2000000, FPS 200.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1571/10000 episodes, total num timesteps 314400/2000000, FPS 200.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1572/10000 episodes, total num timesteps 314600/2000000, FPS 200.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1573/10000 episodes, total num timesteps 314800/2000000, FPS 200.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1574/10000 episodes, total num timesteps 315000/2000000, FPS 200.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1575/10000 episodes, total num timesteps 315200/2000000, FPS 200.

team_policy eval average step individual rewards of agent0: 0.5357122852294175
team_policy eval average team episode rewards of agent0: 117.5
team_policy eval idv catch total num of agent0: 23
team_policy eval team catch total num: 47
team_policy eval average step individual rewards of agent1: 0.5887704490450903
team_policy eval average team episode rewards of agent1: 117.5
team_policy eval idv catch total num of agent1: 25
team_policy eval team catch total num: 47
team_policy eval average step individual rewards of agent2: 0.9940131720731307
team_policy eval average team episode rewards of agent2: 117.5
team_policy eval idv catch total num of agent2: 41
team_policy eval team catch total num: 47
team_policy eval average step individual rewards of agent3: 1.0135234552477548
team_policy eval average team episode rewards of agent3: 117.5
team_policy eval idv catch total num of agent3: 42
team_policy eval team catch total num: 47
team_policy eval average step individual rewards of agent4: 1.0096182750038247
team_policy eval average team episode rewards of agent4: 117.5
team_policy eval idv catch total num of agent4: 42
team_policy eval team catch total num: 47
idv_policy eval average step individual rewards of agent0: 0.7627348854160122
idv_policy eval average team episode rewards of agent0: 92.5
idv_policy eval idv catch total num of agent0: 32
idv_policy eval team catch total num: 37
idv_policy eval average step individual rewards of agent1: 0.8128391995661585
idv_policy eval average team episode rewards of agent1: 92.5
idv_policy eval idv catch total num of agent1: 34
idv_policy eval team catch total num: 37
idv_policy eval average step individual rewards of agent2: 0.6436361549851385
idv_policy eval average team episode rewards of agent2: 92.5
idv_policy eval idv catch total num of agent2: 27
idv_policy eval team catch total num: 37
idv_policy eval average step individual rewards of agent3: 0.5452151673140929
idv_policy eval average team episode rewards of agent3: 92.5
idv_policy eval idv catch total num of agent3: 23
idv_policy eval team catch total num: 37
idv_policy eval average step individual rewards of agent4: 0.89860022479998
idv_policy eval average team episode rewards of agent4: 92.5
idv_policy eval idv catch total num of agent4: 37
idv_policy eval team catch total num: 37

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1576/10000 episodes, total num timesteps 315400/2000000, FPS 200.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1577/10000 episodes, total num timesteps 315600/2000000, FPS 200.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1578/10000 episodes, total num timesteps 315800/2000000, FPS 201.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1579/10000 episodes, total num timesteps 316000/2000000, FPS 201.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1580/10000 episodes, total num timesteps 316200/2000000, FPS 201.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1581/10000 episodes, total num timesteps 316400/2000000, FPS 201.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1582/10000 episodes, total num timesteps 316600/2000000, FPS 201.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1583/10000 episodes, total num timesteps 316800/2000000, FPS 201.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1584/10000 episodes, total num timesteps 317000/2000000, FPS 201.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1585/10000 episodes, total num timesteps 317200/2000000, FPS 201.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1586/10000 episodes, total num timesteps 317400/2000000, FPS 201.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1587/10000 episodes, total num timesteps 317600/2000000, FPS 201.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1588/10000 episodes, total num timesteps 317800/2000000, FPS 201.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1589/10000 episodes, total num timesteps 318000/2000000, FPS 201.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1590/10000 episodes, total num timesteps 318200/2000000, FPS 201.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1591/10000 episodes, total num timesteps 318400/2000000, FPS 201.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1592/10000 episodes, total num timesteps 318600/2000000, FPS 201.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1593/10000 episodes, total num timesteps 318800/2000000, FPS 201.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1594/10000 episodes, total num timesteps 319000/2000000, FPS 201.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1595/10000 episodes, total num timesteps 319200/2000000, FPS 201.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1596/10000 episodes, total num timesteps 319400/2000000, FPS 201.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1597/10000 episodes, total num timesteps 319600/2000000, FPS 201.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1598/10000 episodes, total num timesteps 319800/2000000, FPS 201.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1599/10000 episodes, total num timesteps 320000/2000000, FPS 201.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1600/10000 episodes, total num timesteps 320200/2000000, FPS 201.

team_policy eval average step individual rewards of agent0: 0.7693085069093429
team_policy eval average team episode rewards of agent0: 132.5
team_policy eval idv catch total num of agent0: 32
team_policy eval team catch total num: 53
team_policy eval average step individual rewards of agent1: 0.920220976903844
team_policy eval average team episode rewards of agent1: 132.5
team_policy eval idv catch total num of agent1: 38
team_policy eval team catch total num: 53
team_policy eval average step individual rewards of agent2: 0.8437056389600509
team_policy eval average team episode rewards of agent2: 132.5
team_policy eval idv catch total num of agent2: 35
team_policy eval team catch total num: 53
team_policy eval average step individual rewards of agent3: 0.9398092458847741
team_policy eval average team episode rewards of agent3: 132.5
team_policy eval idv catch total num of agent3: 39
team_policy eval team catch total num: 53
team_policy eval average step individual rewards of agent4: 1.1289801175320386
team_policy eval average team episode rewards of agent4: 132.5
team_policy eval idv catch total num of agent4: 46
team_policy eval team catch total num: 53
idv_policy eval average step individual rewards of agent0: 0.9353986795636137
idv_policy eval average team episode rewards of agent0: 92.5
idv_policy eval idv catch total num of agent0: 39
idv_policy eval team catch total num: 37
idv_policy eval average step individual rewards of agent1: 0.5274887831002095
idv_policy eval average team episode rewards of agent1: 92.5
idv_policy eval idv catch total num of agent1: 23
idv_policy eval team catch total num: 37
idv_policy eval average step individual rewards of agent2: 0.7425242004065871
idv_policy eval average team episode rewards of agent2: 92.5
idv_policy eval idv catch total num of agent2: 31
idv_policy eval team catch total num: 37
idv_policy eval average step individual rewards of agent3: 0.5409342426203118
idv_policy eval average team episode rewards of agent3: 92.5
idv_policy eval idv catch total num of agent3: 24
idv_policy eval team catch total num: 37
idv_policy eval average step individual rewards of agent4: 0.7161163880315962
idv_policy eval average team episode rewards of agent4: 92.5
idv_policy eval idv catch total num of agent4: 30
idv_policy eval team catch total num: 37

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1601/10000 episodes, total num timesteps 320400/2000000, FPS 201.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1602/10000 episodes, total num timesteps 320600/2000000, FPS 201.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1603/10000 episodes, total num timesteps 320800/2000000, FPS 201.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1604/10000 episodes, total num timesteps 321000/2000000, FPS 201.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1605/10000 episodes, total num timesteps 321200/2000000, FPS 201.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1606/10000 episodes, total num timesteps 321400/2000000, FPS 201.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1607/10000 episodes, total num timesteps 321600/2000000, FPS 201.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1608/10000 episodes, total num timesteps 321800/2000000, FPS 201.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1609/10000 episodes, total num timesteps 322000/2000000, FPS 201.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1610/10000 episodes, total num timesteps 322200/2000000, FPS 201.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1611/10000 episodes, total num timesteps 322400/2000000, FPS 201.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1612/10000 episodes, total num timesteps 322600/2000000, FPS 201.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1613/10000 episodes, total num timesteps 322800/2000000, FPS 201.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1614/10000 episodes, total num timesteps 323000/2000000, FPS 201.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1615/10000 episodes, total num timesteps 323200/2000000, FPS 201.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1616/10000 episodes, total num timesteps 323400/2000000, FPS 201.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1617/10000 episodes, total num timesteps 323600/2000000, FPS 201.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1618/10000 episodes, total num timesteps 323800/2000000, FPS 201.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1619/10000 episodes, total num timesteps 324000/2000000, FPS 201.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1620/10000 episodes, total num timesteps 324200/2000000, FPS 201.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1621/10000 episodes, total num timesteps 324400/2000000, FPS 201.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1622/10000 episodes, total num timesteps 324600/2000000, FPS 201.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1623/10000 episodes, total num timesteps 324800/2000000, FPS 201.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1624/10000 episodes, total num timesteps 325000/2000000, FPS 201.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1625/10000 episodes, total num timesteps 325200/2000000, FPS 201.

team_policy eval average step individual rewards of agent0: 1.0117577931011599
team_policy eval average team episode rewards of agent0: 150.0
team_policy eval idv catch total num of agent0: 42
team_policy eval team catch total num: 60
team_policy eval average step individual rewards of agent1: 1.0203873174103566
team_policy eval average team episode rewards of agent1: 150.0
team_policy eval idv catch total num of agent1: 42
team_policy eval team catch total num: 60
team_policy eval average step individual rewards of agent2: 0.9202153505693872
team_policy eval average team episode rewards of agent2: 150.0
team_policy eval idv catch total num of agent2: 38
team_policy eval team catch total num: 60
team_policy eval average step individual rewards of agent3: 1.3962677362165312
team_policy eval average team episode rewards of agent3: 150.0
team_policy eval idv catch total num of agent3: 57
team_policy eval team catch total num: 60
team_policy eval average step individual rewards of agent4: 1.0644750902250701
team_policy eval average team episode rewards of agent4: 150.0
team_policy eval idv catch total num of agent4: 44
team_policy eval team catch total num: 60
idv_policy eval average step individual rewards of agent0: 0.38828244300714176
idv_policy eval average team episode rewards of agent0: 95.0
idv_policy eval idv catch total num of agent0: 17
idv_policy eval team catch total num: 38
idv_policy eval average step individual rewards of agent1: 0.4821755033730693
idv_policy eval average team episode rewards of agent1: 95.0
idv_policy eval idv catch total num of agent1: 21
idv_policy eval team catch total num: 38
idv_policy eval average step individual rewards of agent2: 0.36043924951702316
idv_policy eval average team episode rewards of agent2: 95.0
idv_policy eval idv catch total num of agent2: 16
idv_policy eval team catch total num: 38
idv_policy eval average step individual rewards of agent3: 0.7651256552495564
idv_policy eval average team episode rewards of agent3: 95.0
idv_policy eval idv catch total num of agent3: 32
idv_policy eval team catch total num: 38
idv_policy eval average step individual rewards of agent4: 0.8417252198349385
idv_policy eval average team episode rewards of agent4: 95.0
idv_policy eval idv catch total num of agent4: 35
idv_policy eval team catch total num: 38

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1626/10000 episodes, total num timesteps 325400/2000000, FPS 201.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1627/10000 episodes, total num timesteps 325600/2000000, FPS 201.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1628/10000 episodes, total num timesteps 325800/2000000, FPS 201.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1629/10000 episodes, total num timesteps 326000/2000000, FPS 201.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1630/10000 episodes, total num timesteps 326200/2000000, FPS 201.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1631/10000 episodes, total num timesteps 326400/2000000, FPS 201.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1632/10000 episodes, total num timesteps 326600/2000000, FPS 201.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1633/10000 episodes, total num timesteps 326800/2000000, FPS 201.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1634/10000 episodes, total num timesteps 327000/2000000, FPS 201.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1635/10000 episodes, total num timesteps 327200/2000000, FPS 201.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1636/10000 episodes, total num timesteps 327400/2000000, FPS 201.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1637/10000 episodes, total num timesteps 327600/2000000, FPS 201.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1638/10000 episodes, total num timesteps 327800/2000000, FPS 201.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1639/10000 episodes, total num timesteps 328000/2000000, FPS 201.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1640/10000 episodes, total num timesteps 328200/2000000, FPS 201.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1641/10000 episodes, total num timesteps 328400/2000000, FPS 201.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1642/10000 episodes, total num timesteps 328600/2000000, FPS 201.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1643/10000 episodes, total num timesteps 328800/2000000, FPS 201.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1644/10000 episodes, total num timesteps 329000/2000000, FPS 201.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1645/10000 episodes, total num timesteps 329200/2000000, FPS 201.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1646/10000 episodes, total num timesteps 329400/2000000, FPS 201.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1647/10000 episodes, total num timesteps 329600/2000000, FPS 201.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1648/10000 episodes, total num timesteps 329800/2000000, FPS 201.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1649/10000 episodes, total num timesteps 330000/2000000, FPS 201.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1650/10000 episodes, total num timesteps 330200/2000000, FPS 201.

team_policy eval average step individual rewards of agent0: 0.8947303234411218
team_policy eval average team episode rewards of agent0: 92.5
team_policy eval idv catch total num of agent0: 37
team_policy eval team catch total num: 37
team_policy eval average step individual rewards of agent1: 0.505256808079426
team_policy eval average team episode rewards of agent1: 92.5
team_policy eval idv catch total num of agent1: 22
team_policy eval team catch total num: 37
team_policy eval average step individual rewards of agent2: 0.9471534022302629
team_policy eval average team episode rewards of agent2: 92.5
team_policy eval idv catch total num of agent2: 39
team_policy eval team catch total num: 37
team_policy eval average step individual rewards of agent3: 0.4616956395947743
team_policy eval average team episode rewards of agent3: 92.5
team_policy eval idv catch total num of agent3: 20
team_policy eval team catch total num: 37
team_policy eval average step individual rewards of agent4: 0.7287090850494049
team_policy eval average team episode rewards of agent4: 92.5
team_policy eval idv catch total num of agent4: 31
team_policy eval team catch total num: 37
idv_policy eval average step individual rewards of agent0: 0.6598228470685032
idv_policy eval average team episode rewards of agent0: 90.0
idv_policy eval idv catch total num of agent0: 28
idv_policy eval team catch total num: 36
idv_policy eval average step individual rewards of agent1: 0.5887620663226896
idv_policy eval average team episode rewards of agent1: 90.0
idv_policy eval idv catch total num of agent1: 25
idv_policy eval team catch total num: 36
idv_policy eval average step individual rewards of agent2: 0.7643012437059897
idv_policy eval average team episode rewards of agent2: 90.0
idv_policy eval idv catch total num of agent2: 32
idv_policy eval team catch total num: 36
idv_policy eval average step individual rewards of agent3: 0.6920160086900435
idv_policy eval average team episode rewards of agent3: 90.0
idv_policy eval idv catch total num of agent3: 29
idv_policy eval team catch total num: 36
idv_policy eval average step individual rewards of agent4: 0.7299050060004173
idv_policy eval average team episode rewards of agent4: 90.0
idv_policy eval idv catch total num of agent4: 31
idv_policy eval team catch total num: 36

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1651/10000 episodes, total num timesteps 330400/2000000, FPS 201.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1652/10000 episodes, total num timesteps 330600/2000000, FPS 201.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1653/10000 episodes, total num timesteps 330800/2000000, FPS 201.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1654/10000 episodes, total num timesteps 331000/2000000, FPS 201.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1655/10000 episodes, total num timesteps 331200/2000000, FPS 201.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1656/10000 episodes, total num timesteps 331400/2000000, FPS 202.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1657/10000 episodes, total num timesteps 331600/2000000, FPS 202.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1658/10000 episodes, total num timesteps 331800/2000000, FPS 202.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1659/10000 episodes, total num timesteps 332000/2000000, FPS 202.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1660/10000 episodes, total num timesteps 332200/2000000, FPS 202.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1661/10000 episodes, total num timesteps 332400/2000000, FPS 202.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1662/10000 episodes, total num timesteps 332600/2000000, FPS 202.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1663/10000 episodes, total num timesteps 332800/2000000, FPS 202.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1664/10000 episodes, total num timesteps 333000/2000000, FPS 202.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1665/10000 episodes, total num timesteps 333200/2000000, FPS 202.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1666/10000 episodes, total num timesteps 333400/2000000, FPS 202.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1667/10000 episodes, total num timesteps 333600/2000000, FPS 202.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1668/10000 episodes, total num timesteps 333800/2000000, FPS 202.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1669/10000 episodes, total num timesteps 334000/2000000, FPS 202.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1670/10000 episodes, total num timesteps 334200/2000000, FPS 202.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1671/10000 episodes, total num timesteps 334400/2000000, FPS 202.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1672/10000 episodes, total num timesteps 334600/2000000, FPS 202.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1673/10000 episodes, total num timesteps 334800/2000000, FPS 202.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1674/10000 episodes, total num timesteps 335000/2000000, FPS 202.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1675/10000 episodes, total num timesteps 335200/2000000, FPS 202.

team_policy eval average step individual rewards of agent0: 0.844747936702397
team_policy eval average team episode rewards of agent0: 80.0
team_policy eval idv catch total num of agent0: 35
team_policy eval team catch total num: 32
team_policy eval average step individual rewards of agent1: 0.6832874800331092
team_policy eval average team episode rewards of agent1: 80.0
team_policy eval idv catch total num of agent1: 29
team_policy eval team catch total num: 32
team_policy eval average step individual rewards of agent2: 0.5637269283745199
team_policy eval average team episode rewards of agent2: 80.0
team_policy eval idv catch total num of agent2: 24
team_policy eval team catch total num: 32
team_policy eval average step individual rewards of agent3: 0.36968766251163615
team_policy eval average team episode rewards of agent3: 80.0
team_policy eval idv catch total num of agent3: 17
team_policy eval team catch total num: 32
team_policy eval average step individual rewards of agent4: 0.6592897302478665
team_policy eval average team episode rewards of agent4: 80.0
team_policy eval idv catch total num of agent4: 28
team_policy eval team catch total num: 32
idv_policy eval average step individual rewards of agent0: 0.8149313232692166
idv_policy eval average team episode rewards of agent0: 82.5
idv_policy eval idv catch total num of agent0: 34
idv_policy eval team catch total num: 33
idv_policy eval average step individual rewards of agent1: 0.3518081196842552
idv_policy eval average team episode rewards of agent1: 82.5
idv_policy eval idv catch total num of agent1: 16
idv_policy eval team catch total num: 33
idv_policy eval average step individual rewards of agent2: 0.5337185961960066
idv_policy eval average team episode rewards of agent2: 82.5
idv_policy eval idv catch total num of agent2: 23
idv_policy eval team catch total num: 33
idv_policy eval average step individual rewards of agent3: 0.743541181678193
idv_policy eval average team episode rewards of agent3: 82.5
idv_policy eval idv catch total num of agent3: 31
idv_policy eval team catch total num: 33
idv_policy eval average step individual rewards of agent4: 0.25791696396556835
idv_policy eval average team episode rewards of agent4: 82.5
idv_policy eval idv catch total num of agent4: 12
idv_policy eval team catch total num: 33

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1676/10000 episodes, total num timesteps 335400/2000000, FPS 202.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1677/10000 episodes, total num timesteps 335600/2000000, FPS 202.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1678/10000 episodes, total num timesteps 335800/2000000, FPS 202.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1679/10000 episodes, total num timesteps 336000/2000000, FPS 202.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1680/10000 episodes, total num timesteps 336200/2000000, FPS 202.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1681/10000 episodes, total num timesteps 336400/2000000, FPS 202.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1682/10000 episodes, total num timesteps 336600/2000000, FPS 202.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1683/10000 episodes, total num timesteps 336800/2000000, FPS 202.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1684/10000 episodes, total num timesteps 337000/2000000, FPS 202.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1685/10000 episodes, total num timesteps 337200/2000000, FPS 202.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1686/10000 episodes, total num timesteps 337400/2000000, FPS 202.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1687/10000 episodes, total num timesteps 337600/2000000, FPS 202.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1688/10000 episodes, total num timesteps 337800/2000000, FPS 202.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1689/10000 episodes, total num timesteps 338000/2000000, FPS 202.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1690/10000 episodes, total num timesteps 338200/2000000, FPS 202.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1691/10000 episodes, total num timesteps 338400/2000000, FPS 202.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1692/10000 episodes, total num timesteps 338600/2000000, FPS 202.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1693/10000 episodes, total num timesteps 338800/2000000, FPS 202.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1694/10000 episodes, total num timesteps 339000/2000000, FPS 202.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1695/10000 episodes, total num timesteps 339200/2000000, FPS 202.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1696/10000 episodes, total num timesteps 339400/2000000, FPS 202.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1697/10000 episodes, total num timesteps 339600/2000000, FPS 202.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1698/10000 episodes, total num timesteps 339800/2000000, FPS 202.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1699/10000 episodes, total num timesteps 340000/2000000, FPS 202.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1700/10000 episodes, total num timesteps 340200/2000000, FPS 202.

team_policy eval average step individual rewards of agent0: 0.9388719028396356
team_policy eval average team episode rewards of agent0: 105.0
team_policy eval idv catch total num of agent0: 39
team_policy eval team catch total num: 42
team_policy eval average step individual rewards of agent1: 0.8953782360516168
team_policy eval average team episode rewards of agent1: 105.0
team_policy eval idv catch total num of agent1: 37
team_policy eval team catch total num: 42
team_policy eval average step individual rewards of agent2: 0.7168827394326485
team_policy eval average team episode rewards of agent2: 105.0
team_policy eval idv catch total num of agent2: 30
team_policy eval team catch total num: 42
team_policy eval average step individual rewards of agent3: 0.482405828891619
team_policy eval average team episode rewards of agent3: 105.0
team_policy eval idv catch total num of agent3: 21
team_policy eval team catch total num: 42
team_policy eval average step individual rewards of agent4: 0.5892098085450352
team_policy eval average team episode rewards of agent4: 105.0
team_policy eval idv catch total num of agent4: 25
team_policy eval team catch total num: 42
idv_policy eval average step individual rewards of agent0: 1.0704223108075548
idv_policy eval average team episode rewards of agent0: 152.5
idv_policy eval idv catch total num of agent0: 44
idv_policy eval team catch total num: 61
idv_policy eval average step individual rewards of agent1: 0.9136021397973119
idv_policy eval average team episode rewards of agent1: 152.5
idv_policy eval idv catch total num of agent1: 38
idv_policy eval team catch total num: 61
idv_policy eval average step individual rewards of agent2: 1.2454647960584162
idv_policy eval average team episode rewards of agent2: 152.5
idv_policy eval idv catch total num of agent2: 51
idv_policy eval team catch total num: 61
idv_policy eval average step individual rewards of agent3: 0.8607241825642404
idv_policy eval average team episode rewards of agent3: 152.5
idv_policy eval idv catch total num of agent3: 36
idv_policy eval team catch total num: 61
idv_policy eval average step individual rewards of agent4: 1.498291735562473
idv_policy eval average team episode rewards of agent4: 152.5
idv_policy eval idv catch total num of agent4: 61
idv_policy eval team catch total num: 61

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1701/10000 episodes, total num timesteps 340400/2000000, FPS 202.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1702/10000 episodes, total num timesteps 340600/2000000, FPS 202.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1703/10000 episodes, total num timesteps 340800/2000000, FPS 202.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1704/10000 episodes, total num timesteps 341000/2000000, FPS 202.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1705/10000 episodes, total num timesteps 341200/2000000, FPS 202.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1706/10000 episodes, total num timesteps 341400/2000000, FPS 202.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1707/10000 episodes, total num timesteps 341600/2000000, FPS 202.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1708/10000 episodes, total num timesteps 341800/2000000, FPS 202.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1709/10000 episodes, total num timesteps 342000/2000000, FPS 202.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1710/10000 episodes, total num timesteps 342200/2000000, FPS 202.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1711/10000 episodes, total num timesteps 342400/2000000, FPS 202.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1712/10000 episodes, total num timesteps 342600/2000000, FPS 202.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1713/10000 episodes, total num timesteps 342800/2000000, FPS 202.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1714/10000 episodes, total num timesteps 343000/2000000, FPS 202.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1715/10000 episodes, total num timesteps 343200/2000000, FPS 202.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1716/10000 episodes, total num timesteps 343400/2000000, FPS 202.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1717/10000 episodes, total num timesteps 343600/2000000, FPS 202.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1718/10000 episodes, total num timesteps 343800/2000000, FPS 202.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1719/10000 episodes, total num timesteps 344000/2000000, FPS 202.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1720/10000 episodes, total num timesteps 344200/2000000, FPS 202.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1721/10000 episodes, total num timesteps 344400/2000000, FPS 202.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1722/10000 episodes, total num timesteps 344600/2000000, FPS 202.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1723/10000 episodes, total num timesteps 344800/2000000, FPS 202.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1724/10000 episodes, total num timesteps 345000/2000000, FPS 202.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1725/10000 episodes, total num timesteps 345200/2000000, FPS 202.

team_policy eval average step individual rewards of agent0: 0.479677153869688
team_policy eval average team episode rewards of agent0: 67.5
team_policy eval idv catch total num of agent0: 21
team_policy eval team catch total num: 27
team_policy eval average step individual rewards of agent1: 0.30193160889679177
team_policy eval average team episode rewards of agent1: 67.5
team_policy eval idv catch total num of agent1: 14
team_policy eval team catch total num: 27
team_policy eval average step individual rewards of agent2: 0.48646763749831706
team_policy eval average team episode rewards of agent2: 67.5
team_policy eval idv catch total num of agent2: 21
team_policy eval team catch total num: 27
team_policy eval average step individual rewards of agent3: 0.8081659504850356
team_policy eval average team episode rewards of agent3: 67.5
team_policy eval idv catch total num of agent3: 34
team_policy eval team catch total num: 27
team_policy eval average step individual rewards of agent4: 0.5101941240102081
team_policy eval average team episode rewards of agent4: 67.5
team_policy eval idv catch total num of agent4: 22
team_policy eval team catch total num: 27
idv_policy eval average step individual rewards of agent0: 0.9675333015753332
idv_policy eval average team episode rewards of agent0: 77.5
idv_policy eval idv catch total num of agent0: 40
idv_policy eval team catch total num: 31
idv_policy eval average step individual rewards of agent1: 0.5879433854724259
idv_policy eval average team episode rewards of agent1: 77.5
idv_policy eval idv catch total num of agent1: 25
idv_policy eval team catch total num: 31
idv_policy eval average step individual rewards of agent2: 0.7041695828564878
idv_policy eval average team episode rewards of agent2: 77.5
idv_policy eval idv catch total num of agent2: 30
idv_policy eval team catch total num: 31
idv_policy eval average step individual rewards of agent3: 0.45518682667391075
idv_policy eval average team episode rewards of agent3: 77.5
idv_policy eval idv catch total num of agent3: 20
idv_policy eval team catch total num: 31
idv_policy eval average step individual rewards of agent4: 0.6103801680832754
idv_policy eval average team episode rewards of agent4: 77.5
idv_policy eval idv catch total num of agent4: 26
idv_policy eval team catch total num: 31

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1726/10000 episodes, total num timesteps 345400/2000000, FPS 202.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1727/10000 episodes, total num timesteps 345600/2000000, FPS 202.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1728/10000 episodes, total num timesteps 345800/2000000, FPS 202.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1729/10000 episodes, total num timesteps 346000/2000000, FPS 202.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1730/10000 episodes, total num timesteps 346200/2000000, FPS 202.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1731/10000 episodes, total num timesteps 346400/2000000, FPS 202.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1732/10000 episodes, total num timesteps 346600/2000000, FPS 202.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1733/10000 episodes, total num timesteps 346800/2000000, FPS 202.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1734/10000 episodes, total num timesteps 347000/2000000, FPS 202.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1735/10000 episodes, total num timesteps 347200/2000000, FPS 202.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1736/10000 episodes, total num timesteps 347400/2000000, FPS 202.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1737/10000 episodes, total num timesteps 347600/2000000, FPS 202.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1738/10000 episodes, total num timesteps 347800/2000000, FPS 202.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1739/10000 episodes, total num timesteps 348000/2000000, FPS 203.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1740/10000 episodes, total num timesteps 348200/2000000, FPS 203.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1741/10000 episodes, total num timesteps 348400/2000000, FPS 203.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1742/10000 episodes, total num timesteps 348600/2000000, FPS 203.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1743/10000 episodes, total num timesteps 348800/2000000, FPS 203.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1744/10000 episodes, total num timesteps 349000/2000000, FPS 203.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1745/10000 episodes, total num timesteps 349200/2000000, FPS 203.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1746/10000 episodes, total num timesteps 349400/2000000, FPS 203.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1747/10000 episodes, total num timesteps 349600/2000000, FPS 203.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1748/10000 episodes, total num timesteps 349800/2000000, FPS 203.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1749/10000 episodes, total num timesteps 350000/2000000, FPS 203.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1750/10000 episodes, total num timesteps 350200/2000000, FPS 203.

team_policy eval average step individual rewards of agent0: 0.6184036515398371
team_policy eval average team episode rewards of agent0: 102.5
team_policy eval idv catch total num of agent0: 26
team_policy eval team catch total num: 41
team_policy eval average step individual rewards of agent1: 1.25059477155103
team_policy eval average team episode rewards of agent1: 102.5
team_policy eval idv catch total num of agent1: 51
team_policy eval team catch total num: 41
team_policy eval average step individual rewards of agent2: 1.066737908022476
team_policy eval average team episode rewards of agent2: 102.5
team_policy eval idv catch total num of agent2: 44
team_policy eval team catch total num: 41
team_policy eval average step individual rewards of agent3: 0.6692513001851524
team_policy eval average team episode rewards of agent3: 102.5
team_policy eval idv catch total num of agent3: 28
team_policy eval team catch total num: 41
team_policy eval average step individual rewards of agent4: 0.6153425279257803
team_policy eval average team episode rewards of agent4: 102.5
team_policy eval idv catch total num of agent4: 26
team_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent0: 0.5077018722782748
idv_policy eval average team episode rewards of agent0: 115.0
idv_policy eval idv catch total num of agent0: 22
idv_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent1: 0.9647277978464269
idv_policy eval average team episode rewards of agent1: 115.0
idv_policy eval idv catch total num of agent1: 40
idv_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent2: 0.7660853999787679
idv_policy eval average team episode rewards of agent2: 115.0
idv_policy eval idv catch total num of agent2: 32
idv_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent3: 1.0225903871736746
idv_policy eval average team episode rewards of agent3: 115.0
idv_policy eval idv catch total num of agent3: 42
idv_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent4: 0.9702930238838516
idv_policy eval average team episode rewards of agent4: 115.0
idv_policy eval idv catch total num of agent4: 40
idv_policy eval team catch total num: 46

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1751/10000 episodes, total num timesteps 350400/2000000, FPS 203.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1752/10000 episodes, total num timesteps 350600/2000000, FPS 203.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1753/10000 episodes, total num timesteps 350800/2000000, FPS 203.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1754/10000 episodes, total num timesteps 351000/2000000, FPS 203.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1755/10000 episodes, total num timesteps 351200/2000000, FPS 203.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1756/10000 episodes, total num timesteps 351400/2000000, FPS 203.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1757/10000 episodes, total num timesteps 351600/2000000, FPS 203.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1758/10000 episodes, total num timesteps 351800/2000000, FPS 203.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1759/10000 episodes, total num timesteps 352000/2000000, FPS 203.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1760/10000 episodes, total num timesteps 352200/2000000, FPS 203.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1761/10000 episodes, total num timesteps 352400/2000000, FPS 203.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1762/10000 episodes, total num timesteps 352600/2000000, FPS 203.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1763/10000 episodes, total num timesteps 352800/2000000, FPS 203.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1764/10000 episodes, total num timesteps 353000/2000000, FPS 203.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1765/10000 episodes, total num timesteps 353200/2000000, FPS 203.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1766/10000 episodes, total num timesteps 353400/2000000, FPS 203.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1767/10000 episodes, total num timesteps 353600/2000000, FPS 203.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1768/10000 episodes, total num timesteps 353800/2000000, FPS 203.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1769/10000 episodes, total num timesteps 354000/2000000, FPS 203.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1770/10000 episodes, total num timesteps 354200/2000000, FPS 203.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1771/10000 episodes, total num timesteps 354400/2000000, FPS 203.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1772/10000 episodes, total num timesteps 354600/2000000, FPS 203.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1773/10000 episodes, total num timesteps 354800/2000000, FPS 203.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1774/10000 episodes, total num timesteps 355000/2000000, FPS 203.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1775/10000 episodes, total num timesteps 355200/2000000, FPS 203.

team_policy eval average step individual rewards of agent0: 0.5943525414534494
team_policy eval average team episode rewards of agent0: 97.5
team_policy eval idv catch total num of agent0: 25
team_policy eval team catch total num: 39
team_policy eval average step individual rewards of agent1: 0.9345681191973432
team_policy eval average team episode rewards of agent1: 97.5
team_policy eval idv catch total num of agent1: 39
team_policy eval team catch total num: 39
team_policy eval average step individual rewards of agent2: 0.667595923688086
team_policy eval average team episode rewards of agent2: 97.5
team_policy eval idv catch total num of agent2: 28
team_policy eval team catch total num: 39
team_policy eval average step individual rewards of agent3: 0.6403291524117405
team_policy eval average team episode rewards of agent3: 97.5
team_policy eval idv catch total num of agent3: 27
team_policy eval team catch total num: 39
team_policy eval average step individual rewards of agent4: 0.5611965775558942
team_policy eval average team episode rewards of agent4: 97.5
team_policy eval idv catch total num of agent4: 24
team_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent0: 1.010465487414931
idv_policy eval average team episode rewards of agent0: 90.0
idv_policy eval idv catch total num of agent0: 42
idv_policy eval team catch total num: 36
idv_policy eval average step individual rewards of agent1: 0.6898662984109906
idv_policy eval average team episode rewards of agent1: 90.0
idv_policy eval idv catch total num of agent1: 29
idv_policy eval team catch total num: 36
idv_policy eval average step individual rewards of agent2: 0.6949582701660557
idv_policy eval average team episode rewards of agent2: 90.0
idv_policy eval idv catch total num of agent2: 29
idv_policy eval team catch total num: 36
idv_policy eval average step individual rewards of agent3: 0.3829184072690603
idv_policy eval average team episode rewards of agent3: 90.0
idv_policy eval idv catch total num of agent3: 17
idv_policy eval team catch total num: 36
idv_policy eval average step individual rewards of agent4: 0.620618149310678
idv_policy eval average team episode rewards of agent4: 90.0
idv_policy eval idv catch total num of agent4: 27
idv_policy eval team catch total num: 36

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1776/10000 episodes, total num timesteps 355400/2000000, FPS 203.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1777/10000 episodes, total num timesteps 355600/2000000, FPS 203.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1778/10000 episodes, total num timesteps 355800/2000000, FPS 203.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1779/10000 episodes, total num timesteps 356000/2000000, FPS 203.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1780/10000 episodes, total num timesteps 356200/2000000, FPS 203.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1781/10000 episodes, total num timesteps 356400/2000000, FPS 203.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1782/10000 episodes, total num timesteps 356600/2000000, FPS 203.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1783/10000 episodes, total num timesteps 356800/2000000, FPS 203.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1784/10000 episodes, total num timesteps 357000/2000000, FPS 203.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1785/10000 episodes, total num timesteps 357200/2000000, FPS 203.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1786/10000 episodes, total num timesteps 357400/2000000, FPS 203.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1787/10000 episodes, total num timesteps 357600/2000000, FPS 203.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1788/10000 episodes, total num timesteps 357800/2000000, FPS 203.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1789/10000 episodes, total num timesteps 358000/2000000, FPS 203.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1790/10000 episodes, total num timesteps 358200/2000000, FPS 203.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1791/10000 episodes, total num timesteps 358400/2000000, FPS 203.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1792/10000 episodes, total num timesteps 358600/2000000, FPS 203.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1793/10000 episodes, total num timesteps 358800/2000000, FPS 203.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1794/10000 episodes, total num timesteps 359000/2000000, FPS 203.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1795/10000 episodes, total num timesteps 359200/2000000, FPS 203.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1796/10000 episodes, total num timesteps 359400/2000000, FPS 203.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1797/10000 episodes, total num timesteps 359600/2000000, FPS 203.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1798/10000 episodes, total num timesteps 359800/2000000, FPS 203.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1799/10000 episodes, total num timesteps 360000/2000000, FPS 203.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1800/10000 episodes, total num timesteps 360200/2000000, FPS 203.

team_policy eval average step individual rewards of agent0: 0.563841103122249
team_policy eval average team episode rewards of agent0: 105.0
team_policy eval idv catch total num of agent0: 24
team_policy eval team catch total num: 42
team_policy eval average step individual rewards of agent1: 0.7408393004996
team_policy eval average team episode rewards of agent1: 105.0
team_policy eval idv catch total num of agent1: 31
team_policy eval team catch total num: 42
team_policy eval average step individual rewards of agent2: 1.0477556255703182
team_policy eval average team episode rewards of agent2: 105.0
team_policy eval idv catch total num of agent2: 43
team_policy eval team catch total num: 42
team_policy eval average step individual rewards of agent3: 0.8167864923946694
team_policy eval average team episode rewards of agent3: 105.0
team_policy eval idv catch total num of agent3: 34
team_policy eval team catch total num: 42
team_policy eval average step individual rewards of agent4: 0.5614554383783861
team_policy eval average team episode rewards of agent4: 105.0
team_policy eval idv catch total num of agent4: 24
team_policy eval team catch total num: 42
idv_policy eval average step individual rewards of agent0: 0.7341298179989382
idv_policy eval average team episode rewards of agent0: 90.0
idv_policy eval idv catch total num of agent0: 31
idv_policy eval team catch total num: 36
idv_policy eval average step individual rewards of agent1: 0.41974037974078604
idv_policy eval average team episode rewards of agent1: 90.0
idv_policy eval idv catch total num of agent1: 18
idv_policy eval team catch total num: 36
idv_policy eval average step individual rewards of agent2: 0.7919343033563118
idv_policy eval average team episode rewards of agent2: 90.0
idv_policy eval idv catch total num of agent2: 33
idv_policy eval team catch total num: 36
idv_policy eval average step individual rewards of agent3: 0.43267396648037965
idv_policy eval average team episode rewards of agent3: 90.0
idv_policy eval idv catch total num of agent3: 19
idv_policy eval team catch total num: 36
idv_policy eval average step individual rewards of agent4: 0.5392683474539877
idv_policy eval average team episode rewards of agent4: 90.0
idv_policy eval idv catch total num of agent4: 23
idv_policy eval team catch total num: 36

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1801/10000 episodes, total num timesteps 360400/2000000, FPS 203.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1802/10000 episodes, total num timesteps 360600/2000000, FPS 203.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1803/10000 episodes, total num timesteps 360800/2000000, FPS 203.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1804/10000 episodes, total num timesteps 361000/2000000, FPS 203.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1805/10000 episodes, total num timesteps 361200/2000000, FPS 203.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1806/10000 episodes, total num timesteps 361400/2000000, FPS 203.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1807/10000 episodes, total num timesteps 361600/2000000, FPS 203.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1808/10000 episodes, total num timesteps 361800/2000000, FPS 203.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1809/10000 episodes, total num timesteps 362000/2000000, FPS 203.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1810/10000 episodes, total num timesteps 362200/2000000, FPS 203.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1811/10000 episodes, total num timesteps 362400/2000000, FPS 203.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1812/10000 episodes, total num timesteps 362600/2000000, FPS 203.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1813/10000 episodes, total num timesteps 362800/2000000, FPS 203.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1814/10000 episodes, total num timesteps 363000/2000000, FPS 203.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1815/10000 episodes, total num timesteps 363200/2000000, FPS 203.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1816/10000 episodes, total num timesteps 363400/2000000, FPS 203.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1817/10000 episodes, total num timesteps 363600/2000000, FPS 203.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1818/10000 episodes, total num timesteps 363800/2000000, FPS 203.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1819/10000 episodes, total num timesteps 364000/2000000, FPS 203.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1820/10000 episodes, total num timesteps 364200/2000000, FPS 203.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1821/10000 episodes, total num timesteps 364400/2000000, FPS 203.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1822/10000 episodes, total num timesteps 364600/2000000, FPS 203.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1823/10000 episodes, total num timesteps 364800/2000000, FPS 203.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1824/10000 episodes, total num timesteps 365000/2000000, FPS 203.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1825/10000 episodes, total num timesteps 365200/2000000, FPS 203.

team_policy eval average step individual rewards of agent0: 0.6049285050648404
team_policy eval average team episode rewards of agent0: 77.5
team_policy eval idv catch total num of agent0: 26
team_policy eval team catch total num: 31
team_policy eval average step individual rewards of agent1: 0.6095138439642379
team_policy eval average team episode rewards of agent1: 77.5
team_policy eval idv catch total num of agent1: 26
team_policy eval team catch total num: 31
team_policy eval average step individual rewards of agent2: 0.5390225739836554
team_policy eval average team episode rewards of agent2: 77.5
team_policy eval idv catch total num of agent2: 23
team_policy eval team catch total num: 31
team_policy eval average step individual rewards of agent3: 0.7617845619777257
team_policy eval average team episode rewards of agent3: 77.5
team_policy eval idv catch total num of agent3: 32
team_policy eval team catch total num: 31
team_policy eval average step individual rewards of agent4: 0.5932203221716761
team_policy eval average team episode rewards of agent4: 77.5
team_policy eval idv catch total num of agent4: 25
team_policy eval team catch total num: 31
idv_policy eval average step individual rewards of agent0: 0.6112518767772559
idv_policy eval average team episode rewards of agent0: 115.0
idv_policy eval idv catch total num of agent0: 26
idv_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent1: 0.7194803008848826
idv_policy eval average team episode rewards of agent1: 115.0
idv_policy eval idv catch total num of agent1: 30
idv_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent2: 0.8663332176418729
idv_policy eval average team episode rewards of agent2: 115.0
idv_policy eval idv catch total num of agent2: 36
idv_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent3: 0.9396194118475084
idv_policy eval average team episode rewards of agent3: 115.0
idv_policy eval idv catch total num of agent3: 39
idv_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent4: 0.8392817112630573
idv_policy eval average team episode rewards of agent4: 115.0
idv_policy eval idv catch total num of agent4: 35
idv_policy eval team catch total num: 46

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1826/10000 episodes, total num timesteps 365400/2000000, FPS 203.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1827/10000 episodes, total num timesteps 365600/2000000, FPS 203.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1828/10000 episodes, total num timesteps 365800/2000000, FPS 203.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1829/10000 episodes, total num timesteps 366000/2000000, FPS 203.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1830/10000 episodes, total num timesteps 366200/2000000, FPS 203.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1831/10000 episodes, total num timesteps 366400/2000000, FPS 203.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1832/10000 episodes, total num timesteps 366600/2000000, FPS 203.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1833/10000 episodes, total num timesteps 366800/2000000, FPS 203.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1834/10000 episodes, total num timesteps 367000/2000000, FPS 204.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1835/10000 episodes, total num timesteps 367200/2000000, FPS 204.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1836/10000 episodes, total num timesteps 367400/2000000, FPS 204.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1837/10000 episodes, total num timesteps 367600/2000000, FPS 204.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1838/10000 episodes, total num timesteps 367800/2000000, FPS 204.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1839/10000 episodes, total num timesteps 368000/2000000, FPS 204.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1840/10000 episodes, total num timesteps 368200/2000000, FPS 204.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1841/10000 episodes, total num timesteps 368400/2000000, FPS 204.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1842/10000 episodes, total num timesteps 368600/2000000, FPS 204.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1843/10000 episodes, total num timesteps 368800/2000000, FPS 204.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1844/10000 episodes, total num timesteps 369000/2000000, FPS 204.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1845/10000 episodes, total num timesteps 369200/2000000, FPS 204.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1846/10000 episodes, total num timesteps 369400/2000000, FPS 204.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1847/10000 episodes, total num timesteps 369600/2000000, FPS 204.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1848/10000 episodes, total num timesteps 369800/2000000, FPS 204.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1849/10000 episodes, total num timesteps 370000/2000000, FPS 204.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1850/10000 episodes, total num timesteps 370200/2000000, FPS 204.

team_policy eval average step individual rewards of agent0: 0.6127586192850984
team_policy eval average team episode rewards of agent0: 65.0
team_policy eval idv catch total num of agent0: 26
team_policy eval team catch total num: 26
team_policy eval average step individual rewards of agent1: 0.7072567346298244
team_policy eval average team episode rewards of agent1: 65.0
team_policy eval idv catch total num of agent1: 30
team_policy eval team catch total num: 26
team_policy eval average step individual rewards of agent2: 0.3339045475503329
team_policy eval average team episode rewards of agent2: 65.0
team_policy eval idv catch total num of agent2: 15
team_policy eval team catch total num: 26
team_policy eval average step individual rewards of agent3: 0.12343253233003612
team_policy eval average team episode rewards of agent3: 65.0
team_policy eval idv catch total num of agent3: 7
team_policy eval team catch total num: 26
team_policy eval average step individual rewards of agent4: 0.4044186522310852
team_policy eval average team episode rewards of agent4: 65.0
team_policy eval idv catch total num of agent4: 18
team_policy eval team catch total num: 26
idv_policy eval average step individual rewards of agent0: 0.7344977765872142
idv_policy eval average team episode rewards of agent0: 62.5
idv_policy eval idv catch total num of agent0: 31
idv_policy eval team catch total num: 25
idv_policy eval average step individual rewards of agent1: 0.680115045322848
idv_policy eval average team episode rewards of agent1: 62.5
idv_policy eval idv catch total num of agent1: 29
idv_policy eval team catch total num: 25
idv_policy eval average step individual rewards of agent2: 0.7093135518530856
idv_policy eval average team episode rewards of agent2: 62.5
idv_policy eval idv catch total num of agent2: 30
idv_policy eval team catch total num: 25
idv_policy eval average step individual rewards of agent3: 0.4327226277756149
idv_policy eval average team episode rewards of agent3: 62.5
idv_policy eval idv catch total num of agent3: 19
idv_policy eval team catch total num: 25
idv_policy eval average step individual rewards of agent4: 0.3218545659515019
idv_policy eval average team episode rewards of agent4: 62.5
idv_policy eval idv catch total num of agent4: 15
idv_policy eval team catch total num: 25

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1851/10000 episodes, total num timesteps 370400/2000000, FPS 204.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1852/10000 episodes, total num timesteps 370600/2000000, FPS 204.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1853/10000 episodes, total num timesteps 370800/2000000, FPS 204.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1854/10000 episodes, total num timesteps 371000/2000000, FPS 204.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1855/10000 episodes, total num timesteps 371200/2000000, FPS 204.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1856/10000 episodes, total num timesteps 371400/2000000, FPS 204.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1857/10000 episodes, total num timesteps 371600/2000000, FPS 204.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1858/10000 episodes, total num timesteps 371800/2000000, FPS 204.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1859/10000 episodes, total num timesteps 372000/2000000, FPS 204.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1860/10000 episodes, total num timesteps 372200/2000000, FPS 204.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1861/10000 episodes, total num timesteps 372400/2000000, FPS 204.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1862/10000 episodes, total num timesteps 372600/2000000, FPS 204.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1863/10000 episodes, total num timesteps 372800/2000000, FPS 204.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1864/10000 episodes, total num timesteps 373000/2000000, FPS 204.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1865/10000 episodes, total num timesteps 373200/2000000, FPS 204.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1866/10000 episodes, total num timesteps 373400/2000000, FPS 204.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1867/10000 episodes, total num timesteps 373600/2000000, FPS 204.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1868/10000 episodes, total num timesteps 373800/2000000, FPS 204.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1869/10000 episodes, total num timesteps 374000/2000000, FPS 204.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1870/10000 episodes, total num timesteps 374200/2000000, FPS 204.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1871/10000 episodes, total num timesteps 374400/2000000, FPS 204.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1872/10000 episodes, total num timesteps 374600/2000000, FPS 204.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1873/10000 episodes, total num timesteps 374800/2000000, FPS 204.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1874/10000 episodes, total num timesteps 375000/2000000, FPS 204.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1875/10000 episodes, total num timesteps 375200/2000000, FPS 204.

team_policy eval average step individual rewards of agent0: 1.1938170358262692
team_policy eval average team episode rewards of agent0: 127.5
team_policy eval idv catch total num of agent0: 49
team_policy eval team catch total num: 51
team_policy eval average step individual rewards of agent1: 0.8937125530351195
team_policy eval average team episode rewards of agent1: 127.5
team_policy eval idv catch total num of agent1: 37
team_policy eval team catch total num: 51
team_policy eval average step individual rewards of agent2: 1.2448677492924554
team_policy eval average team episode rewards of agent2: 127.5
team_policy eval idv catch total num of agent2: 51
team_policy eval team catch total num: 51
team_policy eval average step individual rewards of agent3: 0.6060960840005927
team_policy eval average team episode rewards of agent3: 127.5
team_policy eval idv catch total num of agent3: 26
team_policy eval team catch total num: 51
team_policy eval average step individual rewards of agent4: 0.7646362269908968
team_policy eval average team episode rewards of agent4: 127.5
team_policy eval idv catch total num of agent4: 32
team_policy eval team catch total num: 51
idv_policy eval average step individual rewards of agent0: 0.6107132412684603
idv_policy eval average team episode rewards of agent0: 97.5
idv_policy eval idv catch total num of agent0: 26
idv_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent1: 0.5380610076775938
idv_policy eval average team episode rewards of agent1: 97.5
idv_policy eval idv catch total num of agent1: 23
idv_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent2: 0.45514396309446814
idv_policy eval average team episode rewards of agent2: 97.5
idv_policy eval idv catch total num of agent2: 20
idv_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent3: 0.8399409765762238
idv_policy eval average team episode rewards of agent3: 97.5
idv_policy eval idv catch total num of agent3: 35
idv_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent4: 0.814535473584894
idv_policy eval average team episode rewards of agent4: 97.5
idv_policy eval idv catch total num of agent4: 34
idv_policy eval team catch total num: 39

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1876/10000 episodes, total num timesteps 375400/2000000, FPS 204.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1877/10000 episodes, total num timesteps 375600/2000000, FPS 204.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1878/10000 episodes, total num timesteps 375800/2000000, FPS 204.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1879/10000 episodes, total num timesteps 376000/2000000, FPS 204.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1880/10000 episodes, total num timesteps 376200/2000000, FPS 204.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1881/10000 episodes, total num timesteps 376400/2000000, FPS 204.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1882/10000 episodes, total num timesteps 376600/2000000, FPS 204.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1883/10000 episodes, total num timesteps 376800/2000000, FPS 204.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1884/10000 episodes, total num timesteps 377000/2000000, FPS 204.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1885/10000 episodes, total num timesteps 377200/2000000, FPS 204.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1886/10000 episodes, total num timesteps 377400/2000000, FPS 204.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1887/10000 episodes, total num timesteps 377600/2000000, FPS 204.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1888/10000 episodes, total num timesteps 377800/2000000, FPS 204.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1889/10000 episodes, total num timesteps 378000/2000000, FPS 204.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1890/10000 episodes, total num timesteps 378200/2000000, FPS 204.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1891/10000 episodes, total num timesteps 378400/2000000, FPS 204.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1892/10000 episodes, total num timesteps 378600/2000000, FPS 204.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1893/10000 episodes, total num timesteps 378800/2000000, FPS 204.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1894/10000 episodes, total num timesteps 379000/2000000, FPS 204.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1895/10000 episodes, total num timesteps 379200/2000000, FPS 204.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1896/10000 episodes, total num timesteps 379400/2000000, FPS 204.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1897/10000 episodes, total num timesteps 379600/2000000, FPS 204.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1898/10000 episodes, total num timesteps 379800/2000000, FPS 204.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1899/10000 episodes, total num timesteps 380000/2000000, FPS 204.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1900/10000 episodes, total num timesteps 380200/2000000, FPS 204.

team_policy eval average step individual rewards of agent0: 0.7703036546759359
team_policy eval average team episode rewards of agent0: 92.5
team_policy eval idv catch total num of agent0: 32
team_policy eval team catch total num: 37
team_policy eval average step individual rewards of agent1: 0.7184184541202526
team_policy eval average team episode rewards of agent1: 92.5
team_policy eval idv catch total num of agent1: 30
team_policy eval team catch total num: 37
team_policy eval average step individual rewards of agent2: 1.0463976780121123
team_policy eval average team episode rewards of agent2: 92.5
team_policy eval idv catch total num of agent2: 43
team_policy eval team catch total num: 37
team_policy eval average step individual rewards of agent3: 0.9215254943035482
team_policy eval average team episode rewards of agent3: 92.5
team_policy eval idv catch total num of agent3: 38
team_policy eval team catch total num: 37
team_policy eval average step individual rewards of agent4: 0.3774581344293901
team_policy eval average team episode rewards of agent4: 92.5
team_policy eval idv catch total num of agent4: 17
team_policy eval team catch total num: 37
idv_policy eval average step individual rewards of agent0: 0.43091855033462145
idv_policy eval average team episode rewards of agent0: 65.0
idv_policy eval idv catch total num of agent0: 19
idv_policy eval team catch total num: 26
idv_policy eval average step individual rewards of agent1: 0.7210838430106463
idv_policy eval average team episode rewards of agent1: 65.0
idv_policy eval idv catch total num of agent1: 30
idv_policy eval team catch total num: 26
idv_policy eval average step individual rewards of agent2: 0.6184420278114839
idv_policy eval average team episode rewards of agent2: 65.0
idv_policy eval idv catch total num of agent2: 26
idv_policy eval team catch total num: 26
idv_policy eval average step individual rewards of agent3: 0.5586147152507549
idv_policy eval average team episode rewards of agent3: 65.0
idv_policy eval idv catch total num of agent3: 24
idv_policy eval team catch total num: 26
idv_policy eval average step individual rewards of agent4: 0.4684944605348405
idv_policy eval average team episode rewards of agent4: 65.0
idv_policy eval idv catch total num of agent4: 20
idv_policy eval team catch total num: 26

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1901/10000 episodes, total num timesteps 380400/2000000, FPS 204.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1902/10000 episodes, total num timesteps 380600/2000000, FPS 204.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1903/10000 episodes, total num timesteps 380800/2000000, FPS 204.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1904/10000 episodes, total num timesteps 381000/2000000, FPS 204.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1905/10000 episodes, total num timesteps 381200/2000000, FPS 204.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1906/10000 episodes, total num timesteps 381400/2000000, FPS 204.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1907/10000 episodes, total num timesteps 381600/2000000, FPS 204.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1908/10000 episodes, total num timesteps 381800/2000000, FPS 204.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1909/10000 episodes, total num timesteps 382000/2000000, FPS 204.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1910/10000 episodes, total num timesteps 382200/2000000, FPS 204.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1911/10000 episodes, total num timesteps 382400/2000000, FPS 204.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1912/10000 episodes, total num timesteps 382600/2000000, FPS 204.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1913/10000 episodes, total num timesteps 382800/2000000, FPS 204.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1914/10000 episodes, total num timesteps 383000/2000000, FPS 204.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1915/10000 episodes, total num timesteps 383200/2000000, FPS 204.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1916/10000 episodes, total num timesteps 383400/2000000, FPS 204.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1917/10000 episodes, total num timesteps 383600/2000000, FPS 204.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1918/10000 episodes, total num timesteps 383800/2000000, FPS 204.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1919/10000 episodes, total num timesteps 384000/2000000, FPS 204.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1920/10000 episodes, total num timesteps 384200/2000000, FPS 204.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1921/10000 episodes, total num timesteps 384400/2000000, FPS 204.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1922/10000 episodes, total num timesteps 384600/2000000, FPS 204.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1923/10000 episodes, total num timesteps 384800/2000000, FPS 204.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1924/10000 episodes, total num timesteps 385000/2000000, FPS 204.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1925/10000 episodes, total num timesteps 385200/2000000, FPS 204.

team_policy eval average step individual rewards of agent0: 0.9191118126030413
team_policy eval average team episode rewards of agent0: 85.0
team_policy eval idv catch total num of agent0: 38
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent1: 0.7405395160455354
team_policy eval average team episode rewards of agent1: 85.0
team_policy eval idv catch total num of agent1: 31
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent2: 0.8919389274444862
team_policy eval average team episode rewards of agent2: 85.0
team_policy eval idv catch total num of agent2: 37
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent3: 0.4610118750577397
team_policy eval average team episode rewards of agent3: 85.0
team_policy eval idv catch total num of agent3: 20
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent4: 0.6218628634715845
team_policy eval average team episode rewards of agent4: 85.0
team_policy eval idv catch total num of agent4: 27
team_policy eval team catch total num: 34
idv_policy eval average step individual rewards of agent0: 1.039409325951176
idv_policy eval average team episode rewards of agent0: 117.5
idv_policy eval idv catch total num of agent0: 43
idv_policy eval team catch total num: 47
idv_policy eval average step individual rewards of agent1: 0.3858945297362822
idv_policy eval average team episode rewards of agent1: 117.5
idv_policy eval idv catch total num of agent1: 17
idv_policy eval team catch total num: 47
idv_policy eval average step individual rewards of agent2: 0.7666503049838274
idv_policy eval average team episode rewards of agent2: 117.5
idv_policy eval idv catch total num of agent2: 32
idv_policy eval team catch total num: 47
idv_policy eval average step individual rewards of agent3: 0.8370372280195549
idv_policy eval average team episode rewards of agent3: 117.5
idv_policy eval idv catch total num of agent3: 35
idv_policy eval team catch total num: 47
idv_policy eval average step individual rewards of agent4: 1.163895895791523
idv_policy eval average team episode rewards of agent4: 117.5
idv_policy eval idv catch total num of agent4: 48
idv_policy eval team catch total num: 47

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1926/10000 episodes, total num timesteps 385400/2000000, FPS 204.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1927/10000 episodes, total num timesteps 385600/2000000, FPS 204.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1928/10000 episodes, total num timesteps 385800/2000000, FPS 204.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1929/10000 episodes, total num timesteps 386000/2000000, FPS 204.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1930/10000 episodes, total num timesteps 386200/2000000, FPS 204.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1931/10000 episodes, total num timesteps 386400/2000000, FPS 205.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1932/10000 episodes, total num timesteps 386600/2000000, FPS 205.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1933/10000 episodes, total num timesteps 386800/2000000, FPS 205.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1934/10000 episodes, total num timesteps 387000/2000000, FPS 205.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1935/10000 episodes, total num timesteps 387200/2000000, FPS 205.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1936/10000 episodes, total num timesteps 387400/2000000, FPS 205.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1937/10000 episodes, total num timesteps 387600/2000000, FPS 205.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1938/10000 episodes, total num timesteps 387800/2000000, FPS 205.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1939/10000 episodes, total num timesteps 388000/2000000, FPS 205.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1940/10000 episodes, total num timesteps 388200/2000000, FPS 205.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1941/10000 episodes, total num timesteps 388400/2000000, FPS 205.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1942/10000 episodes, total num timesteps 388600/2000000, FPS 205.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1943/10000 episodes, total num timesteps 388800/2000000, FPS 205.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1944/10000 episodes, total num timesteps 389000/2000000, FPS 205.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1945/10000 episodes, total num timesteps 389200/2000000, FPS 205.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1946/10000 episodes, total num timesteps 389400/2000000, FPS 205.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1947/10000 episodes, total num timesteps 389600/2000000, FPS 205.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1948/10000 episodes, total num timesteps 389800/2000000, FPS 205.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1949/10000 episodes, total num timesteps 390000/2000000, FPS 205.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1950/10000 episodes, total num timesteps 390200/2000000, FPS 205.

team_policy eval average step individual rewards of agent0: 0.3711462923420284
team_policy eval average team episode rewards of agent0: 62.5
team_policy eval idv catch total num of agent0: 17
team_policy eval team catch total num: 25
team_policy eval average step individual rewards of agent1: 0.33499536245895456
team_policy eval average team episode rewards of agent1: 62.5
team_policy eval idv catch total num of agent1: 15
team_policy eval team catch total num: 25
team_policy eval average step individual rewards of agent2: 0.7415485534761703
team_policy eval average team episode rewards of agent2: 62.5
team_policy eval idv catch total num of agent2: 31
team_policy eval team catch total num: 25
team_policy eval average step individual rewards of agent3: 0.35518579289441954
team_policy eval average team episode rewards of agent3: 62.5
team_policy eval idv catch total num of agent3: 16
team_policy eval team catch total num: 25
team_policy eval average step individual rewards of agent4: 0.38205095759293956
team_policy eval average team episode rewards of agent4: 62.5
team_policy eval idv catch total num of agent4: 17
team_policy eval team catch total num: 25
idv_policy eval average step individual rewards of agent0: 0.8435135467635914
idv_policy eval average team episode rewards of agent0: 127.5
idv_policy eval idv catch total num of agent0: 35
idv_policy eval team catch total num: 51
idv_policy eval average step individual rewards of agent1: 0.8951233347427219
idv_policy eval average team episode rewards of agent1: 127.5
idv_policy eval idv catch total num of agent1: 37
idv_policy eval team catch total num: 51
idv_policy eval average step individual rewards of agent2: 1.0931440633998761
idv_policy eval average team episode rewards of agent2: 127.5
idv_policy eval idv catch total num of agent2: 45
idv_policy eval team catch total num: 51
idv_policy eval average step individual rewards of agent3: 0.99632742291716
idv_policy eval average team episode rewards of agent3: 127.5
idv_policy eval idv catch total num of agent3: 41
idv_policy eval team catch total num: 51
idv_policy eval average step individual rewards of agent4: 0.5379536098637832
idv_policy eval average team episode rewards of agent4: 127.5
idv_policy eval idv catch total num of agent4: 23
idv_policy eval team catch total num: 51

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1951/10000 episodes, total num timesteps 390400/2000000, FPS 205.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1952/10000 episodes, total num timesteps 390600/2000000, FPS 205.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1953/10000 episodes, total num timesteps 390800/2000000, FPS 205.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1954/10000 episodes, total num timesteps 391000/2000000, FPS 205.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1955/10000 episodes, total num timesteps 391200/2000000, FPS 205.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1956/10000 episodes, total num timesteps 391400/2000000, FPS 205.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1957/10000 episodes, total num timesteps 391600/2000000, FPS 205.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1958/10000 episodes, total num timesteps 391800/2000000, FPS 205.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1959/10000 episodes, total num timesteps 392000/2000000, FPS 205.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1960/10000 episodes, total num timesteps 392200/2000000, FPS 205.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1961/10000 episodes, total num timesteps 392400/2000000, FPS 205.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1962/10000 episodes, total num timesteps 392600/2000000, FPS 205.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1963/10000 episodes, total num timesteps 392800/2000000, FPS 205.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1964/10000 episodes, total num timesteps 393000/2000000, FPS 205.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1965/10000 episodes, total num timesteps 393200/2000000, FPS 205.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1966/10000 episodes, total num timesteps 393400/2000000, FPS 205.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1967/10000 episodes, total num timesteps 393600/2000000, FPS 205.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1968/10000 episodes, total num timesteps 393800/2000000, FPS 205.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1969/10000 episodes, total num timesteps 394000/2000000, FPS 205.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1970/10000 episodes, total num timesteps 394200/2000000, FPS 205.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1971/10000 episodes, total num timesteps 394400/2000000, FPS 205.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1972/10000 episodes, total num timesteps 394600/2000000, FPS 205.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1973/10000 episodes, total num timesteps 394800/2000000, FPS 205.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1974/10000 episodes, total num timesteps 395000/2000000, FPS 205.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1975/10000 episodes, total num timesteps 395200/2000000, FPS 205.

team_policy eval average step individual rewards of agent0: 0.12458668765825529
team_policy eval average team episode rewards of agent0: 30.0
team_policy eval idv catch total num of agent0: 7
team_policy eval team catch total num: 12
team_policy eval average step individual rewards of agent1: 0.14751688164578744
team_policy eval average team episode rewards of agent1: 30.0
team_policy eval idv catch total num of agent1: 8
team_policy eval team catch total num: 12
team_policy eval average step individual rewards of agent2: 0.46199827253471304
team_policy eval average team episode rewards of agent2: 30.0
team_policy eval idv catch total num of agent2: 20
team_policy eval team catch total num: 12
team_policy eval average step individual rewards of agent3: 0.23617542639128405
team_policy eval average team episode rewards of agent3: 30.0
team_policy eval idv catch total num of agent3: 11
team_policy eval team catch total num: 12
team_policy eval average step individual rewards of agent4: 0.1448498980900582
team_policy eval average team episode rewards of agent4: 30.0
team_policy eval idv catch total num of agent4: 8
team_policy eval team catch total num: 12
idv_policy eval average step individual rewards of agent0: 0.30467078798147007
idv_policy eval average team episode rewards of agent0: 62.5
idv_policy eval idv catch total num of agent0: 14
idv_policy eval team catch total num: 25
idv_policy eval average step individual rewards of agent1: 0.6065098426573589
idv_policy eval average team episode rewards of agent1: 62.5
idv_policy eval idv catch total num of agent1: 26
idv_policy eval team catch total num: 25
idv_policy eval average step individual rewards of agent2: 0.46078389742011217
idv_policy eval average team episode rewards of agent2: 62.5
idv_policy eval idv catch total num of agent2: 20
idv_policy eval team catch total num: 25
idv_policy eval average step individual rewards of agent3: 0.5294955847630396
idv_policy eval average team episode rewards of agent3: 62.5
idv_policy eval idv catch total num of agent3: 23
idv_policy eval team catch total num: 25
idv_policy eval average step individual rewards of agent4: 0.4020361767860695
idv_policy eval average team episode rewards of agent4: 62.5
idv_policy eval idv catch total num of agent4: 18
idv_policy eval team catch total num: 25

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1976/10000 episodes, total num timesteps 395400/2000000, FPS 205.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1977/10000 episodes, total num timesteps 395600/2000000, FPS 205.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1978/10000 episodes, total num timesteps 395800/2000000, FPS 205.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1979/10000 episodes, total num timesteps 396000/2000000, FPS 205.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1980/10000 episodes, total num timesteps 396200/2000000, FPS 205.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1981/10000 episodes, total num timesteps 396400/2000000, FPS 205.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1982/10000 episodes, total num timesteps 396600/2000000, FPS 205.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1983/10000 episodes, total num timesteps 396800/2000000, FPS 205.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1984/10000 episodes, total num timesteps 397000/2000000, FPS 205.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1985/10000 episodes, total num timesteps 397200/2000000, FPS 205.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1986/10000 episodes, total num timesteps 397400/2000000, FPS 205.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1987/10000 episodes, total num timesteps 397600/2000000, FPS 205.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1988/10000 episodes, total num timesteps 397800/2000000, FPS 205.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1989/10000 episodes, total num timesteps 398000/2000000, FPS 205.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1990/10000 episodes, total num timesteps 398200/2000000, FPS 205.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1991/10000 episodes, total num timesteps 398400/2000000, FPS 205.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1992/10000 episodes, total num timesteps 398600/2000000, FPS 205.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1993/10000 episodes, total num timesteps 398800/2000000, FPS 205.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1994/10000 episodes, total num timesteps 399000/2000000, FPS 205.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1995/10000 episodes, total num timesteps 399200/2000000, FPS 205.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1996/10000 episodes, total num timesteps 399400/2000000, FPS 205.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1997/10000 episodes, total num timesteps 399600/2000000, FPS 205.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1998/10000 episodes, total num timesteps 399800/2000000, FPS 205.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1999/10000 episodes, total num timesteps 400000/2000000, FPS 205.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2000/10000 episodes, total num timesteps 400200/2000000, FPS 205.

team_policy eval average step individual rewards of agent0: 0.5059244652879843
team_policy eval average team episode rewards of agent0: 122.5
team_policy eval idv catch total num of agent0: 22
team_policy eval team catch total num: 49
team_policy eval average step individual rewards of agent1: 1.1171900521275824
team_policy eval average team episode rewards of agent1: 122.5
team_policy eval idv catch total num of agent1: 46
team_policy eval team catch total num: 49
team_policy eval average step individual rewards of agent2: 0.5613301128321341
team_policy eval average team episode rewards of agent2: 122.5
team_policy eval idv catch total num of agent2: 24
team_policy eval team catch total num: 49
team_policy eval average step individual rewards of agent3: 0.7420270977910041
team_policy eval average team episode rewards of agent3: 122.5
team_policy eval idv catch total num of agent3: 31
team_policy eval team catch total num: 49
team_policy eval average step individual rewards of agent4: 1.2165115781687532
team_policy eval average team episode rewards of agent4: 122.5
team_policy eval idv catch total num of agent4: 50
team_policy eval team catch total num: 49
idv_policy eval average step individual rewards of agent0: 0.8314505070714554
idv_policy eval average team episode rewards of agent0: 95.0
idv_policy eval idv catch total num of agent0: 35
idv_policy eval team catch total num: 38
idv_policy eval average step individual rewards of agent1: 0.7091900036013261
idv_policy eval average team episode rewards of agent1: 95.0
idv_policy eval idv catch total num of agent1: 30
idv_policy eval team catch total num: 38
idv_policy eval average step individual rewards of agent2: 1.013059676654104
idv_policy eval average team episode rewards of agent2: 95.0
idv_policy eval idv catch total num of agent2: 42
idv_policy eval team catch total num: 38
idv_policy eval average step individual rewards of agent3: 0.48158791084656755
idv_policy eval average team episode rewards of agent3: 95.0
idv_policy eval idv catch total num of agent3: 21
idv_policy eval team catch total num: 38
idv_policy eval average step individual rewards of agent4: 0.8176156507285642
idv_policy eval average team episode rewards of agent4: 95.0
idv_policy eval idv catch total num of agent4: 34
idv_policy eval team catch total num: 38

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2001/10000 episodes, total num timesteps 400400/2000000, FPS 205.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2002/10000 episodes, total num timesteps 400600/2000000, FPS 205.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2003/10000 episodes, total num timesteps 400800/2000000, FPS 205.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2004/10000 episodes, total num timesteps 401000/2000000, FPS 205.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2005/10000 episodes, total num timesteps 401200/2000000, FPS 205.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2006/10000 episodes, total num timesteps 401400/2000000, FPS 205.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2007/10000 episodes, total num timesteps 401600/2000000, FPS 205.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2008/10000 episodes, total num timesteps 401800/2000000, FPS 205.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2009/10000 episodes, total num timesteps 402000/2000000, FPS 205.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2010/10000 episodes, total num timesteps 402200/2000000, FPS 205.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2011/10000 episodes, total num timesteps 402400/2000000, FPS 205.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2012/10000 episodes, total num timesteps 402600/2000000, FPS 205.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2013/10000 episodes, total num timesteps 402800/2000000, FPS 205.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2014/10000 episodes, total num timesteps 403000/2000000, FPS 205.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2015/10000 episodes, total num timesteps 403200/2000000, FPS 205.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2016/10000 episodes, total num timesteps 403400/2000000, FPS 205.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2017/10000 episodes, total num timesteps 403600/2000000, FPS 205.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2018/10000 episodes, total num timesteps 403800/2000000, FPS 205.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2019/10000 episodes, total num timesteps 404000/2000000, FPS 205.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2020/10000 episodes, total num timesteps 404200/2000000, FPS 205.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2021/10000 episodes, total num timesteps 404400/2000000, FPS 205.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2022/10000 episodes, total num timesteps 404600/2000000, FPS 205.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2023/10000 episodes, total num timesteps 404800/2000000, FPS 205.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2024/10000 episodes, total num timesteps 405000/2000000, FPS 205.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2025/10000 episodes, total num timesteps 405200/2000000, FPS 205.

team_policy eval average step individual rewards of agent0: 0.6592968075339912
team_policy eval average team episode rewards of agent0: 110.0
team_policy eval idv catch total num of agent0: 28
team_policy eval team catch total num: 44
team_policy eval average step individual rewards of agent1: 0.3957521808745109
team_policy eval average team episode rewards of agent1: 110.0
team_policy eval idv catch total num of agent1: 18
team_policy eval team catch total num: 44
team_policy eval average step individual rewards of agent2: 0.9407422712604167
team_policy eval average team episode rewards of agent2: 110.0
team_policy eval idv catch total num of agent2: 39
team_policy eval team catch total num: 44
team_policy eval average step individual rewards of agent3: 1.1889381759770816
team_policy eval average team episode rewards of agent3: 110.0
team_policy eval idv catch total num of agent3: 49
team_policy eval team catch total num: 44
team_policy eval average step individual rewards of agent4: 0.8517896012338839
team_policy eval average team episode rewards of agent4: 110.0
team_policy eval idv catch total num of agent4: 36
team_policy eval team catch total num: 44
idv_policy eval average step individual rewards of agent0: 1.3703897318316713
idv_policy eval average team episode rewards of agent0: 87.5
idv_policy eval idv catch total num of agent0: 56
idv_policy eval team catch total num: 35
idv_policy eval average step individual rewards of agent1: 0.6420082673139984
idv_policy eval average team episode rewards of agent1: 87.5
idv_policy eval idv catch total num of agent1: 27
idv_policy eval team catch total num: 35
idv_policy eval average step individual rewards of agent2: 0.5309897928877053
idv_policy eval average team episode rewards of agent2: 87.5
idv_policy eval idv catch total num of agent2: 23
idv_policy eval team catch total num: 35
idv_policy eval average step individual rewards of agent3: 0.6627253163545589
idv_policy eval average team episode rewards of agent3: 87.5
idv_policy eval idv catch total num of agent3: 28
idv_policy eval team catch total num: 35
idv_policy eval average step individual rewards of agent4: 0.6108635444579078
idv_policy eval average team episode rewards of agent4: 87.5
idv_policy eval idv catch total num of agent4: 26
idv_policy eval team catch total num: 35

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2026/10000 episodes, total num timesteps 405400/2000000, FPS 205.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2027/10000 episodes, total num timesteps 405600/2000000, FPS 205.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2028/10000 episodes, total num timesteps 405800/2000000, FPS 205.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2029/10000 episodes, total num timesteps 406000/2000000, FPS 205.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2030/10000 episodes, total num timesteps 406200/2000000, FPS 205.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2031/10000 episodes, total num timesteps 406400/2000000, FPS 205.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2032/10000 episodes, total num timesteps 406600/2000000, FPS 205.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2033/10000 episodes, total num timesteps 406800/2000000, FPS 205.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2034/10000 episodes, total num timesteps 407000/2000000, FPS 205.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2035/10000 episodes, total num timesteps 407200/2000000, FPS 205.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2036/10000 episodes, total num timesteps 407400/2000000, FPS 205.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2037/10000 episodes, total num timesteps 407600/2000000, FPS 205.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2038/10000 episodes, total num timesteps 407800/2000000, FPS 205.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2039/10000 episodes, total num timesteps 408000/2000000, FPS 205.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2040/10000 episodes, total num timesteps 408200/2000000, FPS 205.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2041/10000 episodes, total num timesteps 408400/2000000, FPS 205.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2042/10000 episodes, total num timesteps 408600/2000000, FPS 205.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2043/10000 episodes, total num timesteps 408800/2000000, FPS 205.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2044/10000 episodes, total num timesteps 409000/2000000, FPS 205.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2045/10000 episodes, total num timesteps 409200/2000000, FPS 205.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2046/10000 episodes, total num timesteps 409400/2000000, FPS 205.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2047/10000 episodes, total num timesteps 409600/2000000, FPS 205.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2048/10000 episodes, total num timesteps 409800/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2049/10000 episodes, total num timesteps 410000/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2050/10000 episodes, total num timesteps 410200/2000000, FPS 206.

team_policy eval average step individual rewards of agent0: 0.5632944465277798
team_policy eval average team episode rewards of agent0: 85.0
team_policy eval idv catch total num of agent0: 24
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent1: 0.9180083810843462
team_policy eval average team episode rewards of agent1: 85.0
team_policy eval idv catch total num of agent1: 38
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent2: 0.7619133748183069
team_policy eval average team episode rewards of agent2: 85.0
team_policy eval idv catch total num of agent2: 32
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent3: 0.7088040889685147
team_policy eval average team episode rewards of agent3: 85.0
team_policy eval idv catch total num of agent3: 30
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent4: 0.7064920869012963
team_policy eval average team episode rewards of agent4: 85.0
team_policy eval idv catch total num of agent4: 30
team_policy eval team catch total num: 34
idv_policy eval average step individual rewards of agent0: 0.666853605192447
idv_policy eval average team episode rewards of agent0: 87.5
idv_policy eval idv catch total num of agent0: 28
idv_policy eval team catch total num: 35
idv_policy eval average step individual rewards of agent1: 0.7404115294169124
idv_policy eval average team episode rewards of agent1: 87.5
idv_policy eval idv catch total num of agent1: 31
idv_policy eval team catch total num: 35
idv_policy eval average step individual rewards of agent2: 0.287853582459249
idv_policy eval average team episode rewards of agent2: 87.5
idv_policy eval idv catch total num of agent2: 13
idv_policy eval team catch total num: 35
idv_policy eval average step individual rewards of agent3: 0.9438787418592645
idv_policy eval average team episode rewards of agent3: 87.5
idv_policy eval idv catch total num of agent3: 39
idv_policy eval team catch total num: 35
idv_policy eval average step individual rewards of agent4: 0.533509779194501
idv_policy eval average team episode rewards of agent4: 87.5
idv_policy eval idv catch total num of agent4: 23
idv_policy eval team catch total num: 35

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2051/10000 episodes, total num timesteps 410400/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2052/10000 episodes, total num timesteps 410600/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2053/10000 episodes, total num timesteps 410800/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2054/10000 episodes, total num timesteps 411000/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2055/10000 episodes, total num timesteps 411200/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2056/10000 episodes, total num timesteps 411400/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2057/10000 episodes, total num timesteps 411600/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2058/10000 episodes, total num timesteps 411800/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2059/10000 episodes, total num timesteps 412000/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2060/10000 episodes, total num timesteps 412200/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2061/10000 episodes, total num timesteps 412400/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2062/10000 episodes, total num timesteps 412600/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2063/10000 episodes, total num timesteps 412800/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2064/10000 episodes, total num timesteps 413000/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2065/10000 episodes, total num timesteps 413200/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2066/10000 episodes, total num timesteps 413400/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2067/10000 episodes, total num timesteps 413600/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2068/10000 episodes, total num timesteps 413800/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2069/10000 episodes, total num timesteps 414000/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2070/10000 episodes, total num timesteps 414200/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2071/10000 episodes, total num timesteps 414400/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2072/10000 episodes, total num timesteps 414600/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2073/10000 episodes, total num timesteps 414800/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2074/10000 episodes, total num timesteps 415000/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2075/10000 episodes, total num timesteps 415200/2000000, FPS 206.

team_policy eval average step individual rewards of agent0: 0.6609575359343843
team_policy eval average team episode rewards of agent0: 115.0
team_policy eval idv catch total num of agent0: 28
team_policy eval team catch total num: 46
team_policy eval average step individual rewards of agent1: 0.687424862610393
team_policy eval average team episode rewards of agent1: 115.0
team_policy eval idv catch total num of agent1: 29
team_policy eval team catch total num: 46
team_policy eval average step individual rewards of agent2: 0.9094611895789967
team_policy eval average team episode rewards of agent2: 115.0
team_policy eval idv catch total num of agent2: 38
team_policy eval team catch total num: 46
team_policy eval average step individual rewards of agent3: 0.760509392696084
team_policy eval average team episode rewards of agent3: 115.0
team_policy eval idv catch total num of agent3: 32
team_policy eval team catch total num: 46
team_policy eval average step individual rewards of agent4: 0.8468889701307076
team_policy eval average team episode rewards of agent4: 115.0
team_policy eval idv catch total num of agent4: 35
team_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent0: 0.7937136453383401
idv_policy eval average team episode rewards of agent0: 117.5
idv_policy eval idv catch total num of agent0: 33
idv_policy eval team catch total num: 47
idv_policy eval average step individual rewards of agent1: 0.8152784113385212
idv_policy eval average team episode rewards of agent1: 117.5
idv_policy eval idv catch total num of agent1: 34
idv_policy eval team catch total num: 47
idv_policy eval average step individual rewards of agent2: 0.8933632876087552
idv_policy eval average team episode rewards of agent2: 117.5
idv_policy eval idv catch total num of agent2: 37
idv_policy eval team catch total num: 47
idv_policy eval average step individual rewards of agent3: 0.8850652104747004
idv_policy eval average team episode rewards of agent3: 117.5
idv_policy eval idv catch total num of agent3: 37
idv_policy eval team catch total num: 47
idv_policy eval average step individual rewards of agent4: 0.6391011393575462
idv_policy eval average team episode rewards of agent4: 117.5
idv_policy eval idv catch total num of agent4: 27
idv_policy eval team catch total num: 47

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2076/10000 episodes, total num timesteps 415400/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2077/10000 episodes, total num timesteps 415600/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2078/10000 episodes, total num timesteps 415800/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2079/10000 episodes, total num timesteps 416000/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2080/10000 episodes, total num timesteps 416200/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2081/10000 episodes, total num timesteps 416400/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2082/10000 episodes, total num timesteps 416600/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2083/10000 episodes, total num timesteps 416800/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2084/10000 episodes, total num timesteps 417000/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2085/10000 episodes, total num timesteps 417200/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2086/10000 episodes, total num timesteps 417400/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2087/10000 episodes, total num timesteps 417600/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2088/10000 episodes, total num timesteps 417800/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2089/10000 episodes, total num timesteps 418000/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2090/10000 episodes, total num timesteps 418200/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2091/10000 episodes, total num timesteps 418400/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2092/10000 episodes, total num timesteps 418600/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2093/10000 episodes, total num timesteps 418800/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2094/10000 episodes, total num timesteps 419000/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2095/10000 episodes, total num timesteps 419200/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2096/10000 episodes, total num timesteps 419400/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2097/10000 episodes, total num timesteps 419600/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2098/10000 episodes, total num timesteps 419800/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2099/10000 episodes, total num timesteps 420000/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2100/10000 episodes, total num timesteps 420200/2000000, FPS 206.

team_policy eval average step individual rewards of agent0: 0.4869788844285544
team_policy eval average team episode rewards of agent0: 85.0
team_policy eval idv catch total num of agent0: 21
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent1: 0.4386233133219595
team_policy eval average team episode rewards of agent1: 85.0
team_policy eval idv catch total num of agent1: 19
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent2: 0.7710065629045526
team_policy eval average team episode rewards of agent2: 85.0
team_policy eval idv catch total num of agent2: 32
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent3: 0.4693230725114914
team_policy eval average team episode rewards of agent3: 85.0
team_policy eval idv catch total num of agent3: 20
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent4: 0.7998546360977471
team_policy eval average team episode rewards of agent4: 85.0
team_policy eval idv catch total num of agent4: 33
team_policy eval team catch total num: 34
idv_policy eval average step individual rewards of agent0: 0.793930053571331
idv_policy eval average team episode rewards of agent0: 80.0
idv_policy eval idv catch total num of agent0: 33
idv_policy eval team catch total num: 32
idv_policy eval average step individual rewards of agent1: 0.7566056139542192
idv_policy eval average team episode rewards of agent1: 80.0
idv_policy eval idv catch total num of agent1: 32
idv_policy eval team catch total num: 32
idv_policy eval average step individual rewards of agent2: 0.6074236736032407
idv_policy eval average team episode rewards of agent2: 80.0
idv_policy eval idv catch total num of agent2: 26
idv_policy eval team catch total num: 32
idv_policy eval average step individual rewards of agent3: 0.6639982488224875
idv_policy eval average team episode rewards of agent3: 80.0
idv_policy eval idv catch total num of agent3: 28
idv_policy eval team catch total num: 32
idv_policy eval average step individual rewards of agent4: 0.564561392505031
idv_policy eval average team episode rewards of agent4: 80.0
idv_policy eval idv catch total num of agent4: 24
idv_policy eval team catch total num: 32

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2101/10000 episodes, total num timesteps 420400/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2102/10000 episodes, total num timesteps 420600/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2103/10000 episodes, total num timesteps 420800/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2104/10000 episodes, total num timesteps 421000/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2105/10000 episodes, total num timesteps 421200/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2106/10000 episodes, total num timesteps 421400/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2107/10000 episodes, total num timesteps 421600/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2108/10000 episodes, total num timesteps 421800/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2109/10000 episodes, total num timesteps 422000/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2110/10000 episodes, total num timesteps 422200/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2111/10000 episodes, total num timesteps 422400/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2112/10000 episodes, total num timesteps 422600/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2113/10000 episodes, total num timesteps 422800/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2114/10000 episodes, total num timesteps 423000/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2115/10000 episodes, total num timesteps 423200/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2116/10000 episodes, total num timesteps 423400/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2117/10000 episodes, total num timesteps 423600/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2118/10000 episodes, total num timesteps 423800/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2119/10000 episodes, total num timesteps 424000/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2120/10000 episodes, total num timesteps 424200/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2121/10000 episodes, total num timesteps 424400/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2122/10000 episodes, total num timesteps 424600/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2123/10000 episodes, total num timesteps 424800/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2124/10000 episodes, total num timesteps 425000/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2125/10000 episodes, total num timesteps 425200/2000000, FPS 206.

team_policy eval average step individual rewards of agent0: 0.8330409533448301
team_policy eval average team episode rewards of agent0: 117.5
team_policy eval idv catch total num of agent0: 35
team_policy eval team catch total num: 47
team_policy eval average step individual rewards of agent1: 0.6329278572409988
team_policy eval average team episode rewards of agent1: 117.5
team_policy eval idv catch total num of agent1: 27
team_policy eval team catch total num: 47
team_policy eval average step individual rewards of agent2: 1.2657074630106715
team_policy eval average team episode rewards of agent2: 117.5
team_policy eval idv catch total num of agent2: 52
team_policy eval team catch total num: 47
team_policy eval average step individual rewards of agent3: 0.8463284474141516
team_policy eval average team episode rewards of agent3: 117.5
team_policy eval idv catch total num of agent3: 35
team_policy eval team catch total num: 47
team_policy eval average step individual rewards of agent4: 0.5924165343598724
team_policy eval average team episode rewards of agent4: 117.5
team_policy eval idv catch total num of agent4: 25
team_policy eval team catch total num: 47
idv_policy eval average step individual rewards of agent0: 0.6887116260200236
idv_policy eval average team episode rewards of agent0: 102.5
idv_policy eval idv catch total num of agent0: 29
idv_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent1: 1.1162806841524986
idv_policy eval average team episode rewards of agent1: 102.5
idv_policy eval idv catch total num of agent1: 46
idv_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent2: 0.7978182962682706
idv_policy eval average team episode rewards of agent2: 102.5
idv_policy eval idv catch total num of agent2: 34
idv_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent3: 0.7618346571047475
idv_policy eval average team episode rewards of agent3: 102.5
idv_policy eval idv catch total num of agent3: 32
idv_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent4: 0.3969263519120011
idv_policy eval average team episode rewards of agent4: 102.5
idv_policy eval idv catch total num of agent4: 18
idv_policy eval team catch total num: 41

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2126/10000 episodes, total num timesteps 425400/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2127/10000 episodes, total num timesteps 425600/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2128/10000 episodes, total num timesteps 425800/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2129/10000 episodes, total num timesteps 426000/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2130/10000 episodes, total num timesteps 426200/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2131/10000 episodes, total num timesteps 426400/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2132/10000 episodes, total num timesteps 426600/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2133/10000 episodes, total num timesteps 426800/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2134/10000 episodes, total num timesteps 427000/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2135/10000 episodes, total num timesteps 427200/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2136/10000 episodes, total num timesteps 427400/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2137/10000 episodes, total num timesteps 427600/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2138/10000 episodes, total num timesteps 427800/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2139/10000 episodes, total num timesteps 428000/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2140/10000 episodes, total num timesteps 428200/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2141/10000 episodes, total num timesteps 428400/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2142/10000 episodes, total num timesteps 428600/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2143/10000 episodes, total num timesteps 428800/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2144/10000 episodes, total num timesteps 429000/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2145/10000 episodes, total num timesteps 429200/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2146/10000 episodes, total num timesteps 429400/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2147/10000 episodes, total num timesteps 429600/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2148/10000 episodes, total num timesteps 429800/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2149/10000 episodes, total num timesteps 430000/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2150/10000 episodes, total num timesteps 430200/2000000, FPS 206.

team_policy eval average step individual rewards of agent0: 0.5569646820029669
team_policy eval average team episode rewards of agent0: 80.0
team_policy eval idv catch total num of agent0: 24
team_policy eval team catch total num: 32
team_policy eval average step individual rewards of agent1: 0.6897059237371524
team_policy eval average team episode rewards of agent1: 80.0
team_policy eval idv catch total num of agent1: 29
team_policy eval team catch total num: 32
team_policy eval average step individual rewards of agent2: 0.48174366639371013
team_policy eval average team episode rewards of agent2: 80.0
team_policy eval idv catch total num of agent2: 21
team_policy eval team catch total num: 32
team_policy eval average step individual rewards of agent3: 0.557216001079039
team_policy eval average team episode rewards of agent3: 80.0
team_policy eval idv catch total num of agent3: 24
team_policy eval team catch total num: 32
team_policy eval average step individual rewards of agent4: 0.6104046234543452
team_policy eval average team episode rewards of agent4: 80.0
team_policy eval idv catch total num of agent4: 26
team_policy eval team catch total num: 32
idv_policy eval average step individual rewards of agent0: 0.44118782033570264
idv_policy eval average team episode rewards of agent0: 97.5
idv_policy eval idv catch total num of agent0: 19
idv_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent1: 0.5917176531686192
idv_policy eval average team episode rewards of agent1: 97.5
idv_policy eval idv catch total num of agent1: 25
idv_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent2: 0.8689469982213158
idv_policy eval average team episode rewards of agent2: 97.5
idv_policy eval idv catch total num of agent2: 36
idv_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent3: 0.7403677438923927
idv_policy eval average team episode rewards of agent3: 97.5
idv_policy eval idv catch total num of agent3: 31
idv_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent4: 0.5870105189380586
idv_policy eval average team episode rewards of agent4: 97.5
idv_policy eval idv catch total num of agent4: 25
idv_policy eval team catch total num: 39

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2151/10000 episodes, total num timesteps 430400/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2152/10000 episodes, total num timesteps 430600/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2153/10000 episodes, total num timesteps 430800/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2154/10000 episodes, total num timesteps 431000/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2155/10000 episodes, total num timesteps 431200/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2156/10000 episodes, total num timesteps 431400/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2157/10000 episodes, total num timesteps 431600/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2158/10000 episodes, total num timesteps 431800/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2159/10000 episodes, total num timesteps 432000/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2160/10000 episodes, total num timesteps 432200/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2161/10000 episodes, total num timesteps 432400/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2162/10000 episodes, total num timesteps 432600/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2163/10000 episodes, total num timesteps 432800/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2164/10000 episodes, total num timesteps 433000/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2165/10000 episodes, total num timesteps 433200/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2166/10000 episodes, total num timesteps 433400/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2167/10000 episodes, total num timesteps 433600/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2168/10000 episodes, total num timesteps 433800/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2169/10000 episodes, total num timesteps 434000/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2170/10000 episodes, total num timesteps 434200/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2171/10000 episodes, total num timesteps 434400/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2172/10000 episodes, total num timesteps 434600/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2173/10000 episodes, total num timesteps 434800/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2174/10000 episodes, total num timesteps 435000/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2175/10000 episodes, total num timesteps 435200/2000000, FPS 207.

team_policy eval average step individual rewards of agent0: 0.7865307457345919
team_policy eval average team episode rewards of agent0: 122.5
team_policy eval idv catch total num of agent0: 33
team_policy eval team catch total num: 49
team_policy eval average step individual rewards of agent1: 1.1602461890599924
team_policy eval average team episode rewards of agent1: 122.5
team_policy eval idv catch total num of agent1: 48
team_policy eval team catch total num: 49
team_policy eval average step individual rewards of agent2: 0.8781985166506919
team_policy eval average team episode rewards of agent2: 122.5
team_policy eval idv catch total num of agent2: 37
team_policy eval team catch total num: 49
team_policy eval average step individual rewards of agent3: 0.8539919187312414
team_policy eval average team episode rewards of agent3: 122.5
team_policy eval idv catch total num of agent3: 36
team_policy eval team catch total num: 49
team_policy eval average step individual rewards of agent4: 0.613276088418381
team_policy eval average team episode rewards of agent4: 122.5
team_policy eval idv catch total num of agent4: 26
team_policy eval team catch total num: 49
idv_policy eval average step individual rewards of agent0: 0.86694204279265
idv_policy eval average team episode rewards of agent0: 87.5
idv_policy eval idv catch total num of agent0: 36
idv_policy eval team catch total num: 35
idv_policy eval average step individual rewards of agent1: 0.8105861454680416
idv_policy eval average team episode rewards of agent1: 87.5
idv_policy eval idv catch total num of agent1: 34
idv_policy eval team catch total num: 35
idv_policy eval average step individual rewards of agent2: 0.48038653481338683
idv_policy eval average team episode rewards of agent2: 87.5
idv_policy eval idv catch total num of agent2: 21
idv_policy eval team catch total num: 35
idv_policy eval average step individual rewards of agent3: 0.45767640577763374
idv_policy eval average team episode rewards of agent3: 87.5
idv_policy eval idv catch total num of agent3: 20
idv_policy eval team catch total num: 35
idv_policy eval average step individual rewards of agent4: 0.5009707262902106
idv_policy eval average team episode rewards of agent4: 87.5
idv_policy eval idv catch total num of agent4: 22
idv_policy eval team catch total num: 35

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2176/10000 episodes, total num timesteps 435400/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2177/10000 episodes, total num timesteps 435600/2000000, FPS 206.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2178/10000 episodes, total num timesteps 435800/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2179/10000 episodes, total num timesteps 436000/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2180/10000 episodes, total num timesteps 436200/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2181/10000 episodes, total num timesteps 436400/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2182/10000 episodes, total num timesteps 436600/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2183/10000 episodes, total num timesteps 436800/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2184/10000 episodes, total num timesteps 437000/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2185/10000 episodes, total num timesteps 437200/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2186/10000 episodes, total num timesteps 437400/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2187/10000 episodes, total num timesteps 437600/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2188/10000 episodes, total num timesteps 437800/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2189/10000 episodes, total num timesteps 438000/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2190/10000 episodes, total num timesteps 438200/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2191/10000 episodes, total num timesteps 438400/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2192/10000 episodes, total num timesteps 438600/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2193/10000 episodes, total num timesteps 438800/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2194/10000 episodes, total num timesteps 439000/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2195/10000 episodes, total num timesteps 439200/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2196/10000 episodes, total num timesteps 439400/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2197/10000 episodes, total num timesteps 439600/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2198/10000 episodes, total num timesteps 439800/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2199/10000 episodes, total num timesteps 440000/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2200/10000 episodes, total num timesteps 440200/2000000, FPS 207.

team_policy eval average step individual rewards of agent0: 0.7454953974742466
team_policy eval average team episode rewards of agent0: 120.0
team_policy eval idv catch total num of agent0: 31
team_policy eval team catch total num: 48
team_policy eval average step individual rewards of agent1: 0.45955139863705424
team_policy eval average team episode rewards of agent1: 120.0
team_policy eval idv catch total num of agent1: 20
team_policy eval team catch total num: 48
team_policy eval average step individual rewards of agent2: 1.0724092214107903
team_policy eval average team episode rewards of agent2: 120.0
team_policy eval idv catch total num of agent2: 44
team_policy eval team catch total num: 48
team_policy eval average step individual rewards of agent3: 1.0449923827481953
team_policy eval average team episode rewards of agent3: 120.0
team_policy eval idv catch total num of agent3: 43
team_policy eval team catch total num: 48
team_policy eval average step individual rewards of agent4: 0.9749556079125153
team_policy eval average team episode rewards of agent4: 120.0
team_policy eval idv catch total num of agent4: 40
team_policy eval team catch total num: 48
idv_policy eval average step individual rewards of agent0: 0.7280486121667988
idv_policy eval average team episode rewards of agent0: 62.5
idv_policy eval idv catch total num of agent0: 31
idv_policy eval team catch total num: 25
idv_policy eval average step individual rewards of agent1: 0.602597316603971
idv_policy eval average team episode rewards of agent1: 62.5
idv_policy eval idv catch total num of agent1: 26
idv_policy eval team catch total num: 25
idv_policy eval average step individual rewards of agent2: 0.2733986621446653
idv_policy eval average team episode rewards of agent2: 62.5
idv_policy eval idv catch total num of agent2: 13
idv_policy eval team catch total num: 25
idv_policy eval average step individual rewards of agent3: 0.4662046632454511
idv_policy eval average team episode rewards of agent3: 62.5
idv_policy eval idv catch total num of agent3: 21
idv_policy eval team catch total num: 25
idv_policy eval average step individual rewards of agent4: 0.36011484552235445
idv_policy eval average team episode rewards of agent4: 62.5
idv_policy eval idv catch total num of agent4: 16
idv_policy eval team catch total num: 25

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2201/10000 episodes, total num timesteps 440400/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2202/10000 episodes, total num timesteps 440600/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2203/10000 episodes, total num timesteps 440800/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2204/10000 episodes, total num timesteps 441000/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2205/10000 episodes, total num timesteps 441200/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2206/10000 episodes, total num timesteps 441400/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2207/10000 episodes, total num timesteps 441600/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2208/10000 episodes, total num timesteps 441800/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2209/10000 episodes, total num timesteps 442000/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2210/10000 episodes, total num timesteps 442200/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2211/10000 episodes, total num timesteps 442400/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2212/10000 episodes, total num timesteps 442600/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2213/10000 episodes, total num timesteps 442800/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2214/10000 episodes, total num timesteps 443000/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2215/10000 episodes, total num timesteps 443200/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2216/10000 episodes, total num timesteps 443400/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2217/10000 episodes, total num timesteps 443600/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2218/10000 episodes, total num timesteps 443800/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2219/10000 episodes, total num timesteps 444000/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2220/10000 episodes, total num timesteps 444200/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2221/10000 episodes, total num timesteps 444400/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2222/10000 episodes, total num timesteps 444600/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2223/10000 episodes, total num timesteps 444800/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2224/10000 episodes, total num timesteps 445000/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2225/10000 episodes, total num timesteps 445200/2000000, FPS 207.

team_policy eval average step individual rewards of agent0: 0.28275534853784196
team_policy eval average team episode rewards of agent0: 75.0
team_policy eval idv catch total num of agent0: 13
team_policy eval team catch total num: 30
team_policy eval average step individual rewards of agent1: 0.38005963125317066
team_policy eval average team episode rewards of agent1: 75.0
team_policy eval idv catch total num of agent1: 17
team_policy eval team catch total num: 30
team_policy eval average step individual rewards of agent2: 0.6119990679192828
team_policy eval average team episode rewards of agent2: 75.0
team_policy eval idv catch total num of agent2: 26
team_policy eval team catch total num: 30
team_policy eval average step individual rewards of agent3: 0.5634371797950292
team_policy eval average team episode rewards of agent3: 75.0
team_policy eval idv catch total num of agent3: 24
team_policy eval team catch total num: 30
team_policy eval average step individual rewards of agent4: 0.642253671326348
team_policy eval average team episode rewards of agent4: 75.0
team_policy eval idv catch total num of agent4: 27
team_policy eval team catch total num: 30
idv_policy eval average step individual rewards of agent0: 0.9896011480343276
idv_policy eval average team episode rewards of agent0: 97.5
idv_policy eval idv catch total num of agent0: 41
idv_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent1: 0.7378911031745571
idv_policy eval average team episode rewards of agent1: 97.5
idv_policy eval idv catch total num of agent1: 31
idv_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent2: 0.43457599596904745
idv_policy eval average team episode rewards of agent2: 97.5
idv_policy eval idv catch total num of agent2: 19
idv_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent3: 0.8677745757077995
idv_policy eval average team episode rewards of agent3: 97.5
idv_policy eval idv catch total num of agent3: 36
idv_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent4: 0.45900906303796574
idv_policy eval average team episode rewards of agent4: 97.5
idv_policy eval idv catch total num of agent4: 20
idv_policy eval team catch total num: 39

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2226/10000 episodes, total num timesteps 445400/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2227/10000 episodes, total num timesteps 445600/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2228/10000 episodes, total num timesteps 445800/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2229/10000 episodes, total num timesteps 446000/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2230/10000 episodes, total num timesteps 446200/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2231/10000 episodes, total num timesteps 446400/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2232/10000 episodes, total num timesteps 446600/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2233/10000 episodes, total num timesteps 446800/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2234/10000 episodes, total num timesteps 447000/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2235/10000 episodes, total num timesteps 447200/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2236/10000 episodes, total num timesteps 447400/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2237/10000 episodes, total num timesteps 447600/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2238/10000 episodes, total num timesteps 447800/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2239/10000 episodes, total num timesteps 448000/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2240/10000 episodes, total num timesteps 448200/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2241/10000 episodes, total num timesteps 448400/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2242/10000 episodes, total num timesteps 448600/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2243/10000 episodes, total num timesteps 448800/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2244/10000 episodes, total num timesteps 449000/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2245/10000 episodes, total num timesteps 449200/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2246/10000 episodes, total num timesteps 449400/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2247/10000 episodes, total num timesteps 449600/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2248/10000 episodes, total num timesteps 449800/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2249/10000 episodes, total num timesteps 450000/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2250/10000 episodes, total num timesteps 450200/2000000, FPS 207.

team_policy eval average step individual rewards of agent0: 0.7524148499541518
team_policy eval average team episode rewards of agent0: 85.0
team_policy eval idv catch total num of agent0: 32
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent1: 0.7954082376599331
team_policy eval average team episode rewards of agent1: 85.0
team_policy eval idv catch total num of agent1: 33
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent2: 0.6091981893758835
team_policy eval average team episode rewards of agent2: 85.0
team_policy eval idv catch total num of agent2: 26
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent3: 0.6081600309287118
team_policy eval average team episode rewards of agent3: 85.0
team_policy eval idv catch total num of agent3: 26
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent4: 0.3823303838765984
team_policy eval average team episode rewards of agent4: 85.0
team_policy eval idv catch total num of agent4: 17
team_policy eval team catch total num: 34
idv_policy eval average step individual rewards of agent0: 0.1466344531728144
idv_policy eval average team episode rewards of agent0: 90.0
idv_policy eval idv catch total num of agent0: 8
idv_policy eval team catch total num: 36
idv_policy eval average step individual rewards of agent1: 0.8161682433054293
idv_policy eval average team episode rewards of agent1: 90.0
idv_policy eval idv catch total num of agent1: 34
idv_policy eval team catch total num: 36
idv_policy eval average step individual rewards of agent2: 0.8108352266045132
idv_policy eval average team episode rewards of agent2: 90.0
idv_policy eval idv catch total num of agent2: 34
idv_policy eval team catch total num: 36
idv_policy eval average step individual rewards of agent3: 0.47995394565615773
idv_policy eval average team episode rewards of agent3: 90.0
idv_policy eval idv catch total num of agent3: 21
idv_policy eval team catch total num: 36
idv_policy eval average step individual rewards of agent4: 0.9367993131859206
idv_policy eval average team episode rewards of agent4: 90.0
idv_policy eval idv catch total num of agent4: 39
idv_policy eval team catch total num: 36

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2251/10000 episodes, total num timesteps 450400/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2252/10000 episodes, total num timesteps 450600/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2253/10000 episodes, total num timesteps 450800/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2254/10000 episodes, total num timesteps 451000/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2255/10000 episodes, total num timesteps 451200/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2256/10000 episodes, total num timesteps 451400/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2257/10000 episodes, total num timesteps 451600/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2258/10000 episodes, total num timesteps 451800/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2259/10000 episodes, total num timesteps 452000/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2260/10000 episodes, total num timesteps 452200/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2261/10000 episodes, total num timesteps 452400/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2262/10000 episodes, total num timesteps 452600/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2263/10000 episodes, total num timesteps 452800/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2264/10000 episodes, total num timesteps 453000/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2265/10000 episodes, total num timesteps 453200/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2266/10000 episodes, total num timesteps 453400/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2267/10000 episodes, total num timesteps 453600/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2268/10000 episodes, total num timesteps 453800/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2269/10000 episodes, total num timesteps 454000/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2270/10000 episodes, total num timesteps 454200/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2271/10000 episodes, total num timesteps 454400/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2272/10000 episodes, total num timesteps 454600/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2273/10000 episodes, total num timesteps 454800/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2274/10000 episodes, total num timesteps 455000/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2275/10000 episodes, total num timesteps 455200/2000000, FPS 207.

team_policy eval average step individual rewards of agent0: 0.7077584864137364
team_policy eval average team episode rewards of agent0: 102.5
team_policy eval idv catch total num of agent0: 30
team_policy eval team catch total num: 41
team_policy eval average step individual rewards of agent1: 1.0403190678838548
team_policy eval average team episode rewards of agent1: 102.5
team_policy eval idv catch total num of agent1: 43
team_policy eval team catch total num: 41
team_policy eval average step individual rewards of agent2: 0.6934671032747235
team_policy eval average team episode rewards of agent2: 102.5
team_policy eval idv catch total num of agent2: 29
team_policy eval team catch total num: 41
team_policy eval average step individual rewards of agent3: 1.046750343173034
team_policy eval average team episode rewards of agent3: 102.5
team_policy eval idv catch total num of agent3: 43
team_policy eval team catch total num: 41
team_policy eval average step individual rewards of agent4: 0.6545429290829743
team_policy eval average team episode rewards of agent4: 102.5
team_policy eval idv catch total num of agent4: 28
team_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent0: 0.8575302482580176
idv_policy eval average team episode rewards of agent0: 135.0
idv_policy eval idv catch total num of agent0: 36
idv_policy eval team catch total num: 54
idv_policy eval average step individual rewards of agent1: 0.9836956366304829
idv_policy eval average team episode rewards of agent1: 135.0
idv_policy eval idv catch total num of agent1: 41
idv_policy eval team catch total num: 54
idv_policy eval average step individual rewards of agent2: 0.8887483823185852
idv_policy eval average team episode rewards of agent2: 135.0
idv_policy eval idv catch total num of agent2: 37
idv_policy eval team catch total num: 54
idv_policy eval average step individual rewards of agent3: 0.963382155480851
idv_policy eval average team episode rewards of agent3: 135.0
idv_policy eval idv catch total num of agent3: 40
idv_policy eval team catch total num: 54
idv_policy eval average step individual rewards of agent4: 0.7970746606398816
idv_policy eval average team episode rewards of agent4: 135.0
idv_policy eval idv catch total num of agent4: 33
idv_policy eval team catch total num: 54

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2276/10000 episodes, total num timesteps 455400/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2277/10000 episodes, total num timesteps 455600/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2278/10000 episodes, total num timesteps 455800/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2279/10000 episodes, total num timesteps 456000/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2280/10000 episodes, total num timesteps 456200/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2281/10000 episodes, total num timesteps 456400/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2282/10000 episodes, total num timesteps 456600/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2283/10000 episodes, total num timesteps 456800/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2284/10000 episodes, total num timesteps 457000/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2285/10000 episodes, total num timesteps 457200/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2286/10000 episodes, total num timesteps 457400/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2287/10000 episodes, total num timesteps 457600/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2288/10000 episodes, total num timesteps 457800/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2289/10000 episodes, total num timesteps 458000/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2290/10000 episodes, total num timesteps 458200/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2291/10000 episodes, total num timesteps 458400/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2292/10000 episodes, total num timesteps 458600/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2293/10000 episodes, total num timesteps 458800/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2294/10000 episodes, total num timesteps 459000/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2295/10000 episodes, total num timesteps 459200/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2296/10000 episodes, total num timesteps 459400/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2297/10000 episodes, total num timesteps 459600/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2298/10000 episodes, total num timesteps 459800/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2299/10000 episodes, total num timesteps 460000/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2300/10000 episodes, total num timesteps 460200/2000000, FPS 207.

team_policy eval average step individual rewards of agent0: 0.3241358756969459
team_policy eval average team episode rewards of agent0: 60.0
team_policy eval idv catch total num of agent0: 15
team_policy eval team catch total num: 24
team_policy eval average step individual rewards of agent1: 0.8170254078644419
team_policy eval average team episode rewards of agent1: 60.0
team_policy eval idv catch total num of agent1: 34
team_policy eval team catch total num: 24
team_policy eval average step individual rewards of agent2: 0.2787006172401154
team_policy eval average team episode rewards of agent2: 60.0
team_policy eval idv catch total num of agent2: 13
team_policy eval team catch total num: 24
team_policy eval average step individual rewards of agent3: 0.20343455674155086
team_policy eval average team episode rewards of agent3: 60.0
team_policy eval idv catch total num of agent3: 10
team_policy eval team catch total num: 24
team_policy eval average step individual rewards of agent4: 0.6600570370111097
team_policy eval average team episode rewards of agent4: 60.0
team_policy eval idv catch total num of agent4: 28
team_policy eval team catch total num: 24
idv_policy eval average step individual rewards of agent0: 0.6376061387844132
idv_policy eval average team episode rewards of agent0: 70.0
idv_policy eval idv catch total num of agent0: 27
idv_policy eval team catch total num: 28
idv_policy eval average step individual rewards of agent1: 0.3274937328504213
idv_policy eval average team episode rewards of agent1: 70.0
idv_policy eval idv catch total num of agent1: 15
idv_policy eval team catch total num: 28
idv_policy eval average step individual rewards of agent2: 0.7065849343010079
idv_policy eval average team episode rewards of agent2: 70.0
idv_policy eval idv catch total num of agent2: 30
idv_policy eval team catch total num: 28
idv_policy eval average step individual rewards of agent3: 0.5782770898399903
idv_policy eval average team episode rewards of agent3: 70.0
idv_policy eval idv catch total num of agent3: 25
idv_policy eval team catch total num: 28
idv_policy eval average step individual rewards of agent4: 0.6554318697130458
idv_policy eval average team episode rewards of agent4: 70.0
idv_policy eval idv catch total num of agent4: 28
idv_policy eval team catch total num: 28

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2301/10000 episodes, total num timesteps 460400/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2302/10000 episodes, total num timesteps 460600/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2303/10000 episodes, total num timesteps 460800/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2304/10000 episodes, total num timesteps 461000/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2305/10000 episodes, total num timesteps 461200/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2306/10000 episodes, total num timesteps 461400/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2307/10000 episodes, total num timesteps 461600/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2308/10000 episodes, total num timesteps 461800/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2309/10000 episodes, total num timesteps 462000/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2310/10000 episodes, total num timesteps 462200/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2311/10000 episodes, total num timesteps 462400/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2312/10000 episodes, total num timesteps 462600/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2313/10000 episodes, total num timesteps 462800/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2314/10000 episodes, total num timesteps 463000/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2315/10000 episodes, total num timesteps 463200/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2316/10000 episodes, total num timesteps 463400/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2317/10000 episodes, total num timesteps 463600/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2318/10000 episodes, total num timesteps 463800/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2319/10000 episodes, total num timesteps 464000/2000000, FPS 207.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2320/10000 episodes, total num timesteps 464200/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2321/10000 episodes, total num timesteps 464400/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2322/10000 episodes, total num timesteps 464600/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2323/10000 episodes, total num timesteps 464800/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2324/10000 episodes, total num timesteps 465000/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2325/10000 episodes, total num timesteps 465200/2000000, FPS 208.

team_policy eval average step individual rewards of agent0: 0.9408590921053346
team_policy eval average team episode rewards of agent0: 72.5
team_policy eval idv catch total num of agent0: 39
team_policy eval team catch total num: 29
team_policy eval average step individual rewards of agent1: 0.5103128217569035
team_policy eval average team episode rewards of agent1: 72.5
team_policy eval idv catch total num of agent1: 22
team_policy eval team catch total num: 29
team_policy eval average step individual rewards of agent2: 0.4905874872658065
team_policy eval average team episode rewards of agent2: 72.5
team_policy eval idv catch total num of agent2: 21
team_policy eval team catch total num: 29
team_policy eval average step individual rewards of agent3: 0.8893125053409033
team_policy eval average team episode rewards of agent3: 72.5
team_policy eval idv catch total num of agent3: 37
team_policy eval team catch total num: 29
team_policy eval average step individual rewards of agent4: 0.3799770475151324
team_policy eval average team episode rewards of agent4: 72.5
team_policy eval idv catch total num of agent4: 17
team_policy eval team catch total num: 29
idv_policy eval average step individual rewards of agent0: 0.4837249080482124
idv_policy eval average team episode rewards of agent0: 107.5
idv_policy eval idv catch total num of agent0: 21
idv_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent1: 0.9621399526719158
idv_policy eval average team episode rewards of agent1: 107.5
idv_policy eval idv catch total num of agent1: 40
idv_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent2: 0.6823353314859852
idv_policy eval average team episode rewards of agent2: 107.5
idv_policy eval idv catch total num of agent2: 29
idv_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent3: 0.8365775833589661
idv_policy eval average team episode rewards of agent3: 107.5
idv_policy eval idv catch total num of agent3: 35
idv_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent4: 0.8633776019954514
idv_policy eval average team episode rewards of agent4: 107.5
idv_policy eval idv catch total num of agent4: 36
idv_policy eval team catch total num: 43

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2326/10000 episodes, total num timesteps 465400/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2327/10000 episodes, total num timesteps 465600/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2328/10000 episodes, total num timesteps 465800/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2329/10000 episodes, total num timesteps 466000/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2330/10000 episodes, total num timesteps 466200/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2331/10000 episodes, total num timesteps 466400/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2332/10000 episodes, total num timesteps 466600/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2333/10000 episodes, total num timesteps 466800/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2334/10000 episodes, total num timesteps 467000/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2335/10000 episodes, total num timesteps 467200/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2336/10000 episodes, total num timesteps 467400/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2337/10000 episodes, total num timesteps 467600/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2338/10000 episodes, total num timesteps 467800/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2339/10000 episodes, total num timesteps 468000/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2340/10000 episodes, total num timesteps 468200/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2341/10000 episodes, total num timesteps 468400/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2342/10000 episodes, total num timesteps 468600/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2343/10000 episodes, total num timesteps 468800/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2344/10000 episodes, total num timesteps 469000/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2345/10000 episodes, total num timesteps 469200/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2346/10000 episodes, total num timesteps 469400/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2347/10000 episodes, total num timesteps 469600/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2348/10000 episodes, total num timesteps 469800/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2349/10000 episodes, total num timesteps 470000/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2350/10000 episodes, total num timesteps 470200/2000000, FPS 208.

team_policy eval average step individual rewards of agent0: 0.7327269652585519
team_policy eval average team episode rewards of agent0: 87.5
team_policy eval idv catch total num of agent0: 31
team_policy eval team catch total num: 35
team_policy eval average step individual rewards of agent1: 0.3564632497753611
team_policy eval average team episode rewards of agent1: 87.5
team_policy eval idv catch total num of agent1: 16
team_policy eval team catch total num: 35
team_policy eval average step individual rewards of agent2: 0.8926767593306468
team_policy eval average team episode rewards of agent2: 87.5
team_policy eval idv catch total num of agent2: 37
team_policy eval team catch total num: 35
team_policy eval average step individual rewards of agent3: 0.486466275348638
team_policy eval average team episode rewards of agent3: 87.5
team_policy eval idv catch total num of agent3: 21
team_policy eval team catch total num: 35
team_policy eval average step individual rewards of agent4: 0.4055169020431766
team_policy eval average team episode rewards of agent4: 87.5
team_policy eval idv catch total num of agent4: 18
team_policy eval team catch total num: 35
idv_policy eval average step individual rewards of agent0: 0.12814276899266322
idv_policy eval average team episode rewards of agent0: 55.0
idv_policy eval idv catch total num of agent0: 7
idv_policy eval team catch total num: 22
idv_policy eval average step individual rewards of agent1: 0.4319727172642678
idv_policy eval average team episode rewards of agent1: 55.0
idv_policy eval idv catch total num of agent1: 19
idv_policy eval team catch total num: 22
idv_policy eval average step individual rewards of agent2: 0.9384196784670519
idv_policy eval average team episode rewards of agent2: 55.0
idv_policy eval idv catch total num of agent2: 39
idv_policy eval team catch total num: 22
idv_policy eval average step individual rewards of agent3: 0.7359529907528324
idv_policy eval average team episode rewards of agent3: 55.0
idv_policy eval idv catch total num of agent3: 31
idv_policy eval team catch total num: 22
idv_policy eval average step individual rewards of agent4: 0.4827480640365127
idv_policy eval average team episode rewards of agent4: 55.0
idv_policy eval idv catch total num of agent4: 21
idv_policy eval team catch total num: 22

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2351/10000 episodes, total num timesteps 470400/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2352/10000 episodes, total num timesteps 470600/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2353/10000 episodes, total num timesteps 470800/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2354/10000 episodes, total num timesteps 471000/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2355/10000 episodes, total num timesteps 471200/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2356/10000 episodes, total num timesteps 471400/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2357/10000 episodes, total num timesteps 471600/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2358/10000 episodes, total num timesteps 471800/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2359/10000 episodes, total num timesteps 472000/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2360/10000 episodes, total num timesteps 472200/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2361/10000 episodes, total num timesteps 472400/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2362/10000 episodes, total num timesteps 472600/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2363/10000 episodes, total num timesteps 472800/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2364/10000 episodes, total num timesteps 473000/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2365/10000 episodes, total num timesteps 473200/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2366/10000 episodes, total num timesteps 473400/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2367/10000 episodes, total num timesteps 473600/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2368/10000 episodes, total num timesteps 473800/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2369/10000 episodes, total num timesteps 474000/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2370/10000 episodes, total num timesteps 474200/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2371/10000 episodes, total num timesteps 474400/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2372/10000 episodes, total num timesteps 474600/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2373/10000 episodes, total num timesteps 474800/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2374/10000 episodes, total num timesteps 475000/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2375/10000 episodes, total num timesteps 475200/2000000, FPS 208.

team_policy eval average step individual rewards of agent0: 0.5057445658970195
team_policy eval average team episode rewards of agent0: 70.0
team_policy eval idv catch total num of agent0: 22
team_policy eval team catch total num: 28
team_policy eval average step individual rewards of agent1: 0.6840381815876526
team_policy eval average team episode rewards of agent1: 70.0
team_policy eval idv catch total num of agent1: 29
team_policy eval team catch total num: 28
team_policy eval average step individual rewards of agent2: 0.6334385883772252
team_policy eval average team episode rewards of agent2: 70.0
team_policy eval idv catch total num of agent2: 27
team_policy eval team catch total num: 28
team_policy eval average step individual rewards of agent3: 0.6086392196377545
team_policy eval average team episode rewards of agent3: 70.0
team_policy eval idv catch total num of agent3: 26
team_policy eval team catch total num: 28
team_policy eval average step individual rewards of agent4: 0.7043608282891555
team_policy eval average team episode rewards of agent4: 70.0
team_policy eval idv catch total num of agent4: 30
team_policy eval team catch total num: 28
idv_policy eval average step individual rewards of agent0: 0.8022919959065778
idv_policy eval average team episode rewards of agent0: 120.0
idv_policy eval idv catch total num of agent0: 34
idv_policy eval team catch total num: 48
idv_policy eval average step individual rewards of agent1: 0.5065253480022271
idv_policy eval average team episode rewards of agent1: 120.0
idv_policy eval idv catch total num of agent1: 22
idv_policy eval team catch total num: 48
idv_policy eval average step individual rewards of agent2: 0.899638366151648
idv_policy eval average team episode rewards of agent2: 120.0
idv_policy eval idv catch total num of agent2: 37
idv_policy eval team catch total num: 48
idv_policy eval average step individual rewards of agent3: 1.189090074224603
idv_policy eval average team episode rewards of agent3: 120.0
idv_policy eval idv catch total num of agent3: 49
idv_policy eval team catch total num: 48
idv_policy eval average step individual rewards of agent4: 0.7756336301802276
idv_policy eval average team episode rewards of agent4: 120.0
idv_policy eval idv catch total num of agent4: 33
idv_policy eval team catch total num: 48

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2376/10000 episodes, total num timesteps 475400/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2377/10000 episodes, total num timesteps 475600/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2378/10000 episodes, total num timesteps 475800/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2379/10000 episodes, total num timesteps 476000/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2380/10000 episodes, total num timesteps 476200/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2381/10000 episodes, total num timesteps 476400/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2382/10000 episodes, total num timesteps 476600/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2383/10000 episodes, total num timesteps 476800/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2384/10000 episodes, total num timesteps 477000/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2385/10000 episodes, total num timesteps 477200/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2386/10000 episodes, total num timesteps 477400/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2387/10000 episodes, total num timesteps 477600/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2388/10000 episodes, total num timesteps 477800/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2389/10000 episodes, total num timesteps 478000/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2390/10000 episodes, total num timesteps 478200/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2391/10000 episodes, total num timesteps 478400/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2392/10000 episodes, total num timesteps 478600/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2393/10000 episodes, total num timesteps 478800/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2394/10000 episodes, total num timesteps 479000/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2395/10000 episodes, total num timesteps 479200/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2396/10000 episodes, total num timesteps 479400/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2397/10000 episodes, total num timesteps 479600/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2398/10000 episodes, total num timesteps 479800/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2399/10000 episodes, total num timesteps 480000/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2400/10000 episodes, total num timesteps 480200/2000000, FPS 208.

team_policy eval average step individual rewards of agent0: 0.7935436638479387
team_policy eval average team episode rewards of agent0: 92.5
team_policy eval idv catch total num of agent0: 33
team_policy eval team catch total num: 37
team_policy eval average step individual rewards of agent1: 0.5638447298865301
team_policy eval average team episode rewards of agent1: 92.5
team_policy eval idv catch total num of agent1: 24
team_policy eval team catch total num: 37
team_policy eval average step individual rewards of agent2: 0.5367099882247877
team_policy eval average team episode rewards of agent2: 92.5
team_policy eval idv catch total num of agent2: 23
team_policy eval team catch total num: 37
team_policy eval average step individual rewards of agent3: 0.6101574921585261
team_policy eval average team episode rewards of agent3: 92.5
team_policy eval idv catch total num of agent3: 26
team_policy eval team catch total num: 37
team_policy eval average step individual rewards of agent4: 0.9489606960756148
team_policy eval average team episode rewards of agent4: 92.5
team_policy eval idv catch total num of agent4: 39
team_policy eval team catch total num: 37
idv_policy eval average step individual rewards of agent0: 0.7793837241719777
idv_policy eval average team episode rewards of agent0: 75.0
idv_policy eval idv catch total num of agent0: 33
idv_policy eval team catch total num: 30
idv_policy eval average step individual rewards of agent1: 0.5078819228171195
idv_policy eval average team episode rewards of agent1: 75.0
idv_policy eval idv catch total num of agent1: 22
idv_policy eval team catch total num: 30
idv_policy eval average step individual rewards of agent2: 0.13613286333539218
idv_policy eval average team episode rewards of agent2: 75.0
idv_policy eval idv catch total num of agent2: 8
idv_policy eval team catch total num: 30
idv_policy eval average step individual rewards of agent3: 0.8418240621509373
idv_policy eval average team episode rewards of agent3: 75.0
idv_policy eval idv catch total num of agent3: 35
idv_policy eval team catch total num: 30
idv_policy eval average step individual rewards of agent4: 0.8012024172079529
idv_policy eval average team episode rewards of agent4: 75.0
idv_policy eval idv catch total num of agent4: 34
idv_policy eval team catch total num: 30

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2401/10000 episodes, total num timesteps 480400/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2402/10000 episodes, total num timesteps 480600/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2403/10000 episodes, total num timesteps 480800/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2404/10000 episodes, total num timesteps 481000/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2405/10000 episodes, total num timesteps 481200/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2406/10000 episodes, total num timesteps 481400/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2407/10000 episodes, total num timesteps 481600/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2408/10000 episodes, total num timesteps 481800/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2409/10000 episodes, total num timesteps 482000/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2410/10000 episodes, total num timesteps 482200/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2411/10000 episodes, total num timesteps 482400/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2412/10000 episodes, total num timesteps 482600/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2413/10000 episodes, total num timesteps 482800/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2414/10000 episodes, total num timesteps 483000/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2415/10000 episodes, total num timesteps 483200/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2416/10000 episodes, total num timesteps 483400/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2417/10000 episodes, total num timesteps 483600/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2418/10000 episodes, total num timesteps 483800/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2419/10000 episodes, total num timesteps 484000/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2420/10000 episodes, total num timesteps 484200/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2421/10000 episodes, total num timesteps 484400/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2422/10000 episodes, total num timesteps 484600/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2423/10000 episodes, total num timesteps 484800/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2424/10000 episodes, total num timesteps 485000/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2425/10000 episodes, total num timesteps 485200/2000000, FPS 208.

team_policy eval average step individual rewards of agent0: 1.1896635370239026
team_policy eval average team episode rewards of agent0: 130.0
team_policy eval idv catch total num of agent0: 49
team_policy eval team catch total num: 52
team_policy eval average step individual rewards of agent1: 0.8613550282916728
team_policy eval average team episode rewards of agent1: 130.0
team_policy eval idv catch total num of agent1: 36
team_policy eval team catch total num: 52
team_policy eval average step individual rewards of agent2: 0.7150344797476937
team_policy eval average team episode rewards of agent2: 130.0
team_policy eval idv catch total num of agent2: 30
team_policy eval team catch total num: 52
team_policy eval average step individual rewards of agent3: 0.8415132534496206
team_policy eval average team episode rewards of agent3: 130.0
team_policy eval idv catch total num of agent3: 35
team_policy eval team catch total num: 52
team_policy eval average step individual rewards of agent4: 1.1594470148425926
team_policy eval average team episode rewards of agent4: 130.0
team_policy eval idv catch total num of agent4: 48
team_policy eval team catch total num: 52
idv_policy eval average step individual rewards of agent0: 0.7107174568262031
idv_policy eval average team episode rewards of agent0: 160.0
idv_policy eval idv catch total num of agent0: 30
idv_policy eval team catch total num: 64
idv_policy eval average step individual rewards of agent1: 0.9941267872491091
idv_policy eval average team episode rewards of agent1: 160.0
idv_policy eval idv catch total num of agent1: 41
idv_policy eval team catch total num: 64
idv_policy eval average step individual rewards of agent2: 1.4453096526737261
idv_policy eval average team episode rewards of agent2: 160.0
idv_policy eval idv catch total num of agent2: 59
idv_policy eval team catch total num: 64
idv_policy eval average step individual rewards of agent3: 0.7783775878990431
idv_policy eval average team episode rewards of agent3: 160.0
idv_policy eval idv catch total num of agent3: 33
idv_policy eval team catch total num: 64
idv_policy eval average step individual rewards of agent4: 1.115393377316227
idv_policy eval average team episode rewards of agent4: 160.0
idv_policy eval idv catch total num of agent4: 46
idv_policy eval team catch total num: 64

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2426/10000 episodes, total num timesteps 485400/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2427/10000 episodes, total num timesteps 485600/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2428/10000 episodes, total num timesteps 485800/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2429/10000 episodes, total num timesteps 486000/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2430/10000 episodes, total num timesteps 486200/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2431/10000 episodes, total num timesteps 486400/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2432/10000 episodes, total num timesteps 486600/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2433/10000 episodes, total num timesteps 486800/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2434/10000 episodes, total num timesteps 487000/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2435/10000 episodes, total num timesteps 487200/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2436/10000 episodes, total num timesteps 487400/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2437/10000 episodes, total num timesteps 487600/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2438/10000 episodes, total num timesteps 487800/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2439/10000 episodes, total num timesteps 488000/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2440/10000 episodes, total num timesteps 488200/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2441/10000 episodes, total num timesteps 488400/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2442/10000 episodes, total num timesteps 488600/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2443/10000 episodes, total num timesteps 488800/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2444/10000 episodes, total num timesteps 489000/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2445/10000 episodes, total num timesteps 489200/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2446/10000 episodes, total num timesteps 489400/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2447/10000 episodes, total num timesteps 489600/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2448/10000 episodes, total num timesteps 489800/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2449/10000 episodes, total num timesteps 490000/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2450/10000 episodes, total num timesteps 490200/2000000, FPS 208.

team_policy eval average step individual rewards of agent0: 0.8354713441110471
team_policy eval average team episode rewards of agent0: 100.0
team_policy eval idv catch total num of agent0: 35
team_policy eval team catch total num: 40
team_policy eval average step individual rewards of agent1: 0.6118514878753184
team_policy eval average team episode rewards of agent1: 100.0
team_policy eval idv catch total num of agent1: 26
team_policy eval team catch total num: 40
team_policy eval average step individual rewards of agent2: 0.6782832237861293
team_policy eval average team episode rewards of agent2: 100.0
team_policy eval idv catch total num of agent2: 29
team_policy eval team catch total num: 40
team_policy eval average step individual rewards of agent3: 0.6369263111601831
team_policy eval average team episode rewards of agent3: 100.0
team_policy eval idv catch total num of agent3: 27
team_policy eval team catch total num: 40
team_policy eval average step individual rewards of agent4: 0.7826444361545568
team_policy eval average team episode rewards of agent4: 100.0
team_policy eval idv catch total num of agent4: 33
team_policy eval team catch total num: 40
idv_policy eval average step individual rewards of agent0: 0.9689529341634926
idv_policy eval average team episode rewards of agent0: 135.0
idv_policy eval idv catch total num of agent0: 40
idv_policy eval team catch total num: 54
idv_policy eval average step individual rewards of agent1: 0.9451104779700056
idv_policy eval average team episode rewards of agent1: 135.0
idv_policy eval idv catch total num of agent1: 39
idv_policy eval team catch total num: 54
idv_policy eval average step individual rewards of agent2: 0.7438987243038057
idv_policy eval average team episode rewards of agent2: 135.0
idv_policy eval idv catch total num of agent2: 31
idv_policy eval team catch total num: 54
idv_policy eval average step individual rewards of agent3: 1.0198086013511136
idv_policy eval average team episode rewards of agent3: 135.0
idv_policy eval idv catch total num of agent3: 42
idv_policy eval team catch total num: 54
idv_policy eval average step individual rewards of agent4: 0.7918851390337074
idv_policy eval average team episode rewards of agent4: 135.0
idv_policy eval idv catch total num of agent4: 33
idv_policy eval team catch total num: 54

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2451/10000 episodes, total num timesteps 490400/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2452/10000 episodes, total num timesteps 490600/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2453/10000 episodes, total num timesteps 490800/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2454/10000 episodes, total num timesteps 491000/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2455/10000 episodes, total num timesteps 491200/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2456/10000 episodes, total num timesteps 491400/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2457/10000 episodes, total num timesteps 491600/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2458/10000 episodes, total num timesteps 491800/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2459/10000 episodes, total num timesteps 492000/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2460/10000 episodes, total num timesteps 492200/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2461/10000 episodes, total num timesteps 492400/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2462/10000 episodes, total num timesteps 492600/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2463/10000 episodes, total num timesteps 492800/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2464/10000 episodes, total num timesteps 493000/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2465/10000 episodes, total num timesteps 493200/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2466/10000 episodes, total num timesteps 493400/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2467/10000 episodes, total num timesteps 493600/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2468/10000 episodes, total num timesteps 493800/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2469/10000 episodes, total num timesteps 494000/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2470/10000 episodes, total num timesteps 494200/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2471/10000 episodes, total num timesteps 494400/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2472/10000 episodes, total num timesteps 494600/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2473/10000 episodes, total num timesteps 494800/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2474/10000 episodes, total num timesteps 495000/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2475/10000 episodes, total num timesteps 495200/2000000, FPS 208.

team_policy eval average step individual rewards of agent0: 0.6909121769875388
team_policy eval average team episode rewards of agent0: 87.5
team_policy eval idv catch total num of agent0: 29
team_policy eval team catch total num: 35
team_policy eval average step individual rewards of agent1: 0.6256748199949627
team_policy eval average team episode rewards of agent1: 87.5
team_policy eval idv catch total num of agent1: 27
team_policy eval team catch total num: 35
team_policy eval average step individual rewards of agent2: 0.323713882041407
team_policy eval average team episode rewards of agent2: 87.5
team_policy eval idv catch total num of agent2: 15
team_policy eval team catch total num: 35
team_policy eval average step individual rewards of agent3: 0.7799685377807486
team_policy eval average team episode rewards of agent3: 87.5
team_policy eval idv catch total num of agent3: 33
team_policy eval team catch total num: 35
team_policy eval average step individual rewards of agent4: 0.7289013853748186
team_policy eval average team episode rewards of agent4: 87.5
team_policy eval idv catch total num of agent4: 31
team_policy eval team catch total num: 35
idv_policy eval average step individual rewards of agent0: 0.5520280990637566
idv_policy eval average team episode rewards of agent0: 70.0
idv_policy eval idv catch total num of agent0: 24
idv_policy eval team catch total num: 28
idv_policy eval average step individual rewards of agent1: 0.5321895497117047
idv_policy eval average team episode rewards of agent1: 70.0
idv_policy eval idv catch total num of agent1: 23
idv_policy eval team catch total num: 28
idv_policy eval average step individual rewards of agent2: 0.45644977585975466
idv_policy eval average team episode rewards of agent2: 70.0
idv_policy eval idv catch total num of agent2: 20
idv_policy eval team catch total num: 28
idv_policy eval average step individual rewards of agent3: 0.43999269979801425
idv_policy eval average team episode rewards of agent3: 70.0
idv_policy eval idv catch total num of agent3: 20
idv_policy eval team catch total num: 28
idv_policy eval average step individual rewards of agent4: 0.6523970686684956
idv_policy eval average team episode rewards of agent4: 70.0
idv_policy eval idv catch total num of agent4: 28
idv_policy eval team catch total num: 28

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2476/10000 episodes, total num timesteps 495400/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2477/10000 episodes, total num timesteps 495600/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2478/10000 episodes, total num timesteps 495800/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2479/10000 episodes, total num timesteps 496000/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2480/10000 episodes, total num timesteps 496200/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2481/10000 episodes, total num timesteps 496400/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2482/10000 episodes, total num timesteps 496600/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2483/10000 episodes, total num timesteps 496800/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2484/10000 episodes, total num timesteps 497000/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2485/10000 episodes, total num timesteps 497200/2000000, FPS 208.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2486/10000 episodes, total num timesteps 497400/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2487/10000 episodes, total num timesteps 497600/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2488/10000 episodes, total num timesteps 497800/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2489/10000 episodes, total num timesteps 498000/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2490/10000 episodes, total num timesteps 498200/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2491/10000 episodes, total num timesteps 498400/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2492/10000 episodes, total num timesteps 498600/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2493/10000 episodes, total num timesteps 498800/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2494/10000 episodes, total num timesteps 499000/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2495/10000 episodes, total num timesteps 499200/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2496/10000 episodes, total num timesteps 499400/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2497/10000 episodes, total num timesteps 499600/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2498/10000 episodes, total num timesteps 499800/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2499/10000 episodes, total num timesteps 500000/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2500/10000 episodes, total num timesteps 500200/2000000, FPS 209.

team_policy eval average step individual rewards of agent0: 0.6868513503263465
team_policy eval average team episode rewards of agent0: 85.0
team_policy eval idv catch total num of agent0: 29
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent1: 0.713964934627167
team_policy eval average team episode rewards of agent1: 85.0
team_policy eval idv catch total num of agent1: 30
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent2: 0.6620926971254286
team_policy eval average team episode rewards of agent2: 85.0
team_policy eval idv catch total num of agent2: 28
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent3: 0.31088068540953806
team_policy eval average team episode rewards of agent3: 85.0
team_policy eval idv catch total num of agent3: 14
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent4: 0.7358686952313171
team_policy eval average team episode rewards of agent4: 85.0
team_policy eval idv catch total num of agent4: 31
team_policy eval team catch total num: 34
idv_policy eval average step individual rewards of agent0: 0.509524030088977
idv_policy eval average team episode rewards of agent0: 97.5
idv_policy eval idv catch total num of agent0: 22
idv_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent1: 0.5582361513677396
idv_policy eval average team episode rewards of agent1: 97.5
idv_policy eval idv catch total num of agent1: 24
idv_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent2: 0.7425659159297728
idv_policy eval average team episode rewards of agent2: 97.5
idv_policy eval idv catch total num of agent2: 31
idv_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent3: 0.8179633734805128
idv_policy eval average team episode rewards of agent3: 97.5
idv_policy eval idv catch total num of agent3: 34
idv_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent4: 1.0179456573609587
idv_policy eval average team episode rewards of agent4: 97.5
idv_policy eval idv catch total num of agent4: 42
idv_policy eval team catch total num: 39

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2501/10000 episodes, total num timesteps 500400/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2502/10000 episodes, total num timesteps 500600/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2503/10000 episodes, total num timesteps 500800/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2504/10000 episodes, total num timesteps 501000/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2505/10000 episodes, total num timesteps 501200/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2506/10000 episodes, total num timesteps 501400/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2507/10000 episodes, total num timesteps 501600/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2508/10000 episodes, total num timesteps 501800/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2509/10000 episodes, total num timesteps 502000/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2510/10000 episodes, total num timesteps 502200/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2511/10000 episodes, total num timesteps 502400/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2512/10000 episodes, total num timesteps 502600/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2513/10000 episodes, total num timesteps 502800/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2514/10000 episodes, total num timesteps 503000/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2515/10000 episodes, total num timesteps 503200/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2516/10000 episodes, total num timesteps 503400/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2517/10000 episodes, total num timesteps 503600/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2518/10000 episodes, total num timesteps 503800/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2519/10000 episodes, total num timesteps 504000/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2520/10000 episodes, total num timesteps 504200/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2521/10000 episodes, total num timesteps 504400/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2522/10000 episodes, total num timesteps 504600/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2523/10000 episodes, total num timesteps 504800/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2524/10000 episodes, total num timesteps 505000/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2525/10000 episodes, total num timesteps 505200/2000000, FPS 209.

team_policy eval average step individual rewards of agent0: 0.7947382724945407
team_policy eval average team episode rewards of agent0: 90.0
team_policy eval idv catch total num of agent0: 33
team_policy eval team catch total num: 36
team_policy eval average step individual rewards of agent1: 0.48834383612595533
team_policy eval average team episode rewards of agent1: 90.0
team_policy eval idv catch total num of agent1: 21
team_policy eval team catch total num: 36
team_policy eval average step individual rewards of agent2: 0.5348344403052993
team_policy eval average team episode rewards of agent2: 90.0
team_policy eval idv catch total num of agent2: 23
team_policy eval team catch total num: 36
team_policy eval average step individual rewards of agent3: 0.6088788989811932
team_policy eval average team episode rewards of agent3: 90.0
team_policy eval idv catch total num of agent3: 26
team_policy eval team catch total num: 36
team_policy eval average step individual rewards of agent4: 1.270176388565099
team_policy eval average team episode rewards of agent4: 90.0
team_policy eval idv catch total num of agent4: 52
team_policy eval team catch total num: 36
idv_policy eval average step individual rewards of agent0: 0.6548783216762767
idv_policy eval average team episode rewards of agent0: 97.5
idv_policy eval idv catch total num of agent0: 28
idv_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent1: 0.7822673614633419
idv_policy eval average team episode rewards of agent1: 97.5
idv_policy eval idv catch total num of agent1: 33
idv_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent2: 0.40338401061859885
idv_policy eval average team episode rewards of agent2: 97.5
idv_policy eval idv catch total num of agent2: 18
idv_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent3: 0.9186915663253812
idv_policy eval average team episode rewards of agent3: 97.5
idv_policy eval idv catch total num of agent3: 38
idv_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent4: 0.7848292183900325
idv_policy eval average team episode rewards of agent4: 97.5
idv_policy eval idv catch total num of agent4: 33
idv_policy eval team catch total num: 39

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2526/10000 episodes, total num timesteps 505400/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2527/10000 episodes, total num timesteps 505600/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2528/10000 episodes, total num timesteps 505800/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2529/10000 episodes, total num timesteps 506000/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2530/10000 episodes, total num timesteps 506200/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2531/10000 episodes, total num timesteps 506400/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2532/10000 episodes, total num timesteps 506600/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2533/10000 episodes, total num timesteps 506800/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2534/10000 episodes, total num timesteps 507000/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2535/10000 episodes, total num timesteps 507200/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2536/10000 episodes, total num timesteps 507400/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2537/10000 episodes, total num timesteps 507600/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2538/10000 episodes, total num timesteps 507800/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2539/10000 episodes, total num timesteps 508000/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2540/10000 episodes, total num timesteps 508200/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2541/10000 episodes, total num timesteps 508400/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2542/10000 episodes, total num timesteps 508600/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2543/10000 episodes, total num timesteps 508800/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2544/10000 episodes, total num timesteps 509000/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2545/10000 episodes, total num timesteps 509200/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2546/10000 episodes, total num timesteps 509400/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2547/10000 episodes, total num timesteps 509600/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2548/10000 episodes, total num timesteps 509800/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2549/10000 episodes, total num timesteps 510000/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2550/10000 episodes, total num timesteps 510200/2000000, FPS 209.

team_policy eval average step individual rewards of agent0: 0.8348688552082194
team_policy eval average team episode rewards of agent0: 132.5
team_policy eval idv catch total num of agent0: 35
team_policy eval team catch total num: 53
team_policy eval average step individual rewards of agent1: 0.8123818259095568
team_policy eval average team episode rewards of agent1: 132.5
team_policy eval idv catch total num of agent1: 34
team_policy eval team catch total num: 53
team_policy eval average step individual rewards of agent2: 1.0920453948244961
team_policy eval average team episode rewards of agent2: 132.5
team_policy eval idv catch total num of agent2: 45
team_policy eval team catch total num: 53
team_policy eval average step individual rewards of agent3: 0.6357808388342244
team_policy eval average team episode rewards of agent3: 132.5
team_policy eval idv catch total num of agent3: 27
team_policy eval team catch total num: 53
team_policy eval average step individual rewards of agent4: 0.9911937204516655
team_policy eval average team episode rewards of agent4: 132.5
team_policy eval idv catch total num of agent4: 41
team_policy eval team catch total num: 53
idv_policy eval average step individual rewards of agent0: 0.8664344502288656
idv_policy eval average team episode rewards of agent0: 65.0
idv_policy eval idv catch total num of agent0: 36
idv_policy eval team catch total num: 26
idv_policy eval average step individual rewards of agent1: 0.5602661367788908
idv_policy eval average team episode rewards of agent1: 65.0
idv_policy eval idv catch total num of agent1: 24
idv_policy eval team catch total num: 26
idv_policy eval average step individual rewards of agent2: 0.6585443305625011
idv_policy eval average team episode rewards of agent2: 65.0
idv_policy eval idv catch total num of agent2: 28
idv_policy eval team catch total num: 26
idv_policy eval average step individual rewards of agent3: 0.42890796690934335
idv_policy eval average team episode rewards of agent3: 65.0
idv_policy eval idv catch total num of agent3: 19
idv_policy eval team catch total num: 26
idv_policy eval average step individual rewards of agent4: 0.4011746649605237
idv_policy eval average team episode rewards of agent4: 65.0
idv_policy eval idv catch total num of agent4: 18
idv_policy eval team catch total num: 26

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2551/10000 episodes, total num timesteps 510400/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2552/10000 episodes, total num timesteps 510600/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2553/10000 episodes, total num timesteps 510800/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2554/10000 episodes, total num timesteps 511000/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2555/10000 episodes, total num timesteps 511200/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2556/10000 episodes, total num timesteps 511400/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2557/10000 episodes, total num timesteps 511600/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2558/10000 episodes, total num timesteps 511800/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2559/10000 episodes, total num timesteps 512000/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2560/10000 episodes, total num timesteps 512200/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2561/10000 episodes, total num timesteps 512400/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2562/10000 episodes, total num timesteps 512600/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2563/10000 episodes, total num timesteps 512800/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2564/10000 episodes, total num timesteps 513000/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2565/10000 episodes, total num timesteps 513200/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2566/10000 episodes, total num timesteps 513400/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2567/10000 episodes, total num timesteps 513600/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2568/10000 episodes, total num timesteps 513800/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2569/10000 episodes, total num timesteps 514000/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2570/10000 episodes, total num timesteps 514200/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2571/10000 episodes, total num timesteps 514400/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2572/10000 episodes, total num timesteps 514600/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2573/10000 episodes, total num timesteps 514800/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2574/10000 episodes, total num timesteps 515000/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2575/10000 episodes, total num timesteps 515200/2000000, FPS 209.

team_policy eval average step individual rewards of agent0: 0.46900728378878775
team_policy eval average team episode rewards of agent0: 97.5
team_policy eval idv catch total num of agent0: 21
team_policy eval team catch total num: 39
team_policy eval average step individual rewards of agent1: 0.48909581678040476
team_policy eval average team episode rewards of agent1: 97.5
team_policy eval idv catch total num of agent1: 21
team_policy eval team catch total num: 39
team_policy eval average step individual rewards of agent2: 0.7661946541051585
team_policy eval average team episode rewards of agent2: 97.5
team_policy eval idv catch total num of agent2: 32
team_policy eval team catch total num: 39
team_policy eval average step individual rewards of agent3: 0.5072738665825017
team_policy eval average team episode rewards of agent3: 97.5
team_policy eval idv catch total num of agent3: 22
team_policy eval team catch total num: 39
team_policy eval average step individual rewards of agent4: 0.9458427836591977
team_policy eval average team episode rewards of agent4: 97.5
team_policy eval idv catch total num of agent4: 39
team_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent0: 0.9689807376196068
idv_policy eval average team episode rewards of agent0: 115.0
idv_policy eval idv catch total num of agent0: 40
idv_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent1: 1.067557054262262
idv_policy eval average team episode rewards of agent1: 115.0
idv_policy eval idv catch total num of agent1: 44
idv_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent2: 0.6819865073105575
idv_policy eval average team episode rewards of agent2: 115.0
idv_policy eval idv catch total num of agent2: 29
idv_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent3: 0.9716586761516018
idv_policy eval average team episode rewards of agent3: 115.0
idv_policy eval idv catch total num of agent3: 40
idv_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent4: 1.0690006388698905
idv_policy eval average team episode rewards of agent4: 115.0
idv_policy eval idv catch total num of agent4: 44
idv_policy eval team catch total num: 46

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2576/10000 episodes, total num timesteps 515400/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2577/10000 episodes, total num timesteps 515600/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2578/10000 episodes, total num timesteps 515800/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2579/10000 episodes, total num timesteps 516000/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2580/10000 episodes, total num timesteps 516200/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2581/10000 episodes, total num timesteps 516400/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2582/10000 episodes, total num timesteps 516600/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2583/10000 episodes, total num timesteps 516800/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2584/10000 episodes, total num timesteps 517000/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2585/10000 episodes, total num timesteps 517200/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2586/10000 episodes, total num timesteps 517400/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2587/10000 episodes, total num timesteps 517600/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2588/10000 episodes, total num timesteps 517800/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2589/10000 episodes, total num timesteps 518000/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2590/10000 episodes, total num timesteps 518200/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2591/10000 episodes, total num timesteps 518400/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2592/10000 episodes, total num timesteps 518600/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2593/10000 episodes, total num timesteps 518800/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2594/10000 episodes, total num timesteps 519000/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2595/10000 episodes, total num timesteps 519200/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2596/10000 episodes, total num timesteps 519400/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2597/10000 episodes, total num timesteps 519600/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2598/10000 episodes, total num timesteps 519800/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2599/10000 episodes, total num timesteps 520000/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2600/10000 episodes, total num timesteps 520200/2000000, FPS 209.

team_policy eval average step individual rewards of agent0: 0.639394538838877
team_policy eval average team episode rewards of agent0: 100.0
team_policy eval idv catch total num of agent0: 27
team_policy eval team catch total num: 40
team_policy eval average step individual rewards of agent1: 0.6100059229636716
team_policy eval average team episode rewards of agent1: 100.0
team_policy eval idv catch total num of agent1: 26
team_policy eval team catch total num: 40
team_policy eval average step individual rewards of agent2: 0.7467128018967424
team_policy eval average team episode rewards of agent2: 100.0
team_policy eval idv catch total num of agent2: 31
team_policy eval team catch total num: 40
team_policy eval average step individual rewards of agent3: 0.42779144914346945
team_policy eval average team episode rewards of agent3: 100.0
team_policy eval idv catch total num of agent3: 19
team_policy eval team catch total num: 40
team_policy eval average step individual rewards of agent4: 1.0166235965345658
team_policy eval average team episode rewards of agent4: 100.0
team_policy eval idv catch total num of agent4: 42
team_policy eval team catch total num: 40
idv_policy eval average step individual rewards of agent0: 0.9608629696318414
idv_policy eval average team episode rewards of agent0: 117.5
idv_policy eval idv catch total num of agent0: 40
idv_policy eval team catch total num: 47
idv_policy eval average step individual rewards of agent1: 0.5497660501681282
idv_policy eval average team episode rewards of agent1: 117.5
idv_policy eval idv catch total num of agent1: 24
idv_policy eval team catch total num: 47
idv_policy eval average step individual rewards of agent2: 0.6060951811405596
idv_policy eval average team episode rewards of agent2: 117.5
idv_policy eval idv catch total num of agent2: 26
idv_policy eval team catch total num: 47
idv_policy eval average step individual rewards of agent3: 1.3129021754050563
idv_policy eval average team episode rewards of agent3: 117.5
idv_policy eval idv catch total num of agent3: 54
idv_policy eval team catch total num: 47
idv_policy eval average step individual rewards of agent4: 0.9931772750300745
idv_policy eval average team episode rewards of agent4: 117.5
idv_policy eval idv catch total num of agent4: 41
idv_policy eval team catch total num: 47

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2601/10000 episodes, total num timesteps 520400/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2602/10000 episodes, total num timesteps 520600/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2603/10000 episodes, total num timesteps 520800/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2604/10000 episodes, total num timesteps 521000/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2605/10000 episodes, total num timesteps 521200/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2606/10000 episodes, total num timesteps 521400/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2607/10000 episodes, total num timesteps 521600/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2608/10000 episodes, total num timesteps 521800/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2609/10000 episodes, total num timesteps 522000/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2610/10000 episodes, total num timesteps 522200/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2611/10000 episodes, total num timesteps 522400/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2612/10000 episodes, total num timesteps 522600/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2613/10000 episodes, total num timesteps 522800/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2614/10000 episodes, total num timesteps 523000/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2615/10000 episodes, total num timesteps 523200/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2616/10000 episodes, total num timesteps 523400/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2617/10000 episodes, total num timesteps 523600/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2618/10000 episodes, total num timesteps 523800/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2619/10000 episodes, total num timesteps 524000/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2620/10000 episodes, total num timesteps 524200/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2621/10000 episodes, total num timesteps 524400/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2622/10000 episodes, total num timesteps 524600/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2623/10000 episodes, total num timesteps 524800/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2624/10000 episodes, total num timesteps 525000/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2625/10000 episodes, total num timesteps 525200/2000000, FPS 209.

team_policy eval average step individual rewards of agent0: 0.9618980243684747
team_policy eval average team episode rewards of agent0: 120.0
team_policy eval idv catch total num of agent0: 40
team_policy eval team catch total num: 48
team_policy eval average step individual rewards of agent1: 0.8610111347698176
team_policy eval average team episode rewards of agent1: 120.0
team_policy eval idv catch total num of agent1: 36
team_policy eval team catch total num: 48
team_policy eval average step individual rewards of agent2: 0.9680926761594774
team_policy eval average team episode rewards of agent2: 120.0
team_policy eval idv catch total num of agent2: 40
team_policy eval team catch total num: 48
team_policy eval average step individual rewards of agent3: 0.3784699087515054
team_policy eval average team episode rewards of agent3: 120.0
team_policy eval idv catch total num of agent3: 17
team_policy eval team catch total num: 48
team_policy eval average step individual rewards of agent4: 0.9106395500431574
team_policy eval average team episode rewards of agent4: 120.0
team_policy eval idv catch total num of agent4: 38
team_policy eval team catch total num: 48
idv_policy eval average step individual rewards of agent0: 0.32596546610985044
idv_policy eval average team episode rewards of agent0: 50.0
idv_policy eval idv catch total num of agent0: 15
idv_policy eval team catch total num: 20
idv_policy eval average step individual rewards of agent1: 0.660982854019948
idv_policy eval average team episode rewards of agent1: 50.0
idv_policy eval idv catch total num of agent1: 28
idv_policy eval team catch total num: 20
idv_policy eval average step individual rewards of agent2: 0.7678078779613879
idv_policy eval average team episode rewards of agent2: 50.0
idv_policy eval idv catch total num of agent2: 32
idv_policy eval team catch total num: 20
idv_policy eval average step individual rewards of agent3: 0.07565613854364321
idv_policy eval average team episode rewards of agent3: 50.0
idv_policy eval idv catch total num of agent3: 5
idv_policy eval team catch total num: 20
idv_policy eval average step individual rewards of agent4: 0.5064986541993541
idv_policy eval average team episode rewards of agent4: 50.0
idv_policy eval idv catch total num of agent4: 22
idv_policy eval team catch total num: 20

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2626/10000 episodes, total num timesteps 525400/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2627/10000 episodes, total num timesteps 525600/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2628/10000 episodes, total num timesteps 525800/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2629/10000 episodes, total num timesteps 526000/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2630/10000 episodes, total num timesteps 526200/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2631/10000 episodes, total num timesteps 526400/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2632/10000 episodes, total num timesteps 526600/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2633/10000 episodes, total num timesteps 526800/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2634/10000 episodes, total num timesteps 527000/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2635/10000 episodes, total num timesteps 527200/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2636/10000 episodes, total num timesteps 527400/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2637/10000 episodes, total num timesteps 527600/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2638/10000 episodes, total num timesteps 527800/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2639/10000 episodes, total num timesteps 528000/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2640/10000 episodes, total num timesteps 528200/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2641/10000 episodes, total num timesteps 528400/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2642/10000 episodes, total num timesteps 528600/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2643/10000 episodes, total num timesteps 528800/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2644/10000 episodes, total num timesteps 529000/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2645/10000 episodes, total num timesteps 529200/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2646/10000 episodes, total num timesteps 529400/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2647/10000 episodes, total num timesteps 529600/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2648/10000 episodes, total num timesteps 529800/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2649/10000 episodes, total num timesteps 530000/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2650/10000 episodes, total num timesteps 530200/2000000, FPS 209.

team_policy eval average step individual rewards of agent0: 0.5420219243610443
team_policy eval average team episode rewards of agent0: 97.5
team_policy eval idv catch total num of agent0: 23
team_policy eval team catch total num: 39
team_policy eval average step individual rewards of agent1: 0.6318291120643922
team_policy eval average team episode rewards of agent1: 97.5
team_policy eval idv catch total num of agent1: 27
team_policy eval team catch total num: 39
team_policy eval average step individual rewards of agent2: 0.8448092010964959
team_policy eval average team episode rewards of agent2: 97.5
team_policy eval idv catch total num of agent2: 35
team_policy eval team catch total num: 39
team_policy eval average step individual rewards of agent3: 0.7639277723152768
team_policy eval average team episode rewards of agent3: 97.5
team_policy eval idv catch total num of agent3: 32
team_policy eval team catch total num: 39
team_policy eval average step individual rewards of agent4: 0.7787380818490169
team_policy eval average team episode rewards of agent4: 97.5
team_policy eval idv catch total num of agent4: 33
team_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent0: 0.9125967760797659
idv_policy eval average team episode rewards of agent0: 87.5
idv_policy eval idv catch total num of agent0: 38
idv_policy eval team catch total num: 35
idv_policy eval average step individual rewards of agent1: 0.45204417884978526
idv_policy eval average team episode rewards of agent1: 87.5
idv_policy eval idv catch total num of agent1: 20
idv_policy eval team catch total num: 35
idv_policy eval average step individual rewards of agent2: 0.5303968699481735
idv_policy eval average team episode rewards of agent2: 87.5
idv_policy eval idv catch total num of agent2: 23
idv_policy eval team catch total num: 35
idv_policy eval average step individual rewards of agent3: 0.8653060046556081
idv_policy eval average team episode rewards of agent3: 87.5
idv_policy eval idv catch total num of agent3: 36
idv_policy eval team catch total num: 35
idv_policy eval average step individual rewards of agent4: 0.6523799722070305
idv_policy eval average team episode rewards of agent4: 87.5
idv_policy eval idv catch total num of agent4: 28
idv_policy eval team catch total num: 35

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2651/10000 episodes, total num timesteps 530400/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2652/10000 episodes, total num timesteps 530600/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2653/10000 episodes, total num timesteps 530800/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2654/10000 episodes, total num timesteps 531000/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2655/10000 episodes, total num timesteps 531200/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2656/10000 episodes, total num timesteps 531400/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2657/10000 episodes, total num timesteps 531600/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2658/10000 episodes, total num timesteps 531800/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2659/10000 episodes, total num timesteps 532000/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2660/10000 episodes, total num timesteps 532200/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2661/10000 episodes, total num timesteps 532400/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2662/10000 episodes, total num timesteps 532600/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2663/10000 episodes, total num timesteps 532800/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2664/10000 episodes, total num timesteps 533000/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2665/10000 episodes, total num timesteps 533200/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2666/10000 episodes, total num timesteps 533400/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2667/10000 episodes, total num timesteps 533600/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2668/10000 episodes, total num timesteps 533800/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2669/10000 episodes, total num timesteps 534000/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2670/10000 episodes, total num timesteps 534200/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2671/10000 episodes, total num timesteps 534400/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2672/10000 episodes, total num timesteps 534600/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2673/10000 episodes, total num timesteps 534800/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2674/10000 episodes, total num timesteps 535000/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2675/10000 episodes, total num timesteps 535200/2000000, FPS 209.

team_policy eval average step individual rewards of agent0: 0.9604506364320313
team_policy eval average team episode rewards of agent0: 95.0
team_policy eval idv catch total num of agent0: 40
team_policy eval team catch total num: 38
team_policy eval average step individual rewards of agent1: 0.8325887158377862
team_policy eval average team episode rewards of agent1: 95.0
team_policy eval idv catch total num of agent1: 35
team_policy eval team catch total num: 38
team_policy eval average step individual rewards of agent2: 0.48369249093824296
team_policy eval average team episode rewards of agent2: 95.0
team_policy eval idv catch total num of agent2: 21
team_policy eval team catch total num: 38
team_policy eval average step individual rewards of agent3: 0.40862637520731293
team_policy eval average team episode rewards of agent3: 95.0
team_policy eval idv catch total num of agent3: 18
team_policy eval team catch total num: 38
team_policy eval average step individual rewards of agent4: 0.8471062504137323
team_policy eval average team episode rewards of agent4: 95.0
team_policy eval idv catch total num of agent4: 35
team_policy eval team catch total num: 38
idv_policy eval average step individual rewards of agent0: 0.9589207616819628
idv_policy eval average team episode rewards of agent0: 120.0
idv_policy eval idv catch total num of agent0: 40
idv_policy eval team catch total num: 48
idv_policy eval average step individual rewards of agent1: 1.3392071104282572
idv_policy eval average team episode rewards of agent1: 120.0
idv_policy eval idv catch total num of agent1: 55
idv_policy eval team catch total num: 48
idv_policy eval average step individual rewards of agent2: 0.9232684932840782
idv_policy eval average team episode rewards of agent2: 120.0
idv_policy eval idv catch total num of agent2: 38
idv_policy eval team catch total num: 48
idv_policy eval average step individual rewards of agent3: 0.8396177535009861
idv_policy eval average team episode rewards of agent3: 120.0
idv_policy eval idv catch total num of agent3: 35
idv_policy eval team catch total num: 48
idv_policy eval average step individual rewards of agent4: 0.8620646451603418
idv_policy eval average team episode rewards of agent4: 120.0
idv_policy eval idv catch total num of agent4: 36
idv_policy eval team catch total num: 48

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2676/10000 episodes, total num timesteps 535400/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2677/10000 episodes, total num timesteps 535600/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2678/10000 episodes, total num timesteps 535800/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2679/10000 episodes, total num timesteps 536000/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2680/10000 episodes, total num timesteps 536200/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2681/10000 episodes, total num timesteps 536400/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2682/10000 episodes, total num timesteps 536600/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2683/10000 episodes, total num timesteps 536800/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2684/10000 episodes, total num timesteps 537000/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2685/10000 episodes, total num timesteps 537200/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2686/10000 episodes, total num timesteps 537400/2000000, FPS 209.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2687/10000 episodes, total num timesteps 537600/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2688/10000 episodes, total num timesteps 537800/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2689/10000 episodes, total num timesteps 538000/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2690/10000 episodes, total num timesteps 538200/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2691/10000 episodes, total num timesteps 538400/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2692/10000 episodes, total num timesteps 538600/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2693/10000 episodes, total num timesteps 538800/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2694/10000 episodes, total num timesteps 539000/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2695/10000 episodes, total num timesteps 539200/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2696/10000 episodes, total num timesteps 539400/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2697/10000 episodes, total num timesteps 539600/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2698/10000 episodes, total num timesteps 539800/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2699/10000 episodes, total num timesteps 540000/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2700/10000 episodes, total num timesteps 540200/2000000, FPS 210.

team_policy eval average step individual rewards of agent0: 0.9175816096599277
team_policy eval average team episode rewards of agent0: 107.5
team_policy eval idv catch total num of agent0: 38
team_policy eval team catch total num: 43
team_policy eval average step individual rewards of agent1: 0.9401252649831533
team_policy eval average team episode rewards of agent1: 107.5
team_policy eval idv catch total num of agent1: 39
team_policy eval team catch total num: 43
team_policy eval average step individual rewards of agent2: 0.7382612269945733
team_policy eval average team episode rewards of agent2: 107.5
team_policy eval idv catch total num of agent2: 31
team_policy eval team catch total num: 43
team_policy eval average step individual rewards of agent3: 0.4612887724810675
team_policy eval average team episode rewards of agent3: 107.5
team_policy eval idv catch total num of agent3: 20
team_policy eval team catch total num: 43
team_policy eval average step individual rewards of agent4: 0.6088541072081765
team_policy eval average team episode rewards of agent4: 107.5
team_policy eval idv catch total num of agent4: 26
team_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent0: 0.20007876161023913
idv_policy eval average team episode rewards of agent0: 82.5
idv_policy eval idv catch total num of agent0: 10
idv_policy eval team catch total num: 33
idv_policy eval average step individual rewards of agent1: 0.6115418940279607
idv_policy eval average team episode rewards of agent1: 82.5
idv_policy eval idv catch total num of agent1: 26
idv_policy eval team catch total num: 33
idv_policy eval average step individual rewards of agent2: 0.6594053385195804
idv_policy eval average team episode rewards of agent2: 82.5
idv_policy eval idv catch total num of agent2: 28
idv_policy eval team catch total num: 33
idv_policy eval average step individual rewards of agent3: 0.837433348373033
idv_policy eval average team episode rewards of agent3: 82.5
idv_policy eval idv catch total num of agent3: 35
idv_policy eval team catch total num: 33
idv_policy eval average step individual rewards of agent4: 0.7344815267894299
idv_policy eval average team episode rewards of agent4: 82.5
idv_policy eval idv catch total num of agent4: 31
idv_policy eval team catch total num: 33

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2701/10000 episodes, total num timesteps 540400/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2702/10000 episodes, total num timesteps 540600/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2703/10000 episodes, total num timesteps 540800/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2704/10000 episodes, total num timesteps 541000/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2705/10000 episodes, total num timesteps 541200/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2706/10000 episodes, total num timesteps 541400/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2707/10000 episodes, total num timesteps 541600/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2708/10000 episodes, total num timesteps 541800/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2709/10000 episodes, total num timesteps 542000/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2710/10000 episodes, total num timesteps 542200/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2711/10000 episodes, total num timesteps 542400/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2712/10000 episodes, total num timesteps 542600/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2713/10000 episodes, total num timesteps 542800/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2714/10000 episodes, total num timesteps 543000/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2715/10000 episodes, total num timesteps 543200/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2716/10000 episodes, total num timesteps 543400/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2717/10000 episodes, total num timesteps 543600/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2718/10000 episodes, total num timesteps 543800/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2719/10000 episodes, total num timesteps 544000/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2720/10000 episodes, total num timesteps 544200/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2721/10000 episodes, total num timesteps 544400/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2722/10000 episodes, total num timesteps 544600/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2723/10000 episodes, total num timesteps 544800/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2724/10000 episodes, total num timesteps 545000/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2725/10000 episodes, total num timesteps 545200/2000000, FPS 210.

team_policy eval average step individual rewards of agent0: 0.9273489722839091
team_policy eval average team episode rewards of agent0: 97.5
team_policy eval idv catch total num of agent0: 39
team_policy eval team catch total num: 39
team_policy eval average step individual rewards of agent1: 0.863439826804243
team_policy eval average team episode rewards of agent1: 97.5
team_policy eval idv catch total num of agent1: 36
team_policy eval team catch total num: 39
team_policy eval average step individual rewards of agent2: 0.29939739907481616
team_policy eval average team episode rewards of agent2: 97.5
team_policy eval idv catch total num of agent2: 14
team_policy eval team catch total num: 39
team_policy eval average step individual rewards of agent3: 0.5516948871571262
team_policy eval average team episode rewards of agent3: 97.5
team_policy eval idv catch total num of agent3: 24
team_policy eval team catch total num: 39
team_policy eval average step individual rewards of agent4: 0.6058542941522203
team_policy eval average team episode rewards of agent4: 97.5
team_policy eval idv catch total num of agent4: 26
team_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent0: 0.7387636982954728
idv_policy eval average team episode rewards of agent0: 112.5
idv_policy eval idv catch total num of agent0: 31
idv_policy eval team catch total num: 45
idv_policy eval average step individual rewards of agent1: 0.7060692890270945
idv_policy eval average team episode rewards of agent1: 112.5
idv_policy eval idv catch total num of agent1: 30
idv_policy eval team catch total num: 45
idv_policy eval average step individual rewards of agent2: 0.7052433249414726
idv_policy eval average team episode rewards of agent2: 112.5
idv_policy eval idv catch total num of agent2: 30
idv_policy eval team catch total num: 45
idv_policy eval average step individual rewards of agent3: 1.0388544598703702
idv_policy eval average team episode rewards of agent3: 112.5
idv_policy eval idv catch total num of agent3: 43
idv_policy eval team catch total num: 45
idv_policy eval average step individual rewards of agent4: 0.9221144843386341
idv_policy eval average team episode rewards of agent4: 112.5
idv_policy eval idv catch total num of agent4: 38
idv_policy eval team catch total num: 45

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2726/10000 episodes, total num timesteps 545400/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2727/10000 episodes, total num timesteps 545600/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2728/10000 episodes, total num timesteps 545800/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2729/10000 episodes, total num timesteps 546000/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2730/10000 episodes, total num timesteps 546200/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2731/10000 episodes, total num timesteps 546400/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2732/10000 episodes, total num timesteps 546600/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2733/10000 episodes, total num timesteps 546800/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2734/10000 episodes, total num timesteps 547000/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2735/10000 episodes, total num timesteps 547200/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2736/10000 episodes, total num timesteps 547400/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2737/10000 episodes, total num timesteps 547600/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2738/10000 episodes, total num timesteps 547800/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2739/10000 episodes, total num timesteps 548000/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2740/10000 episodes, total num timesteps 548200/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2741/10000 episodes, total num timesteps 548400/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2742/10000 episodes, total num timesteps 548600/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2743/10000 episodes, total num timesteps 548800/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2744/10000 episodes, total num timesteps 549000/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2745/10000 episodes, total num timesteps 549200/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2746/10000 episodes, total num timesteps 549400/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2747/10000 episodes, total num timesteps 549600/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2748/10000 episodes, total num timesteps 549800/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2749/10000 episodes, total num timesteps 550000/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2750/10000 episodes, total num timesteps 550200/2000000, FPS 210.

team_policy eval average step individual rewards of agent0: 0.9770233289880698
team_policy eval average team episode rewards of agent0: 115.0
team_policy eval idv catch total num of agent0: 41
team_policy eval team catch total num: 46
team_policy eval average step individual rewards of agent1: 0.6489109804819857
team_policy eval average team episode rewards of agent1: 115.0
team_policy eval idv catch total num of agent1: 28
team_policy eval team catch total num: 46
team_policy eval average step individual rewards of agent2: 1.0644101841594356
team_policy eval average team episode rewards of agent2: 115.0
team_policy eval idv catch total num of agent2: 44
team_policy eval team catch total num: 46
team_policy eval average step individual rewards of agent3: 0.8603597110298326
team_policy eval average team episode rewards of agent3: 115.0
team_policy eval idv catch total num of agent3: 36
team_policy eval team catch total num: 46
team_policy eval average step individual rewards of agent4: 0.9157132239401453
team_policy eval average team episode rewards of agent4: 115.0
team_policy eval idv catch total num of agent4: 38
team_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent0: 0.8486563632403005
idv_policy eval average team episode rewards of agent0: 115.0
idv_policy eval idv catch total num of agent0: 35
idv_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent1: 0.9204406881059978
idv_policy eval average team episode rewards of agent1: 115.0
idv_policy eval idv catch total num of agent1: 38
idv_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent2: 0.7871628845948081
idv_policy eval average team episode rewards of agent2: 115.0
idv_policy eval idv catch total num of agent2: 33
idv_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent3: 0.6147401168176099
idv_policy eval average team episode rewards of agent3: 115.0
idv_policy eval idv catch total num of agent3: 26
idv_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent4: 0.8719961244478551
idv_policy eval average team episode rewards of agent4: 115.0
idv_policy eval idv catch total num of agent4: 36
idv_policy eval team catch total num: 46

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2751/10000 episodes, total num timesteps 550400/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2752/10000 episodes, total num timesteps 550600/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2753/10000 episodes, total num timesteps 550800/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2754/10000 episodes, total num timesteps 551000/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2755/10000 episodes, total num timesteps 551200/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2756/10000 episodes, total num timesteps 551400/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2757/10000 episodes, total num timesteps 551600/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2758/10000 episodes, total num timesteps 551800/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2759/10000 episodes, total num timesteps 552000/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2760/10000 episodes, total num timesteps 552200/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2761/10000 episodes, total num timesteps 552400/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2762/10000 episodes, total num timesteps 552600/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2763/10000 episodes, total num timesteps 552800/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2764/10000 episodes, total num timesteps 553000/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2765/10000 episodes, total num timesteps 553200/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2766/10000 episodes, total num timesteps 553400/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2767/10000 episodes, total num timesteps 553600/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2768/10000 episodes, total num timesteps 553800/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2769/10000 episodes, total num timesteps 554000/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2770/10000 episodes, total num timesteps 554200/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2771/10000 episodes, total num timesteps 554400/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2772/10000 episodes, total num timesteps 554600/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2773/10000 episodes, total num timesteps 554800/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2774/10000 episodes, total num timesteps 555000/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2775/10000 episodes, total num timesteps 555200/2000000, FPS 210.

team_policy eval average step individual rewards of agent0: 0.5905523720138985
team_policy eval average team episode rewards of agent0: 72.5
team_policy eval idv catch total num of agent0: 26
team_policy eval team catch total num: 29
team_policy eval average step individual rewards of agent1: 0.6373362743045302
team_policy eval average team episode rewards of agent1: 72.5
team_policy eval idv catch total num of agent1: 27
team_policy eval team catch total num: 29
team_policy eval average step individual rewards of agent2: 0.44545418814881077
team_policy eval average team episode rewards of agent2: 72.5
team_policy eval idv catch total num of agent2: 20
team_policy eval team catch total num: 29
team_policy eval average step individual rewards of agent3: 0.4415645347682263
team_policy eval average team episode rewards of agent3: 72.5
team_policy eval idv catch total num of agent3: 20
team_policy eval team catch total num: 29
team_policy eval average step individual rewards of agent4: 0.5557646940264511
team_policy eval average team episode rewards of agent4: 72.5
team_policy eval idv catch total num of agent4: 24
team_policy eval team catch total num: 29
idv_policy eval average step individual rewards of agent0: 0.6112001669628175
idv_policy eval average team episode rewards of agent0: 145.0
idv_policy eval idv catch total num of agent0: 26
idv_policy eval team catch total num: 58
idv_policy eval average step individual rewards of agent1: 1.0704012688439493
idv_policy eval average team episode rewards of agent1: 145.0
idv_policy eval idv catch total num of agent1: 44
idv_policy eval team catch total num: 58
idv_policy eval average step individual rewards of agent2: 1.1227128875564671
idv_policy eval average team episode rewards of agent2: 145.0
idv_policy eval idv catch total num of agent2: 46
idv_policy eval team catch total num: 58
idv_policy eval average step individual rewards of agent3: 1.189840678444837
idv_policy eval average team episode rewards of agent3: 145.0
idv_policy eval idv catch total num of agent3: 49
idv_policy eval team catch total num: 58
idv_policy eval average step individual rewards of agent4: 1.1702684827524847
idv_policy eval average team episode rewards of agent4: 145.0
idv_policy eval idv catch total num of agent4: 48
idv_policy eval team catch total num: 58

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2776/10000 episodes, total num timesteps 555400/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2777/10000 episodes, total num timesteps 555600/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2778/10000 episodes, total num timesteps 555800/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2779/10000 episodes, total num timesteps 556000/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2780/10000 episodes, total num timesteps 556200/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2781/10000 episodes, total num timesteps 556400/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2782/10000 episodes, total num timesteps 556600/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2783/10000 episodes, total num timesteps 556800/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2784/10000 episodes, total num timesteps 557000/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2785/10000 episodes, total num timesteps 557200/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2786/10000 episodes, total num timesteps 557400/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2787/10000 episodes, total num timesteps 557600/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2788/10000 episodes, total num timesteps 557800/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2789/10000 episodes, total num timesteps 558000/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2790/10000 episodes, total num timesteps 558200/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2791/10000 episodes, total num timesteps 558400/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2792/10000 episodes, total num timesteps 558600/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2793/10000 episodes, total num timesteps 558800/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2794/10000 episodes, total num timesteps 559000/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2795/10000 episodes, total num timesteps 559200/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2796/10000 episodes, total num timesteps 559400/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2797/10000 episodes, total num timesteps 559600/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2798/10000 episodes, total num timesteps 559800/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2799/10000 episodes, total num timesteps 560000/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2800/10000 episodes, total num timesteps 560200/2000000, FPS 210.

team_policy eval average step individual rewards of agent0: 0.6062031346946608
team_policy eval average team episode rewards of agent0: 115.0
team_policy eval idv catch total num of agent0: 26
team_policy eval team catch total num: 46
team_policy eval average step individual rewards of agent1: 1.192814061956948
team_policy eval average team episode rewards of agent1: 115.0
team_policy eval idv catch total num of agent1: 49
team_policy eval team catch total num: 46
team_policy eval average step individual rewards of agent2: 0.7687476369849713
team_policy eval average team episode rewards of agent2: 115.0
team_policy eval idv catch total num of agent2: 32
team_policy eval team catch total num: 46
team_policy eval average step individual rewards of agent3: 0.9911433340660076
team_policy eval average team episode rewards of agent3: 115.0
team_policy eval idv catch total num of agent3: 41
team_policy eval team catch total num: 46
team_policy eval average step individual rewards of agent4: 0.8881748360675212
team_policy eval average team episode rewards of agent4: 115.0
team_policy eval idv catch total num of agent4: 37
team_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent0: 0.2600042466686572
idv_policy eval average team episode rewards of agent0: 77.5
idv_policy eval idv catch total num of agent0: 12
idv_policy eval team catch total num: 31
idv_policy eval average step individual rewards of agent1: 0.8158255678722796
idv_policy eval average team episode rewards of agent1: 77.5
idv_policy eval idv catch total num of agent1: 34
idv_policy eval team catch total num: 31
idv_policy eval average step individual rewards of agent2: 0.7675420543490779
idv_policy eval average team episode rewards of agent2: 77.5
idv_policy eval idv catch total num of agent2: 32
idv_policy eval team catch total num: 31
idv_policy eval average step individual rewards of agent3: 0.5685146356634426
idv_policy eval average team episode rewards of agent3: 77.5
idv_policy eval idv catch total num of agent3: 24
idv_policy eval team catch total num: 31
idv_policy eval average step individual rewards of agent4: 0.5603263113998319
idv_policy eval average team episode rewards of agent4: 77.5
idv_policy eval idv catch total num of agent4: 24
idv_policy eval team catch total num: 31

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2801/10000 episodes, total num timesteps 560400/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2802/10000 episodes, total num timesteps 560600/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2803/10000 episodes, total num timesteps 560800/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2804/10000 episodes, total num timesteps 561000/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2805/10000 episodes, total num timesteps 561200/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2806/10000 episodes, total num timesteps 561400/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2807/10000 episodes, total num timesteps 561600/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2808/10000 episodes, total num timesteps 561800/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2809/10000 episodes, total num timesteps 562000/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2810/10000 episodes, total num timesteps 562200/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2811/10000 episodes, total num timesteps 562400/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2812/10000 episodes, total num timesteps 562600/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2813/10000 episodes, total num timesteps 562800/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2814/10000 episodes, total num timesteps 563000/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2815/10000 episodes, total num timesteps 563200/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2816/10000 episodes, total num timesteps 563400/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2817/10000 episodes, total num timesteps 563600/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2818/10000 episodes, total num timesteps 563800/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2819/10000 episodes, total num timesteps 564000/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2820/10000 episodes, total num timesteps 564200/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2821/10000 episodes, total num timesteps 564400/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2822/10000 episodes, total num timesteps 564600/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2823/10000 episodes, total num timesteps 564800/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2824/10000 episodes, total num timesteps 565000/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2825/10000 episodes, total num timesteps 565200/2000000, FPS 210.

team_policy eval average step individual rewards of agent0: 0.575840203521411
team_policy eval average team episode rewards of agent0: 75.0
team_policy eval idv catch total num of agent0: 25
team_policy eval team catch total num: 30
team_policy eval average step individual rewards of agent1: 0.680965101060268
team_policy eval average team episode rewards of agent1: 75.0
team_policy eval idv catch total num of agent1: 29
team_policy eval team catch total num: 30
team_policy eval average step individual rewards of agent2: 0.5788938591739811
team_policy eval average team episode rewards of agent2: 75.0
team_policy eval idv catch total num of agent2: 25
team_policy eval team catch total num: 30
team_policy eval average step individual rewards of agent3: 0.269197592010954
team_policy eval average team episode rewards of agent3: 75.0
team_policy eval idv catch total num of agent3: 13
team_policy eval team catch total num: 30
team_policy eval average step individual rewards of agent4: 0.8132488471239248
team_policy eval average team episode rewards of agent4: 75.0
team_policy eval idv catch total num of agent4: 34
team_policy eval team catch total num: 30
idv_policy eval average step individual rewards of agent0: 0.8087426374622868
idv_policy eval average team episode rewards of agent0: 80.0
idv_policy eval idv catch total num of agent0: 34
idv_policy eval team catch total num: 32
idv_policy eval average step individual rewards of agent1: 0.5568862611160422
idv_policy eval average team episode rewards of agent1: 80.0
idv_policy eval idv catch total num of agent1: 24
idv_policy eval team catch total num: 32
idv_policy eval average step individual rewards of agent2: 0.9155461413649827
idv_policy eval average team episode rewards of agent2: 80.0
idv_policy eval idv catch total num of agent2: 38
idv_policy eval team catch total num: 32
idv_policy eval average step individual rewards of agent3: 0.5996367437031567
idv_policy eval average team episode rewards of agent3: 80.0
idv_policy eval idv catch total num of agent3: 26
idv_policy eval team catch total num: 32
idv_policy eval average step individual rewards of agent4: 0.4303266894234784
idv_policy eval average team episode rewards of agent4: 80.0
idv_policy eval idv catch total num of agent4: 19
idv_policy eval team catch total num: 32

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2826/10000 episodes, total num timesteps 565400/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2827/10000 episodes, total num timesteps 565600/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2828/10000 episodes, total num timesteps 565800/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2829/10000 episodes, total num timesteps 566000/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2830/10000 episodes, total num timesteps 566200/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2831/10000 episodes, total num timesteps 566400/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2832/10000 episodes, total num timesteps 566600/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2833/10000 episodes, total num timesteps 566800/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2834/10000 episodes, total num timesteps 567000/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2835/10000 episodes, total num timesteps 567200/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2836/10000 episodes, total num timesteps 567400/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2837/10000 episodes, total num timesteps 567600/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2838/10000 episodes, total num timesteps 567800/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2839/10000 episodes, total num timesteps 568000/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2840/10000 episodes, total num timesteps 568200/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2841/10000 episodes, total num timesteps 568400/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2842/10000 episodes, total num timesteps 568600/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2843/10000 episodes, total num timesteps 568800/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2844/10000 episodes, total num timesteps 569000/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2845/10000 episodes, total num timesteps 569200/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2846/10000 episodes, total num timesteps 569400/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2847/10000 episodes, total num timesteps 569600/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2848/10000 episodes, total num timesteps 569800/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2849/10000 episodes, total num timesteps 570000/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2850/10000 episodes, total num timesteps 570200/2000000, FPS 210.

team_policy eval average step individual rewards of agent0: 0.761776306816446
team_policy eval average team episode rewards of agent0: 117.5
team_policy eval idv catch total num of agent0: 32
team_policy eval team catch total num: 47
team_policy eval average step individual rewards of agent1: 0.9142176029554158
team_policy eval average team episode rewards of agent1: 117.5
team_policy eval idv catch total num of agent1: 38
team_policy eval team catch total num: 47
team_policy eval average step individual rewards of agent2: 0.6551607305713789
team_policy eval average team episode rewards of agent2: 117.5
team_policy eval idv catch total num of agent2: 28
team_policy eval team catch total num: 47
team_policy eval average step individual rewards of agent3: 0.8616954703784007
team_policy eval average team episode rewards of agent3: 117.5
team_policy eval idv catch total num of agent3: 36
team_policy eval team catch total num: 47
team_policy eval average step individual rewards of agent4: 0.763465835704525
team_policy eval average team episode rewards of agent4: 117.5
team_policy eval idv catch total num of agent4: 32
team_policy eval team catch total num: 47
idv_policy eval average step individual rewards of agent0: 0.8856886198467804
idv_policy eval average team episode rewards of agent0: 97.5
idv_policy eval idv catch total num of agent0: 37
idv_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent1: 0.5507389156913878
idv_policy eval average team episode rewards of agent1: 97.5
idv_policy eval idv catch total num of agent1: 24
idv_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent2: 0.6826226147396723
idv_policy eval average team episode rewards of agent2: 97.5
idv_policy eval idv catch total num of agent2: 29
idv_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent3: 0.8146404653602272
idv_policy eval average team episode rewards of agent3: 97.5
idv_policy eval idv catch total num of agent3: 34
idv_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent4: 0.8849300926413768
idv_policy eval average team episode rewards of agent4: 97.5
idv_policy eval idv catch total num of agent4: 37
idv_policy eval team catch total num: 39

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2851/10000 episodes, total num timesteps 570400/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2852/10000 episodes, total num timesteps 570600/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2853/10000 episodes, total num timesteps 570800/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2854/10000 episodes, total num timesteps 571000/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2855/10000 episodes, total num timesteps 571200/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2856/10000 episodes, total num timesteps 571400/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2857/10000 episodes, total num timesteps 571600/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2858/10000 episodes, total num timesteps 571800/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2859/10000 episodes, total num timesteps 572000/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2860/10000 episodes, total num timesteps 572200/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2861/10000 episodes, total num timesteps 572400/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2862/10000 episodes, total num timesteps 572600/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2863/10000 episodes, total num timesteps 572800/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2864/10000 episodes, total num timesteps 573000/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2865/10000 episodes, total num timesteps 573200/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2866/10000 episodes, total num timesteps 573400/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2867/10000 episodes, total num timesteps 573600/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2868/10000 episodes, total num timesteps 573800/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2869/10000 episodes, total num timesteps 574000/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2870/10000 episodes, total num timesteps 574200/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2871/10000 episodes, total num timesteps 574400/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2872/10000 episodes, total num timesteps 574600/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2873/10000 episodes, total num timesteps 574800/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2874/10000 episodes, total num timesteps 575000/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2875/10000 episodes, total num timesteps 575200/2000000, FPS 210.

team_policy eval average step individual rewards of agent0: 1.0144147300029174
team_policy eval average team episode rewards of agent0: 97.5
team_policy eval idv catch total num of agent0: 42
team_policy eval team catch total num: 39
team_policy eval average step individual rewards of agent1: 0.6767705267581242
team_policy eval average team episode rewards of agent1: 97.5
team_policy eval idv catch total num of agent1: 29
team_policy eval team catch total num: 39
team_policy eval average step individual rewards of agent2: 0.7329503791495128
team_policy eval average team episode rewards of agent2: 97.5
team_policy eval idv catch total num of agent2: 31
team_policy eval team catch total num: 39
team_policy eval average step individual rewards of agent3: 0.7050518498959643
team_policy eval average team episode rewards of agent3: 97.5
team_policy eval idv catch total num of agent3: 30
team_policy eval team catch total num: 39
team_policy eval average step individual rewards of agent4: 0.6570177691288532
team_policy eval average team episode rewards of agent4: 97.5
team_policy eval idv catch total num of agent4: 28
team_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent0: 1.0439524592481921
idv_policy eval average team episode rewards of agent0: 97.5
idv_policy eval idv catch total num of agent0: 43
idv_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent1: 0.8988485604406692
idv_policy eval average team episode rewards of agent1: 97.5
idv_policy eval idv catch total num of agent1: 37
idv_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent2: 0.3896832975060917
idv_policy eval average team episode rewards of agent2: 97.5
idv_policy eval idv catch total num of agent2: 17
idv_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent3: 0.5111708345997712
idv_policy eval average team episode rewards of agent3: 97.5
idv_policy eval idv catch total num of agent3: 22
idv_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent4: 0.5633175067395452
idv_policy eval average team episode rewards of agent4: 97.5
idv_policy eval idv catch total num of agent4: 24
idv_policy eval team catch total num: 39

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2876/10000 episodes, total num timesteps 575400/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2877/10000 episodes, total num timesteps 575600/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2878/10000 episodes, total num timesteps 575800/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2879/10000 episodes, total num timesteps 576000/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2880/10000 episodes, total num timesteps 576200/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2881/10000 episodes, total num timesteps 576400/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2882/10000 episodes, total num timesteps 576600/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2883/10000 episodes, total num timesteps 576800/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2884/10000 episodes, total num timesteps 577000/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2885/10000 episodes, total num timesteps 577200/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2886/10000 episodes, total num timesteps 577400/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2887/10000 episodes, total num timesteps 577600/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2888/10000 episodes, total num timesteps 577800/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2889/10000 episodes, total num timesteps 578000/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2890/10000 episodes, total num timesteps 578200/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2891/10000 episodes, total num timesteps 578400/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2892/10000 episodes, total num timesteps 578600/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2893/10000 episodes, total num timesteps 578800/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2894/10000 episodes, total num timesteps 579000/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2895/10000 episodes, total num timesteps 579200/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2896/10000 episodes, total num timesteps 579400/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2897/10000 episodes, total num timesteps 579600/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2898/10000 episodes, total num timesteps 579800/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2899/10000 episodes, total num timesteps 580000/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2900/10000 episodes, total num timesteps 580200/2000000, FPS 210.

team_policy eval average step individual rewards of agent0: 1.2707236347646638
team_policy eval average team episode rewards of agent0: 140.0
team_policy eval idv catch total num of agent0: 52
team_policy eval team catch total num: 56
team_policy eval average step individual rewards of agent1: 0.9553513919287069
team_policy eval average team episode rewards of agent1: 140.0
team_policy eval idv catch total num of agent1: 40
team_policy eval team catch total num: 56
team_policy eval average step individual rewards of agent2: 0.7390535281003525
team_policy eval average team episode rewards of agent2: 140.0
team_policy eval idv catch total num of agent2: 31
team_policy eval team catch total num: 56
team_policy eval average step individual rewards of agent3: 1.015318775511077
team_policy eval average team episode rewards of agent3: 140.0
team_policy eval idv catch total num of agent3: 42
team_policy eval team catch total num: 56
team_policy eval average step individual rewards of agent4: 0.8453315063147968
team_policy eval average team episode rewards of agent4: 140.0
team_policy eval idv catch total num of agent4: 35
team_policy eval team catch total num: 56
idv_policy eval average step individual rewards of agent0: 0.7987703737082144
idv_policy eval average team episode rewards of agent0: 102.5
idv_policy eval idv catch total num of agent0: 33
idv_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent1: 1.0893135324912118
idv_policy eval average team episode rewards of agent1: 102.5
idv_policy eval idv catch total num of agent1: 45
idv_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent2: 0.9088650692479425
idv_policy eval average team episode rewards of agent2: 102.5
idv_policy eval idv catch total num of agent2: 38
idv_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent3: 0.5832450581685568
idv_policy eval average team episode rewards of agent3: 102.5
idv_policy eval idv catch total num of agent3: 25
idv_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent4: 0.2600622837908341
idv_policy eval average team episode rewards of agent4: 102.5
idv_policy eval idv catch total num of agent4: 12
idv_policy eval team catch total num: 41

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2901/10000 episodes, total num timesteps 580400/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2902/10000 episodes, total num timesteps 580600/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2903/10000 episodes, total num timesteps 580800/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2904/10000 episodes, total num timesteps 581000/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2905/10000 episodes, total num timesteps 581200/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2906/10000 episodes, total num timesteps 581400/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2907/10000 episodes, total num timesteps 581600/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2908/10000 episodes, total num timesteps 581800/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2909/10000 episodes, total num timesteps 582000/2000000, FPS 210.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2910/10000 episodes, total num timesteps 582200/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2911/10000 episodes, total num timesteps 582400/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2912/10000 episodes, total num timesteps 582600/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2913/10000 episodes, total num timesteps 582800/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2914/10000 episodes, total num timesteps 583000/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2915/10000 episodes, total num timesteps 583200/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2916/10000 episodes, total num timesteps 583400/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2917/10000 episodes, total num timesteps 583600/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2918/10000 episodes, total num timesteps 583800/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2919/10000 episodes, total num timesteps 584000/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2920/10000 episodes, total num timesteps 584200/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2921/10000 episodes, total num timesteps 584400/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2922/10000 episodes, total num timesteps 584600/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2923/10000 episodes, total num timesteps 584800/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2924/10000 episodes, total num timesteps 585000/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2925/10000 episodes, total num timesteps 585200/2000000, FPS 211.

team_policy eval average step individual rewards of agent0: 0.43496117206743184
team_policy eval average team episode rewards of agent0: 77.5
team_policy eval idv catch total num of agent0: 19
team_policy eval team catch total num: 31
team_policy eval average step individual rewards of agent1: 0.5595645447926892
team_policy eval average team episode rewards of agent1: 77.5
team_policy eval idv catch total num of agent1: 24
team_policy eval team catch total num: 31
team_policy eval average step individual rewards of agent2: 0.7627548158113394
team_policy eval average team episode rewards of agent2: 77.5
team_policy eval idv catch total num of agent2: 32
team_policy eval team catch total num: 31
team_policy eval average step individual rewards of agent3: 0.6630883540392943
team_policy eval average team episode rewards of agent3: 77.5
team_policy eval idv catch total num of agent3: 28
team_policy eval team catch total num: 31
team_policy eval average step individual rewards of agent4: 0.46216693003253995
team_policy eval average team episode rewards of agent4: 77.5
team_policy eval idv catch total num of agent4: 20
team_policy eval team catch total num: 31
idv_policy eval average step individual rewards of agent0: 0.7332067569569132
idv_policy eval average team episode rewards of agent0: 90.0
idv_policy eval idv catch total num of agent0: 31
idv_policy eval team catch total num: 36
idv_policy eval average step individual rewards of agent1: 0.491453110962144
idv_policy eval average team episode rewards of agent1: 90.0
idv_policy eval idv catch total num of agent1: 21
idv_policy eval team catch total num: 36
idv_policy eval average step individual rewards of agent2: 0.7808307990922476
idv_policy eval average team episode rewards of agent2: 90.0
idv_policy eval idv catch total num of agent2: 33
idv_policy eval team catch total num: 36
idv_policy eval average step individual rewards of agent3: 0.37888650465743384
idv_policy eval average team episode rewards of agent3: 90.0
idv_policy eval idv catch total num of agent3: 17
idv_policy eval team catch total num: 36
idv_policy eval average step individual rewards of agent4: 0.8217662938175262
idv_policy eval average team episode rewards of agent4: 90.0
idv_policy eval idv catch total num of agent4: 34
idv_policy eval team catch total num: 36

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2926/10000 episodes, total num timesteps 585400/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2927/10000 episodes, total num timesteps 585600/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2928/10000 episodes, total num timesteps 585800/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2929/10000 episodes, total num timesteps 586000/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2930/10000 episodes, total num timesteps 586200/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2931/10000 episodes, total num timesteps 586400/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2932/10000 episodes, total num timesteps 586600/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2933/10000 episodes, total num timesteps 586800/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2934/10000 episodes, total num timesteps 587000/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2935/10000 episodes, total num timesteps 587200/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2936/10000 episodes, total num timesteps 587400/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2937/10000 episodes, total num timesteps 587600/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2938/10000 episodes, total num timesteps 587800/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2939/10000 episodes, total num timesteps 588000/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2940/10000 episodes, total num timesteps 588200/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2941/10000 episodes, total num timesteps 588400/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2942/10000 episodes, total num timesteps 588600/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2943/10000 episodes, total num timesteps 588800/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2944/10000 episodes, total num timesteps 589000/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2945/10000 episodes, total num timesteps 589200/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2946/10000 episodes, total num timesteps 589400/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2947/10000 episodes, total num timesteps 589600/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2948/10000 episodes, total num timesteps 589800/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2949/10000 episodes, total num timesteps 590000/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2950/10000 episodes, total num timesteps 590200/2000000, FPS 211.

team_policy eval average step individual rewards of agent0: 0.32723341851432197
team_policy eval average team episode rewards of agent0: 52.5
team_policy eval idv catch total num of agent0: 15
team_policy eval team catch total num: 21
team_policy eval average step individual rewards of agent1: 0.317923545140748
team_policy eval average team episode rewards of agent1: 52.5
team_policy eval idv catch total num of agent1: 15
team_policy eval team catch total num: 21
team_policy eval average step individual rewards of agent2: 0.40012860529377003
team_policy eval average team episode rewards of agent2: 52.5
team_policy eval idv catch total num of agent2: 18
team_policy eval team catch total num: 21
team_policy eval average step individual rewards of agent3: 0.4268690759870708
team_policy eval average team episode rewards of agent3: 52.5
team_policy eval idv catch total num of agent3: 19
team_policy eval team catch total num: 21
team_policy eval average step individual rewards of agent4: 0.43420319921044354
team_policy eval average team episode rewards of agent4: 52.5
team_policy eval idv catch total num of agent4: 19
team_policy eval team catch total num: 21
idv_policy eval average step individual rewards of agent0: 0.7940661625151404
idv_policy eval average team episode rewards of agent0: 135.0
idv_policy eval idv catch total num of agent0: 33
idv_policy eval team catch total num: 54
idv_policy eval average step individual rewards of agent1: 0.7936523978304121
idv_policy eval average team episode rewards of agent1: 135.0
idv_policy eval idv catch total num of agent1: 33
idv_policy eval team catch total num: 54
idv_policy eval average step individual rewards of agent2: 1.1205593671634448
idv_policy eval average team episode rewards of agent2: 135.0
idv_policy eval idv catch total num of agent2: 46
idv_policy eval team catch total num: 54
idv_policy eval average step individual rewards of agent3: 0.765325787452
idv_policy eval average team episode rewards of agent3: 135.0
idv_policy eval idv catch total num of agent3: 32
idv_policy eval team catch total num: 54
idv_policy eval average step individual rewards of agent4: 0.9965697287953915
idv_policy eval average team episode rewards of agent4: 135.0
idv_policy eval idv catch total num of agent4: 41
idv_policy eval team catch total num: 54

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2951/10000 episodes, total num timesteps 590400/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2952/10000 episodes, total num timesteps 590600/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2953/10000 episodes, total num timesteps 590800/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2954/10000 episodes, total num timesteps 591000/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2955/10000 episodes, total num timesteps 591200/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2956/10000 episodes, total num timesteps 591400/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2957/10000 episodes, total num timesteps 591600/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2958/10000 episodes, total num timesteps 591800/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2959/10000 episodes, total num timesteps 592000/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2960/10000 episodes, total num timesteps 592200/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2961/10000 episodes, total num timesteps 592400/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2962/10000 episodes, total num timesteps 592600/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2963/10000 episodes, total num timesteps 592800/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2964/10000 episodes, total num timesteps 593000/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2965/10000 episodes, total num timesteps 593200/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2966/10000 episodes, total num timesteps 593400/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2967/10000 episodes, total num timesteps 593600/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2968/10000 episodes, total num timesteps 593800/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2969/10000 episodes, total num timesteps 594000/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2970/10000 episodes, total num timesteps 594200/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2971/10000 episodes, total num timesteps 594400/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2972/10000 episodes, total num timesteps 594600/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2973/10000 episodes, total num timesteps 594800/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2974/10000 episodes, total num timesteps 595000/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2975/10000 episodes, total num timesteps 595200/2000000, FPS 211.

team_policy eval average step individual rewards of agent0: 0.5362175103696991
team_policy eval average team episode rewards of agent0: 75.0
team_policy eval idv catch total num of agent0: 23
team_policy eval team catch total num: 30
team_policy eval average step individual rewards of agent1: 0.5824420898502182
team_policy eval average team episode rewards of agent1: 75.0
team_policy eval idv catch total num of agent1: 25
team_policy eval team catch total num: 30
team_policy eval average step individual rewards of agent2: 0.4638960309649113
team_policy eval average team episode rewards of agent2: 75.0
team_policy eval idv catch total num of agent2: 20
team_policy eval team catch total num: 30
team_policy eval average step individual rewards of agent3: 0.7420572890930421
team_policy eval average team episode rewards of agent3: 75.0
team_policy eval idv catch total num of agent3: 31
team_policy eval team catch total num: 30
team_policy eval average step individual rewards of agent4: 0.5233576199101706
team_policy eval average team episode rewards of agent4: 75.0
team_policy eval idv catch total num of agent4: 23
team_policy eval team catch total num: 30
idv_policy eval average step individual rewards of agent0: 0.41283540991320045
idv_policy eval average team episode rewards of agent0: 107.5
idv_policy eval idv catch total num of agent0: 18
idv_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent1: 0.5064223845876206
idv_policy eval average team episode rewards of agent1: 107.5
idv_policy eval idv catch total num of agent1: 22
idv_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent2: 1.0112431387471437
idv_policy eval average team episode rewards of agent2: 107.5
idv_policy eval idv catch total num of agent2: 42
idv_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent3: 1.1745246091167612
idv_policy eval average team episode rewards of agent3: 107.5
idv_policy eval idv catch total num of agent3: 48
idv_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent4: 0.8717545519464918
idv_policy eval average team episode rewards of agent4: 107.5
idv_policy eval idv catch total num of agent4: 36
idv_policy eval team catch total num: 43

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2976/10000 episodes, total num timesteps 595400/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2977/10000 episodes, total num timesteps 595600/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2978/10000 episodes, total num timesteps 595800/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2979/10000 episodes, total num timesteps 596000/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2980/10000 episodes, total num timesteps 596200/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2981/10000 episodes, total num timesteps 596400/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2982/10000 episodes, total num timesteps 596600/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2983/10000 episodes, total num timesteps 596800/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2984/10000 episodes, total num timesteps 597000/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2985/10000 episodes, total num timesteps 597200/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2986/10000 episodes, total num timesteps 597400/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2987/10000 episodes, total num timesteps 597600/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2988/10000 episodes, total num timesteps 597800/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2989/10000 episodes, total num timesteps 598000/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2990/10000 episodes, total num timesteps 598200/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2991/10000 episodes, total num timesteps 598400/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2992/10000 episodes, total num timesteps 598600/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2993/10000 episodes, total num timesteps 598800/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2994/10000 episodes, total num timesteps 599000/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2995/10000 episodes, total num timesteps 599200/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2996/10000 episodes, total num timesteps 599400/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2997/10000 episodes, total num timesteps 599600/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2998/10000 episodes, total num timesteps 599800/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2999/10000 episodes, total num timesteps 600000/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3000/10000 episodes, total num timesteps 600200/2000000, FPS 211.

team_policy eval average step individual rewards of agent0: 0.9126238757283547
team_policy eval average team episode rewards of agent0: 100.0
team_policy eval idv catch total num of agent0: 38
team_policy eval team catch total num: 40
team_policy eval average step individual rewards of agent1: 0.6856992204618478
team_policy eval average team episode rewards of agent1: 100.0
team_policy eval idv catch total num of agent1: 29
team_policy eval team catch total num: 40
team_policy eval average step individual rewards of agent2: 0.9369029216951574
team_policy eval average team episode rewards of agent2: 100.0
team_policy eval idv catch total num of agent2: 39
team_policy eval team catch total num: 40
team_policy eval average step individual rewards of agent3: 0.7883719728262072
team_policy eval average team episode rewards of agent3: 100.0
team_policy eval idv catch total num of agent3: 33
team_policy eval team catch total num: 40
team_policy eval average step individual rewards of agent4: 0.81705510647713
team_policy eval average team episode rewards of agent4: 100.0
team_policy eval idv catch total num of agent4: 34
team_policy eval team catch total num: 40
idv_policy eval average step individual rewards of agent0: 0.8389039868643957
idv_policy eval average team episode rewards of agent0: 132.5
idv_policy eval idv catch total num of agent0: 35
idv_policy eval team catch total num: 53
idv_policy eval average step individual rewards of agent1: 0.993363469047391
idv_policy eval average team episode rewards of agent1: 132.5
idv_policy eval idv catch total num of agent1: 41
idv_policy eval team catch total num: 53
idv_policy eval average step individual rewards of agent2: 1.1921177349139036
idv_policy eval average team episode rewards of agent2: 132.5
idv_policy eval idv catch total num of agent2: 49
idv_policy eval team catch total num: 53
idv_policy eval average step individual rewards of agent3: 0.7177992810553738
idv_policy eval average team episode rewards of agent3: 132.5
idv_policy eval idv catch total num of agent3: 30
idv_policy eval team catch total num: 53
idv_policy eval average step individual rewards of agent4: 1.0955542142884753
idv_policy eval average team episode rewards of agent4: 132.5
idv_policy eval idv catch total num of agent4: 45
idv_policy eval team catch total num: 53

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3001/10000 episodes, total num timesteps 600400/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3002/10000 episodes, total num timesteps 600600/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3003/10000 episodes, total num timesteps 600800/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3004/10000 episodes, total num timesteps 601000/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3005/10000 episodes, total num timesteps 601200/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3006/10000 episodes, total num timesteps 601400/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3007/10000 episodes, total num timesteps 601600/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3008/10000 episodes, total num timesteps 601800/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3009/10000 episodes, total num timesteps 602000/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3010/10000 episodes, total num timesteps 602200/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3011/10000 episodes, total num timesteps 602400/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3012/10000 episodes, total num timesteps 602600/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3013/10000 episodes, total num timesteps 602800/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3014/10000 episodes, total num timesteps 603000/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3015/10000 episodes, total num timesteps 603200/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3016/10000 episodes, total num timesteps 603400/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3017/10000 episodes, total num timesteps 603600/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3018/10000 episodes, total num timesteps 603800/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3019/10000 episodes, total num timesteps 604000/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3020/10000 episodes, total num timesteps 604200/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3021/10000 episodes, total num timesteps 604400/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3022/10000 episodes, total num timesteps 604600/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3023/10000 episodes, total num timesteps 604800/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3024/10000 episodes, total num timesteps 605000/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3025/10000 episodes, total num timesteps 605200/2000000, FPS 211.

team_policy eval average step individual rewards of agent0: 0.5288524249074426
team_policy eval average team episode rewards of agent0: 87.5
team_policy eval idv catch total num of agent0: 23
team_policy eval team catch total num: 35
team_policy eval average step individual rewards of agent1: 0.7893613965211269
team_policy eval average team episode rewards of agent1: 87.5
team_policy eval idv catch total num of agent1: 33
team_policy eval team catch total num: 35
team_policy eval average step individual rewards of agent2: 0.7518800344040527
team_policy eval average team episode rewards of agent2: 87.5
team_policy eval idv catch total num of agent2: 32
team_policy eval team catch total num: 35
team_policy eval average step individual rewards of agent3: 0.6077264977718646
team_policy eval average team episode rewards of agent3: 87.5
team_policy eval idv catch total num of agent3: 26
team_policy eval team catch total num: 35
team_policy eval average step individual rewards of agent4: 0.5871812196231784
team_policy eval average team episode rewards of agent4: 87.5
team_policy eval idv catch total num of agent4: 25
team_policy eval team catch total num: 35
idv_policy eval average step individual rewards of agent0: 0.3593792859391493
idv_policy eval average team episode rewards of agent0: 60.0
idv_policy eval idv catch total num of agent0: 16
idv_policy eval team catch total num: 24
idv_policy eval average step individual rewards of agent1: 0.7185508930029062
idv_policy eval average team episode rewards of agent1: 60.0
idv_policy eval idv catch total num of agent1: 30
idv_policy eval team catch total num: 24
idv_policy eval average step individual rewards of agent2: 0.10118052425825741
idv_policy eval average team episode rewards of agent2: 60.0
idv_policy eval idv catch total num of agent2: 6
idv_policy eval team catch total num: 24
idv_policy eval average step individual rewards of agent3: 0.7691660043963248
idv_policy eval average team episode rewards of agent3: 60.0
idv_policy eval idv catch total num of agent3: 32
idv_policy eval team catch total num: 24
idv_policy eval average step individual rewards of agent4: 0.3266644801101657
idv_policy eval average team episode rewards of agent4: 60.0
idv_policy eval idv catch total num of agent4: 15
idv_policy eval team catch total num: 24

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3026/10000 episodes, total num timesteps 605400/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3027/10000 episodes, total num timesteps 605600/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3028/10000 episodes, total num timesteps 605800/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3029/10000 episodes, total num timesteps 606000/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3030/10000 episodes, total num timesteps 606200/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3031/10000 episodes, total num timesteps 606400/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3032/10000 episodes, total num timesteps 606600/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3033/10000 episodes, total num timesteps 606800/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3034/10000 episodes, total num timesteps 607000/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3035/10000 episodes, total num timesteps 607200/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3036/10000 episodes, total num timesteps 607400/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3037/10000 episodes, total num timesteps 607600/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3038/10000 episodes, total num timesteps 607800/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3039/10000 episodes, total num timesteps 608000/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3040/10000 episodes, total num timesteps 608200/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3041/10000 episodes, total num timesteps 608400/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3042/10000 episodes, total num timesteps 608600/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3043/10000 episodes, total num timesteps 608800/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3044/10000 episodes, total num timesteps 609000/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3045/10000 episodes, total num timesteps 609200/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3046/10000 episodes, total num timesteps 609400/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3047/10000 episodes, total num timesteps 609600/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3048/10000 episodes, total num timesteps 609800/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3049/10000 episodes, total num timesteps 610000/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3050/10000 episodes, total num timesteps 610200/2000000, FPS 211.

team_policy eval average step individual rewards of agent0: 0.8441690818608408
team_policy eval average team episode rewards of agent0: 150.0
team_policy eval idv catch total num of agent0: 35
team_policy eval team catch total num: 60
team_policy eval average step individual rewards of agent1: 1.2450877005106578
team_policy eval average team episode rewards of agent1: 150.0
team_policy eval idv catch total num of agent1: 51
team_policy eval team catch total num: 60
team_policy eval average step individual rewards of agent2: 1.0214025687548385
team_policy eval average team episode rewards of agent2: 150.0
team_policy eval idv catch total num of agent2: 42
team_policy eval team catch total num: 60
team_policy eval average step individual rewards of agent3: 1.1005999536276239
team_policy eval average team episode rewards of agent3: 150.0
team_policy eval idv catch total num of agent3: 45
team_policy eval team catch total num: 60
team_policy eval average step individual rewards of agent4: 0.9185046593732474
team_policy eval average team episode rewards of agent4: 150.0
team_policy eval idv catch total num of agent4: 38
team_policy eval team catch total num: 60
idv_policy eval average step individual rewards of agent0: 0.7343217236991205
idv_policy eval average team episode rewards of agent0: 90.0
idv_policy eval idv catch total num of agent0: 31
idv_policy eval team catch total num: 36
idv_policy eval average step individual rewards of agent1: 0.42265700001653833
idv_policy eval average team episode rewards of agent1: 90.0
idv_policy eval idv catch total num of agent1: 19
idv_policy eval team catch total num: 36
idv_policy eval average step individual rewards of agent2: 1.04053473945138
idv_policy eval average team episode rewards of agent2: 90.0
idv_policy eval idv catch total num of agent2: 43
idv_policy eval team catch total num: 36
idv_policy eval average step individual rewards of agent3: 0.6561372140613548
idv_policy eval average team episode rewards of agent3: 90.0
idv_policy eval idv catch total num of agent3: 28
idv_policy eval team catch total num: 36
idv_policy eval average step individual rewards of agent4: 0.6315674827414817
idv_policy eval average team episode rewards of agent4: 90.0
idv_policy eval idv catch total num of agent4: 27
idv_policy eval team catch total num: 36

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3051/10000 episodes, total num timesteps 610400/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3052/10000 episodes, total num timesteps 610600/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3053/10000 episodes, total num timesteps 610800/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3054/10000 episodes, total num timesteps 611000/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3055/10000 episodes, total num timesteps 611200/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3056/10000 episodes, total num timesteps 611400/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3057/10000 episodes, total num timesteps 611600/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3058/10000 episodes, total num timesteps 611800/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3059/10000 episodes, total num timesteps 612000/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3060/10000 episodes, total num timesteps 612200/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3061/10000 episodes, total num timesteps 612400/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3062/10000 episodes, total num timesteps 612600/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3063/10000 episodes, total num timesteps 612800/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3064/10000 episodes, total num timesteps 613000/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3065/10000 episodes, total num timesteps 613200/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3066/10000 episodes, total num timesteps 613400/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3067/10000 episodes, total num timesteps 613600/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3068/10000 episodes, total num timesteps 613800/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3069/10000 episodes, total num timesteps 614000/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3070/10000 episodes, total num timesteps 614200/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3071/10000 episodes, total num timesteps 614400/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3072/10000 episodes, total num timesteps 614600/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3073/10000 episodes, total num timesteps 614800/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3074/10000 episodes, total num timesteps 615000/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3075/10000 episodes, total num timesteps 615200/2000000, FPS 211.

team_policy eval average step individual rewards of agent0: 0.6602039160542609
team_policy eval average team episode rewards of agent0: 117.5
team_policy eval idv catch total num of agent0: 28
team_policy eval team catch total num: 47
team_policy eval average step individual rewards of agent1: 0.9853461060411481
team_policy eval average team episode rewards of agent1: 117.5
team_policy eval idv catch total num of agent1: 41
team_policy eval team catch total num: 47
team_policy eval average step individual rewards of agent2: 0.6858506078557035
team_policy eval average team episode rewards of agent2: 117.5
team_policy eval idv catch total num of agent2: 29
team_policy eval team catch total num: 47
team_policy eval average step individual rewards of agent3: 1.0312896195517771
team_policy eval average team episode rewards of agent3: 117.5
team_policy eval idv catch total num of agent3: 43
team_policy eval team catch total num: 47
team_policy eval average step individual rewards of agent4: 0.9483161258521747
team_policy eval average team episode rewards of agent4: 117.5
team_policy eval idv catch total num of agent4: 39
team_policy eval team catch total num: 47
idv_policy eval average step individual rewards of agent0: 0.6889277491327421
idv_policy eval average team episode rewards of agent0: 62.5
idv_policy eval idv catch total num of agent0: 29
idv_policy eval team catch total num: 25
idv_policy eval average step individual rewards of agent1: 0.27790768224347984
idv_policy eval average team episode rewards of agent1: 62.5
idv_policy eval idv catch total num of agent1: 13
idv_policy eval team catch total num: 25
idv_policy eval average step individual rewards of agent2: 0.33440755791197724
idv_policy eval average team episode rewards of agent2: 62.5
idv_policy eval idv catch total num of agent2: 15
idv_policy eval team catch total num: 25
idv_policy eval average step individual rewards of agent3: 0.29919990575263455
idv_policy eval average team episode rewards of agent3: 62.5
idv_policy eval idv catch total num of agent3: 14
idv_policy eval team catch total num: 25
idv_policy eval average step individual rewards of agent4: 0.6101005454407794
idv_policy eval average team episode rewards of agent4: 62.5
idv_policy eval idv catch total num of agent4: 26
idv_policy eval team catch total num: 25

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3076/10000 episodes, total num timesteps 615400/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3077/10000 episodes, total num timesteps 615600/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3078/10000 episodes, total num timesteps 615800/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3079/10000 episodes, total num timesteps 616000/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3080/10000 episodes, total num timesteps 616200/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3081/10000 episodes, total num timesteps 616400/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3082/10000 episodes, total num timesteps 616600/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3083/10000 episodes, total num timesteps 616800/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3084/10000 episodes, total num timesteps 617000/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3085/10000 episodes, total num timesteps 617200/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3086/10000 episodes, total num timesteps 617400/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3087/10000 episodes, total num timesteps 617600/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3088/10000 episodes, total num timesteps 617800/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3089/10000 episodes, total num timesteps 618000/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3090/10000 episodes, total num timesteps 618200/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3091/10000 episodes, total num timesteps 618400/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3092/10000 episodes, total num timesteps 618600/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3093/10000 episodes, total num timesteps 618800/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3094/10000 episodes, total num timesteps 619000/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3095/10000 episodes, total num timesteps 619200/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3096/10000 episodes, total num timesteps 619400/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3097/10000 episodes, total num timesteps 619600/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3098/10000 episodes, total num timesteps 619800/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3099/10000 episodes, total num timesteps 620000/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3100/10000 episodes, total num timesteps 620200/2000000, FPS 211.

team_policy eval average step individual rewards of agent0: 0.45684476291037285
team_policy eval average team episode rewards of agent0: 147.5
team_policy eval idv catch total num of agent0: 20
team_policy eval team catch total num: 59
team_policy eval average step individual rewards of agent1: 1.4533292682569896
team_policy eval average team episode rewards of agent1: 147.5
team_policy eval idv catch total num of agent1: 59
team_policy eval team catch total num: 59
team_policy eval average step individual rewards of agent2: 0.9434253170331143
team_policy eval average team episode rewards of agent2: 147.5
team_policy eval idv catch total num of agent2: 39
team_policy eval team catch total num: 59
team_policy eval average step individual rewards of agent3: 1.0385533677422574
team_policy eval average team episode rewards of agent3: 147.5
team_policy eval idv catch total num of agent3: 43
team_policy eval team catch total num: 59
team_policy eval average step individual rewards of agent4: 1.022973730032114
team_policy eval average team episode rewards of agent4: 147.5
team_policy eval idv catch total num of agent4: 42
team_policy eval team catch total num: 59
idv_policy eval average step individual rewards of agent0: 0.9478527754911006
idv_policy eval average team episode rewards of agent0: 122.5
idv_policy eval idv catch total num of agent0: 39
idv_policy eval team catch total num: 49
idv_policy eval average step individual rewards of agent1: 1.2471910028944955
idv_policy eval average team episode rewards of agent1: 122.5
idv_policy eval idv catch total num of agent1: 51
idv_policy eval team catch total num: 49
idv_policy eval average step individual rewards of agent2: 1.1931892148521406
idv_policy eval average team episode rewards of agent2: 122.5
idv_policy eval idv catch total num of agent2: 49
idv_policy eval team catch total num: 49
idv_policy eval average step individual rewards of agent3: 0.7146982178873256
idv_policy eval average team episode rewards of agent3: 122.5
idv_policy eval idv catch total num of agent3: 30
idv_policy eval team catch total num: 49
idv_policy eval average step individual rewards of agent4: 0.6380959977611957
idv_policy eval average team episode rewards of agent4: 122.5
idv_policy eval idv catch total num of agent4: 27
idv_policy eval team catch total num: 49

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3101/10000 episodes, total num timesteps 620400/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3102/10000 episodes, total num timesteps 620600/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3103/10000 episodes, total num timesteps 620800/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3104/10000 episodes, total num timesteps 621000/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3105/10000 episodes, total num timesteps 621200/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3106/10000 episodes, total num timesteps 621400/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3107/10000 episodes, total num timesteps 621600/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3108/10000 episodes, total num timesteps 621800/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3109/10000 episodes, total num timesteps 622000/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3110/10000 episodes, total num timesteps 622200/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3111/10000 episodes, total num timesteps 622400/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3112/10000 episodes, total num timesteps 622600/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3113/10000 episodes, total num timesteps 622800/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3114/10000 episodes, total num timesteps 623000/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3115/10000 episodes, total num timesteps 623200/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3116/10000 episodes, total num timesteps 623400/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3117/10000 episodes, total num timesteps 623600/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3118/10000 episodes, total num timesteps 623800/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3119/10000 episodes, total num timesteps 624000/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3120/10000 episodes, total num timesteps 624200/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3121/10000 episodes, total num timesteps 624400/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3122/10000 episodes, total num timesteps 624600/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3123/10000 episodes, total num timesteps 624800/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3124/10000 episodes, total num timesteps 625000/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3125/10000 episodes, total num timesteps 625200/2000000, FPS 211.

team_policy eval average step individual rewards of agent0: 0.8048463417434293
team_policy eval average team episode rewards of agent0: 97.5
team_policy eval idv catch total num of agent0: 34
team_policy eval team catch total num: 39
team_policy eval average step individual rewards of agent1: 0.5612884132173224
team_policy eval average team episode rewards of agent1: 97.5
team_policy eval idv catch total num of agent1: 24
team_policy eval team catch total num: 39
team_policy eval average step individual rewards of agent2: 0.7307880015308292
team_policy eval average team episode rewards of agent2: 97.5
team_policy eval idv catch total num of agent2: 31
team_policy eval team catch total num: 39
team_policy eval average step individual rewards of agent3: 0.8133748862201958
team_policy eval average team episode rewards of agent3: 97.5
team_policy eval idv catch total num of agent3: 34
team_policy eval team catch total num: 39
team_policy eval average step individual rewards of agent4: 0.5716834792984496
team_policy eval average team episode rewards of agent4: 97.5
team_policy eval idv catch total num of agent4: 25
team_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent0: 0.7946881763766498
idv_policy eval average team episode rewards of agent0: 87.5
idv_policy eval idv catch total num of agent0: 33
idv_policy eval team catch total num: 35
idv_policy eval average step individual rewards of agent1: 0.4219281571927636
idv_policy eval average team episode rewards of agent1: 87.5
idv_policy eval idv catch total num of agent1: 19
idv_policy eval team catch total num: 35
idv_policy eval average step individual rewards of agent2: 0.6922829166676236
idv_policy eval average team episode rewards of agent2: 87.5
idv_policy eval idv catch total num of agent2: 29
idv_policy eval team catch total num: 35
idv_policy eval average step individual rewards of agent3: 0.5866346115471974
idv_policy eval average team episode rewards of agent3: 87.5
idv_policy eval idv catch total num of agent3: 25
idv_policy eval team catch total num: 35
idv_policy eval average step individual rewards of agent4: 1.112568836991664
idv_policy eval average team episode rewards of agent4: 87.5
idv_policy eval idv catch total num of agent4: 46
idv_policy eval team catch total num: 35

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3126/10000 episodes, total num timesteps 625400/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3127/10000 episodes, total num timesteps 625600/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3128/10000 episodes, total num timesteps 625800/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3129/10000 episodes, total num timesteps 626000/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3130/10000 episodes, total num timesteps 626200/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3131/10000 episodes, total num timesteps 626400/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3132/10000 episodes, total num timesteps 626600/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3133/10000 episodes, total num timesteps 626800/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3134/10000 episodes, total num timesteps 627000/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3135/10000 episodes, total num timesteps 627200/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3136/10000 episodes, total num timesteps 627400/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3137/10000 episodes, total num timesteps 627600/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3138/10000 episodes, total num timesteps 627800/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3139/10000 episodes, total num timesteps 628000/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3140/10000 episodes, total num timesteps 628200/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3141/10000 episodes, total num timesteps 628400/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3142/10000 episodes, total num timesteps 628600/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3143/10000 episodes, total num timesteps 628800/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3144/10000 episodes, total num timesteps 629000/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3145/10000 episodes, total num timesteps 629200/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3146/10000 episodes, total num timesteps 629400/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3147/10000 episodes, total num timesteps 629600/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3148/10000 episodes, total num timesteps 629800/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3149/10000 episodes, total num timesteps 630000/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3150/10000 episodes, total num timesteps 630200/2000000, FPS 211.

team_policy eval average step individual rewards of agent0: 1.3967664845337366
team_policy eval average team episode rewards of agent0: 170.0
team_policy eval idv catch total num of agent0: 57
team_policy eval team catch total num: 68
team_policy eval average step individual rewards of agent1: 1.0698198327986
team_policy eval average team episode rewards of agent1: 170.0
team_policy eval idv catch total num of agent1: 44
team_policy eval team catch total num: 68
team_policy eval average step individual rewards of agent2: 1.2950536912580455
team_policy eval average team episode rewards of agent2: 170.0
team_policy eval idv catch total num of agent2: 53
team_policy eval team catch total num: 68
team_policy eval average step individual rewards of agent3: 1.225687654024081
team_policy eval average team episode rewards of agent3: 170.0
team_policy eval idv catch total num of agent3: 50
team_policy eval team catch total num: 68
team_policy eval average step individual rewards of agent4: 1.0405077671021257
team_policy eval average team episode rewards of agent4: 170.0
team_policy eval idv catch total num of agent4: 43
team_policy eval team catch total num: 68
idv_policy eval average step individual rewards of agent0: 0.6633862517717474
idv_policy eval average team episode rewards of agent0: 97.5
idv_policy eval idv catch total num of agent0: 28
idv_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent1: 0.9416068393934682
idv_policy eval average team episode rewards of agent1: 97.5
idv_policy eval idv catch total num of agent1: 39
idv_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent2: 0.514317812825841
idv_policy eval average team episode rewards of agent2: 97.5
idv_policy eval idv catch total num of agent2: 22
idv_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent3: 1.222145273240393
idv_policy eval average team episode rewards of agent3: 97.5
idv_policy eval idv catch total num of agent3: 50
idv_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent4: 0.6972793683328373
idv_policy eval average team episode rewards of agent4: 97.5
idv_policy eval idv catch total num of agent4: 30
idv_policy eval team catch total num: 39

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3151/10000 episodes, total num timesteps 630400/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3152/10000 episodes, total num timesteps 630600/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3153/10000 episodes, total num timesteps 630800/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3154/10000 episodes, total num timesteps 631000/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3155/10000 episodes, total num timesteps 631200/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3156/10000 episodes, total num timesteps 631400/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3157/10000 episodes, total num timesteps 631600/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3158/10000 episodes, total num timesteps 631800/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3159/10000 episodes, total num timesteps 632000/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3160/10000 episodes, total num timesteps 632200/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3161/10000 episodes, total num timesteps 632400/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3162/10000 episodes, total num timesteps 632600/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3163/10000 episodes, total num timesteps 632800/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3164/10000 episodes, total num timesteps 633000/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3165/10000 episodes, total num timesteps 633200/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3166/10000 episodes, total num timesteps 633400/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3167/10000 episodes, total num timesteps 633600/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3168/10000 episodes, total num timesteps 633800/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3169/10000 episodes, total num timesteps 634000/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3170/10000 episodes, total num timesteps 634200/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3171/10000 episodes, total num timesteps 634400/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3172/10000 episodes, total num timesteps 634600/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3173/10000 episodes, total num timesteps 634800/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3174/10000 episodes, total num timesteps 635000/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3175/10000 episodes, total num timesteps 635200/2000000, FPS 211.

team_policy eval average step individual rewards of agent0: 0.1539702556301239
team_policy eval average team episode rewards of agent0: 72.5
team_policy eval idv catch total num of agent0: 8
team_policy eval team catch total num: 29
team_policy eval average step individual rewards of agent1: 0.4696298979050766
team_policy eval average team episode rewards of agent1: 72.5
team_policy eval idv catch total num of agent1: 20
team_policy eval team catch total num: 29
team_policy eval average step individual rewards of agent2: 0.6418217175895689
team_policy eval average team episode rewards of agent2: 72.5
team_policy eval idv catch total num of agent2: 27
team_policy eval team catch total num: 29
team_policy eval average step individual rewards of agent3: 0.4317043080799884
team_policy eval average team episode rewards of agent3: 72.5
team_policy eval idv catch total num of agent3: 19
team_policy eval team catch total num: 29
team_policy eval average step individual rewards of agent4: 0.946509308992633
team_policy eval average team episode rewards of agent4: 72.5
team_policy eval idv catch total num of agent4: 39
team_policy eval team catch total num: 29
idv_policy eval average step individual rewards of agent0: 0.6385535649208492
idv_policy eval average team episode rewards of agent0: 115.0
idv_policy eval idv catch total num of agent0: 27
idv_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent1: 0.9830973195225033
idv_policy eval average team episode rewards of agent1: 115.0
idv_policy eval idv catch total num of agent1: 41
idv_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent2: 1.1510601388184531
idv_policy eval average team episode rewards of agent2: 115.0
idv_policy eval idv catch total num of agent2: 47
idv_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent3: 1.0977019645996102
idv_policy eval average team episode rewards of agent3: 115.0
idv_policy eval idv catch total num of agent3: 45
idv_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent4: 0.764294842235257
idv_policy eval average team episode rewards of agent4: 115.0
idv_policy eval idv catch total num of agent4: 32
idv_policy eval team catch total num: 46

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3176/10000 episodes, total num timesteps 635400/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3177/10000 episodes, total num timesteps 635600/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3178/10000 episodes, total num timesteps 635800/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3179/10000 episodes, total num timesteps 636000/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3180/10000 episodes, total num timesteps 636200/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3181/10000 episodes, total num timesteps 636400/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3182/10000 episodes, total num timesteps 636600/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3183/10000 episodes, total num timesteps 636800/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3184/10000 episodes, total num timesteps 637000/2000000, FPS 211.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3185/10000 episodes, total num timesteps 637200/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3186/10000 episodes, total num timesteps 637400/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3187/10000 episodes, total num timesteps 637600/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3188/10000 episodes, total num timesteps 637800/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3189/10000 episodes, total num timesteps 638000/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3190/10000 episodes, total num timesteps 638200/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3191/10000 episodes, total num timesteps 638400/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3192/10000 episodes, total num timesteps 638600/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3193/10000 episodes, total num timesteps 638800/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3194/10000 episodes, total num timesteps 639000/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3195/10000 episodes, total num timesteps 639200/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3196/10000 episodes, total num timesteps 639400/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3197/10000 episodes, total num timesteps 639600/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3198/10000 episodes, total num timesteps 639800/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3199/10000 episodes, total num timesteps 640000/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3200/10000 episodes, total num timesteps 640200/2000000, FPS 212.

team_policy eval average step individual rewards of agent0: 0.45757961021312993
team_policy eval average team episode rewards of agent0: 85.0
team_policy eval idv catch total num of agent0: 20
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent1: 0.5884734341815756
team_policy eval average team episode rewards of agent1: 85.0
team_policy eval idv catch total num of agent1: 25
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent2: 0.407766059038933
team_policy eval average team episode rewards of agent2: 85.0
team_policy eval idv catch total num of agent2: 18
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent3: 0.7176230302063255
team_policy eval average team episode rewards of agent3: 85.0
team_policy eval idv catch total num of agent3: 30
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent4: 0.7442905324831177
team_policy eval average team episode rewards of agent4: 85.0
team_policy eval idv catch total num of agent4: 31
team_policy eval team catch total num: 34
idv_policy eval average step individual rewards of agent0: 0.992337513847431
idv_policy eval average team episode rewards of agent0: 137.5
idv_policy eval idv catch total num of agent0: 41
idv_policy eval team catch total num: 55
idv_policy eval average step individual rewards of agent1: 0.7512375291077537
idv_policy eval average team episode rewards of agent1: 137.5
idv_policy eval idv catch total num of agent1: 32
idv_policy eval team catch total num: 55
idv_policy eval average step individual rewards of agent2: 1.1197576221624084
idv_policy eval average team episode rewards of agent2: 137.5
idv_policy eval idv catch total num of agent2: 46
idv_policy eval team catch total num: 55
idv_policy eval average step individual rewards of agent3: 0.7249427875446134
idv_policy eval average team episode rewards of agent3: 137.5
idv_policy eval idv catch total num of agent3: 31
idv_policy eval team catch total num: 55
idv_policy eval average step individual rewards of agent4: 0.8945039945283682
idv_policy eval average team episode rewards of agent4: 137.5
idv_policy eval idv catch total num of agent4: 37
idv_policy eval team catch total num: 55

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3201/10000 episodes, total num timesteps 640400/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3202/10000 episodes, total num timesteps 640600/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3203/10000 episodes, total num timesteps 640800/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3204/10000 episodes, total num timesteps 641000/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3205/10000 episodes, total num timesteps 641200/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3206/10000 episodes, total num timesteps 641400/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3207/10000 episodes, total num timesteps 641600/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3208/10000 episodes, total num timesteps 641800/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3209/10000 episodes, total num timesteps 642000/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3210/10000 episodes, total num timesteps 642200/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3211/10000 episodes, total num timesteps 642400/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3212/10000 episodes, total num timesteps 642600/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3213/10000 episodes, total num timesteps 642800/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3214/10000 episodes, total num timesteps 643000/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3215/10000 episodes, total num timesteps 643200/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3216/10000 episodes, total num timesteps 643400/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3217/10000 episodes, total num timesteps 643600/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3218/10000 episodes, total num timesteps 643800/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3219/10000 episodes, total num timesteps 644000/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3220/10000 episodes, total num timesteps 644200/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3221/10000 episodes, total num timesteps 644400/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3222/10000 episodes, total num timesteps 644600/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3223/10000 episodes, total num timesteps 644800/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3224/10000 episodes, total num timesteps 645000/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3225/10000 episodes, total num timesteps 645200/2000000, FPS 212.

team_policy eval average step individual rewards of agent0: 0.5100878510537692
team_policy eval average team episode rewards of agent0: 90.0
team_policy eval idv catch total num of agent0: 22
team_policy eval team catch total num: 36
team_policy eval average step individual rewards of agent1: 0.8374331246117437
team_policy eval average team episode rewards of agent1: 90.0
team_policy eval idv catch total num of agent1: 35
team_policy eval team catch total num: 36
team_policy eval average step individual rewards of agent2: 0.7821602323919741
team_policy eval average team episode rewards of agent2: 90.0
team_policy eval idv catch total num of agent2: 33
team_policy eval team catch total num: 36
team_policy eval average step individual rewards of agent3: 0.4565216807997558
team_policy eval average team episode rewards of agent3: 90.0
team_policy eval idv catch total num of agent3: 20
team_policy eval team catch total num: 36
team_policy eval average step individual rewards of agent4: 1.0978696191586688
team_policy eval average team episode rewards of agent4: 90.0
team_policy eval idv catch total num of agent4: 45
team_policy eval team catch total num: 36
idv_policy eval average step individual rewards of agent0: 1.2254985786521584
idv_policy eval average team episode rewards of agent0: 125.0
idv_policy eval idv catch total num of agent0: 50
idv_policy eval team catch total num: 50
idv_policy eval average step individual rewards of agent1: 0.9155750693856689
idv_policy eval average team episode rewards of agent1: 125.0
idv_policy eval idv catch total num of agent1: 38
idv_policy eval team catch total num: 50
idv_policy eval average step individual rewards of agent2: 0.25486649956404855
idv_policy eval average team episode rewards of agent2: 125.0
idv_policy eval idv catch total num of agent2: 13
idv_policy eval team catch total num: 50
idv_policy eval average step individual rewards of agent3: 1.21390970058437
idv_policy eval average team episode rewards of agent3: 125.0
idv_policy eval idv catch total num of agent3: 50
idv_policy eval team catch total num: 50
idv_policy eval average step individual rewards of agent4: 1.1130707508243802
idv_policy eval average team episode rewards of agent4: 125.0
idv_policy eval idv catch total num of agent4: 46
idv_policy eval team catch total num: 50

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3226/10000 episodes, total num timesteps 645400/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3227/10000 episodes, total num timesteps 645600/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3228/10000 episodes, total num timesteps 645800/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3229/10000 episodes, total num timesteps 646000/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3230/10000 episodes, total num timesteps 646200/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3231/10000 episodes, total num timesteps 646400/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3232/10000 episodes, total num timesteps 646600/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3233/10000 episodes, total num timesteps 646800/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3234/10000 episodes, total num timesteps 647000/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3235/10000 episodes, total num timesteps 647200/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3236/10000 episodes, total num timesteps 647400/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3237/10000 episodes, total num timesteps 647600/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3238/10000 episodes, total num timesteps 647800/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3239/10000 episodes, total num timesteps 648000/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3240/10000 episodes, total num timesteps 648200/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3241/10000 episodes, total num timesteps 648400/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3242/10000 episodes, total num timesteps 648600/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3243/10000 episodes, total num timesteps 648800/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3244/10000 episodes, total num timesteps 649000/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3245/10000 episodes, total num timesteps 649200/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3246/10000 episodes, total num timesteps 649400/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3247/10000 episodes, total num timesteps 649600/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3248/10000 episodes, total num timesteps 649800/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3249/10000 episodes, total num timesteps 650000/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3250/10000 episodes, total num timesteps 650200/2000000, FPS 212.

team_policy eval average step individual rewards of agent0: 0.6838224961999596
team_policy eval average team episode rewards of agent0: 112.5
team_policy eval idv catch total num of agent0: 29
team_policy eval team catch total num: 45
team_policy eval average step individual rewards of agent1: 0.8856274140027852
team_policy eval average team episode rewards of agent1: 112.5
team_policy eval idv catch total num of agent1: 37
team_policy eval team catch total num: 45
team_policy eval average step individual rewards of agent2: 0.6750897563027702
team_policy eval average team episode rewards of agent2: 112.5
team_policy eval idv catch total num of agent2: 29
team_policy eval team catch total num: 45
team_policy eval average step individual rewards of agent3: 0.9083204715400202
team_policy eval average team episode rewards of agent3: 112.5
team_policy eval idv catch total num of agent3: 38
team_policy eval team catch total num: 45
team_policy eval average step individual rewards of agent4: 0.4647326289070564
team_policy eval average team episode rewards of agent4: 112.5
team_policy eval idv catch total num of agent4: 20
team_policy eval team catch total num: 45
idv_policy eval average step individual rewards of agent0: 0.6801109669278302
idv_policy eval average team episode rewards of agent0: 107.5
idv_policy eval idv catch total num of agent0: 29
idv_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent1: 1.2402733411772418
idv_policy eval average team episode rewards of agent1: 107.5
idv_policy eval idv catch total num of agent1: 51
idv_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent2: 0.7154794084373483
idv_policy eval average team episode rewards of agent2: 107.5
idv_policy eval idv catch total num of agent2: 30
idv_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent3: 0.9120927875520372
idv_policy eval average team episode rewards of agent3: 107.5
idv_policy eval idv catch total num of agent3: 38
idv_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent4: 0.621327820950683
idv_policy eval average team episode rewards of agent4: 107.5
idv_policy eval idv catch total num of agent4: 27
idv_policy eval team catch total num: 43

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3251/10000 episodes, total num timesteps 650400/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3252/10000 episodes, total num timesteps 650600/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3253/10000 episodes, total num timesteps 650800/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3254/10000 episodes, total num timesteps 651000/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3255/10000 episodes, total num timesteps 651200/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3256/10000 episodes, total num timesteps 651400/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3257/10000 episodes, total num timesteps 651600/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3258/10000 episodes, total num timesteps 651800/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3259/10000 episodes, total num timesteps 652000/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3260/10000 episodes, total num timesteps 652200/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3261/10000 episodes, total num timesteps 652400/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3262/10000 episodes, total num timesteps 652600/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3263/10000 episodes, total num timesteps 652800/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3264/10000 episodes, total num timesteps 653000/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3265/10000 episodes, total num timesteps 653200/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3266/10000 episodes, total num timesteps 653400/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3267/10000 episodes, total num timesteps 653600/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3268/10000 episodes, total num timesteps 653800/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3269/10000 episodes, total num timesteps 654000/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3270/10000 episodes, total num timesteps 654200/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3271/10000 episodes, total num timesteps 654400/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3272/10000 episodes, total num timesteps 654600/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3273/10000 episodes, total num timesteps 654800/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3274/10000 episodes, total num timesteps 655000/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3275/10000 episodes, total num timesteps 655200/2000000, FPS 212.

team_policy eval average step individual rewards of agent0: 1.149753915942004
team_policy eval average team episode rewards of agent0: 85.0
team_policy eval idv catch total num of agent0: 47
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent1: 0.4124481169443858
team_policy eval average team episode rewards of agent1: 85.0
team_policy eval idv catch total num of agent1: 18
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent2: 0.5039713315182319
team_policy eval average team episode rewards of agent2: 85.0
team_policy eval idv catch total num of agent2: 22
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent3: 0.9939219597497561
team_policy eval average team episode rewards of agent3: 85.0
team_policy eval idv catch total num of agent3: 41
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent4: 0.6092691580383537
team_policy eval average team episode rewards of agent4: 85.0
team_policy eval idv catch total num of agent4: 26
team_policy eval team catch total num: 34
idv_policy eval average step individual rewards of agent0: 0.7081052627395374
idv_policy eval average team episode rewards of agent0: 117.5
idv_policy eval idv catch total num of agent0: 30
idv_policy eval team catch total num: 47
idv_policy eval average step individual rewards of agent1: 0.9866496562999342
idv_policy eval average team episode rewards of agent1: 117.5
idv_policy eval idv catch total num of agent1: 41
idv_policy eval team catch total num: 47
idv_policy eval average step individual rewards of agent2: 0.4586280166853235
idv_policy eval average team episode rewards of agent2: 117.5
idv_policy eval idv catch total num of agent2: 20
idv_policy eval team catch total num: 47
idv_policy eval average step individual rewards of agent3: 0.9127939506034269
idv_policy eval average team episode rewards of agent3: 117.5
idv_policy eval idv catch total num of agent3: 38
idv_policy eval team catch total num: 47
idv_policy eval average step individual rewards of agent4: 0.4721381785949969
idv_policy eval average team episode rewards of agent4: 117.5
idv_policy eval idv catch total num of agent4: 21
idv_policy eval team catch total num: 47

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3276/10000 episodes, total num timesteps 655400/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3277/10000 episodes, total num timesteps 655600/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3278/10000 episodes, total num timesteps 655800/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3279/10000 episodes, total num timesteps 656000/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3280/10000 episodes, total num timesteps 656200/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3281/10000 episodes, total num timesteps 656400/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3282/10000 episodes, total num timesteps 656600/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3283/10000 episodes, total num timesteps 656800/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3284/10000 episodes, total num timesteps 657000/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3285/10000 episodes, total num timesteps 657200/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3286/10000 episodes, total num timesteps 657400/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3287/10000 episodes, total num timesteps 657600/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3288/10000 episodes, total num timesteps 657800/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3289/10000 episodes, total num timesteps 658000/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3290/10000 episodes, total num timesteps 658200/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3291/10000 episodes, total num timesteps 658400/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3292/10000 episodes, total num timesteps 658600/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3293/10000 episodes, total num timesteps 658800/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3294/10000 episodes, total num timesteps 659000/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3295/10000 episodes, total num timesteps 659200/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3296/10000 episodes, total num timesteps 659400/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3297/10000 episodes, total num timesteps 659600/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3298/10000 episodes, total num timesteps 659800/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3299/10000 episodes, total num timesteps 660000/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3300/10000 episodes, total num timesteps 660200/2000000, FPS 212.

team_policy eval average step individual rewards of agent0: 0.681297359165639
team_policy eval average team episode rewards of agent0: 85.0
team_policy eval idv catch total num of agent0: 29
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent1: 0.33648078258870484
team_policy eval average team episode rewards of agent1: 85.0
team_policy eval idv catch total num of agent1: 15
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent2: 0.48209054426063985
team_policy eval average team episode rewards of agent2: 85.0
team_policy eval idv catch total num of agent2: 21
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent3: 0.6823190546372799
team_policy eval average team episode rewards of agent3: 85.0
team_policy eval idv catch total num of agent3: 29
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent4: 0.9450877007099673
team_policy eval average team episode rewards of agent4: 85.0
team_policy eval idv catch total num of agent4: 39
team_policy eval team catch total num: 34
idv_policy eval average step individual rewards of agent0: 0.8426947656658448
idv_policy eval average team episode rewards of agent0: 95.0
idv_policy eval idv catch total num of agent0: 35
idv_policy eval team catch total num: 38
idv_policy eval average step individual rewards of agent1: 0.5627034703799596
idv_policy eval average team episode rewards of agent1: 95.0
idv_policy eval idv catch total num of agent1: 24
idv_policy eval team catch total num: 38
idv_policy eval average step individual rewards of agent2: 0.7849973664168749
idv_policy eval average team episode rewards of agent2: 95.0
idv_policy eval idv catch total num of agent2: 33
idv_policy eval team catch total num: 38
idv_policy eval average step individual rewards of agent3: 0.5381120152489562
idv_policy eval average team episode rewards of agent3: 95.0
idv_policy eval idv catch total num of agent3: 23
idv_policy eval team catch total num: 38
idv_policy eval average step individual rewards of agent4: 0.6820072339276309
idv_policy eval average team episode rewards of agent4: 95.0
idv_policy eval idv catch total num of agent4: 29
idv_policy eval team catch total num: 38

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3301/10000 episodes, total num timesteps 660400/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3302/10000 episodes, total num timesteps 660600/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3303/10000 episodes, total num timesteps 660800/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3304/10000 episodes, total num timesteps 661000/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3305/10000 episodes, total num timesteps 661200/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3306/10000 episodes, total num timesteps 661400/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3307/10000 episodes, total num timesteps 661600/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3308/10000 episodes, total num timesteps 661800/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3309/10000 episodes, total num timesteps 662000/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3310/10000 episodes, total num timesteps 662200/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3311/10000 episodes, total num timesteps 662400/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3312/10000 episodes, total num timesteps 662600/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3313/10000 episodes, total num timesteps 662800/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3314/10000 episodes, total num timesteps 663000/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3315/10000 episodes, total num timesteps 663200/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3316/10000 episodes, total num timesteps 663400/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3317/10000 episodes, total num timesteps 663600/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3318/10000 episodes, total num timesteps 663800/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3319/10000 episodes, total num timesteps 664000/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3320/10000 episodes, total num timesteps 664200/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3321/10000 episodes, total num timesteps 664400/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3322/10000 episodes, total num timesteps 664600/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3323/10000 episodes, total num timesteps 664800/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3324/10000 episodes, total num timesteps 665000/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3325/10000 episodes, total num timesteps 665200/2000000, FPS 212.

team_policy eval average step individual rewards of agent0: 0.39663107884839277
team_policy eval average team episode rewards of agent0: 85.0
team_policy eval idv catch total num of agent0: 18
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent1: 0.8162696997853388
team_policy eval average team episode rewards of agent1: 85.0
team_policy eval idv catch total num of agent1: 34
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent2: 0.8364658888544659
team_policy eval average team episode rewards of agent2: 85.0
team_policy eval idv catch total num of agent2: 35
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent3: 0.5563832071966337
team_policy eval average team episode rewards of agent3: 85.0
team_policy eval idv catch total num of agent3: 24
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent4: 0.5369612459170806
team_policy eval average team episode rewards of agent4: 85.0
team_policy eval idv catch total num of agent4: 23
team_policy eval team catch total num: 34
idv_policy eval average step individual rewards of agent0: 0.45372655233824266
idv_policy eval average team episode rewards of agent0: 107.5
idv_policy eval idv catch total num of agent0: 20
idv_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent1: 0.921513264944615
idv_policy eval average team episode rewards of agent1: 107.5
idv_policy eval idv catch total num of agent1: 38
idv_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent2: 0.5550085995968522
idv_policy eval average team episode rewards of agent2: 107.5
idv_policy eval idv catch total num of agent2: 24
idv_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent3: 0.922115056972239
idv_policy eval average team episode rewards of agent3: 107.5
idv_policy eval idv catch total num of agent3: 38
idv_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent4: 0.7392345425131075
idv_policy eval average team episode rewards of agent4: 107.5
idv_policy eval idv catch total num of agent4: 31
idv_policy eval team catch total num: 43

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3326/10000 episodes, total num timesteps 665400/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3327/10000 episodes, total num timesteps 665600/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3328/10000 episodes, total num timesteps 665800/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3329/10000 episodes, total num timesteps 666000/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3330/10000 episodes, total num timesteps 666200/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3331/10000 episodes, total num timesteps 666400/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3332/10000 episodes, total num timesteps 666600/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3333/10000 episodes, total num timesteps 666800/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3334/10000 episodes, total num timesteps 667000/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3335/10000 episodes, total num timesteps 667200/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3336/10000 episodes, total num timesteps 667400/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3337/10000 episodes, total num timesteps 667600/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3338/10000 episodes, total num timesteps 667800/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3339/10000 episodes, total num timesteps 668000/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3340/10000 episodes, total num timesteps 668200/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3341/10000 episodes, total num timesteps 668400/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3342/10000 episodes, total num timesteps 668600/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3343/10000 episodes, total num timesteps 668800/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3344/10000 episodes, total num timesteps 669000/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3345/10000 episodes, total num timesteps 669200/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3346/10000 episodes, total num timesteps 669400/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3347/10000 episodes, total num timesteps 669600/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3348/10000 episodes, total num timesteps 669800/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3349/10000 episodes, total num timesteps 670000/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3350/10000 episodes, total num timesteps 670200/2000000, FPS 212.

team_policy eval average step individual rewards of agent0: 0.5848465641718537
team_policy eval average team episode rewards of agent0: 80.0
team_policy eval idv catch total num of agent0: 25
team_policy eval team catch total num: 32
team_policy eval average step individual rewards of agent1: 0.4594236854405254
team_policy eval average team episode rewards of agent1: 80.0
team_policy eval idv catch total num of agent1: 20
team_policy eval team catch total num: 32
team_policy eval average step individual rewards of agent2: 0.6327943432812465
team_policy eval average team episode rewards of agent2: 80.0
team_policy eval idv catch total num of agent2: 27
team_policy eval team catch total num: 32
team_policy eval average step individual rewards of agent3: 0.8382436284370296
team_policy eval average team episode rewards of agent3: 80.0
team_policy eval idv catch total num of agent3: 35
team_policy eval team catch total num: 32
team_policy eval average step individual rewards of agent4: 0.5153454712535271
team_policy eval average team episode rewards of agent4: 80.0
team_policy eval idv catch total num of agent4: 22
team_policy eval team catch total num: 32
idv_policy eval average step individual rewards of agent0: 0.4263023280327938
idv_policy eval average team episode rewards of agent0: 102.5
idv_policy eval idv catch total num of agent0: 19
idv_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent1: 0.9552901247713962
idv_policy eval average team episode rewards of agent1: 102.5
idv_policy eval idv catch total num of agent1: 40
idv_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent2: 0.8090591620718931
idv_policy eval average team episode rewards of agent2: 102.5
idv_policy eval idv catch total num of agent2: 34
idv_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent3: 0.5509602710571978
idv_policy eval average team episode rewards of agent3: 102.5
idv_policy eval idv catch total num of agent3: 24
idv_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent4: 0.6589421177993857
idv_policy eval average team episode rewards of agent4: 102.5
idv_policy eval idv catch total num of agent4: 28
idv_policy eval team catch total num: 41

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3351/10000 episodes, total num timesteps 670400/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3352/10000 episodes, total num timesteps 670600/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3353/10000 episodes, total num timesteps 670800/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3354/10000 episodes, total num timesteps 671000/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3355/10000 episodes, total num timesteps 671200/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3356/10000 episodes, total num timesteps 671400/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3357/10000 episodes, total num timesteps 671600/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3358/10000 episodes, total num timesteps 671800/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3359/10000 episodes, total num timesteps 672000/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3360/10000 episodes, total num timesteps 672200/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3361/10000 episodes, total num timesteps 672400/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3362/10000 episodes, total num timesteps 672600/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3363/10000 episodes, total num timesteps 672800/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3364/10000 episodes, total num timesteps 673000/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3365/10000 episodes, total num timesteps 673200/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3366/10000 episodes, total num timesteps 673400/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3367/10000 episodes, total num timesteps 673600/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3368/10000 episodes, total num timesteps 673800/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3369/10000 episodes, total num timesteps 674000/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3370/10000 episodes, total num timesteps 674200/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3371/10000 episodes, total num timesteps 674400/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3372/10000 episodes, total num timesteps 674600/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3373/10000 episodes, total num timesteps 674800/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3374/10000 episodes, total num timesteps 675000/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3375/10000 episodes, total num timesteps 675200/2000000, FPS 212.

team_policy eval average step individual rewards of agent0: 0.6435391785841371
team_policy eval average team episode rewards of agent0: 100.0
team_policy eval idv catch total num of agent0: 27
team_policy eval team catch total num: 40
team_policy eval average step individual rewards of agent1: 0.48377552011431246
team_policy eval average team episode rewards of agent1: 100.0
team_policy eval idv catch total num of agent1: 21
team_policy eval team catch total num: 40
team_policy eval average step individual rewards of agent2: 0.8446937601087895
team_policy eval average team episode rewards of agent2: 100.0
team_policy eval idv catch total num of agent2: 35
team_policy eval team catch total num: 40
team_policy eval average step individual rewards of agent3: 0.7397234502417214
team_policy eval average team episode rewards of agent3: 100.0
team_policy eval idv catch total num of agent3: 31
team_policy eval team catch total num: 40
team_policy eval average step individual rewards of agent4: 0.9187668121112118
team_policy eval average team episode rewards of agent4: 100.0
team_policy eval idv catch total num of agent4: 38
team_policy eval team catch total num: 40
idv_policy eval average step individual rewards of agent0: 0.2792517087448759
idv_policy eval average team episode rewards of agent0: 37.5
idv_policy eval idv catch total num of agent0: 13
idv_policy eval team catch total num: 15
idv_policy eval average step individual rewards of agent1: 0.5053640041927229
idv_policy eval average team episode rewards of agent1: 37.5
idv_policy eval idv catch total num of agent1: 22
idv_policy eval team catch total num: 15
idv_policy eval average step individual rewards of agent2: 0.4853133660242406
idv_policy eval average team episode rewards of agent2: 37.5
idv_policy eval idv catch total num of agent2: 21
idv_policy eval team catch total num: 15
idv_policy eval average step individual rewards of agent3: 0.24171276027167796
idv_policy eval average team episode rewards of agent3: 37.5
idv_policy eval idv catch total num of agent3: 12
idv_policy eval team catch total num: 15
idv_policy eval average step individual rewards of agent4: 0.5267138564593266
idv_policy eval average team episode rewards of agent4: 37.5
idv_policy eval idv catch total num of agent4: 23
idv_policy eval team catch total num: 15

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3376/10000 episodes, total num timesteps 675400/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3377/10000 episodes, total num timesteps 675600/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3378/10000 episodes, total num timesteps 675800/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3379/10000 episodes, total num timesteps 676000/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3380/10000 episodes, total num timesteps 676200/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3381/10000 episodes, total num timesteps 676400/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3382/10000 episodes, total num timesteps 676600/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3383/10000 episodes, total num timesteps 676800/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3384/10000 episodes, total num timesteps 677000/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3385/10000 episodes, total num timesteps 677200/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3386/10000 episodes, total num timesteps 677400/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3387/10000 episodes, total num timesteps 677600/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3388/10000 episodes, total num timesteps 677800/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3389/10000 episodes, total num timesteps 678000/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3390/10000 episodes, total num timesteps 678200/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3391/10000 episodes, total num timesteps 678400/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3392/10000 episodes, total num timesteps 678600/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3393/10000 episodes, total num timesteps 678800/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3394/10000 episodes, total num timesteps 679000/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3395/10000 episodes, total num timesteps 679200/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3396/10000 episodes, total num timesteps 679400/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3397/10000 episodes, total num timesteps 679600/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3398/10000 episodes, total num timesteps 679800/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3399/10000 episodes, total num timesteps 680000/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3400/10000 episodes, total num timesteps 680200/2000000, FPS 212.

team_policy eval average step individual rewards of agent0: 1.3237108966788405
team_policy eval average team episode rewards of agent0: 127.5
team_policy eval idv catch total num of agent0: 54
team_policy eval team catch total num: 51
team_policy eval average step individual rewards of agent1: 0.4029890104333901
team_policy eval average team episode rewards of agent1: 127.5
team_policy eval idv catch total num of agent1: 18
team_policy eval team catch total num: 51
team_policy eval average step individual rewards of agent2: 0.7896998841920144
team_policy eval average team episode rewards of agent2: 127.5
team_policy eval idv catch total num of agent2: 33
team_policy eval team catch total num: 51
team_policy eval average step individual rewards of agent3: 1.0479811612201102
team_policy eval average team episode rewards of agent3: 127.5
team_policy eval idv catch total num of agent3: 43
team_policy eval team catch total num: 51
team_policy eval average step individual rewards of agent4: 0.9797596141687392
team_policy eval average team episode rewards of agent4: 127.5
team_policy eval idv catch total num of agent4: 41
team_policy eval team catch total num: 51
idv_policy eval average step individual rewards of agent0: 0.5866570507603409
idv_policy eval average team episode rewards of agent0: 77.5
idv_policy eval idv catch total num of agent0: 25
idv_policy eval team catch total num: 31
idv_policy eval average step individual rewards of agent1: 0.3559136385722265
idv_policy eval average team episode rewards of agent1: 77.5
idv_policy eval idv catch total num of agent1: 16
idv_policy eval team catch total num: 31
idv_policy eval average step individual rewards of agent2: 0.48455268731757
idv_policy eval average team episode rewards of agent2: 77.5
idv_policy eval idv catch total num of agent2: 21
idv_policy eval team catch total num: 31
idv_policy eval average step individual rewards of agent3: 0.6365968828276468
idv_policy eval average team episode rewards of agent3: 77.5
idv_policy eval idv catch total num of agent3: 27
idv_policy eval team catch total num: 31
idv_policy eval average step individual rewards of agent4: 0.7898516998862017
idv_policy eval average team episode rewards of agent4: 77.5
idv_policy eval idv catch total num of agent4: 33
idv_policy eval team catch total num: 31

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3401/10000 episodes, total num timesteps 680400/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3402/10000 episodes, total num timesteps 680600/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3403/10000 episodes, total num timesteps 680800/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3404/10000 episodes, total num timesteps 681000/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3405/10000 episodes, total num timesteps 681200/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3406/10000 episodes, total num timesteps 681400/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3407/10000 episodes, total num timesteps 681600/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3408/10000 episodes, total num timesteps 681800/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3409/10000 episodes, total num timesteps 682000/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3410/10000 episodes, total num timesteps 682200/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3411/10000 episodes, total num timesteps 682400/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3412/10000 episodes, total num timesteps 682600/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3413/10000 episodes, total num timesteps 682800/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3414/10000 episodes, total num timesteps 683000/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3415/10000 episodes, total num timesteps 683200/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3416/10000 episodes, total num timesteps 683400/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3417/10000 episodes, total num timesteps 683600/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3418/10000 episodes, total num timesteps 683800/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3419/10000 episodes, total num timesteps 684000/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3420/10000 episodes, total num timesteps 684200/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3421/10000 episodes, total num timesteps 684400/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3422/10000 episodes, total num timesteps 684600/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3423/10000 episodes, total num timesteps 684800/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3424/10000 episodes, total num timesteps 685000/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3425/10000 episodes, total num timesteps 685200/2000000, FPS 212.

team_policy eval average step individual rewards of agent0: 0.8412892776744197
team_policy eval average team episode rewards of agent0: 82.5
team_policy eval idv catch total num of agent0: 35
team_policy eval team catch total num: 33
team_policy eval average step individual rewards of agent1: 0.6545947806694055
team_policy eval average team episode rewards of agent1: 82.5
team_policy eval idv catch total num of agent1: 28
team_policy eval team catch total num: 33
team_policy eval average step individual rewards of agent2: 0.5098019645247509
team_policy eval average team episode rewards of agent2: 82.5
team_policy eval idv catch total num of agent2: 22
team_policy eval team catch total num: 33
team_policy eval average step individual rewards of agent3: 0.7121138236466967
team_policy eval average team episode rewards of agent3: 82.5
team_policy eval idv catch total num of agent3: 30
team_policy eval team catch total num: 33
team_policy eval average step individual rewards of agent4: 0.37346650781196883
team_policy eval average team episode rewards of agent4: 82.5
team_policy eval idv catch total num of agent4: 17
team_policy eval team catch total num: 33
idv_policy eval average step individual rewards of agent0: 1.0427916121043106
idv_policy eval average team episode rewards of agent0: 152.5
idv_policy eval idv catch total num of agent0: 43
idv_policy eval team catch total num: 61
idv_policy eval average step individual rewards of agent1: 1.319535234825305
idv_policy eval average team episode rewards of agent1: 152.5
idv_policy eval idv catch total num of agent1: 54
idv_policy eval team catch total num: 61
idv_policy eval average step individual rewards of agent2: 1.4486639367209435
idv_policy eval average team episode rewards of agent2: 152.5
idv_policy eval idv catch total num of agent2: 59
idv_policy eval team catch total num: 61
idv_policy eval average step individual rewards of agent3: 1.0166269167455684
idv_policy eval average team episode rewards of agent3: 152.5
idv_policy eval idv catch total num of agent3: 42
idv_policy eval team catch total num: 61
idv_policy eval average step individual rewards of agent4: 0.918474671741456
idv_policy eval average team episode rewards of agent4: 152.5
idv_policy eval idv catch total num of agent4: 38
idv_policy eval team catch total num: 61

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3426/10000 episodes, total num timesteps 685400/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3427/10000 episodes, total num timesteps 685600/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3428/10000 episodes, total num timesteps 685800/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3429/10000 episodes, total num timesteps 686000/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3430/10000 episodes, total num timesteps 686200/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3431/10000 episodes, total num timesteps 686400/2000000, FPS 212.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3432/10000 episodes, total num timesteps 686600/2000000, FPS 213.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3433/10000 episodes, total num timesteps 686800/2000000, FPS 213.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3434/10000 episodes, total num timesteps 687000/2000000, FPS 213.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3435/10000 episodes, total num timesteps 687200/2000000, FPS 213.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3436/10000 episodes, total num timesteps 687400/2000000, FPS 213.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3437/10000 episodes, total num timesteps 687600/2000000, FPS 213.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3438/10000 episodes, total num timesteps 687800/2000000, FPS 213.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3439/10000 episodes, total num timesteps 688000/2000000, FPS 213.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3440/10000 episodes, total num timesteps 688200/2000000, FPS 213.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3441/10000 episodes, total num timesteps 688400/2000000, FPS 213.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3442/10000 episodes, total num timesteps 688600/2000000, FPS 213.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3443/10000 episodes, total num timesteps 688800/2000000, FPS 213.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3444/10000 episodes, total num timesteps 689000/2000000, FPS 213.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3445/10000 episodes, total num timesteps 689200/2000000, FPS 213.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3446/10000 episodes, total num timesteps 689400/2000000, FPS 213.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3447/10000 episodes, total num timesteps 689600/2000000, FPS 213.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3448/10000 episodes, total num timesteps 689800/2000000, FPS 213.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3449/10000 episodes, total num timesteps 690000/2000000, FPS 213.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3450/10000 episodes, total num timesteps 690200/2000000, FPS 213.

team_policy eval average step individual rewards of agent0: 0.5141769460818004
team_policy eval average team episode rewards of agent0: 122.5
team_policy eval idv catch total num of agent0: 22
team_policy eval team catch total num: 49
team_policy eval average step individual rewards of agent1: 1.3382990946469795
team_policy eval average team episode rewards of agent1: 122.5
team_policy eval idv catch total num of agent1: 55
team_policy eval team catch total num: 49
team_policy eval average step individual rewards of agent2: 0.9686861465401762
team_policy eval average team episode rewards of agent2: 122.5
team_policy eval idv catch total num of agent2: 40
team_policy eval team catch total num: 49
team_policy eval average step individual rewards of agent3: 0.9427891672736037
team_policy eval average team episode rewards of agent3: 122.5
team_policy eval idv catch total num of agent3: 39
team_policy eval team catch total num: 49
team_policy eval average step individual rewards of agent4: 0.6874187403832309
team_policy eval average team episode rewards of agent4: 122.5
team_policy eval idv catch total num of agent4: 29
team_policy eval team catch total num: 49
idv_policy eval average step individual rewards of agent0: 0.4316025262110499
idv_policy eval average team episode rewards of agent0: 70.0
idv_policy eval idv catch total num of agent0: 19
idv_policy eval team catch total num: 28
idv_policy eval average step individual rewards of agent1: 0.20827530515504108
idv_policy eval average team episode rewards of agent1: 70.0
idv_policy eval idv catch total num of agent1: 10
idv_policy eval team catch total num: 28
idv_policy eval average step individual rewards of agent2: 0.8671381268766137
idv_policy eval average team episode rewards of agent2: 70.0
idv_policy eval idv catch total num of agent2: 36
idv_policy eval team catch total num: 28
idv_policy eval average step individual rewards of agent3: 0.691629299134195
idv_policy eval average team episode rewards of agent3: 70.0
idv_policy eval idv catch total num of agent3: 29
idv_policy eval team catch total num: 28
idv_policy eval average step individual rewards of agent4: 0.6118393262834522
idv_policy eval average team episode rewards of agent4: 70.0
idv_policy eval idv catch total num of agent4: 26
idv_policy eval team catch total num: 28

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3451/10000 episodes, total num timesteps 690400/2000000, FPS 213.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3452/10000 episodes, total num timesteps 690600/2000000, FPS 213.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3453/10000 episodes, total num timesteps 690800/2000000, FPS 213.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3454/10000 episodes, total num timesteps 691000/2000000, FPS 213.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3455/10000 episodes, total num timesteps 691200/2000000, FPS 213.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3456/10000 episodes, total num timesteps 691400/2000000, FPS 213.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3457/10000 episodes, total num timesteps 691600/2000000, FPS 213.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3458/10000 episodes, total num timesteps 691800/2000000, FPS 213.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3459/10000 episodes, total num timesteps 692000/2000000, FPS 213.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3460/10000 episodes, total num timesteps 692200/2000000, FPS 213.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3461/10000 episodes, total num timesteps 692400/2000000, FPS 213.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3462/10000 episodes, total num timesteps 692600/2000000, FPS 213.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3463/10000 episodes, total num timesteps 692800/2000000, FPS 213.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3464/10000 episodes, total num timesteps 693000/2000000, FPS 213.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3465/10000 episodes, total num timesteps 693200/2000000, FPS 213.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3466/10000 episodes, total num timesteps 693400/2000000, FPS 213.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3467/10000 episodes, total num timesteps 693600/2000000, FPS 213.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3468/10000 episodes, total num timesteps 693800/2000000, FPS 213.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3469/10000 episodes, total num timesteps 694000/2000000, FPS 213.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3470/10000 episodes, total num timesteps 694200/2000000, FPS 213.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3471/10000 episodes, total num timesteps 694400/2000000, FPS 213.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3472/10000 episodes, total num timesteps 694600/2000000, FPS 213.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3473/10000 episodes, total num timesteps 694800/2000000, FPS 213.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3474/10000 episodes, total num timesteps 695000/2000000, FPS 213.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3475/10000 episodes, total num timesteps 695200/2000000, FPS 213.

team_policy eval average step individual rewards of agent0: 0.5553360714867708
team_policy eval average team episode rewards of agent0: 112.5
team_policy eval idv catch total num of agent0: 24
team_policy eval team catch total num: 45
team_policy eval average step individual rewards of agent1: 0.3065844840377384
team_policy eval average team episode rewards of agent1: 112.5
team_policy eval idv catch total num of agent1: 14
team_policy eval team catch total num: 45
team_policy eval average step individual rewards of agent2: 0.9309680027945096
team_policy eval average team episode rewards of agent2: 112.5
team_policy eval idv catch total num of agent2: 39
team_policy eval team catch total num: 45
team_policy eval average step individual rewards of agent3: 0.9613536092015839
team_policy eval average team episode rewards of agent3: 112.5
team_policy eval idv catch total num of agent3: 40
team_policy eval team catch total num: 45
team_policy eval average step individual rewards of agent4: 1.0179345303246268
team_policy eval average team episode rewards of agent4: 112.5
team_policy eval idv catch total num of agent4: 42
team_policy eval team catch total num: 45
idv_policy eval average step individual rewards of agent0: 0.6099188539313737
idv_policy eval average team episode rewards of agent0: 145.0
idv_policy eval idv catch total num of agent0: 26
idv_policy eval team catch total num: 58
idv_policy eval average step individual rewards of agent1: 0.6104932452774373
idv_policy eval average team episode rewards of agent1: 145.0
idv_policy eval idv catch total num of agent1: 26
idv_policy eval team catch total num: 58
idv_policy eval average step individual rewards of agent2: 0.7873305893147142
idv_policy eval average team episode rewards of agent2: 145.0
idv_policy eval idv catch total num of agent2: 33
idv_policy eval team catch total num: 58
idv_policy eval average step individual rewards of agent3: 1.1579143084893844
idv_policy eval average team episode rewards of agent3: 145.0
idv_policy eval idv catch total num of agent3: 48
idv_policy eval team catch total num: 58
idv_policy eval average step individual rewards of agent4: 1.293527149508488
idv_policy eval average team episode rewards of agent4: 145.0
idv_policy eval idv catch total num of agent4: 53
idv_policy eval team catch total num: 58

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3476/10000 episodes, total num timesteps 695400/2000000, FPS 213.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3477/10000 episodes, total num timesteps 695600/2000000, FPS 213.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3478/10000 episodes, total num timesteps 695800/2000000, FPS 213.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3479/10000 episodes, total num timesteps 696000/2000000, FPS 213.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3480/10000 episodes, total num timesteps 696200/2000000, FPS 213.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3481/10000 episodes, total num timesteps 696400/2000000, FPS 213.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3482/10000 episodes, total num timesteps 696600/2000000, FPS 213.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3483/10000 episodes, total num timesteps 696800/2000000, FPS 213.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3484/10000 episodes, total num timesteps 697000/2000000, FPS 213.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3485/10000 episodes, total num timesteps 697200/2000000, FPS 213.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3486/10000 episodes, total num timesteps 697400/2000000, FPS 213.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3487/10000 episodes, total num timesteps 697600/2000000, FPS 213.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3488/10000 episodes, total num timesteps 697800/2000000, FPS 213.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3489/10000 episodes, total num timesteps 698000/2000000, FPS 213.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3490/10000 episodes, total num timesteps 698200/2000000, FPS 213.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3491/10000 episodes, total num timesteps 698400/2000000, FPS 213.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3492/10000 episodes, total num timesteps 698600/2000000, FPS 213.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3493/10000 episodes, total num timesteps 698800/2000000, FPS 213.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3494/10000 episodes, total num timesteps 699000/2000000, FPS 213.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3495/10000 episodes, total num timesteps 699200/2000000, FPS 213.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3496/10000 episodes, total num timesteps 699400/2000000, FPS 213.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3497/10000 episodes, total num timesteps 699600/2000000, FPS 213.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3498/10000 episodes, total num timesteps 699800/2000000, FPS 213.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3499/10000 episodes, total num timesteps 700000/2000000, FPS 213.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3500/10000 episodes, total num timesteps 700200/2000000, FPS 213.

team_policy eval average step individual rewards of agent0: 0.5843064406345175
team_policy eval average team episode rewards of agent0: 117.5
team_policy eval idv catch total num of agent0: 25
team_policy eval team catch total num: 47
team_policy eval average step individual rewards of agent1: 0.9926664828507791
team_policy eval average team episode rewards of agent1: 117.5
team_policy eval idv catch total num of agent1: 41
team_policy eval team catch total num: 47
team_policy eval average step individual rewards of agent2: 1.022899368494514
team_policy eval average team episode rewards of agent2: 117.5
team_policy eval idv catch total num of agent2: 42
team_policy eval team catch total num: 47
team_policy eval average step individual rewards of agent3: 1.145777660467059
team_policy eval average team episode rewards of agent3: 117.5
team_policy eval idv catch total num of agent3: 47
team_policy eval team catch total num: 47
team_policy eval average step individual rewards of agent4: 0.6162544603725607
team_policy eval average team episode rewards of agent4: 117.5
team_policy eval idv catch total num of agent4: 26
team_policy eval team catch total num: 47
idv_policy eval average step individual rewards of agent0: 0.7931162156659429
idv_policy eval average team episode rewards of agent0: 162.5
idv_policy eval idv catch total num of agent0: 34
idv_policy eval team catch total num: 65
idv_policy eval average step individual rewards of agent1: 0.9422690982489504
idv_policy eval average team episode rewards of agent1: 162.5
idv_policy eval idv catch total num of agent1: 39
idv_policy eval team catch total num: 65
idv_policy eval average step individual rewards of agent2: 1.291133700816356
idv_policy eval average team episode rewards of agent2: 162.5
idv_policy eval idv catch total num of agent2: 53
idv_policy eval team catch total num: 65
idv_policy eval average step individual rewards of agent3: 1.6021406310979092
idv_policy eval average team episode rewards of agent3: 162.5
idv_policy eval idv catch total num of agent3: 65
idv_policy eval team catch total num: 65
idv_policy eval average step individual rewards of agent4: 1.1087784157654066
idv_policy eval average team episode rewards of agent4: 162.5
idv_policy eval idv catch total num of agent4: 46
idv_policy eval team catch total num: 65

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3501/10000 episodes, total num timesteps 700400/2000000, FPS 213.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3502/10000 episodes, total num timesteps 700600/2000000, FPS 213.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3503/10000 episodes, total num timesteps 700800/2000000, FPS 213.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3504/10000 episodes, total num timesteps 701000/2000000, FPS 213.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3505/10000 episodes, total num timesteps 701200/2000000, FPS 213.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3506/10000 episodes, total num timesteps 701400/2000000, FPS 213.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3507/10000 episodes, total num timesteps 701600/2000000, FPS 213.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3508/10000 episodes, total num timesteps 701800/2000000, FPS 213.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3509/10000 episodes, total num timesteps 702000/2000000, FPS 213.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3510/10000 episodes, total num timesteps 702200/2000000, FPS 213.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3511/10000 episodes, total num timesteps 702400/2000000, FPS 213.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3512/10000 episodes, total num timesteps 702600/2000000, FPS 213.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3513/10000 episodes, total num timesteps 702800/2000000, FPS 213.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3514/10000 episodes, total num timesteps 703000/2000000, FPS 213.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3515/10000 episodes, total num timesteps 703200/2000000, FPS 213.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3516/10000 episodes, total num timesteps 703400/2000000, FPS 213.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3517/10000 episodes, total num timesteps 703600/2000000, FPS 213.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3518/10000 episodes, total num timesteps 703800/2000000, FPS 213.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3519/10000 episodes, total num timesteps 704000/2000000, FPS 213.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3520/10000 episodes, total num timesteps 704200/2000000, FPS 213.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3521/10000 episodes, total num timesteps 704400/2000000, FPS 213.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3522/10000 episodes, total num timesteps 704600/2000000, FPS 213.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3523/10000 episodes, total num timesteps 704800/2000000, FPS 213.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3524/10000 episodes, total num timesteps 705000/2000000, FPS 213.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3525/10000 episodes, total num timesteps 705200/2000000, FPS 213.

team_policy eval average step individual rewards of agent0: 0.5573803393244142
team_policy eval average team episode rewards of agent0: 82.5
team_policy eval idv catch total num of agent0: 24
team_policy eval team catch total num: 33
team_policy eval average step individual rewards of agent1: 0.48677974808695523
team_policy eval average team episode rewards of agent1: 82.5
team_policy eval idv catch total num of agent1: 21
team_policy eval team catch total num: 33
team_policy eval average step individual rewards of agent2: 0.5303760753599901
team_policy eval average team episode rewards of agent2: 82.5
team_policy eval idv catch total num of agent2: 23
team_policy eval team catch total num: 33
team_policy eval average step individual rewards of agent3: 0.7918198728338425
team_policy eval average team episode rewards of agent3: 82.5
team_policy eval idv catch total num of agent3: 33
team_policy eval team catch total num: 33
team_policy eval average step individual rewards of agent4: 0.689488354172806
team_policy eval average team episode rewards of agent4: 82.5
team_policy eval idv catch total num of agent4: 29
team_policy eval team catch total num: 33
idv_policy eval average step individual rewards of agent0: 0.42143111097637204
idv_policy eval average team episode rewards of agent0: 67.5
idv_policy eval idv catch total num of agent0: 19
idv_policy eval team catch total num: 27
idv_policy eval average step individual rewards of agent1: 0.4959833073865056
idv_policy eval average team episode rewards of agent1: 67.5
idv_policy eval idv catch total num of agent1: 22
idv_policy eval team catch total num: 27
idv_policy eval average step individual rewards of agent2: 0.23878401683316464
idv_policy eval average team episode rewards of agent2: 67.5
idv_policy eval idv catch total num of agent2: 12
idv_policy eval team catch total num: 27
idv_policy eval average step individual rewards of agent3: 0.570115047226899
idv_policy eval average team episode rewards of agent3: 67.5
idv_policy eval idv catch total num of agent3: 25
idv_policy eval team catch total num: 27
idv_policy eval average step individual rewards of agent4: 0.4578236317371857
idv_policy eval average team episode rewards of agent4: 67.5
idv_policy eval idv catch total num of agent4: 20
idv_policy eval team catch total num: 27

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3526/10000 episodes, total num timesteps 705400/2000000, FPS 213.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3527/10000 episodes, total num timesteps 705600/2000000, FPS 213.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3528/10000 episodes, total num timesteps 705800/2000000, FPS 213.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3529/10000 episodes, total num timesteps 706000/2000000, FPS 213.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3530/10000 episodes, total num timesteps 706200/2000000, FPS 213.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3531/10000 episodes, total num timesteps 706400/2000000, FPS 213.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3532/10000 episodes, total num timesteps 706600/2000000, FPS 214.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3533/10000 episodes, total num timesteps 706800/2000000, FPS 214.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3534/10000 episodes, total num timesteps 707000/2000000, FPS 214.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3535/10000 episodes, total num timesteps 707200/2000000, FPS 214.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3536/10000 episodes, total num timesteps 707400/2000000, FPS 214.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3537/10000 episodes, total num timesteps 707600/2000000, FPS 214.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3538/10000 episodes, total num timesteps 707800/2000000, FPS 214.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3539/10000 episodes, total num timesteps 708000/2000000, FPS 214.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3540/10000 episodes, total num timesteps 708200/2000000, FPS 214.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3541/10000 episodes, total num timesteps 708400/2000000, FPS 214.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3542/10000 episodes, total num timesteps 708600/2000000, FPS 214.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3543/10000 episodes, total num timesteps 708800/2000000, FPS 214.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3544/10000 episodes, total num timesteps 709000/2000000, FPS 214.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3545/10000 episodes, total num timesteps 709200/2000000, FPS 214.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3546/10000 episodes, total num timesteps 709400/2000000, FPS 214.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3547/10000 episodes, total num timesteps 709600/2000000, FPS 214.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3548/10000 episodes, total num timesteps 709800/2000000, FPS 214.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3549/10000 episodes, total num timesteps 710000/2000000, FPS 214.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3550/10000 episodes, total num timesteps 710200/2000000, FPS 214.

team_policy eval average step individual rewards of agent0: 0.9694143419295403
team_policy eval average team episode rewards of agent0: 102.5
team_policy eval idv catch total num of agent0: 40
team_policy eval team catch total num: 41
team_policy eval average step individual rewards of agent1: 0.7295184598416452
team_policy eval average team episode rewards of agent1: 102.5
team_policy eval idv catch total num of agent1: 31
team_policy eval team catch total num: 41
team_policy eval average step individual rewards of agent2: 0.6003455832670596
team_policy eval average team episode rewards of agent2: 102.5
team_policy eval idv catch total num of agent2: 26
team_policy eval team catch total num: 41
team_policy eval average step individual rewards of agent3: 0.7498061662050399
team_policy eval average team episode rewards of agent3: 102.5
team_policy eval idv catch total num of agent3: 32
team_policy eval team catch total num: 41
team_policy eval average step individual rewards of agent4: 0.5871637716101406
team_policy eval average team episode rewards of agent4: 102.5
team_policy eval idv catch total num of agent4: 25
team_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent0: 0.7379746549347835
idv_policy eval average team episode rewards of agent0: 100.0
idv_policy eval idv catch total num of agent0: 31
idv_policy eval team catch total num: 40
idv_policy eval average step individual rewards of agent1: 0.5005679780271549
idv_policy eval average team episode rewards of agent1: 100.0
idv_policy eval idv catch total num of agent1: 22
idv_policy eval team catch total num: 40
idv_policy eval average step individual rewards of agent2: 0.6911091108934354
idv_policy eval average team episode rewards of agent2: 100.0
idv_policy eval idv catch total num of agent2: 29
idv_policy eval team catch total num: 40
idv_policy eval average step individual rewards of agent3: 1.2170996503336824
idv_policy eval average team episode rewards of agent3: 100.0
idv_policy eval idv catch total num of agent3: 50
idv_policy eval team catch total num: 40
idv_policy eval average step individual rewards of agent4: 0.7366944428220122
idv_policy eval average team episode rewards of agent4: 100.0
idv_policy eval idv catch total num of agent4: 31
idv_policy eval team catch total num: 40

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3551/10000 episodes, total num timesteps 710400/2000000, FPS 214.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3552/10000 episodes, total num timesteps 710600/2000000, FPS 214.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3553/10000 episodes, total num timesteps 710800/2000000, FPS 214.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3554/10000 episodes, total num timesteps 711000/2000000, FPS 214.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3555/10000 episodes, total num timesteps 711200/2000000, FPS 214.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3556/10000 episodes, total num timesteps 711400/2000000, FPS 214.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3557/10000 episodes, total num timesteps 711600/2000000, FPS 214.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3558/10000 episodes, total num timesteps 711800/2000000, FPS 214.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3559/10000 episodes, total num timesteps 712000/2000000, FPS 214.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3560/10000 episodes, total num timesteps 712200/2000000, FPS 214.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3561/10000 episodes, total num timesteps 712400/2000000, FPS 214.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3562/10000 episodes, total num timesteps 712600/2000000, FPS 214.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3563/10000 episodes, total num timesteps 712800/2000000, FPS 214.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3564/10000 episodes, total num timesteps 713000/2000000, FPS 214.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3565/10000 episodes, total num timesteps 713200/2000000, FPS 214.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3566/10000 episodes, total num timesteps 713400/2000000, FPS 214.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3567/10000 episodes, total num timesteps 713600/2000000, FPS 214.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3568/10000 episodes, total num timesteps 713800/2000000, FPS 214.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3569/10000 episodes, total num timesteps 714000/2000000, FPS 214.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3570/10000 episodes, total num timesteps 714200/2000000, FPS 214.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3571/10000 episodes, total num timesteps 714400/2000000, FPS 214.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3572/10000 episodes, total num timesteps 714600/2000000, FPS 214.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3573/10000 episodes, total num timesteps 714800/2000000, FPS 214.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3574/10000 episodes, total num timesteps 715000/2000000, FPS 214.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3575/10000 episodes, total num timesteps 715200/2000000, FPS 214.

team_policy eval average step individual rewards of agent0: 0.53693476926659
team_policy eval average team episode rewards of agent0: 75.0
team_policy eval idv catch total num of agent0: 23
team_policy eval team catch total num: 30
team_policy eval average step individual rewards of agent1: 0.8852631212784121
team_policy eval average team episode rewards of agent1: 75.0
team_policy eval idv catch total num of agent1: 37
team_policy eval team catch total num: 30
team_policy eval average step individual rewards of agent2: 0.3358659608888115
team_policy eval average team episode rewards of agent2: 75.0
team_policy eval idv catch total num of agent2: 15
team_policy eval team catch total num: 30
team_policy eval average step individual rewards of agent3: 0.8442141853862271
team_policy eval average team episode rewards of agent3: 75.0
team_policy eval idv catch total num of agent3: 35
team_policy eval team catch total num: 30
team_policy eval average step individual rewards of agent4: 0.4532306190336215
team_policy eval average team episode rewards of agent4: 75.0
team_policy eval idv catch total num of agent4: 20
team_policy eval team catch total num: 30
idv_policy eval average step individual rewards of agent0: 0.26341260237235864
idv_policy eval average team episode rewards of agent0: 67.5
idv_policy eval idv catch total num of agent0: 12
idv_policy eval team catch total num: 27
idv_policy eval average step individual rewards of agent1: 0.4849587710030827
idv_policy eval average team episode rewards of agent1: 67.5
idv_policy eval idv catch total num of agent1: 21
idv_policy eval team catch total num: 27
idv_policy eval average step individual rewards of agent2: 0.6364646832170094
idv_policy eval average team episode rewards of agent2: 67.5
idv_policy eval idv catch total num of agent2: 27
idv_policy eval team catch total num: 27
idv_policy eval average step individual rewards of agent3: 0.6543616412407229
idv_policy eval average team episode rewards of agent3: 67.5
idv_policy eval idv catch total num of agent3: 28
idv_policy eval team catch total num: 27
idv_policy eval average step individual rewards of agent4: 0.14615968386033912
idv_policy eval average team episode rewards of agent4: 67.5
idv_policy eval idv catch total num of agent4: 8
idv_policy eval team catch total num: 27

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3576/10000 episodes, total num timesteps 715400/2000000, FPS 214.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3577/10000 episodes, total num timesteps 715600/2000000, FPS 214.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3578/10000 episodes, total num timesteps 715800/2000000, FPS 214.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3579/10000 episodes, total num timesteps 716000/2000000, FPS 214.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3580/10000 episodes, total num timesteps 716200/2000000, FPS 214.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3581/10000 episodes, total num timesteps 716400/2000000, FPS 214.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3582/10000 episodes, total num timesteps 716600/2000000, FPS 214.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3583/10000 episodes, total num timesteps 716800/2000000, FPS 214.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3584/10000 episodes, total num timesteps 717000/2000000, FPS 214.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3585/10000 episodes, total num timesteps 717200/2000000, FPS 214.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3586/10000 episodes, total num timesteps 717400/2000000, FPS 214.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3587/10000 episodes, total num timesteps 717600/2000000, FPS 214.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3588/10000 episodes, total num timesteps 717800/2000000, FPS 214.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3589/10000 episodes, total num timesteps 718000/2000000, FPS 214.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3590/10000 episodes, total num timesteps 718200/2000000, FPS 214.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3591/10000 episodes, total num timesteps 718400/2000000, FPS 214.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3592/10000 episodes, total num timesteps 718600/2000000, FPS 214.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3593/10000 episodes, total num timesteps 718800/2000000, FPS 214.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3594/10000 episodes, total num timesteps 719000/2000000, FPS 214.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3595/10000 episodes, total num timesteps 719200/2000000, FPS 214.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3596/10000 episodes, total num timesteps 719400/2000000, FPS 214.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3597/10000 episodes, total num timesteps 719600/2000000, FPS 214.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3598/10000 episodes, total num timesteps 719800/2000000, FPS 214.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3599/10000 episodes, total num timesteps 720000/2000000, FPS 214.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3600/10000 episodes, total num timesteps 720200/2000000, FPS 214.

team_policy eval average step individual rewards of agent0: 1.1694501244916033
team_policy eval average team episode rewards of agent0: 155.0
team_policy eval idv catch total num of agent0: 48
team_policy eval team catch total num: 62
team_policy eval average step individual rewards of agent1: 0.8129299207050193
team_policy eval average team episode rewards of agent1: 155.0
team_policy eval idv catch total num of agent1: 34
team_policy eval team catch total num: 62
team_policy eval average step individual rewards of agent2: 0.9718922003762042
team_policy eval average team episode rewards of agent2: 155.0
team_policy eval idv catch total num of agent2: 40
team_policy eval team catch total num: 62
team_policy eval average step individual rewards of agent3: 1.4236743874150348
team_policy eval average team episode rewards of agent3: 155.0
team_policy eval idv catch total num of agent3: 58
team_policy eval team catch total num: 62
team_policy eval average step individual rewards of agent4: 1.245068792987643
team_policy eval average team episode rewards of agent4: 155.0
team_policy eval idv catch total num of agent4: 51
team_policy eval team catch total num: 62
idv_policy eval average step individual rewards of agent0: 0.6328019778189775
idv_policy eval average team episode rewards of agent0: 85.0
idv_policy eval idv catch total num of agent0: 27
idv_policy eval team catch total num: 34
idv_policy eval average step individual rewards of agent1: 0.4377046955405114
idv_policy eval average team episode rewards of agent1: 85.0
idv_policy eval idv catch total num of agent1: 20
idv_policy eval team catch total num: 34
idv_policy eval average step individual rewards of agent2: 0.3585518983471466
idv_policy eval average team episode rewards of agent2: 85.0
idv_policy eval idv catch total num of agent2: 16
idv_policy eval team catch total num: 34
idv_policy eval average step individual rewards of agent3: 0.5521167359022112
idv_policy eval average team episode rewards of agent3: 85.0
idv_policy eval idv catch total num of agent3: 24
idv_policy eval team catch total num: 34
idv_policy eval average step individual rewards of agent4: 0.788227107897363
idv_policy eval average team episode rewards of agent4: 85.0
idv_policy eval idv catch total num of agent4: 33
idv_policy eval team catch total num: 34

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3601/10000 episodes, total num timesteps 720400/2000000, FPS 214.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3602/10000 episodes, total num timesteps 720600/2000000, FPS 214.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3603/10000 episodes, total num timesteps 720800/2000000, FPS 214.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3604/10000 episodes, total num timesteps 721000/2000000, FPS 214.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3605/10000 episodes, total num timesteps 721200/2000000, FPS 214.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3606/10000 episodes, total num timesteps 721400/2000000, FPS 214.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3607/10000 episodes, total num timesteps 721600/2000000, FPS 214.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3608/10000 episodes, total num timesteps 721800/2000000, FPS 214.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3609/10000 episodes, total num timesteps 722000/2000000, FPS 215.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3610/10000 episodes, total num timesteps 722200/2000000, FPS 215.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3611/10000 episodes, total num timesteps 722400/2000000, FPS 215.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3612/10000 episodes, total num timesteps 722600/2000000, FPS 215.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3613/10000 episodes, total num timesteps 722800/2000000, FPS 215.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3614/10000 episodes, total num timesteps 723000/2000000, FPS 215.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3615/10000 episodes, total num timesteps 723200/2000000, FPS 215.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3616/10000 episodes, total num timesteps 723400/2000000, FPS 215.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3617/10000 episodes, total num timesteps 723600/2000000, FPS 215.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3618/10000 episodes, total num timesteps 723800/2000000, FPS 215.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3619/10000 episodes, total num timesteps 724000/2000000, FPS 215.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3620/10000 episodes, total num timesteps 724200/2000000, FPS 215.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3621/10000 episodes, total num timesteps 724400/2000000, FPS 215.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3622/10000 episodes, total num timesteps 724600/2000000, FPS 215.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3623/10000 episodes, total num timesteps 724800/2000000, FPS 215.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3624/10000 episodes, total num timesteps 725000/2000000, FPS 215.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3625/10000 episodes, total num timesteps 725200/2000000, FPS 215.

team_policy eval average step individual rewards of agent0: 0.5776199662053262
team_policy eval average team episode rewards of agent0: 82.5
team_policy eval idv catch total num of agent0: 25
team_policy eval team catch total num: 33
team_policy eval average step individual rewards of agent1: 0.8926589306939996
team_policy eval average team episode rewards of agent1: 82.5
team_policy eval idv catch total num of agent1: 37
team_policy eval team catch total num: 33
team_policy eval average step individual rewards of agent2: 0.6972364821559188
team_policy eval average team episode rewards of agent2: 82.5
team_policy eval idv catch total num of agent2: 29
team_policy eval team catch total num: 33
team_policy eval average step individual rewards of agent3: 0.7126298195278413
team_policy eval average team episode rewards of agent3: 82.5
team_policy eval idv catch total num of agent3: 30
team_policy eval team catch total num: 33
team_policy eval average step individual rewards of agent4: 0.37125331169224585
team_policy eval average team episode rewards of agent4: 82.5
team_policy eval idv catch total num of agent4: 17
team_policy eval team catch total num: 33
idv_policy eval average step individual rewards of agent0: 0.9426857198698744
idv_policy eval average team episode rewards of agent0: 77.5
idv_policy eval idv catch total num of agent0: 39
idv_policy eval team catch total num: 31
idv_policy eval average step individual rewards of agent1: 0.6340580498239156
idv_policy eval average team episode rewards of agent1: 77.5
idv_policy eval idv catch total num of agent1: 27
idv_policy eval team catch total num: 31
idv_policy eval average step individual rewards of agent2: 0.5373433975214624
idv_policy eval average team episode rewards of agent2: 77.5
idv_policy eval idv catch total num of agent2: 23
idv_policy eval team catch total num: 31
idv_policy eval average step individual rewards of agent3: 0.6402222530339678
idv_policy eval average team episode rewards of agent3: 77.5
idv_policy eval idv catch total num of agent3: 27
idv_policy eval team catch total num: 31
idv_policy eval average step individual rewards of agent4: 0.440221340696924
idv_policy eval average team episode rewards of agent4: 77.5
idv_policy eval idv catch total num of agent4: 19
idv_policy eval team catch total num: 31

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3626/10000 episodes, total num timesteps 725400/2000000, FPS 215.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3627/10000 episodes, total num timesteps 725600/2000000, FPS 215.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3628/10000 episodes, total num timesteps 725800/2000000, FPS 215.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3629/10000 episodes, total num timesteps 726000/2000000, FPS 215.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3630/10000 episodes, total num timesteps 726200/2000000, FPS 215.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3631/10000 episodes, total num timesteps 726400/2000000, FPS 215.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3632/10000 episodes, total num timesteps 726600/2000000, FPS 215.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3633/10000 episodes, total num timesteps 726800/2000000, FPS 215.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3634/10000 episodes, total num timesteps 727000/2000000, FPS 215.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3635/10000 episodes, total num timesteps 727200/2000000, FPS 215.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3636/10000 episodes, total num timesteps 727400/2000000, FPS 215.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3637/10000 episodes, total num timesteps 727600/2000000, FPS 215.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3638/10000 episodes, total num timesteps 727800/2000000, FPS 215.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3639/10000 episodes, total num timesteps 728000/2000000, FPS 215.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3640/10000 episodes, total num timesteps 728200/2000000, FPS 215.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3641/10000 episodes, total num timesteps 728400/2000000, FPS 215.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3642/10000 episodes, total num timesteps 728600/2000000, FPS 215.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3643/10000 episodes, total num timesteps 728800/2000000, FPS 215.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3644/10000 episodes, total num timesteps 729000/2000000, FPS 215.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3645/10000 episodes, total num timesteps 729200/2000000, FPS 215.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3646/10000 episodes, total num timesteps 729400/2000000, FPS 215.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3647/10000 episodes, total num timesteps 729600/2000000, FPS 215.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3648/10000 episodes, total num timesteps 729800/2000000, FPS 215.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3649/10000 episodes, total num timesteps 730000/2000000, FPS 215.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3650/10000 episodes, total num timesteps 730200/2000000, FPS 215.

team_policy eval average step individual rewards of agent0: 0.711073331014536
team_policy eval average team episode rewards of agent0: 97.5
team_policy eval idv catch total num of agent0: 30
team_policy eval team catch total num: 39
team_policy eval average step individual rewards of agent1: 0.7339044684394054
team_policy eval average team episode rewards of agent1: 97.5
team_policy eval idv catch total num of agent1: 31
team_policy eval team catch total num: 39
team_policy eval average step individual rewards of agent2: 0.3989186548695421
team_policy eval average team episode rewards of agent2: 97.5
team_policy eval idv catch total num of agent2: 18
team_policy eval team catch total num: 39
team_policy eval average step individual rewards of agent3: 1.0378409656176855
team_policy eval average team episode rewards of agent3: 97.5
team_policy eval idv catch total num of agent3: 43
team_policy eval team catch total num: 39
team_policy eval average step individual rewards of agent4: 0.964012972405459
team_policy eval average team episode rewards of agent4: 97.5
team_policy eval idv catch total num of agent4: 40
team_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent0: 0.7186172184565182
idv_policy eval average team episode rewards of agent0: 97.5
idv_policy eval idv catch total num of agent0: 30
idv_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent1: 0.9422171190795544
idv_policy eval average team episode rewards of agent1: 97.5
idv_policy eval idv catch total num of agent1: 39
idv_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent2: 0.7684353235469588
idv_policy eval average team episode rewards of agent2: 97.5
idv_policy eval idv catch total num of agent2: 32
idv_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent3: 0.7367179338180299
idv_policy eval average team episode rewards of agent3: 97.5
idv_policy eval idv catch total num of agent3: 31
idv_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent4: 0.4254272840025154
idv_policy eval average team episode rewards of agent4: 97.5
idv_policy eval idv catch total num of agent4: 19
idv_policy eval team catch total num: 39

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3651/10000 episodes, total num timesteps 730400/2000000, FPS 215.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3652/10000 episodes, total num timesteps 730600/2000000, FPS 215.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3653/10000 episodes, total num timesteps 730800/2000000, FPS 215.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3654/10000 episodes, total num timesteps 731000/2000000, FPS 215.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3655/10000 episodes, total num timesteps 731200/2000000, FPS 215.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3656/10000 episodes, total num timesteps 731400/2000000, FPS 215.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3657/10000 episodes, total num timesteps 731600/2000000, FPS 215.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3658/10000 episodes, total num timesteps 731800/2000000, FPS 215.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3659/10000 episodes, total num timesteps 732000/2000000, FPS 215.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3660/10000 episodes, total num timesteps 732200/2000000, FPS 215.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3661/10000 episodes, total num timesteps 732400/2000000, FPS 215.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3662/10000 episodes, total num timesteps 732600/2000000, FPS 215.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3663/10000 episodes, total num timesteps 732800/2000000, FPS 215.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3664/10000 episodes, total num timesteps 733000/2000000, FPS 215.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3665/10000 episodes, total num timesteps 733200/2000000, FPS 215.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3666/10000 episodes, total num timesteps 733400/2000000, FPS 215.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3667/10000 episodes, total num timesteps 733600/2000000, FPS 215.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3668/10000 episodes, total num timesteps 733800/2000000, FPS 215.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3669/10000 episodes, total num timesteps 734000/2000000, FPS 215.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3670/10000 episodes, total num timesteps 734200/2000000, FPS 215.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3671/10000 episodes, total num timesteps 734400/2000000, FPS 215.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3672/10000 episodes, total num timesteps 734600/2000000, FPS 215.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3673/10000 episodes, total num timesteps 734800/2000000, FPS 215.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3674/10000 episodes, total num timesteps 735000/2000000, FPS 215.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3675/10000 episodes, total num timesteps 735200/2000000, FPS 215.

team_policy eval average step individual rewards of agent0: 0.7586780092635007
team_policy eval average team episode rewards of agent0: 95.0
team_policy eval idv catch total num of agent0: 32
team_policy eval team catch total num: 38
team_policy eval average step individual rewards of agent1: 0.7344608791700112
team_policy eval average team episode rewards of agent1: 95.0
team_policy eval idv catch total num of agent1: 31
team_policy eval team catch total num: 38
team_policy eval average step individual rewards of agent2: 0.6336506912779635
team_policy eval average team episode rewards of agent2: 95.0
team_policy eval idv catch total num of agent2: 27
team_policy eval team catch total num: 38
team_policy eval average step individual rewards of agent3: 0.8118347857966154
team_policy eval average team episode rewards of agent3: 95.0
team_policy eval idv catch total num of agent3: 34
team_policy eval team catch total num: 38
team_policy eval average step individual rewards of agent4: 0.6047596728464059
team_policy eval average team episode rewards of agent4: 95.0
team_policy eval idv catch total num of agent4: 26
team_policy eval team catch total num: 38
idv_policy eval average step individual rewards of agent0: 0.7621891256684705
idv_policy eval average team episode rewards of agent0: 87.5
idv_policy eval idv catch total num of agent0: 32
idv_policy eval team catch total num: 35
idv_policy eval average step individual rewards of agent1: 0.19682897872114316
idv_policy eval average team episode rewards of agent1: 87.5
idv_policy eval idv catch total num of agent1: 10
idv_policy eval team catch total num: 35
idv_policy eval average step individual rewards of agent2: 0.9703804076508092
idv_policy eval average team episode rewards of agent2: 87.5
idv_policy eval idv catch total num of agent2: 40
idv_policy eval team catch total num: 35
idv_policy eval average step individual rewards of agent3: 0.6335268877903998
idv_policy eval average team episode rewards of agent3: 87.5
idv_policy eval idv catch total num of agent3: 27
idv_policy eval team catch total num: 35
idv_policy eval average step individual rewards of agent4: 0.8602027687902928
idv_policy eval average team episode rewards of agent4: 87.5
idv_policy eval idv catch total num of agent4: 36
idv_policy eval team catch total num: 35

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3676/10000 episodes, total num timesteps 735400/2000000, FPS 215.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3677/10000 episodes, total num timesteps 735600/2000000, FPS 215.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3678/10000 episodes, total num timesteps 735800/2000000, FPS 215.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3679/10000 episodes, total num timesteps 736000/2000000, FPS 215.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3680/10000 episodes, total num timesteps 736200/2000000, FPS 215.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3681/10000 episodes, total num timesteps 736400/2000000, FPS 215.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3682/10000 episodes, total num timesteps 736600/2000000, FPS 215.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3683/10000 episodes, total num timesteps 736800/2000000, FPS 215.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3684/10000 episodes, total num timesteps 737000/2000000, FPS 216.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3685/10000 episodes, total num timesteps 737200/2000000, FPS 216.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3686/10000 episodes, total num timesteps 737400/2000000, FPS 216.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3687/10000 episodes, total num timesteps 737600/2000000, FPS 216.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3688/10000 episodes, total num timesteps 737800/2000000, FPS 216.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3689/10000 episodes, total num timesteps 738000/2000000, FPS 216.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3690/10000 episodes, total num timesteps 738200/2000000, FPS 216.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3691/10000 episodes, total num timesteps 738400/2000000, FPS 216.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3692/10000 episodes, total num timesteps 738600/2000000, FPS 216.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3693/10000 episodes, total num timesteps 738800/2000000, FPS 216.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3694/10000 episodes, total num timesteps 739000/2000000, FPS 216.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3695/10000 episodes, total num timesteps 739200/2000000, FPS 216.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3696/10000 episodes, total num timesteps 739400/2000000, FPS 216.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3697/10000 episodes, total num timesteps 739600/2000000, FPS 216.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3698/10000 episodes, total num timesteps 739800/2000000, FPS 216.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3699/10000 episodes, total num timesteps 740000/2000000, FPS 216.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3700/10000 episodes, total num timesteps 740200/2000000, FPS 216.

team_policy eval average step individual rewards of agent0: 1.2161753814280543
team_policy eval average team episode rewards of agent0: 152.5
team_policy eval idv catch total num of agent0: 50
team_policy eval team catch total num: 61
team_policy eval average step individual rewards of agent1: 1.0870500369319673
team_policy eval average team episode rewards of agent1: 152.5
team_policy eval idv catch total num of agent1: 45
team_policy eval team catch total num: 61
team_policy eval average step individual rewards of agent2: 1.0483286303322148
team_policy eval average team episode rewards of agent2: 152.5
team_policy eval idv catch total num of agent2: 43
team_policy eval team catch total num: 61
team_policy eval average step individual rewards of agent3: 0.7787954597034537
team_policy eval average team episode rewards of agent3: 152.5
team_policy eval idv catch total num of agent3: 33
team_policy eval team catch total num: 61
team_policy eval average step individual rewards of agent4: 1.091214153163444
team_policy eval average team episode rewards of agent4: 152.5
team_policy eval idv catch total num of agent4: 45
team_policy eval team catch total num: 61
idv_policy eval average step individual rewards of agent0: 0.7535341272336382
idv_policy eval average team episode rewards of agent0: 107.5
idv_policy eval idv catch total num of agent0: 32
idv_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent1: 0.7174070277887934
idv_policy eval average team episode rewards of agent1: 107.5
idv_policy eval idv catch total num of agent1: 30
idv_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent2: 0.8571581209447338
idv_policy eval average team episode rewards of agent2: 107.5
idv_policy eval idv catch total num of agent2: 36
idv_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent3: 0.7874432368155189
idv_policy eval average team episode rewards of agent3: 107.5
idv_policy eval idv catch total num of agent3: 33
idv_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent4: 0.8052485934452147
idv_policy eval average team episode rewards of agent4: 107.5
idv_policy eval idv catch total num of agent4: 34
idv_policy eval team catch total num: 43

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3701/10000 episodes, total num timesteps 740400/2000000, FPS 216.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3702/10000 episodes, total num timesteps 740600/2000000, FPS 216.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3703/10000 episodes, total num timesteps 740800/2000000, FPS 216.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3704/10000 episodes, total num timesteps 741000/2000000, FPS 216.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3705/10000 episodes, total num timesteps 741200/2000000, FPS 216.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3706/10000 episodes, total num timesteps 741400/2000000, FPS 216.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3707/10000 episodes, total num timesteps 741600/2000000, FPS 216.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3708/10000 episodes, total num timesteps 741800/2000000, FPS 216.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3709/10000 episodes, total num timesteps 742000/2000000, FPS 216.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3710/10000 episodes, total num timesteps 742200/2000000, FPS 216.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3711/10000 episodes, total num timesteps 742400/2000000, FPS 216.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3712/10000 episodes, total num timesteps 742600/2000000, FPS 216.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3713/10000 episodes, total num timesteps 742800/2000000, FPS 216.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3714/10000 episodes, total num timesteps 743000/2000000, FPS 216.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3715/10000 episodes, total num timesteps 743200/2000000, FPS 216.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3716/10000 episodes, total num timesteps 743400/2000000, FPS 216.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3717/10000 episodes, total num timesteps 743600/2000000, FPS 216.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3718/10000 episodes, total num timesteps 743800/2000000, FPS 216.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3719/10000 episodes, total num timesteps 744000/2000000, FPS 216.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3720/10000 episodes, total num timesteps 744200/2000000, FPS 216.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3721/10000 episodes, total num timesteps 744400/2000000, FPS 216.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3722/10000 episodes, total num timesteps 744600/2000000, FPS 216.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3723/10000 episodes, total num timesteps 744800/2000000, FPS 216.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3724/10000 episodes, total num timesteps 745000/2000000, FPS 216.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3725/10000 episodes, total num timesteps 745200/2000000, FPS 216.

team_policy eval average step individual rewards of agent0: 1.3444955975920598
team_policy eval average team episode rewards of agent0: 117.5
team_policy eval idv catch total num of agent0: 55
team_policy eval team catch total num: 47
team_policy eval average step individual rewards of agent1: 0.7360277064164248
team_policy eval average team episode rewards of agent1: 117.5
team_policy eval idv catch total num of agent1: 31
team_policy eval team catch total num: 47
team_policy eval average step individual rewards of agent2: 0.7936204135734519
team_policy eval average team episode rewards of agent2: 117.5
team_policy eval idv catch total num of agent2: 33
team_policy eval team catch total num: 47
team_policy eval average step individual rewards of agent3: 0.8104588780049599
team_policy eval average team episode rewards of agent3: 117.5
team_policy eval idv catch total num of agent3: 34
team_policy eval team catch total num: 47
team_policy eval average step individual rewards of agent4: 0.9379700744610489
team_policy eval average team episode rewards of agent4: 117.5
team_policy eval idv catch total num of agent4: 39
team_policy eval team catch total num: 47
idv_policy eval average step individual rewards of agent0: 0.608054951485577
idv_policy eval average team episode rewards of agent0: 87.5
idv_policy eval idv catch total num of agent0: 26
idv_policy eval team catch total num: 35
idv_policy eval average step individual rewards of agent1: 0.7159936055753593
idv_policy eval average team episode rewards of agent1: 87.5
idv_policy eval idv catch total num of agent1: 30
idv_policy eval team catch total num: 35
idv_policy eval average step individual rewards of agent2: 0.7145420407495163
idv_policy eval average team episode rewards of agent2: 87.5
idv_policy eval idv catch total num of agent2: 30
idv_policy eval team catch total num: 35
idv_policy eval average step individual rewards of agent3: 1.0849226608891498
idv_policy eval average team episode rewards of agent3: 87.5
idv_policy eval idv catch total num of agent3: 45
idv_policy eval team catch total num: 35
idv_policy eval average step individual rewards of agent4: 0.7171070088540518
idv_policy eval average team episode rewards of agent4: 87.5
idv_policy eval idv catch total num of agent4: 30
idv_policy eval team catch total num: 35

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3726/10000 episodes, total num timesteps 745400/2000000, FPS 216.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3727/10000 episodes, total num timesteps 745600/2000000, FPS 216.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3728/10000 episodes, total num timesteps 745800/2000000, FPS 216.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3729/10000 episodes, total num timesteps 746000/2000000, FPS 216.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3730/10000 episodes, total num timesteps 746200/2000000, FPS 216.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3731/10000 episodes, total num timesteps 746400/2000000, FPS 216.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3732/10000 episodes, total num timesteps 746600/2000000, FPS 216.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3733/10000 episodes, total num timesteps 746800/2000000, FPS 216.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3734/10000 episodes, total num timesteps 747000/2000000, FPS 216.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3735/10000 episodes, total num timesteps 747200/2000000, FPS 216.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3736/10000 episodes, total num timesteps 747400/2000000, FPS 216.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3737/10000 episodes, total num timesteps 747600/2000000, FPS 216.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3738/10000 episodes, total num timesteps 747800/2000000, FPS 216.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3739/10000 episodes, total num timesteps 748000/2000000, FPS 216.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3740/10000 episodes, total num timesteps 748200/2000000, FPS 216.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3741/10000 episodes, total num timesteps 748400/2000000, FPS 216.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3742/10000 episodes, total num timesteps 748600/2000000, FPS 216.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3743/10000 episodes, total num timesteps 748800/2000000, FPS 216.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3744/10000 episodes, total num timesteps 749000/2000000, FPS 216.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3745/10000 episodes, total num timesteps 749200/2000000, FPS 216.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3746/10000 episodes, total num timesteps 749400/2000000, FPS 216.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3747/10000 episodes, total num timesteps 749600/2000000, FPS 216.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3748/10000 episodes, total num timesteps 749800/2000000, FPS 216.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3749/10000 episodes, total num timesteps 750000/2000000, FPS 216.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3750/10000 episodes, total num timesteps 750200/2000000, FPS 217.

team_policy eval average step individual rewards of agent0: 0.590439250044808
team_policy eval average team episode rewards of agent0: 137.5
team_policy eval idv catch total num of agent0: 25
team_policy eval team catch total num: 55
team_policy eval average step individual rewards of agent1: 0.9111229661296839
team_policy eval average team episode rewards of agent1: 137.5
team_policy eval idv catch total num of agent1: 38
team_policy eval team catch total num: 55
team_policy eval average step individual rewards of agent2: 0.9420849857461632
team_policy eval average team episode rewards of agent2: 137.5
team_policy eval idv catch total num of agent2: 39
team_policy eval team catch total num: 55
team_policy eval average step individual rewards of agent3: 0.9404852067897252
team_policy eval average team episode rewards of agent3: 137.5
team_policy eval idv catch total num of agent3: 39
team_policy eval team catch total num: 55
team_policy eval average step individual rewards of agent4: 0.712865782147318
team_policy eval average team episode rewards of agent4: 137.5
team_policy eval idv catch total num of agent4: 30
team_policy eval team catch total num: 55
idv_policy eval average step individual rewards of agent0: 0.7031778905674897
idv_policy eval average team episode rewards of agent0: 97.5
idv_policy eval idv catch total num of agent0: 30
idv_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent1: 0.38171451267168666
idv_policy eval average team episode rewards of agent1: 97.5
idv_policy eval idv catch total num of agent1: 17
idv_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent2: 1.146354982362046
idv_policy eval average team episode rewards of agent2: 97.5
idv_policy eval idv catch total num of agent2: 47
idv_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent3: 0.6830350992904078
idv_policy eval average team episode rewards of agent3: 97.5
idv_policy eval idv catch total num of agent3: 29
idv_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent4: 0.6868441489246514
idv_policy eval average team episode rewards of agent4: 97.5
idv_policy eval idv catch total num of agent4: 29
idv_policy eval team catch total num: 39

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3751/10000 episodes, total num timesteps 750400/2000000, FPS 217.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3752/10000 episodes, total num timesteps 750600/2000000, FPS 217.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3753/10000 episodes, total num timesteps 750800/2000000, FPS 217.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3754/10000 episodes, total num timesteps 751000/2000000, FPS 217.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3755/10000 episodes, total num timesteps 751200/2000000, FPS 217.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3756/10000 episodes, total num timesteps 751400/2000000, FPS 217.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3757/10000 episodes, total num timesteps 751600/2000000, FPS 217.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3758/10000 episodes, total num timesteps 751800/2000000, FPS 217.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3759/10000 episodes, total num timesteps 752000/2000000, FPS 217.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3760/10000 episodes, total num timesteps 752200/2000000, FPS 217.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3761/10000 episodes, total num timesteps 752400/2000000, FPS 217.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3762/10000 episodes, total num timesteps 752600/2000000, FPS 217.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3763/10000 episodes, total num timesteps 752800/2000000, FPS 217.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3764/10000 episodes, total num timesteps 753000/2000000, FPS 217.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3765/10000 episodes, total num timesteps 753200/2000000, FPS 217.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3766/10000 episodes, total num timesteps 753400/2000000, FPS 217.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3767/10000 episodes, total num timesteps 753600/2000000, FPS 217.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3768/10000 episodes, total num timesteps 753800/2000000, FPS 217.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3769/10000 episodes, total num timesteps 754000/2000000, FPS 217.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3770/10000 episodes, total num timesteps 754200/2000000, FPS 217.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3771/10000 episodes, total num timesteps 754400/2000000, FPS 217.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3772/10000 episodes, total num timesteps 754600/2000000, FPS 217.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3773/10000 episodes, total num timesteps 754800/2000000, FPS 217.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3774/10000 episodes, total num timesteps 755000/2000000, FPS 217.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3775/10000 episodes, total num timesteps 755200/2000000, FPS 217.

team_policy eval average step individual rewards of agent0: 0.40909104538300023
team_policy eval average team episode rewards of agent0: 92.5
team_policy eval idv catch total num of agent0: 18
team_policy eval team catch total num: 37
team_policy eval average step individual rewards of agent1: 0.9872795154394839
team_policy eval average team episode rewards of agent1: 92.5
team_policy eval idv catch total num of agent1: 41
team_policy eval team catch total num: 37
team_policy eval average step individual rewards of agent2: 0.6880743732335602
team_policy eval average team episode rewards of agent2: 92.5
team_policy eval idv catch total num of agent2: 29
team_policy eval team catch total num: 37
team_policy eval average step individual rewards of agent3: 0.8349890959723854
team_policy eval average team episode rewards of agent3: 92.5
team_policy eval idv catch total num of agent3: 35
team_policy eval team catch total num: 37
team_policy eval average step individual rewards of agent4: 0.5513202443804568
team_policy eval average team episode rewards of agent4: 92.5
team_policy eval idv catch total num of agent4: 24
team_policy eval team catch total num: 37
idv_policy eval average step individual rewards of agent0: 0.7226775336719021
idv_policy eval average team episode rewards of agent0: 100.0
idv_policy eval idv catch total num of agent0: 30
idv_policy eval team catch total num: 40
idv_policy eval average step individual rewards of agent1: 0.7907185023798813
idv_policy eval average team episode rewards of agent1: 100.0
idv_policy eval idv catch total num of agent1: 33
idv_policy eval team catch total num: 40
idv_policy eval average step individual rewards of agent2: 0.8382098411498646
idv_policy eval average team episode rewards of agent2: 100.0
idv_policy eval idv catch total num of agent2: 35
idv_policy eval team catch total num: 40
idv_policy eval average step individual rewards of agent3: 0.6122476642741407
idv_policy eval average team episode rewards of agent3: 100.0
idv_policy eval idv catch total num of agent3: 26
idv_policy eval team catch total num: 40
idv_policy eval average step individual rewards of agent4: 0.7944903659434156
idv_policy eval average team episode rewards of agent4: 100.0
idv_policy eval idv catch total num of agent4: 33
idv_policy eval team catch total num: 40

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3776/10000 episodes, total num timesteps 755400/2000000, FPS 217.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3777/10000 episodes, total num timesteps 755600/2000000, FPS 217.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3778/10000 episodes, total num timesteps 755800/2000000, FPS 217.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3779/10000 episodes, total num timesteps 756000/2000000, FPS 217.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3780/10000 episodes, total num timesteps 756200/2000000, FPS 217.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3781/10000 episodes, total num timesteps 756400/2000000, FPS 217.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3782/10000 episodes, total num timesteps 756600/2000000, FPS 217.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3783/10000 episodes, total num timesteps 756800/2000000, FPS 217.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3784/10000 episodes, total num timesteps 757000/2000000, FPS 217.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3785/10000 episodes, total num timesteps 757200/2000000, FPS 217.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3786/10000 episodes, total num timesteps 757400/2000000, FPS 217.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3787/10000 episodes, total num timesteps 757600/2000000, FPS 217.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3788/10000 episodes, total num timesteps 757800/2000000, FPS 217.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3789/10000 episodes, total num timesteps 758000/2000000, FPS 217.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3790/10000 episodes, total num timesteps 758200/2000000, FPS 217.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3791/10000 episodes, total num timesteps 758400/2000000, FPS 217.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3792/10000 episodes, total num timesteps 758600/2000000, FPS 217.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3793/10000 episodes, total num timesteps 758800/2000000, FPS 217.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3794/10000 episodes, total num timesteps 759000/2000000, FPS 217.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3795/10000 episodes, total num timesteps 759200/2000000, FPS 217.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3796/10000 episodes, total num timesteps 759400/2000000, FPS 217.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3797/10000 episodes, total num timesteps 759600/2000000, FPS 217.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3798/10000 episodes, total num timesteps 759800/2000000, FPS 217.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3799/10000 episodes, total num timesteps 760000/2000000, FPS 217.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3800/10000 episodes, total num timesteps 760200/2000000, FPS 217.

team_policy eval average step individual rewards of agent0: 0.8109108119516562
team_policy eval average team episode rewards of agent0: 145.0
team_policy eval idv catch total num of agent0: 34
team_policy eval team catch total num: 58
team_policy eval average step individual rewards of agent1: 1.1668160197152118
team_policy eval average team episode rewards of agent1: 145.0
team_policy eval idv catch total num of agent1: 48
team_policy eval team catch total num: 58
team_policy eval average step individual rewards of agent2: 0.7917291479688597
team_policy eval average team episode rewards of agent2: 145.0
team_policy eval idv catch total num of agent2: 33
team_policy eval team catch total num: 58
team_policy eval average step individual rewards of agent3: 1.212968759159887
team_policy eval average team episode rewards of agent3: 145.0
team_policy eval idv catch total num of agent3: 50
team_policy eval team catch total num: 58
team_policy eval average step individual rewards of agent4: 0.9739518925464288
team_policy eval average team episode rewards of agent4: 145.0
team_policy eval idv catch total num of agent4: 40
team_policy eval team catch total num: 58
idv_policy eval average step individual rewards of agent0: 1.1657640347146425
idv_policy eval average team episode rewards of agent0: 152.5
idv_policy eval idv catch total num of agent0: 48
idv_policy eval team catch total num: 61
idv_policy eval average step individual rewards of agent1: 1.0408818608237498
idv_policy eval average team episode rewards of agent1: 152.5
idv_policy eval idv catch total num of agent1: 43
idv_policy eval team catch total num: 61
idv_policy eval average step individual rewards of agent2: 0.9911630750059992
idv_policy eval average team episode rewards of agent2: 152.5
idv_policy eval idv catch total num of agent2: 41
idv_policy eval team catch total num: 61
idv_policy eval average step individual rewards of agent3: 1.2184665190382433
idv_policy eval average team episode rewards of agent3: 152.5
idv_policy eval idv catch total num of agent3: 50
idv_policy eval team catch total num: 61
idv_policy eval average step individual rewards of agent4: 0.9651685790416107
idv_policy eval average team episode rewards of agent4: 152.5
idv_policy eval idv catch total num of agent4: 40
idv_policy eval team catch total num: 61

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3801/10000 episodes, total num timesteps 760400/2000000, FPS 217.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3802/10000 episodes, total num timesteps 760600/2000000, FPS 217.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3803/10000 episodes, total num timesteps 760800/2000000, FPS 217.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3804/10000 episodes, total num timesteps 761000/2000000, FPS 217.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3805/10000 episodes, total num timesteps 761200/2000000, FPS 217.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3806/10000 episodes, total num timesteps 761400/2000000, FPS 217.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3807/10000 episodes, total num timesteps 761600/2000000, FPS 217.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3808/10000 episodes, total num timesteps 761800/2000000, FPS 217.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3809/10000 episodes, total num timesteps 762000/2000000, FPS 217.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3810/10000 episodes, total num timesteps 762200/2000000, FPS 217.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3811/10000 episodes, total num timesteps 762400/2000000, FPS 217.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3812/10000 episodes, total num timesteps 762600/2000000, FPS 217.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3813/10000 episodes, total num timesteps 762800/2000000, FPS 217.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3814/10000 episodes, total num timesteps 763000/2000000, FPS 217.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3815/10000 episodes, total num timesteps 763200/2000000, FPS 217.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3816/10000 episodes, total num timesteps 763400/2000000, FPS 217.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3817/10000 episodes, total num timesteps 763600/2000000, FPS 217.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3818/10000 episodes, total num timesteps 763800/2000000, FPS 217.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3819/10000 episodes, total num timesteps 764000/2000000, FPS 218.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3820/10000 episodes, total num timesteps 764200/2000000, FPS 218.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3821/10000 episodes, total num timesteps 764400/2000000, FPS 218.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3822/10000 episodes, total num timesteps 764600/2000000, FPS 218.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3823/10000 episodes, total num timesteps 764800/2000000, FPS 218.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3824/10000 episodes, total num timesteps 765000/2000000, FPS 218.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3825/10000 episodes, total num timesteps 765200/2000000, FPS 218.

team_policy eval average step individual rewards of agent0: 0.8304800990815305
team_policy eval average team episode rewards of agent0: 117.5
team_policy eval idv catch total num of agent0: 35
team_policy eval team catch total num: 47
team_policy eval average step individual rewards of agent1: 0.7569647832478791
team_policy eval average team episode rewards of agent1: 117.5
team_policy eval idv catch total num of agent1: 32
team_policy eval team catch total num: 47
team_policy eval average step individual rewards of agent2: 1.2166820182906115
team_policy eval average team episode rewards of agent2: 117.5
team_policy eval idv catch total num of agent2: 50
team_policy eval team catch total num: 47
team_policy eval average step individual rewards of agent3: 0.8933417818933427
team_policy eval average team episode rewards of agent3: 117.5
team_policy eval idv catch total num of agent3: 37
team_policy eval team catch total num: 47
team_policy eval average step individual rewards of agent4: 0.5608791329966154
team_policy eval average team episode rewards of agent4: 117.5
team_policy eval idv catch total num of agent4: 24
team_policy eval team catch total num: 47
idv_policy eval average step individual rewards of agent0: 0.6339536432461351
idv_policy eval average team episode rewards of agent0: 107.5
idv_policy eval idv catch total num of agent0: 27
idv_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent1: 0.5336972108216511
idv_policy eval average team episode rewards of agent1: 107.5
idv_policy eval idv catch total num of agent1: 23
idv_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent2: 0.9125155054363162
idv_policy eval average team episode rewards of agent2: 107.5
idv_policy eval idv catch total num of agent2: 38
idv_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent3: 0.5793015058542861
idv_policy eval average team episode rewards of agent3: 107.5
idv_policy eval idv catch total num of agent3: 25
idv_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent4: 0.6015399275805627
idv_policy eval average team episode rewards of agent4: 107.5
idv_policy eval idv catch total num of agent4: 26
idv_policy eval team catch total num: 43

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3826/10000 episodes, total num timesteps 765400/2000000, FPS 218.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3827/10000 episodes, total num timesteps 765600/2000000, FPS 218.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3828/10000 episodes, total num timesteps 765800/2000000, FPS 218.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3829/10000 episodes, total num timesteps 766000/2000000, FPS 218.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3830/10000 episodes, total num timesteps 766200/2000000, FPS 218.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3831/10000 episodes, total num timesteps 766400/2000000, FPS 218.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3832/10000 episodes, total num timesteps 766600/2000000, FPS 218.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3833/10000 episodes, total num timesteps 766800/2000000, FPS 218.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3834/10000 episodes, total num timesteps 767000/2000000, FPS 218.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3835/10000 episodes, total num timesteps 767200/2000000, FPS 218.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3836/10000 episodes, total num timesteps 767400/2000000, FPS 218.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3837/10000 episodes, total num timesteps 767600/2000000, FPS 218.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3838/10000 episodes, total num timesteps 767800/2000000, FPS 218.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3839/10000 episodes, total num timesteps 768000/2000000, FPS 218.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3840/10000 episodes, total num timesteps 768200/2000000, FPS 218.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3841/10000 episodes, total num timesteps 768400/2000000, FPS 218.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3842/10000 episodes, total num timesteps 768600/2000000, FPS 218.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3843/10000 episodes, total num timesteps 768800/2000000, FPS 218.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3844/10000 episodes, total num timesteps 769000/2000000, FPS 218.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3845/10000 episodes, total num timesteps 769200/2000000, FPS 218.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3846/10000 episodes, total num timesteps 769400/2000000, FPS 218.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3847/10000 episodes, total num timesteps 769600/2000000, FPS 218.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3848/10000 episodes, total num timesteps 769800/2000000, FPS 218.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3849/10000 episodes, total num timesteps 770000/2000000, FPS 218.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3850/10000 episodes, total num timesteps 770200/2000000, FPS 218.

team_policy eval average step individual rewards of agent0: 1.0241992300402698
team_policy eval average team episode rewards of agent0: 135.0
team_policy eval idv catch total num of agent0: 42
team_policy eval team catch total num: 54
team_policy eval average step individual rewards of agent1: 0.8841245390527759
team_policy eval average team episode rewards of agent1: 135.0
team_policy eval idv catch total num of agent1: 37
team_policy eval team catch total num: 54
team_policy eval average step individual rewards of agent2: 0.9910098363487294
team_policy eval average team episode rewards of agent2: 135.0
team_policy eval idv catch total num of agent2: 41
team_policy eval team catch total num: 54
team_policy eval average step individual rewards of agent3: 0.9968454958239283
team_policy eval average team episode rewards of agent3: 135.0
team_policy eval idv catch total num of agent3: 41
team_policy eval team catch total num: 54
team_policy eval average step individual rewards of agent4: 0.9582888911220085
team_policy eval average team episode rewards of agent4: 135.0
team_policy eval idv catch total num of agent4: 40
team_policy eval team catch total num: 54
idv_policy eval average step individual rewards of agent0: 1.1993010193459182
idv_policy eval average team episode rewards of agent0: 102.5
idv_policy eval idv catch total num of agent0: 49
idv_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent1: 0.6324039021725929
idv_policy eval average team episode rewards of agent1: 102.5
idv_policy eval idv catch total num of agent1: 27
idv_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent2: 0.6343505021279684
idv_policy eval average team episode rewards of agent2: 102.5
idv_policy eval idv catch total num of agent2: 27
idv_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent3: 0.6825094992865408
idv_policy eval average team episode rewards of agent3: 102.5
idv_policy eval idv catch total num of agent3: 29
idv_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent4: 0.556401198046313
idv_policy eval average team episode rewards of agent4: 102.5
idv_policy eval idv catch total num of agent4: 24
idv_policy eval team catch total num: 41

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3851/10000 episodes, total num timesteps 770400/2000000, FPS 218.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3852/10000 episodes, total num timesteps 770600/2000000, FPS 218.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3853/10000 episodes, total num timesteps 770800/2000000, FPS 218.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3854/10000 episodes, total num timesteps 771000/2000000, FPS 218.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3855/10000 episodes, total num timesteps 771200/2000000, FPS 218.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3856/10000 episodes, total num timesteps 771400/2000000, FPS 218.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3857/10000 episodes, total num timesteps 771600/2000000, FPS 218.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3858/10000 episodes, total num timesteps 771800/2000000, FPS 218.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3859/10000 episodes, total num timesteps 772000/2000000, FPS 218.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3860/10000 episodes, total num timesteps 772200/2000000, FPS 218.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3861/10000 episodes, total num timesteps 772400/2000000, FPS 218.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3862/10000 episodes, total num timesteps 772600/2000000, FPS 218.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3863/10000 episodes, total num timesteps 772800/2000000, FPS 218.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3864/10000 episodes, total num timesteps 773000/2000000, FPS 218.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3865/10000 episodes, total num timesteps 773200/2000000, FPS 218.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3866/10000 episodes, total num timesteps 773400/2000000, FPS 218.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3867/10000 episodes, total num timesteps 773600/2000000, FPS 218.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3868/10000 episodes, total num timesteps 773800/2000000, FPS 218.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3869/10000 episodes, total num timesteps 774000/2000000, FPS 218.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3870/10000 episodes, total num timesteps 774200/2000000, FPS 218.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3871/10000 episodes, total num timesteps 774400/2000000, FPS 218.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3872/10000 episodes, total num timesteps 774600/2000000, FPS 218.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3873/10000 episodes, total num timesteps 774800/2000000, FPS 218.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3874/10000 episodes, total num timesteps 775000/2000000, FPS 218.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3875/10000 episodes, total num timesteps 775200/2000000, FPS 218.

team_policy eval average step individual rewards of agent0: 0.42016341087784764
team_policy eval average team episode rewards of agent0: 85.0
team_policy eval idv catch total num of agent0: 19
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent1: 0.7586808924711336
team_policy eval average team episode rewards of agent1: 85.0
team_policy eval idv catch total num of agent1: 32
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent2: 0.833139627206083
team_policy eval average team episode rewards of agent2: 85.0
team_policy eval idv catch total num of agent2: 35
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent3: 0.6092118910336641
team_policy eval average team episode rewards of agent3: 85.0
team_policy eval idv catch total num of agent3: 26
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent4: 0.32282038898170934
team_policy eval average team episode rewards of agent4: 85.0
team_policy eval idv catch total num of agent4: 15
team_policy eval team catch total num: 34
idv_policy eval average step individual rewards of agent0: 0.501323776862624
idv_policy eval average team episode rewards of agent0: 127.5
idv_policy eval idv catch total num of agent0: 22
idv_policy eval team catch total num: 51
idv_policy eval average step individual rewards of agent1: 1.0926883943482713
idv_policy eval average team episode rewards of agent1: 127.5
idv_policy eval idv catch total num of agent1: 45
idv_policy eval team catch total num: 51
idv_policy eval average step individual rewards of agent2: 1.016117447753688
idv_policy eval average team episode rewards of agent2: 127.5
idv_policy eval idv catch total num of agent2: 42
idv_policy eval team catch total num: 51
idv_policy eval average step individual rewards of agent3: 0.9423369552430312
idv_policy eval average team episode rewards of agent3: 127.5
idv_policy eval idv catch total num of agent3: 39
idv_policy eval team catch total num: 51
idv_policy eval average step individual rewards of agent4: 0.7291302366852207
idv_policy eval average team episode rewards of agent4: 127.5
idv_policy eval idv catch total num of agent4: 31
idv_policy eval team catch total num: 51

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3876/10000 episodes, total num timesteps 775400/2000000, FPS 218.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3877/10000 episodes, total num timesteps 775600/2000000, FPS 218.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3878/10000 episodes, total num timesteps 775800/2000000, FPS 218.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3879/10000 episodes, total num timesteps 776000/2000000, FPS 218.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3880/10000 episodes, total num timesteps 776200/2000000, FPS 218.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3881/10000 episodes, total num timesteps 776400/2000000, FPS 218.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3882/10000 episodes, total num timesteps 776600/2000000, FPS 218.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3883/10000 episodes, total num timesteps 776800/2000000, FPS 218.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3884/10000 episodes, total num timesteps 777000/2000000, FPS 218.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3885/10000 episodes, total num timesteps 777200/2000000, FPS 218.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3886/10000 episodes, total num timesteps 777400/2000000, FPS 218.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3887/10000 episodes, total num timesteps 777600/2000000, FPS 218.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3888/10000 episodes, total num timesteps 777800/2000000, FPS 218.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3889/10000 episodes, total num timesteps 778000/2000000, FPS 218.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3890/10000 episodes, total num timesteps 778200/2000000, FPS 219.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3891/10000 episodes, total num timesteps 778400/2000000, FPS 219.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3892/10000 episodes, total num timesteps 778600/2000000, FPS 219.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3893/10000 episodes, total num timesteps 778800/2000000, FPS 219.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3894/10000 episodes, total num timesteps 779000/2000000, FPS 219.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3895/10000 episodes, total num timesteps 779200/2000000, FPS 219.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3896/10000 episodes, total num timesteps 779400/2000000, FPS 219.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3897/10000 episodes, total num timesteps 779600/2000000, FPS 219.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3898/10000 episodes, total num timesteps 779800/2000000, FPS 219.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3899/10000 episodes, total num timesteps 780000/2000000, FPS 219.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3900/10000 episodes, total num timesteps 780200/2000000, FPS 219.

team_policy eval average step individual rewards of agent0: 1.0432503731269087
team_policy eval average team episode rewards of agent0: 105.0
team_policy eval idv catch total num of agent0: 43
team_policy eval team catch total num: 42
team_policy eval average step individual rewards of agent1: 0.7576472975021972
team_policy eval average team episode rewards of agent1: 105.0
team_policy eval idv catch total num of agent1: 32
team_policy eval team catch total num: 42
team_policy eval average step individual rewards of agent2: 0.40835271262354594
team_policy eval average team episode rewards of agent2: 105.0
team_policy eval idv catch total num of agent2: 18
team_policy eval team catch total num: 42
team_policy eval average step individual rewards of agent3: 0.8619337408424655
team_policy eval average team episode rewards of agent3: 105.0
team_policy eval idv catch total num of agent3: 36
team_policy eval team catch total num: 42
team_policy eval average step individual rewards of agent4: 0.7533693628793813
team_policy eval average team episode rewards of agent4: 105.0
team_policy eval idv catch total num of agent4: 32
team_policy eval team catch total num: 42
idv_policy eval average step individual rewards of agent0: 1.16540388776347
idv_policy eval average team episode rewards of agent0: 132.5
idv_policy eval idv catch total num of agent0: 48
idv_policy eval team catch total num: 53
idv_policy eval average step individual rewards of agent1: 0.5397516516518635
idv_policy eval average team episode rewards of agent1: 132.5
idv_policy eval idv catch total num of agent1: 23
idv_policy eval team catch total num: 53
idv_policy eval average step individual rewards of agent2: 0.8708437863182215
idv_policy eval average team episode rewards of agent2: 132.5
idv_policy eval idv catch total num of agent2: 36
idv_policy eval team catch total num: 53
idv_policy eval average step individual rewards of agent3: 0.8691355617732066
idv_policy eval average team episode rewards of agent3: 132.5
idv_policy eval idv catch total num of agent3: 36
idv_policy eval team catch total num: 53
idv_policy eval average step individual rewards of agent4: 1.02254184672832
idv_policy eval average team episode rewards of agent4: 132.5
idv_policy eval idv catch total num of agent4: 42
idv_policy eval team catch total num: 53

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3901/10000 episodes, total num timesteps 780400/2000000, FPS 219.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3902/10000 episodes, total num timesteps 780600/2000000, FPS 219.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3903/10000 episodes, total num timesteps 780800/2000000, FPS 219.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3904/10000 episodes, total num timesteps 781000/2000000, FPS 219.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3905/10000 episodes, total num timesteps 781200/2000000, FPS 219.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3906/10000 episodes, total num timesteps 781400/2000000, FPS 219.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3907/10000 episodes, total num timesteps 781600/2000000, FPS 219.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3908/10000 episodes, total num timesteps 781800/2000000, FPS 219.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3909/10000 episodes, total num timesteps 782000/2000000, FPS 219.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3910/10000 episodes, total num timesteps 782200/2000000, FPS 219.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3911/10000 episodes, total num timesteps 782400/2000000, FPS 219.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3912/10000 episodes, total num timesteps 782600/2000000, FPS 219.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3913/10000 episodes, total num timesteps 782800/2000000, FPS 219.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3914/10000 episodes, total num timesteps 783000/2000000, FPS 219.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3915/10000 episodes, total num timesteps 783200/2000000, FPS 219.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3916/10000 episodes, total num timesteps 783400/2000000, FPS 219.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3917/10000 episodes, total num timesteps 783600/2000000, FPS 219.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3918/10000 episodes, total num timesteps 783800/2000000, FPS 219.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3919/10000 episodes, total num timesteps 784000/2000000, FPS 219.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3920/10000 episodes, total num timesteps 784200/2000000, FPS 219.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3921/10000 episodes, total num timesteps 784400/2000000, FPS 219.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3922/10000 episodes, total num timesteps 784600/2000000, FPS 219.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3923/10000 episodes, total num timesteps 784800/2000000, FPS 219.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3924/10000 episodes, total num timesteps 785000/2000000, FPS 219.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3925/10000 episodes, total num timesteps 785200/2000000, FPS 219.

team_policy eval average step individual rewards of agent0: 0.37703696887296345
team_policy eval average team episode rewards of agent0: 65.0
team_policy eval idv catch total num of agent0: 17
team_policy eval team catch total num: 26
team_policy eval average step individual rewards of agent1: 0.5070647011352686
team_policy eval average team episode rewards of agent1: 65.0
team_policy eval idv catch total num of agent1: 22
team_policy eval team catch total num: 26
team_policy eval average step individual rewards of agent2: 0.3990876004954503
team_policy eval average team episode rewards of agent2: 65.0
team_policy eval idv catch total num of agent2: 18
team_policy eval team catch total num: 26
team_policy eval average step individual rewards of agent3: 0.5278891538639114
team_policy eval average team episode rewards of agent3: 65.0
team_policy eval idv catch total num of agent3: 23
team_policy eval team catch total num: 26
team_policy eval average step individual rewards of agent4: 0.906110517010827
team_policy eval average team episode rewards of agent4: 65.0
team_policy eval idv catch total num of agent4: 38
team_policy eval team catch total num: 26
idv_policy eval average step individual rewards of agent0: 0.9387896777001474
idv_policy eval average team episode rewards of agent0: 132.5
idv_policy eval idv catch total num of agent0: 39
idv_policy eval team catch total num: 53
idv_policy eval average step individual rewards of agent1: 0.5815938129556427
idv_policy eval average team episode rewards of agent1: 132.5
idv_policy eval idv catch total num of agent1: 25
idv_policy eval team catch total num: 53
idv_policy eval average step individual rewards of agent2: 0.6540311677463139
idv_policy eval average team episode rewards of agent2: 132.5
idv_policy eval idv catch total num of agent2: 28
idv_policy eval team catch total num: 53
idv_policy eval average step individual rewards of agent3: 1.0186487295262447
idv_policy eval average team episode rewards of agent3: 132.5
idv_policy eval idv catch total num of agent3: 42
idv_policy eval team catch total num: 53
idv_policy eval average step individual rewards of agent4: 0.983381010580684
idv_policy eval average team episode rewards of agent4: 132.5
idv_policy eval idv catch total num of agent4: 41
idv_policy eval team catch total num: 53

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3926/10000 episodes, total num timesteps 785400/2000000, FPS 219.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3927/10000 episodes, total num timesteps 785600/2000000, FPS 219.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3928/10000 episodes, total num timesteps 785800/2000000, FPS 219.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3929/10000 episodes, total num timesteps 786000/2000000, FPS 219.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3930/10000 episodes, total num timesteps 786200/2000000, FPS 219.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3931/10000 episodes, total num timesteps 786400/2000000, FPS 219.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3932/10000 episodes, total num timesteps 786600/2000000, FPS 219.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3933/10000 episodes, total num timesteps 786800/2000000, FPS 219.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3934/10000 episodes, total num timesteps 787000/2000000, FPS 219.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3935/10000 episodes, total num timesteps 787200/2000000, FPS 219.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3936/10000 episodes, total num timesteps 787400/2000000, FPS 219.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3937/10000 episodes, total num timesteps 787600/2000000, FPS 219.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3938/10000 episodes, total num timesteps 787800/2000000, FPS 219.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3939/10000 episodes, total num timesteps 788000/2000000, FPS 219.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3940/10000 episodes, total num timesteps 788200/2000000, FPS 219.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3941/10000 episodes, total num timesteps 788400/2000000, FPS 219.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3942/10000 episodes, total num timesteps 788600/2000000, FPS 219.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3943/10000 episodes, total num timesteps 788800/2000000, FPS 219.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3944/10000 episodes, total num timesteps 789000/2000000, FPS 219.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3945/10000 episodes, total num timesteps 789200/2000000, FPS 219.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3946/10000 episodes, total num timesteps 789400/2000000, FPS 219.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3947/10000 episodes, total num timesteps 789600/2000000, FPS 219.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3948/10000 episodes, total num timesteps 789800/2000000, FPS 219.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3949/10000 episodes, total num timesteps 790000/2000000, FPS 219.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3950/10000 episodes, total num timesteps 790200/2000000, FPS 219.

team_policy eval average step individual rewards of agent0: 0.5911318457843562
team_policy eval average team episode rewards of agent0: 97.5
team_policy eval idv catch total num of agent0: 25
team_policy eval team catch total num: 39
team_policy eval average step individual rewards of agent1: 0.5300037043273046
team_policy eval average team episode rewards of agent1: 97.5
team_policy eval idv catch total num of agent1: 23
team_policy eval team catch total num: 39
team_policy eval average step individual rewards of agent2: 0.762102367328621
team_policy eval average team episode rewards of agent2: 97.5
team_policy eval idv catch total num of agent2: 32
team_policy eval team catch total num: 39
team_policy eval average step individual rewards of agent3: 0.9869981046804006
team_policy eval average team episode rewards of agent3: 97.5
team_policy eval idv catch total num of agent3: 41
team_policy eval team catch total num: 39
team_policy eval average step individual rewards of agent4: 1.2466930567506458
team_policy eval average team episode rewards of agent4: 97.5
team_policy eval idv catch total num of agent4: 51
team_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent0: 0.8397828691852592
idv_policy eval average team episode rewards of agent0: 115.0
idv_policy eval idv catch total num of agent0: 35
idv_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent1: 0.9871813143740066
idv_policy eval average team episode rewards of agent1: 115.0
idv_policy eval idv catch total num of agent1: 41
idv_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent2: 0.6612884935214322
idv_policy eval average team episode rewards of agent2: 115.0
idv_policy eval idv catch total num of agent2: 28
idv_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent3: 0.891630759918383
idv_policy eval average team episode rewards of agent3: 115.0
idv_policy eval idv catch total num of agent3: 37
idv_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent4: 0.6312961938354141
idv_policy eval average team episode rewards of agent4: 115.0
idv_policy eval idv catch total num of agent4: 27
idv_policy eval team catch total num: 46

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3951/10000 episodes, total num timesteps 790400/2000000, FPS 219.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3952/10000 episodes, total num timesteps 790600/2000000, FPS 219.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3953/10000 episodes, total num timesteps 790800/2000000, FPS 219.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3954/10000 episodes, total num timesteps 791000/2000000, FPS 219.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3955/10000 episodes, total num timesteps 791200/2000000, FPS 219.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3956/10000 episodes, total num timesteps 791400/2000000, FPS 219.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3957/10000 episodes, total num timesteps 791600/2000000, FPS 219.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3958/10000 episodes, total num timesteps 791800/2000000, FPS 219.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3959/10000 episodes, total num timesteps 792000/2000000, FPS 219.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3960/10000 episodes, total num timesteps 792200/2000000, FPS 219.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3961/10000 episodes, total num timesteps 792400/2000000, FPS 219.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3962/10000 episodes, total num timesteps 792600/2000000, FPS 219.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3963/10000 episodes, total num timesteps 792800/2000000, FPS 219.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3964/10000 episodes, total num timesteps 793000/2000000, FPS 219.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3965/10000 episodes, total num timesteps 793200/2000000, FPS 219.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3966/10000 episodes, total num timesteps 793400/2000000, FPS 219.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3967/10000 episodes, total num timesteps 793600/2000000, FPS 220.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3968/10000 episodes, total num timesteps 793800/2000000, FPS 220.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3969/10000 episodes, total num timesteps 794000/2000000, FPS 220.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3970/10000 episodes, total num timesteps 794200/2000000, FPS 220.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3971/10000 episodes, total num timesteps 794400/2000000, FPS 220.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3972/10000 episodes, total num timesteps 794600/2000000, FPS 220.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3973/10000 episodes, total num timesteps 794800/2000000, FPS 220.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3974/10000 episodes, total num timesteps 795000/2000000, FPS 220.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3975/10000 episodes, total num timesteps 795200/2000000, FPS 220.

team_policy eval average step individual rewards of agent0: 0.3715430165431005
team_policy eval average team episode rewards of agent0: 115.0
team_policy eval idv catch total num of agent0: 17
team_policy eval team catch total num: 46
team_policy eval average step individual rewards of agent1: 1.165296859719521
team_policy eval average team episode rewards of agent1: 115.0
team_policy eval idv catch total num of agent1: 48
team_policy eval team catch total num: 46
team_policy eval average step individual rewards of agent2: 0.761023520911388
team_policy eval average team episode rewards of agent2: 115.0
team_policy eval idv catch total num of agent2: 32
team_policy eval team catch total num: 46
team_policy eval average step individual rewards of agent3: 0.6847047957496727
team_policy eval average team episode rewards of agent3: 115.0
team_policy eval idv catch total num of agent3: 29
team_policy eval team catch total num: 46
team_policy eval average step individual rewards of agent4: 0.9932754519095854
team_policy eval average team episode rewards of agent4: 115.0
team_policy eval idv catch total num of agent4: 41
team_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent0: 0.8115317179249558
idv_policy eval average team episode rewards of agent0: 72.5
idv_policy eval idv catch total num of agent0: 34
idv_policy eval team catch total num: 29
idv_policy eval average step individual rewards of agent1: 0.5038773007007227
idv_policy eval average team episode rewards of agent1: 72.5
idv_policy eval idv catch total num of agent1: 22
idv_policy eval team catch total num: 29
idv_policy eval average step individual rewards of agent2: 0.5361798670212875
idv_policy eval average team episode rewards of agent2: 72.5
idv_policy eval idv catch total num of agent2: 23
idv_policy eval team catch total num: 29
idv_policy eval average step individual rewards of agent3: 0.3392359060916481
idv_policy eval average team episode rewards of agent3: 72.5
idv_policy eval idv catch total num of agent3: 15
idv_policy eval team catch total num: 29
idv_policy eval average step individual rewards of agent4: 0.44074832684302906
idv_policy eval average team episode rewards of agent4: 72.5
idv_policy eval idv catch total num of agent4: 19
idv_policy eval team catch total num: 29

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3976/10000 episodes, total num timesteps 795400/2000000, FPS 220.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3977/10000 episodes, total num timesteps 795600/2000000, FPS 220.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3978/10000 episodes, total num timesteps 795800/2000000, FPS 220.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3979/10000 episodes, total num timesteps 796000/2000000, FPS 220.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3980/10000 episodes, total num timesteps 796200/2000000, FPS 220.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3981/10000 episodes, total num timesteps 796400/2000000, FPS 220.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3982/10000 episodes, total num timesteps 796600/2000000, FPS 220.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3983/10000 episodes, total num timesteps 796800/2000000, FPS 220.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3984/10000 episodes, total num timesteps 797000/2000000, FPS 220.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3985/10000 episodes, total num timesteps 797200/2000000, FPS 220.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3986/10000 episodes, total num timesteps 797400/2000000, FPS 220.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3987/10000 episodes, total num timesteps 797600/2000000, FPS 220.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3988/10000 episodes, total num timesteps 797800/2000000, FPS 220.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3989/10000 episodes, total num timesteps 798000/2000000, FPS 220.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3990/10000 episodes, total num timesteps 798200/2000000, FPS 220.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3991/10000 episodes, total num timesteps 798400/2000000, FPS 220.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3992/10000 episodes, total num timesteps 798600/2000000, FPS 220.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3993/10000 episodes, total num timesteps 798800/2000000, FPS 220.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3994/10000 episodes, total num timesteps 799000/2000000, FPS 220.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3995/10000 episodes, total num timesteps 799200/2000000, FPS 220.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3996/10000 episodes, total num timesteps 799400/2000000, FPS 220.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3997/10000 episodes, total num timesteps 799600/2000000, FPS 220.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3998/10000 episodes, total num timesteps 799800/2000000, FPS 220.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3999/10000 episodes, total num timesteps 800000/2000000, FPS 220.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4000/10000 episodes, total num timesteps 800200/2000000, FPS 220.

team_policy eval average step individual rewards of agent0: 0.7129409710344615
team_policy eval average team episode rewards of agent0: 102.5
team_policy eval idv catch total num of agent0: 30
team_policy eval team catch total num: 41
team_policy eval average step individual rewards of agent1: 0.5125145617759
team_policy eval average team episode rewards of agent1: 102.5
team_policy eval idv catch total num of agent1: 22
team_policy eval team catch total num: 41
team_policy eval average step individual rewards of agent2: 1.0406547422771952
team_policy eval average team episode rewards of agent2: 102.5
team_policy eval idv catch total num of agent2: 43
team_policy eval team catch total num: 41
team_policy eval average step individual rewards of agent3: 0.7623554118352938
team_policy eval average team episode rewards of agent3: 102.5
team_policy eval idv catch total num of agent3: 32
team_policy eval team catch total num: 41
team_policy eval average step individual rewards of agent4: 0.8668610311124574
team_policy eval average team episode rewards of agent4: 102.5
team_policy eval idv catch total num of agent4: 36
team_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent0: 0.7661541333915959
idv_policy eval average team episode rewards of agent0: 100.0
idv_policy eval idv catch total num of agent0: 32
idv_policy eval team catch total num: 40
idv_policy eval average step individual rewards of agent1: 0.9114556333829152
idv_policy eval average team episode rewards of agent1: 100.0
idv_policy eval idv catch total num of agent1: 38
idv_policy eval team catch total num: 40
idv_policy eval average step individual rewards of agent2: 0.6093054058769443
idv_policy eval average team episode rewards of agent2: 100.0
idv_policy eval idv catch total num of agent2: 26
idv_policy eval team catch total num: 40
idv_policy eval average step individual rewards of agent3: 0.6059137127444683
idv_policy eval average team episode rewards of agent3: 100.0
idv_policy eval idv catch total num of agent3: 26
idv_policy eval team catch total num: 40
idv_policy eval average step individual rewards of agent4: 0.8423814712383035
idv_policy eval average team episode rewards of agent4: 100.0
idv_policy eval idv catch total num of agent4: 35
idv_policy eval team catch total num: 40

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4001/10000 episodes, total num timesteps 800400/2000000, FPS 220.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4002/10000 episodes, total num timesteps 800600/2000000, FPS 220.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4003/10000 episodes, total num timesteps 800800/2000000, FPS 220.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4004/10000 episodes, total num timesteps 801000/2000000, FPS 220.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4005/10000 episodes, total num timesteps 801200/2000000, FPS 220.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4006/10000 episodes, total num timesteps 801400/2000000, FPS 220.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4007/10000 episodes, total num timesteps 801600/2000000, FPS 220.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4008/10000 episodes, total num timesteps 801800/2000000, FPS 220.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4009/10000 episodes, total num timesteps 802000/2000000, FPS 220.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4010/10000 episodes, total num timesteps 802200/2000000, FPS 220.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4011/10000 episodes, total num timesteps 802400/2000000, FPS 220.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4012/10000 episodes, total num timesteps 802600/2000000, FPS 220.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4013/10000 episodes, total num timesteps 802800/2000000, FPS 220.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4014/10000 episodes, total num timesteps 803000/2000000, FPS 220.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4015/10000 episodes, total num timesteps 803200/2000000, FPS 220.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4016/10000 episodes, total num timesteps 803400/2000000, FPS 220.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4017/10000 episodes, total num timesteps 803600/2000000, FPS 220.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4018/10000 episodes, total num timesteps 803800/2000000, FPS 220.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4019/10000 episodes, total num timesteps 804000/2000000, FPS 220.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4020/10000 episodes, total num timesteps 804200/2000000, FPS 220.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4021/10000 episodes, total num timesteps 804400/2000000, FPS 220.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4022/10000 episodes, total num timesteps 804600/2000000, FPS 220.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4023/10000 episodes, total num timesteps 804800/2000000, FPS 220.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4024/10000 episodes, total num timesteps 805000/2000000, FPS 220.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4025/10000 episodes, total num timesteps 805200/2000000, FPS 220.

team_policy eval average step individual rewards of agent0: 0.48798225245940235
team_policy eval average team episode rewards of agent0: 57.5
team_policy eval idv catch total num of agent0: 22
team_policy eval team catch total num: 23
team_policy eval average step individual rewards of agent1: 0.5293098325069095
team_policy eval average team episode rewards of agent1: 57.5
team_policy eval idv catch total num of agent1: 23
team_policy eval team catch total num: 23
team_policy eval average step individual rewards of agent2: 0.5191264997656017
team_policy eval average team episode rewards of agent2: 57.5
team_policy eval idv catch total num of agent2: 23
team_policy eval team catch total num: 23
team_policy eval average step individual rewards of agent3: 0.44306880725860937
team_policy eval average team episode rewards of agent3: 57.5
team_policy eval idv catch total num of agent3: 20
team_policy eval team catch total num: 23
team_policy eval average step individual rewards of agent4: 0.25264359143063786
team_policy eval average team episode rewards of agent4: 57.5
team_policy eval idv catch total num of agent4: 12
team_policy eval team catch total num: 23
idv_policy eval average step individual rewards of agent0: 0.9506662307180949
idv_policy eval average team episode rewards of agent0: 115.0
idv_policy eval idv catch total num of agent0: 39
idv_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent1: 0.9913871357301441
idv_policy eval average team episode rewards of agent1: 115.0
idv_policy eval idv catch total num of agent1: 41
idv_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent2: 0.32814217515280214
idv_policy eval average team episode rewards of agent2: 115.0
idv_policy eval idv catch total num of agent2: 15
idv_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent3: 1.0157498175102133
idv_policy eval average team episode rewards of agent3: 115.0
idv_policy eval idv catch total num of agent3: 42
idv_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent4: 0.5902930686341098
idv_policy eval average team episode rewards of agent4: 115.0
idv_policy eval idv catch total num of agent4: 25
idv_policy eval team catch total num: 46

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4026/10000 episodes, total num timesteps 805400/2000000, FPS 220.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4027/10000 episodes, total num timesteps 805600/2000000, FPS 220.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4028/10000 episodes, total num timesteps 805800/2000000, FPS 220.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4029/10000 episodes, total num timesteps 806000/2000000, FPS 220.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4030/10000 episodes, total num timesteps 806200/2000000, FPS 220.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4031/10000 episodes, total num timesteps 806400/2000000, FPS 220.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4032/10000 episodes, total num timesteps 806600/2000000, FPS 220.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4033/10000 episodes, total num timesteps 806800/2000000, FPS 220.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4034/10000 episodes, total num timesteps 807000/2000000, FPS 220.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4035/10000 episodes, total num timesteps 807200/2000000, FPS 220.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4036/10000 episodes, total num timesteps 807400/2000000, FPS 220.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4037/10000 episodes, total num timesteps 807600/2000000, FPS 220.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4038/10000 episodes, total num timesteps 807800/2000000, FPS 220.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4039/10000 episodes, total num timesteps 808000/2000000, FPS 220.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4040/10000 episodes, total num timesteps 808200/2000000, FPS 221.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4041/10000 episodes, total num timesteps 808400/2000000, FPS 221.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4042/10000 episodes, total num timesteps 808600/2000000, FPS 221.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4043/10000 episodes, total num timesteps 808800/2000000, FPS 221.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4044/10000 episodes, total num timesteps 809000/2000000, FPS 221.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4045/10000 episodes, total num timesteps 809200/2000000, FPS 221.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4046/10000 episodes, total num timesteps 809400/2000000, FPS 221.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4047/10000 episodes, total num timesteps 809600/2000000, FPS 221.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4048/10000 episodes, total num timesteps 809800/2000000, FPS 221.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4049/10000 episodes, total num timesteps 810000/2000000, FPS 221.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4050/10000 episodes, total num timesteps 810200/2000000, FPS 221.

team_policy eval average step individual rewards of agent0: 0.9168682428665019
team_policy eval average team episode rewards of agent0: 140.0
team_policy eval idv catch total num of agent0: 38
team_policy eval team catch total num: 56
team_policy eval average step individual rewards of agent1: 0.7631451905931208
team_policy eval average team episode rewards of agent1: 140.0
team_policy eval idv catch total num of agent1: 32
team_policy eval team catch total num: 56
team_policy eval average step individual rewards of agent2: 0.9208756763439743
team_policy eval average team episode rewards of agent2: 140.0
team_policy eval idv catch total num of agent2: 38
team_policy eval team catch total num: 56
team_policy eval average step individual rewards of agent3: 1.0682845950917408
team_policy eval average team episode rewards of agent3: 140.0
team_policy eval idv catch total num of agent3: 44
team_policy eval team catch total num: 56
team_policy eval average step individual rewards of agent4: 0.9447959670000787
team_policy eval average team episode rewards of agent4: 140.0
team_policy eval idv catch total num of agent4: 39
team_policy eval team catch total num: 56
idv_policy eval average step individual rewards of agent0: 0.2985027363985564
idv_policy eval average team episode rewards of agent0: 52.5
idv_policy eval idv catch total num of agent0: 14
idv_policy eval team catch total num: 21
idv_policy eval average step individual rewards of agent1: 0.8647397385613206
idv_policy eval average team episode rewards of agent1: 52.5
idv_policy eval idv catch total num of agent1: 36
idv_policy eval team catch total num: 21
idv_policy eval average step individual rewards of agent2: 0.12156642898058667
idv_policy eval average team episode rewards of agent2: 52.5
idv_policy eval idv catch total num of agent2: 7
idv_policy eval team catch total num: 21
idv_policy eval average step individual rewards of agent3: 0.6065366458922743
idv_policy eval average team episode rewards of agent3: 52.5
idv_policy eval idv catch total num of agent3: 26
idv_policy eval team catch total num: 21
idv_policy eval average step individual rewards of agent4: 0.39174902370971987
idv_policy eval average team episode rewards of agent4: 52.5
idv_policy eval idv catch total num of agent4: 17
idv_policy eval team catch total num: 21

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4051/10000 episodes, total num timesteps 810400/2000000, FPS 221.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4052/10000 episodes, total num timesteps 810600/2000000, FPS 221.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4053/10000 episodes, total num timesteps 810800/2000000, FPS 221.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4054/10000 episodes, total num timesteps 811000/2000000, FPS 221.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4055/10000 episodes, total num timesteps 811200/2000000, FPS 221.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4056/10000 episodes, total num timesteps 811400/2000000, FPS 221.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4057/10000 episodes, total num timesteps 811600/2000000, FPS 221.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4058/10000 episodes, total num timesteps 811800/2000000, FPS 221.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4059/10000 episodes, total num timesteps 812000/2000000, FPS 221.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4060/10000 episodes, total num timesteps 812200/2000000, FPS 221.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4061/10000 episodes, total num timesteps 812400/2000000, FPS 221.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4062/10000 episodes, total num timesteps 812600/2000000, FPS 221.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4063/10000 episodes, total num timesteps 812800/2000000, FPS 221.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4064/10000 episodes, total num timesteps 813000/2000000, FPS 221.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4065/10000 episodes, total num timesteps 813200/2000000, FPS 221.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4066/10000 episodes, total num timesteps 813400/2000000, FPS 221.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4067/10000 episodes, total num timesteps 813600/2000000, FPS 221.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4068/10000 episodes, total num timesteps 813800/2000000, FPS 221.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4069/10000 episodes, total num timesteps 814000/2000000, FPS 221.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4070/10000 episodes, total num timesteps 814200/2000000, FPS 221.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4071/10000 episodes, total num timesteps 814400/2000000, FPS 221.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4072/10000 episodes, total num timesteps 814600/2000000, FPS 221.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4073/10000 episodes, total num timesteps 814800/2000000, FPS 221.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4074/10000 episodes, total num timesteps 815000/2000000, FPS 221.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4075/10000 episodes, total num timesteps 815200/2000000, FPS 221.

team_policy eval average step individual rewards of agent0: 0.5727679432219042
team_policy eval average team episode rewards of agent0: 87.5
team_policy eval idv catch total num of agent0: 25
team_policy eval team catch total num: 35
team_policy eval average step individual rewards of agent1: 0.913778580220362
team_policy eval average team episode rewards of agent1: 87.5
team_policy eval idv catch total num of agent1: 38
team_policy eval team catch total num: 35
team_policy eval average step individual rewards of agent2: 0.689129881564363
team_policy eval average team episode rewards of agent2: 87.5
team_policy eval idv catch total num of agent2: 29
team_policy eval team catch total num: 35
team_policy eval average step individual rewards of agent3: 0.46106984962263736
team_policy eval average team episode rewards of agent3: 87.5
team_policy eval idv catch total num of agent3: 20
team_policy eval team catch total num: 35
team_policy eval average step individual rewards of agent4: 0.55790396024411
team_policy eval average team episode rewards of agent4: 87.5
team_policy eval idv catch total num of agent4: 24
team_policy eval team catch total num: 35
idv_policy eval average step individual rewards of agent0: 0.6030987754638093
idv_policy eval average team episode rewards of agent0: 75.0
idv_policy eval idv catch total num of agent0: 26
idv_policy eval team catch total num: 30
idv_policy eval average step individual rewards of agent1: 0.47994753738184776
idv_policy eval average team episode rewards of agent1: 75.0
idv_policy eval idv catch total num of agent1: 21
idv_policy eval team catch total num: 30
idv_policy eval average step individual rewards of agent2: 0.6841795991885653
idv_policy eval average team episode rewards of agent2: 75.0
idv_policy eval idv catch total num of agent2: 29
idv_policy eval team catch total num: 30
idv_policy eval average step individual rewards of agent3: 0.34906169138195614
idv_policy eval average team episode rewards of agent3: 75.0
idv_policy eval idv catch total num of agent3: 16
idv_policy eval team catch total num: 30
idv_policy eval average step individual rewards of agent4: 0.8054566563355651
idv_policy eval average team episode rewards of agent4: 75.0
idv_policy eval idv catch total num of agent4: 34
idv_policy eval team catch total num: 30

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4076/10000 episodes, total num timesteps 815400/2000000, FPS 221.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4077/10000 episodes, total num timesteps 815600/2000000, FPS 221.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4078/10000 episodes, total num timesteps 815800/2000000, FPS 221.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4079/10000 episodes, total num timesteps 816000/2000000, FPS 221.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4080/10000 episodes, total num timesteps 816200/2000000, FPS 221.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4081/10000 episodes, total num timesteps 816400/2000000, FPS 221.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4082/10000 episodes, total num timesteps 816600/2000000, FPS 221.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4083/10000 episodes, total num timesteps 816800/2000000, FPS 221.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4084/10000 episodes, total num timesteps 817000/2000000, FPS 221.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4085/10000 episodes, total num timesteps 817200/2000000, FPS 221.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4086/10000 episodes, total num timesteps 817400/2000000, FPS 221.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4087/10000 episodes, total num timesteps 817600/2000000, FPS 221.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4088/10000 episodes, total num timesteps 817800/2000000, FPS 221.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4089/10000 episodes, total num timesteps 818000/2000000, FPS 221.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4090/10000 episodes, total num timesteps 818200/2000000, FPS 221.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4091/10000 episodes, total num timesteps 818400/2000000, FPS 221.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4092/10000 episodes, total num timesteps 818600/2000000, FPS 221.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4093/10000 episodes, total num timesteps 818800/2000000, FPS 221.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4094/10000 episodes, total num timesteps 819000/2000000, FPS 221.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4095/10000 episodes, total num timesteps 819200/2000000, FPS 221.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4096/10000 episodes, total num timesteps 819400/2000000, FPS 221.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4097/10000 episodes, total num timesteps 819600/2000000, FPS 221.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4098/10000 episodes, total num timesteps 819800/2000000, FPS 221.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4099/10000 episodes, total num timesteps 820000/2000000, FPS 221.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4100/10000 episodes, total num timesteps 820200/2000000, FPS 221.

team_policy eval average step individual rewards of agent0: 0.7738465757416141
team_policy eval average team episode rewards of agent0: 85.0
team_policy eval idv catch total num of agent0: 33
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent1: 0.7355466840110948
team_policy eval average team episode rewards of agent1: 85.0
team_policy eval idv catch total num of agent1: 31
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent2: 0.32859470192358187
team_policy eval average team episode rewards of agent2: 85.0
team_policy eval idv catch total num of agent2: 15
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent3: 0.7580708648708695
team_policy eval average team episode rewards of agent3: 85.0
team_policy eval idv catch total num of agent3: 32
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent4: 0.4854527490049103
team_policy eval average team episode rewards of agent4: 85.0
team_policy eval idv catch total num of agent4: 21
team_policy eval team catch total num: 34
idv_policy eval average step individual rewards of agent0: 0.6289259016028589
idv_policy eval average team episode rewards of agent0: 50.0
idv_policy eval idv catch total num of agent0: 27
idv_policy eval team catch total num: 20
idv_policy eval average step individual rewards of agent1: 0.5834653694176396
idv_policy eval average team episode rewards of agent1: 50.0
idv_policy eval idv catch total num of agent1: 25
idv_policy eval team catch total num: 20
idv_policy eval average step individual rewards of agent2: 0.23283221538632268
idv_policy eval average team episode rewards of agent2: 50.0
idv_policy eval idv catch total num of agent2: 12
idv_policy eval team catch total num: 20
idv_policy eval average step individual rewards of agent3: 0.2369620946800616
idv_policy eval average team episode rewards of agent3: 50.0
idv_policy eval idv catch total num of agent3: 12
idv_policy eval team catch total num: 20
idv_policy eval average step individual rewards of agent4: 0.33596987378992715
idv_policy eval average team episode rewards of agent4: 50.0
idv_policy eval idv catch total num of agent4: 16
idv_policy eval team catch total num: 20

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4101/10000 episodes, total num timesteps 820400/2000000, FPS 221.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4102/10000 episodes, total num timesteps 820600/2000000, FPS 221.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4103/10000 episodes, total num timesteps 820800/2000000, FPS 221.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4104/10000 episodes, total num timesteps 821000/2000000, FPS 221.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4105/10000 episodes, total num timesteps 821200/2000000, FPS 221.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4106/10000 episodes, total num timesteps 821400/2000000, FPS 221.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4107/10000 episodes, total num timesteps 821600/2000000, FPS 221.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4108/10000 episodes, total num timesteps 821800/2000000, FPS 221.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4109/10000 episodes, total num timesteps 822000/2000000, FPS 221.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4110/10000 episodes, total num timesteps 822200/2000000, FPS 221.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4111/10000 episodes, total num timesteps 822400/2000000, FPS 221.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4112/10000 episodes, total num timesteps 822600/2000000, FPS 221.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4113/10000 episodes, total num timesteps 822800/2000000, FPS 221.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4114/10000 episodes, total num timesteps 823000/2000000, FPS 221.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4115/10000 episodes, total num timesteps 823200/2000000, FPS 221.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4116/10000 episodes, total num timesteps 823400/2000000, FPS 221.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4117/10000 episodes, total num timesteps 823600/2000000, FPS 222.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4118/10000 episodes, total num timesteps 823800/2000000, FPS 222.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4119/10000 episodes, total num timesteps 824000/2000000, FPS 222.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4120/10000 episodes, total num timesteps 824200/2000000, FPS 222.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4121/10000 episodes, total num timesteps 824400/2000000, FPS 222.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4122/10000 episodes, total num timesteps 824600/2000000, FPS 222.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4123/10000 episodes, total num timesteps 824800/2000000, FPS 222.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4124/10000 episodes, total num timesteps 825000/2000000, FPS 222.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4125/10000 episodes, total num timesteps 825200/2000000, FPS 222.

team_policy eval average step individual rewards of agent0: 1.274112818527716
team_policy eval average team episode rewards of agent0: 147.5
team_policy eval idv catch total num of agent0: 52
team_policy eval team catch total num: 59
team_policy eval average step individual rewards of agent1: 1.001368936333465
team_policy eval average team episode rewards of agent1: 147.5
team_policy eval idv catch total num of agent1: 41
team_policy eval team catch total num: 59
team_policy eval average step individual rewards of agent2: 1.226183846940782
team_policy eval average team episode rewards of agent2: 147.5
team_policy eval idv catch total num of agent2: 50
team_policy eval team catch total num: 59
team_policy eval average step individual rewards of agent3: 0.7646304872541191
team_policy eval average team episode rewards of agent3: 147.5
team_policy eval idv catch total num of agent3: 32
team_policy eval team catch total num: 59
team_policy eval average step individual rewards of agent4: 1.0874009327044183
team_policy eval average team episode rewards of agent4: 147.5
team_policy eval idv catch total num of agent4: 45
team_policy eval team catch total num: 59
idv_policy eval average step individual rewards of agent0: 1.0007837334533527
idv_policy eval average team episode rewards of agent0: 130.0
idv_policy eval idv catch total num of agent0: 41
idv_policy eval team catch total num: 52
idv_policy eval average step individual rewards of agent1: 0.8739282431287149
idv_policy eval average team episode rewards of agent1: 130.0
idv_policy eval idv catch total num of agent1: 36
idv_policy eval team catch total num: 52
idv_policy eval average step individual rewards of agent2: 1.2766986883208082
idv_policy eval average team episode rewards of agent2: 130.0
idv_policy eval idv catch total num of agent2: 52
idv_policy eval team catch total num: 52
idv_policy eval average step individual rewards of agent3: 0.6928604574504549
idv_policy eval average team episode rewards of agent3: 130.0
idv_policy eval idv catch total num of agent3: 29
idv_policy eval team catch total num: 52
idv_policy eval average step individual rewards of agent4: 0.8632972361479445
idv_policy eval average team episode rewards of agent4: 130.0
idv_policy eval idv catch total num of agent4: 36
idv_policy eval team catch total num: 52

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4126/10000 episodes, total num timesteps 825400/2000000, FPS 222.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4127/10000 episodes, total num timesteps 825600/2000000, FPS 222.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4128/10000 episodes, total num timesteps 825800/2000000, FPS 222.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4129/10000 episodes, total num timesteps 826000/2000000, FPS 222.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4130/10000 episodes, total num timesteps 826200/2000000, FPS 222.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4131/10000 episodes, total num timesteps 826400/2000000, FPS 222.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4132/10000 episodes, total num timesteps 826600/2000000, FPS 222.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4133/10000 episodes, total num timesteps 826800/2000000, FPS 222.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4134/10000 episodes, total num timesteps 827000/2000000, FPS 222.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4135/10000 episodes, total num timesteps 827200/2000000, FPS 222.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4136/10000 episodes, total num timesteps 827400/2000000, FPS 222.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4137/10000 episodes, total num timesteps 827600/2000000, FPS 222.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4138/10000 episodes, total num timesteps 827800/2000000, FPS 222.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4139/10000 episodes, total num timesteps 828000/2000000, FPS 222.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4140/10000 episodes, total num timesteps 828200/2000000, FPS 222.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4141/10000 episodes, total num timesteps 828400/2000000, FPS 222.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4142/10000 episodes, total num timesteps 828600/2000000, FPS 222.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4143/10000 episodes, total num timesteps 828800/2000000, FPS 222.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4144/10000 episodes, total num timesteps 829000/2000000, FPS 222.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4145/10000 episodes, total num timesteps 829200/2000000, FPS 222.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4146/10000 episodes, total num timesteps 829400/2000000, FPS 222.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4147/10000 episodes, total num timesteps 829600/2000000, FPS 222.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4148/10000 episodes, total num timesteps 829800/2000000, FPS 222.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4149/10000 episodes, total num timesteps 830000/2000000, FPS 222.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4150/10000 episodes, total num timesteps 830200/2000000, FPS 222.

team_policy eval average step individual rewards of agent0: 0.8947892682260458
team_policy eval average team episode rewards of agent0: 120.0
team_policy eval idv catch total num of agent0: 37
team_policy eval team catch total num: 48
team_policy eval average step individual rewards of agent1: 0.45166594275098704
team_policy eval average team episode rewards of agent1: 120.0
team_policy eval idv catch total num of agent1: 20
team_policy eval team catch total num: 48
team_policy eval average step individual rewards of agent2: 0.9605529080749986
team_policy eval average team episode rewards of agent2: 120.0
team_policy eval idv catch total num of agent2: 40
team_policy eval team catch total num: 48
team_policy eval average step individual rewards of agent3: 1.2476695087239522
team_policy eval average team episode rewards of agent3: 120.0
team_policy eval idv catch total num of agent3: 51
team_policy eval team catch total num: 48
team_policy eval average step individual rewards of agent4: 0.7935860171875223
team_policy eval average team episode rewards of agent4: 120.0
team_policy eval idv catch total num of agent4: 33
team_policy eval team catch total num: 48
idv_policy eval average step individual rewards of agent0: 0.604608926146614
idv_policy eval average team episode rewards of agent0: 130.0
idv_policy eval idv catch total num of agent0: 26
idv_policy eval team catch total num: 52
idv_policy eval average step individual rewards of agent1: 0.42470902564302526
idv_policy eval average team episode rewards of agent1: 130.0
idv_policy eval idv catch total num of agent1: 19
idv_policy eval team catch total num: 52
idv_policy eval average step individual rewards of agent2: 0.9860037757753412
idv_policy eval average team episode rewards of agent2: 130.0
idv_policy eval idv catch total num of agent2: 41
idv_policy eval team catch total num: 52
idv_policy eval average step individual rewards of agent3: 0.9619070778725972
idv_policy eval average team episode rewards of agent3: 130.0
idv_policy eval idv catch total num of agent3: 40
idv_policy eval team catch total num: 52
idv_policy eval average step individual rewards of agent4: 0.9162249908531794
idv_policy eval average team episode rewards of agent4: 130.0
idv_policy eval idv catch total num of agent4: 38
idv_policy eval team catch total num: 52

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4151/10000 episodes, total num timesteps 830400/2000000, FPS 222.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4152/10000 episodes, total num timesteps 830600/2000000, FPS 222.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4153/10000 episodes, total num timesteps 830800/2000000, FPS 222.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4154/10000 episodes, total num timesteps 831000/2000000, FPS 222.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4155/10000 episodes, total num timesteps 831200/2000000, FPS 222.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4156/10000 episodes, total num timesteps 831400/2000000, FPS 222.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4157/10000 episodes, total num timesteps 831600/2000000, FPS 222.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4158/10000 episodes, total num timesteps 831800/2000000, FPS 222.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4159/10000 episodes, total num timesteps 832000/2000000, FPS 222.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4160/10000 episodes, total num timesteps 832200/2000000, FPS 222.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4161/10000 episodes, total num timesteps 832400/2000000, FPS 222.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4162/10000 episodes, total num timesteps 832600/2000000, FPS 222.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4163/10000 episodes, total num timesteps 832800/2000000, FPS 222.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4164/10000 episodes, total num timesteps 833000/2000000, FPS 222.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4165/10000 episodes, total num timesteps 833200/2000000, FPS 222.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4166/10000 episodes, total num timesteps 833400/2000000, FPS 222.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4167/10000 episodes, total num timesteps 833600/2000000, FPS 222.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4168/10000 episodes, total num timesteps 833800/2000000, FPS 222.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4169/10000 episodes, total num timesteps 834000/2000000, FPS 222.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4170/10000 episodes, total num timesteps 834200/2000000, FPS 222.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4171/10000 episodes, total num timesteps 834400/2000000, FPS 222.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4172/10000 episodes, total num timesteps 834600/2000000, FPS 222.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4173/10000 episodes, total num timesteps 834800/2000000, FPS 222.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4174/10000 episodes, total num timesteps 835000/2000000, FPS 222.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4175/10000 episodes, total num timesteps 835200/2000000, FPS 222.

team_policy eval average step individual rewards of agent0: 0.47327303464938436
team_policy eval average team episode rewards of agent0: 75.0
team_policy eval idv catch total num of agent0: 21
team_policy eval team catch total num: 30
team_policy eval average step individual rewards of agent1: 0.524081482317984
team_policy eval average team episode rewards of agent1: 75.0
team_policy eval idv catch total num of agent1: 23
team_policy eval team catch total num: 30
team_policy eval average step individual rewards of agent2: 0.5622667971525966
team_policy eval average team episode rewards of agent2: 75.0
team_policy eval idv catch total num of agent2: 24
team_policy eval team catch total num: 30
team_policy eval average step individual rewards of agent3: 0.5274386999410009
team_policy eval average team episode rewards of agent3: 75.0
team_policy eval idv catch total num of agent3: 23
team_policy eval team catch total num: 30
team_policy eval average step individual rewards of agent4: 0.4798626612151445
team_policy eval average team episode rewards of agent4: 75.0
team_policy eval idv catch total num of agent4: 21
team_policy eval team catch total num: 30
idv_policy eval average step individual rewards of agent0: 1.019323350825998
idv_policy eval average team episode rewards of agent0: 137.5
idv_policy eval idv catch total num of agent0: 42
idv_policy eval team catch total num: 55
idv_policy eval average step individual rewards of agent1: 1.1416393820211377
idv_policy eval average team episode rewards of agent1: 137.5
idv_policy eval idv catch total num of agent1: 47
idv_policy eval team catch total num: 55
idv_policy eval average step individual rewards of agent2: 0.935386720016753
idv_policy eval average team episode rewards of agent2: 137.5
idv_policy eval idv catch total num of agent2: 39
idv_policy eval team catch total num: 55
idv_policy eval average step individual rewards of agent3: 0.9441487145265377
idv_policy eval average team episode rewards of agent3: 137.5
idv_policy eval idv catch total num of agent3: 39
idv_policy eval team catch total num: 55
idv_policy eval average step individual rewards of agent4: 0.8879108938369281
idv_policy eval average team episode rewards of agent4: 137.5
idv_policy eval idv catch total num of agent4: 37
idv_policy eval team catch total num: 55

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4176/10000 episodes, total num timesteps 835400/2000000, FPS 222.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4177/10000 episodes, total num timesteps 835600/2000000, FPS 222.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4178/10000 episodes, total num timesteps 835800/2000000, FPS 222.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4179/10000 episodes, total num timesteps 836000/2000000, FPS 222.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4180/10000 episodes, total num timesteps 836200/2000000, FPS 222.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4181/10000 episodes, total num timesteps 836400/2000000, FPS 222.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4182/10000 episodes, total num timesteps 836600/2000000, FPS 222.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4183/10000 episodes, total num timesteps 836800/2000000, FPS 222.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4184/10000 episodes, total num timesteps 837000/2000000, FPS 222.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4185/10000 episodes, total num timesteps 837200/2000000, FPS 222.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4186/10000 episodes, total num timesteps 837400/2000000, FPS 222.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4187/10000 episodes, total num timesteps 837600/2000000, FPS 222.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4188/10000 episodes, total num timesteps 837800/2000000, FPS 222.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4189/10000 episodes, total num timesteps 838000/2000000, FPS 222.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4190/10000 episodes, total num timesteps 838200/2000000, FPS 222.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4191/10000 episodes, total num timesteps 838400/2000000, FPS 222.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4192/10000 episodes, total num timesteps 838600/2000000, FPS 222.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4193/10000 episodes, total num timesteps 838800/2000000, FPS 222.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4194/10000 episodes, total num timesteps 839000/2000000, FPS 222.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4195/10000 episodes, total num timesteps 839200/2000000, FPS 222.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4196/10000 episodes, total num timesteps 839400/2000000, FPS 223.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4197/10000 episodes, total num timesteps 839600/2000000, FPS 223.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4198/10000 episodes, total num timesteps 839800/2000000, FPS 223.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4199/10000 episodes, total num timesteps 840000/2000000, FPS 223.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4200/10000 episodes, total num timesteps 840200/2000000, FPS 223.

team_policy eval average step individual rewards of agent0: 0.5158467663933687
team_policy eval average team episode rewards of agent0: 97.5
team_policy eval idv catch total num of agent0: 22
team_policy eval team catch total num: 39
team_policy eval average step individual rewards of agent1: 0.858909125869951
team_policy eval average team episode rewards of agent1: 97.5
team_policy eval idv catch total num of agent1: 36
team_policy eval team catch total num: 39
team_policy eval average step individual rewards of agent2: 0.6841767427666714
team_policy eval average team episode rewards of agent2: 97.5
team_policy eval idv catch total num of agent2: 29
team_policy eval team catch total num: 39
team_policy eval average step individual rewards of agent3: 0.9377229316777316
team_policy eval average team episode rewards of agent3: 97.5
team_policy eval idv catch total num of agent3: 39
team_policy eval team catch total num: 39
team_policy eval average step individual rewards of agent4: 0.6584466800551312
team_policy eval average team episode rewards of agent4: 97.5
team_policy eval idv catch total num of agent4: 28
team_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent0: 0.6401200384104859
idv_policy eval average team episode rewards of agent0: 92.5
idv_policy eval idv catch total num of agent0: 27
idv_policy eval team catch total num: 37
idv_policy eval average step individual rewards of agent1: 0.4915668356315699
idv_policy eval average team episode rewards of agent1: 92.5
idv_policy eval idv catch total num of agent1: 21
idv_policy eval team catch total num: 37
idv_policy eval average step individual rewards of agent2: 0.8369336009068655
idv_policy eval average team episode rewards of agent2: 92.5
idv_policy eval idv catch total num of agent2: 35
idv_policy eval team catch total num: 37
idv_policy eval average step individual rewards of agent3: 0.7388129043560332
idv_policy eval average team episode rewards of agent3: 92.5
idv_policy eval idv catch total num of agent3: 31
idv_policy eval team catch total num: 37
idv_policy eval average step individual rewards of agent4: 0.9726097401136892
idv_policy eval average team episode rewards of agent4: 92.5
idv_policy eval idv catch total num of agent4: 40
idv_policy eval team catch total num: 37

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4201/10000 episodes, total num timesteps 840400/2000000, FPS 223.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4202/10000 episodes, total num timesteps 840600/2000000, FPS 223.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4203/10000 episodes, total num timesteps 840800/2000000, FPS 223.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4204/10000 episodes, total num timesteps 841000/2000000, FPS 223.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4205/10000 episodes, total num timesteps 841200/2000000, FPS 223.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4206/10000 episodes, total num timesteps 841400/2000000, FPS 223.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4207/10000 episodes, total num timesteps 841600/2000000, FPS 223.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4208/10000 episodes, total num timesteps 841800/2000000, FPS 223.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4209/10000 episodes, total num timesteps 842000/2000000, FPS 223.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4210/10000 episodes, total num timesteps 842200/2000000, FPS 223.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4211/10000 episodes, total num timesteps 842400/2000000, FPS 223.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4212/10000 episodes, total num timesteps 842600/2000000, FPS 223.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4213/10000 episodes, total num timesteps 842800/2000000, FPS 223.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4214/10000 episodes, total num timesteps 843000/2000000, FPS 223.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4215/10000 episodes, total num timesteps 843200/2000000, FPS 223.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4216/10000 episodes, total num timesteps 843400/2000000, FPS 223.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4217/10000 episodes, total num timesteps 843600/2000000, FPS 223.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4218/10000 episodes, total num timesteps 843800/2000000, FPS 223.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4219/10000 episodes, total num timesteps 844000/2000000, FPS 223.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4220/10000 episodes, total num timesteps 844200/2000000, FPS 223.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4221/10000 episodes, total num timesteps 844400/2000000, FPS 223.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4222/10000 episodes, total num timesteps 844600/2000000, FPS 223.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4223/10000 episodes, total num timesteps 844800/2000000, FPS 223.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4224/10000 episodes, total num timesteps 845000/2000000, FPS 223.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4225/10000 episodes, total num timesteps 845200/2000000, FPS 223.

team_policy eval average step individual rewards of agent0: 0.7652172133618927
team_policy eval average team episode rewards of agent0: 122.5
team_policy eval idv catch total num of agent0: 32
team_policy eval team catch total num: 49
team_policy eval average step individual rewards of agent1: 0.8678180832167492
team_policy eval average team episode rewards of agent1: 122.5
team_policy eval idv catch total num of agent1: 36
team_policy eval team catch total num: 49
team_policy eval average step individual rewards of agent2: 1.2759781097322662
team_policy eval average team episode rewards of agent2: 122.5
team_policy eval idv catch total num of agent2: 52
team_policy eval team catch total num: 49
team_policy eval average step individual rewards of agent3: 0.8946497416512526
team_policy eval average team episode rewards of agent3: 122.5
team_policy eval idv catch total num of agent3: 37
team_policy eval team catch total num: 49
team_policy eval average step individual rewards of agent4: 0.8369401085854242
team_policy eval average team episode rewards of agent4: 122.5
team_policy eval idv catch total num of agent4: 35
team_policy eval team catch total num: 49
idv_policy eval average step individual rewards of agent0: 0.5757374116950599
idv_policy eval average team episode rewards of agent0: 127.5
idv_policy eval idv catch total num of agent0: 25
idv_policy eval team catch total num: 51
idv_policy eval average step individual rewards of agent1: 1.0693580236600264
idv_policy eval average team episode rewards of agent1: 127.5
idv_policy eval idv catch total num of agent1: 44
idv_policy eval team catch total num: 51
idv_policy eval average step individual rewards of agent2: 1.2189335824078187
idv_policy eval average team episode rewards of agent2: 127.5
idv_policy eval idv catch total num of agent2: 50
idv_policy eval team catch total num: 51
idv_policy eval average step individual rewards of agent3: 0.2495480634052182
idv_policy eval average team episode rewards of agent3: 127.5
idv_policy eval idv catch total num of agent3: 12
idv_policy eval team catch total num: 51
idv_policy eval average step individual rewards of agent4: 1.3250400814884977
idv_policy eval average team episode rewards of agent4: 127.5
idv_policy eval idv catch total num of agent4: 54
idv_policy eval team catch total num: 51

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4226/10000 episodes, total num timesteps 845400/2000000, FPS 223.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4227/10000 episodes, total num timesteps 845600/2000000, FPS 223.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4228/10000 episodes, total num timesteps 845800/2000000, FPS 223.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4229/10000 episodes, total num timesteps 846000/2000000, FPS 223.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4230/10000 episodes, total num timesteps 846200/2000000, FPS 223.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4231/10000 episodes, total num timesteps 846400/2000000, FPS 223.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4232/10000 episodes, total num timesteps 846600/2000000, FPS 223.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4233/10000 episodes, total num timesteps 846800/2000000, FPS 223.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4234/10000 episodes, total num timesteps 847000/2000000, FPS 223.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4235/10000 episodes, total num timesteps 847200/2000000, FPS 223.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4236/10000 episodes, total num timesteps 847400/2000000, FPS 223.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4237/10000 episodes, total num timesteps 847600/2000000, FPS 223.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4238/10000 episodes, total num timesteps 847800/2000000, FPS 223.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4239/10000 episodes, total num timesteps 848000/2000000, FPS 223.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4240/10000 episodes, total num timesteps 848200/2000000, FPS 223.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4241/10000 episodes, total num timesteps 848400/2000000, FPS 223.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4242/10000 episodes, total num timesteps 848600/2000000, FPS 223.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4243/10000 episodes, total num timesteps 848800/2000000, FPS 223.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4244/10000 episodes, total num timesteps 849000/2000000, FPS 223.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4245/10000 episodes, total num timesteps 849200/2000000, FPS 223.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4246/10000 episodes, total num timesteps 849400/2000000, FPS 223.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4247/10000 episodes, total num timesteps 849600/2000000, FPS 223.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4248/10000 episodes, total num timesteps 849800/2000000, FPS 223.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4249/10000 episodes, total num timesteps 850000/2000000, FPS 223.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4250/10000 episodes, total num timesteps 850200/2000000, FPS 223.

team_policy eval average step individual rewards of agent0: 1.4136254905741734
team_policy eval average team episode rewards of agent0: 130.0
team_policy eval idv catch total num of agent0: 58
team_policy eval team catch total num: 52
team_policy eval average step individual rewards of agent1: 0.5624177371895983
team_policy eval average team episode rewards of agent1: 130.0
team_policy eval idv catch total num of agent1: 24
team_policy eval team catch total num: 52
team_policy eval average step individual rewards of agent2: 0.83595428366155
team_policy eval average team episode rewards of agent2: 130.0
team_policy eval idv catch total num of agent2: 35
team_policy eval team catch total num: 52
team_policy eval average step individual rewards of agent3: 0.9652433100198065
team_policy eval average team episode rewards of agent3: 130.0
team_policy eval idv catch total num of agent3: 40
team_policy eval team catch total num: 52
team_policy eval average step individual rewards of agent4: 0.6909625033402023
team_policy eval average team episode rewards of agent4: 130.0
team_policy eval idv catch total num of agent4: 29
team_policy eval team catch total num: 52
idv_policy eval average step individual rewards of agent0: 1.092642357731371
idv_policy eval average team episode rewards of agent0: 132.5
idv_policy eval idv catch total num of agent0: 45
idv_policy eval team catch total num: 53
idv_policy eval average step individual rewards of agent1: 0.7321070675800309
idv_policy eval average team episode rewards of agent1: 132.5
idv_policy eval idv catch total num of agent1: 31
idv_policy eval team catch total num: 53
idv_policy eval average step individual rewards of agent2: 0.7651239303842537
idv_policy eval average team episode rewards of agent2: 132.5
idv_policy eval idv catch total num of agent2: 32
idv_policy eval team catch total num: 53
idv_policy eval average step individual rewards of agent3: 0.7692774927555622
idv_policy eval average team episode rewards of agent3: 132.5
idv_policy eval idv catch total num of agent3: 32
idv_policy eval team catch total num: 53
idv_policy eval average step individual rewards of agent4: 1.1976694492110072
idv_policy eval average team episode rewards of agent4: 132.5
idv_policy eval idv catch total num of agent4: 49
idv_policy eval team catch total num: 53

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4251/10000 episodes, total num timesteps 850400/2000000, FPS 223.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4252/10000 episodes, total num timesteps 850600/2000000, FPS 223.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4253/10000 episodes, total num timesteps 850800/2000000, FPS 223.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4254/10000 episodes, total num timesteps 851000/2000000, FPS 223.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4255/10000 episodes, total num timesteps 851200/2000000, FPS 223.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4256/10000 episodes, total num timesteps 851400/2000000, FPS 223.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4257/10000 episodes, total num timesteps 851600/2000000, FPS 223.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4258/10000 episodes, total num timesteps 851800/2000000, FPS 223.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4259/10000 episodes, total num timesteps 852000/2000000, FPS 223.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4260/10000 episodes, total num timesteps 852200/2000000, FPS 223.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4261/10000 episodes, total num timesteps 852400/2000000, FPS 223.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4262/10000 episodes, total num timesteps 852600/2000000, FPS 223.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4263/10000 episodes, total num timesteps 852800/2000000, FPS 223.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4264/10000 episodes, total num timesteps 853000/2000000, FPS 223.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4265/10000 episodes, total num timesteps 853200/2000000, FPS 223.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4266/10000 episodes, total num timesteps 853400/2000000, FPS 223.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4267/10000 episodes, total num timesteps 853600/2000000, FPS 223.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4268/10000 episodes, total num timesteps 853800/2000000, FPS 223.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4269/10000 episodes, total num timesteps 854000/2000000, FPS 223.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4270/10000 episodes, total num timesteps 854200/2000000, FPS 223.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4271/10000 episodes, total num timesteps 854400/2000000, FPS 223.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4272/10000 episodes, total num timesteps 854600/2000000, FPS 223.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4273/10000 episodes, total num timesteps 854800/2000000, FPS 223.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4274/10000 episodes, total num timesteps 855000/2000000, FPS 223.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4275/10000 episodes, total num timesteps 855200/2000000, FPS 223.

team_policy eval average step individual rewards of agent0: 0.7188281735755919
team_policy eval average team episode rewards of agent0: 77.5
team_policy eval idv catch total num of agent0: 30
team_policy eval team catch total num: 31
team_policy eval average step individual rewards of agent1: 0.5624632570303257
team_policy eval average team episode rewards of agent1: 77.5
team_policy eval idv catch total num of agent1: 24
team_policy eval team catch total num: 31
team_policy eval average step individual rewards of agent2: 0.6874564320520633
team_policy eval average team episode rewards of agent2: 77.5
team_policy eval idv catch total num of agent2: 29
team_policy eval team catch total num: 31
team_policy eval average step individual rewards of agent3: 0.35634516167036695
team_policy eval average team episode rewards of agent3: 77.5
team_policy eval idv catch total num of agent3: 16
team_policy eval team catch total num: 31
team_policy eval average step individual rewards of agent4: 0.6434763299442864
team_policy eval average team episode rewards of agent4: 77.5
team_policy eval idv catch total num of agent4: 27
team_policy eval team catch total num: 31
idv_policy eval average step individual rewards of agent0: 1.2241196562309615
idv_policy eval average team episode rewards of agent0: 142.5
idv_policy eval idv catch total num of agent0: 50
idv_policy eval team catch total num: 57
idv_policy eval average step individual rewards of agent1: 1.4449990430430528
idv_policy eval average team episode rewards of agent1: 142.5
idv_policy eval idv catch total num of agent1: 59
idv_policy eval team catch total num: 57
idv_policy eval average step individual rewards of agent2: 0.686535071266408
idv_policy eval average team episode rewards of agent2: 142.5
idv_policy eval idv catch total num of agent2: 29
idv_policy eval team catch total num: 57
idv_policy eval average step individual rewards of agent3: 0.7663172559529962
idv_policy eval average team episode rewards of agent3: 142.5
idv_policy eval idv catch total num of agent3: 32
idv_policy eval team catch total num: 57
idv_policy eval average step individual rewards of agent4: 0.946078479108046
idv_policy eval average team episode rewards of agent4: 142.5
idv_policy eval idv catch total num of agent4: 39
idv_policy eval team catch total num: 57

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4276/10000 episodes, total num timesteps 855400/2000000, FPS 223.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4277/10000 episodes, total num timesteps 855600/2000000, FPS 223.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4278/10000 episodes, total num timesteps 855800/2000000, FPS 224.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4279/10000 episodes, total num timesteps 856000/2000000, FPS 224.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4280/10000 episodes, total num timesteps 856200/2000000, FPS 224.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4281/10000 episodes, total num timesteps 856400/2000000, FPS 224.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4282/10000 episodes, total num timesteps 856600/2000000, FPS 224.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4283/10000 episodes, total num timesteps 856800/2000000, FPS 224.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4284/10000 episodes, total num timesteps 857000/2000000, FPS 224.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4285/10000 episodes, total num timesteps 857200/2000000, FPS 224.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4286/10000 episodes, total num timesteps 857400/2000000, FPS 224.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4287/10000 episodes, total num timesteps 857600/2000000, FPS 224.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4288/10000 episodes, total num timesteps 857800/2000000, FPS 224.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4289/10000 episodes, total num timesteps 858000/2000000, FPS 224.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4290/10000 episodes, total num timesteps 858200/2000000, FPS 224.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4291/10000 episodes, total num timesteps 858400/2000000, FPS 224.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4292/10000 episodes, total num timesteps 858600/2000000, FPS 224.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4293/10000 episodes, total num timesteps 858800/2000000, FPS 224.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4294/10000 episodes, total num timesteps 859000/2000000, FPS 224.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4295/10000 episodes, total num timesteps 859200/2000000, FPS 224.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4296/10000 episodes, total num timesteps 859400/2000000, FPS 224.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4297/10000 episodes, total num timesteps 859600/2000000, FPS 224.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4298/10000 episodes, total num timesteps 859800/2000000, FPS 224.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4299/10000 episodes, total num timesteps 860000/2000000, FPS 224.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4300/10000 episodes, total num timesteps 860200/2000000, FPS 224.

team_policy eval average step individual rewards of agent0: 0.7475996786997794
team_policy eval average team episode rewards of agent0: 85.0
team_policy eval idv catch total num of agent0: 32
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent1: 0.8445768715415202
team_policy eval average team episode rewards of agent1: 85.0
team_policy eval idv catch total num of agent1: 35
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent2: 0.1864862782839544
team_policy eval average team episode rewards of agent2: 85.0
team_policy eval idv catch total num of agent2: 10
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent3: 0.5533826718940912
team_policy eval average team episode rewards of agent3: 85.0
team_policy eval idv catch total num of agent3: 24
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent4: 0.7683390858415672
team_policy eval average team episode rewards of agent4: 85.0
team_policy eval idv catch total num of agent4: 32
team_policy eval team catch total num: 34
idv_policy eval average step individual rewards of agent0: 0.809315137966482
idv_policy eval average team episode rewards of agent0: 120.0
idv_policy eval idv catch total num of agent0: 34
idv_policy eval team catch total num: 48
idv_policy eval average step individual rewards of agent1: 1.0361216400780118
idv_policy eval average team episode rewards of agent1: 120.0
idv_policy eval idv catch total num of agent1: 43
idv_policy eval team catch total num: 48
idv_policy eval average step individual rewards of agent2: 0.5356334442117103
idv_policy eval average team episode rewards of agent2: 120.0
idv_policy eval idv catch total num of agent2: 23
idv_policy eval team catch total num: 48
idv_policy eval average step individual rewards of agent3: 1.1182452572604342
idv_policy eval average team episode rewards of agent3: 120.0
idv_policy eval idv catch total num of agent3: 46
idv_policy eval team catch total num: 48
idv_policy eval average step individual rewards of agent4: 0.5300905030982633
idv_policy eval average team episode rewards of agent4: 120.0
idv_policy eval idv catch total num of agent4: 23
idv_policy eval team catch total num: 48

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4301/10000 episodes, total num timesteps 860400/2000000, FPS 224.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4302/10000 episodes, total num timesteps 860600/2000000, FPS 224.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4303/10000 episodes, total num timesteps 860800/2000000, FPS 224.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4304/10000 episodes, total num timesteps 861000/2000000, FPS 224.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4305/10000 episodes, total num timesteps 861200/2000000, FPS 224.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4306/10000 episodes, total num timesteps 861400/2000000, FPS 224.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4307/10000 episodes, total num timesteps 861600/2000000, FPS 224.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4308/10000 episodes, total num timesteps 861800/2000000, FPS 224.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4309/10000 episodes, total num timesteps 862000/2000000, FPS 224.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4310/10000 episodes, total num timesteps 862200/2000000, FPS 224.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4311/10000 episodes, total num timesteps 862400/2000000, FPS 224.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4312/10000 episodes, total num timesteps 862600/2000000, FPS 224.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4313/10000 episodes, total num timesteps 862800/2000000, FPS 224.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4314/10000 episodes, total num timesteps 863000/2000000, FPS 224.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4315/10000 episodes, total num timesteps 863200/2000000, FPS 224.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4316/10000 episodes, total num timesteps 863400/2000000, FPS 224.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4317/10000 episodes, total num timesteps 863600/2000000, FPS 224.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4318/10000 episodes, total num timesteps 863800/2000000, FPS 224.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4319/10000 episodes, total num timesteps 864000/2000000, FPS 224.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4320/10000 episodes, total num timesteps 864200/2000000, FPS 224.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4321/10000 episodes, total num timesteps 864400/2000000, FPS 224.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4322/10000 episodes, total num timesteps 864600/2000000, FPS 224.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4323/10000 episodes, total num timesteps 864800/2000000, FPS 224.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4324/10000 episodes, total num timesteps 865000/2000000, FPS 224.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4325/10000 episodes, total num timesteps 865200/2000000, FPS 224.

team_policy eval average step individual rewards of agent0: 0.696643399030048
team_policy eval average team episode rewards of agent0: 90.0
team_policy eval idv catch total num of agent0: 29
team_policy eval team catch total num: 36
team_policy eval average step individual rewards of agent1: 0.8444587606123456
team_policy eval average team episode rewards of agent1: 90.0
team_policy eval idv catch total num of agent1: 35
team_policy eval team catch total num: 36
team_policy eval average step individual rewards of agent2: 0.6877222553345846
team_policy eval average team episode rewards of agent2: 90.0
team_policy eval idv catch total num of agent2: 29
team_policy eval team catch total num: 36
team_policy eval average step individual rewards of agent3: 0.8350527583490306
team_policy eval average team episode rewards of agent3: 90.0
team_policy eval idv catch total num of agent3: 35
team_policy eval team catch total num: 36
team_policy eval average step individual rewards of agent4: 0.607616306460327
team_policy eval average team episode rewards of agent4: 90.0
team_policy eval idv catch total num of agent4: 26
team_policy eval team catch total num: 36
idv_policy eval average step individual rewards of agent0: 0.7395046186797984
idv_policy eval average team episode rewards of agent0: 125.0
idv_policy eval idv catch total num of agent0: 31
idv_policy eval team catch total num: 50
idv_policy eval average step individual rewards of agent1: 1.034137194965088
idv_policy eval average team episode rewards of agent1: 125.0
idv_policy eval idv catch total num of agent1: 43
idv_policy eval team catch total num: 50
idv_policy eval average step individual rewards of agent2: 0.865052493423872
idv_policy eval average team episode rewards of agent2: 125.0
idv_policy eval idv catch total num of agent2: 36
idv_policy eval team catch total num: 50
idv_policy eval average step individual rewards of agent3: 0.7582312470927058
idv_policy eval average team episode rewards of agent3: 125.0
idv_policy eval idv catch total num of agent3: 32
idv_policy eval team catch total num: 50
idv_policy eval average step individual rewards of agent4: 0.9449073559450664
idv_policy eval average team episode rewards of agent4: 125.0
idv_policy eval idv catch total num of agent4: 39
idv_policy eval team catch total num: 50

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4326/10000 episodes, total num timesteps 865400/2000000, FPS 224.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4327/10000 episodes, total num timesteps 865600/2000000, FPS 224.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4328/10000 episodes, total num timesteps 865800/2000000, FPS 224.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4329/10000 episodes, total num timesteps 866000/2000000, FPS 224.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4330/10000 episodes, total num timesteps 866200/2000000, FPS 224.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4331/10000 episodes, total num timesteps 866400/2000000, FPS 224.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4332/10000 episodes, total num timesteps 866600/2000000, FPS 224.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4333/10000 episodes, total num timesteps 866800/2000000, FPS 224.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4334/10000 episodes, total num timesteps 867000/2000000, FPS 224.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4335/10000 episodes, total num timesteps 867200/2000000, FPS 224.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4336/10000 episodes, total num timesteps 867400/2000000, FPS 224.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4337/10000 episodes, total num timesteps 867600/2000000, FPS 224.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4338/10000 episodes, total num timesteps 867800/2000000, FPS 224.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4339/10000 episodes, total num timesteps 868000/2000000, FPS 224.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4340/10000 episodes, total num timesteps 868200/2000000, FPS 224.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4341/10000 episodes, total num timesteps 868400/2000000, FPS 224.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4342/10000 episodes, total num timesteps 868600/2000000, FPS 224.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4343/10000 episodes, total num timesteps 868800/2000000, FPS 224.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4344/10000 episodes, total num timesteps 869000/2000000, FPS 224.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4345/10000 episodes, total num timesteps 869200/2000000, FPS 224.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4346/10000 episodes, total num timesteps 869400/2000000, FPS 224.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4347/10000 episodes, total num timesteps 869600/2000000, FPS 224.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4348/10000 episodes, total num timesteps 869800/2000000, FPS 224.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4349/10000 episodes, total num timesteps 870000/2000000, FPS 224.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4350/10000 episodes, total num timesteps 870200/2000000, FPS 224.

team_policy eval average step individual rewards of agent0: 0.9635442493574063
team_policy eval average team episode rewards of agent0: 125.0
team_policy eval idv catch total num of agent0: 40
team_policy eval team catch total num: 50
team_policy eval average step individual rewards of agent1: 0.7130600139494015
team_policy eval average team episode rewards of agent1: 125.0
team_policy eval idv catch total num of agent1: 30
team_policy eval team catch total num: 50
team_policy eval average step individual rewards of agent2: 0.9933216945001757
team_policy eval average team episode rewards of agent2: 125.0
team_policy eval idv catch total num of agent2: 41
team_policy eval team catch total num: 50
team_policy eval average step individual rewards of agent3: 1.1373529953180832
team_policy eval average team episode rewards of agent3: 125.0
team_policy eval idv catch total num of agent3: 47
team_policy eval team catch total num: 50
team_policy eval average step individual rewards of agent4: 0.6404775031787815
team_policy eval average team episode rewards of agent4: 125.0
team_policy eval idv catch total num of agent4: 27
team_policy eval team catch total num: 50
idv_policy eval average step individual rewards of agent0: 0.6153078399122474
idv_policy eval average team episode rewards of agent0: 90.0
idv_policy eval idv catch total num of agent0: 26
idv_policy eval team catch total num: 36
idv_policy eval average step individual rewards of agent1: 0.5647528629523955
idv_policy eval average team episode rewards of agent1: 90.0
idv_policy eval idv catch total num of agent1: 24
idv_policy eval team catch total num: 36
idv_policy eval average step individual rewards of agent2: 0.5358337561632059
idv_policy eval average team episode rewards of agent2: 90.0
idv_policy eval idv catch total num of agent2: 23
idv_policy eval team catch total num: 36
idv_policy eval average step individual rewards of agent3: 0.7539308128940148
idv_policy eval average team episode rewards of agent3: 90.0
idv_policy eval idv catch total num of agent3: 32
idv_policy eval team catch total num: 36
idv_policy eval average step individual rewards of agent4: 0.9696925754991138
idv_policy eval average team episode rewards of agent4: 90.0
idv_policy eval idv catch total num of agent4: 40
idv_policy eval team catch total num: 36

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4351/10000 episodes, total num timesteps 870400/2000000, FPS 224.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4352/10000 episodes, total num timesteps 870600/2000000, FPS 224.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4353/10000 episodes, total num timesteps 870800/2000000, FPS 224.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4354/10000 episodes, total num timesteps 871000/2000000, FPS 224.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4355/10000 episodes, total num timesteps 871200/2000000, FPS 224.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4356/10000 episodes, total num timesteps 871400/2000000, FPS 224.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4357/10000 episodes, total num timesteps 871600/2000000, FPS 224.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4358/10000 episodes, total num timesteps 871800/2000000, FPS 224.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4359/10000 episodes, total num timesteps 872000/2000000, FPS 224.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4360/10000 episodes, total num timesteps 872200/2000000, FPS 224.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4361/10000 episodes, total num timesteps 872400/2000000, FPS 224.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4362/10000 episodes, total num timesteps 872600/2000000, FPS 224.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4363/10000 episodes, total num timesteps 872800/2000000, FPS 225.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4364/10000 episodes, total num timesteps 873000/2000000, FPS 225.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4365/10000 episodes, total num timesteps 873200/2000000, FPS 225.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4366/10000 episodes, total num timesteps 873400/2000000, FPS 225.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4367/10000 episodes, total num timesteps 873600/2000000, FPS 225.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4368/10000 episodes, total num timesteps 873800/2000000, FPS 225.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4369/10000 episodes, total num timesteps 874000/2000000, FPS 225.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4370/10000 episodes, total num timesteps 874200/2000000, FPS 225.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4371/10000 episodes, total num timesteps 874400/2000000, FPS 225.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4372/10000 episodes, total num timesteps 874600/2000000, FPS 225.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4373/10000 episodes, total num timesteps 874800/2000000, FPS 225.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4374/10000 episodes, total num timesteps 875000/2000000, FPS 225.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4375/10000 episodes, total num timesteps 875200/2000000, FPS 225.

team_policy eval average step individual rewards of agent0: 0.8149632922500182
team_policy eval average team episode rewards of agent0: 92.5
team_policy eval idv catch total num of agent0: 34
team_policy eval team catch total num: 37
team_policy eval average step individual rewards of agent1: 0.7126583072563115
team_policy eval average team episode rewards of agent1: 92.5
team_policy eval idv catch total num of agent1: 30
team_policy eval team catch total num: 37
team_policy eval average step individual rewards of agent2: 0.6419096792649209
team_policy eval average team episode rewards of agent2: 92.5
team_policy eval idv catch total num of agent2: 27
team_policy eval team catch total num: 37
team_policy eval average step individual rewards of agent3: 0.6334476790424803
team_policy eval average team episode rewards of agent3: 92.5
team_policy eval idv catch total num of agent3: 27
team_policy eval team catch total num: 37
team_policy eval average step individual rewards of agent4: 0.45755527745167085
team_policy eval average team episode rewards of agent4: 92.5
team_policy eval idv catch total num of agent4: 20
team_policy eval team catch total num: 37
idv_policy eval average step individual rewards of agent0: 0.7908343258942979
idv_policy eval average team episode rewards of agent0: 130.0
idv_policy eval idv catch total num of agent0: 33
idv_policy eval team catch total num: 52
idv_policy eval average step individual rewards of agent1: 0.801825737298422
idv_policy eval average team episode rewards of agent1: 130.0
idv_policy eval idv catch total num of agent1: 34
idv_policy eval team catch total num: 52
idv_policy eval average step individual rewards of agent2: 0.9743384937004441
idv_policy eval average team episode rewards of agent2: 130.0
idv_policy eval idv catch total num of agent2: 40
idv_policy eval team catch total num: 52
idv_policy eval average step individual rewards of agent3: 0.8845972511506375
idv_policy eval average team episode rewards of agent3: 130.0
idv_policy eval idv catch total num of agent3: 37
idv_policy eval team catch total num: 52
idv_policy eval average step individual rewards of agent4: 1.0366985996890097
idv_policy eval average team episode rewards of agent4: 130.0
idv_policy eval idv catch total num of agent4: 43
idv_policy eval team catch total num: 52

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4376/10000 episodes, total num timesteps 875400/2000000, FPS 225.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4377/10000 episodes, total num timesteps 875600/2000000, FPS 225.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4378/10000 episodes, total num timesteps 875800/2000000, FPS 225.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4379/10000 episodes, total num timesteps 876000/2000000, FPS 225.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4380/10000 episodes, total num timesteps 876200/2000000, FPS 225.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4381/10000 episodes, total num timesteps 876400/2000000, FPS 225.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4382/10000 episodes, total num timesteps 876600/2000000, FPS 225.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4383/10000 episodes, total num timesteps 876800/2000000, FPS 225.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4384/10000 episodes, total num timesteps 877000/2000000, FPS 225.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4385/10000 episodes, total num timesteps 877200/2000000, FPS 225.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4386/10000 episodes, total num timesteps 877400/2000000, FPS 225.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4387/10000 episodes, total num timesteps 877600/2000000, FPS 225.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4388/10000 episodes, total num timesteps 877800/2000000, FPS 225.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4389/10000 episodes, total num timesteps 878000/2000000, FPS 225.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4390/10000 episodes, total num timesteps 878200/2000000, FPS 225.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4391/10000 episodes, total num timesteps 878400/2000000, FPS 225.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4392/10000 episodes, total num timesteps 878600/2000000, FPS 225.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4393/10000 episodes, total num timesteps 878800/2000000, FPS 225.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4394/10000 episodes, total num timesteps 879000/2000000, FPS 225.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4395/10000 episodes, total num timesteps 879200/2000000, FPS 225.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4396/10000 episodes, total num timesteps 879400/2000000, FPS 225.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4397/10000 episodes, total num timesteps 879600/2000000, FPS 225.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4398/10000 episodes, total num timesteps 879800/2000000, FPS 225.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4399/10000 episodes, total num timesteps 880000/2000000, FPS 225.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4400/10000 episodes, total num timesteps 880200/2000000, FPS 225.

team_policy eval average step individual rewards of agent0: 0.30970785182761845
team_policy eval average team episode rewards of agent0: 57.5
team_policy eval idv catch total num of agent0: 14
team_policy eval team catch total num: 23
team_policy eval average step individual rewards of agent1: 0.3090997222320814
team_policy eval average team episode rewards of agent1: 57.5
team_policy eval idv catch total num of agent1: 14
team_policy eval team catch total num: 23
team_policy eval average step individual rewards of agent2: 1.0208022177221168
team_policy eval average team episode rewards of agent2: 57.5
team_policy eval idv catch total num of agent2: 42
team_policy eval team catch total num: 23
team_policy eval average step individual rewards of agent3: 0.48072330247612155
team_policy eval average team episode rewards of agent3: 57.5
team_policy eval idv catch total num of agent3: 21
team_policy eval team catch total num: 23
team_policy eval average step individual rewards of agent4: 0.28580429407795277
team_policy eval average team episode rewards of agent4: 57.5
team_policy eval idv catch total num of agent4: 13
team_policy eval team catch total num: 23
idv_policy eval average step individual rewards of agent0: 1.0173135990018638
idv_policy eval average team episode rewards of agent0: 112.5
idv_policy eval idv catch total num of agent0: 42
idv_policy eval team catch total num: 45
idv_policy eval average step individual rewards of agent1: 0.8105656911011485
idv_policy eval average team episode rewards of agent1: 112.5
idv_policy eval idv catch total num of agent1: 34
idv_policy eval team catch total num: 45
idv_policy eval average step individual rewards of agent2: 0.8117951264215083
idv_policy eval average team episode rewards of agent2: 112.5
idv_policy eval idv catch total num of agent2: 34
idv_policy eval team catch total num: 45
idv_policy eval average step individual rewards of agent3: 0.9175414048317354
idv_policy eval average team episode rewards of agent3: 112.5
idv_policy eval idv catch total num of agent3: 38
idv_policy eval team catch total num: 45
idv_policy eval average step individual rewards of agent4: 0.763978865036051
idv_policy eval average team episode rewards of agent4: 112.5
idv_policy eval idv catch total num of agent4: 32
idv_policy eval team catch total num: 45

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4401/10000 episodes, total num timesteps 880400/2000000, FPS 225.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4402/10000 episodes, total num timesteps 880600/2000000, FPS 225.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4403/10000 episodes, total num timesteps 880800/2000000, FPS 225.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4404/10000 episodes, total num timesteps 881000/2000000, FPS 225.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4405/10000 episodes, total num timesteps 881200/2000000, FPS 225.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4406/10000 episodes, total num timesteps 881400/2000000, FPS 225.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4407/10000 episodes, total num timesteps 881600/2000000, FPS 225.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4408/10000 episodes, total num timesteps 881800/2000000, FPS 225.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4409/10000 episodes, total num timesteps 882000/2000000, FPS 225.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4410/10000 episodes, total num timesteps 882200/2000000, FPS 225.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4411/10000 episodes, total num timesteps 882400/2000000, FPS 225.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4412/10000 episodes, total num timesteps 882600/2000000, FPS 225.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4413/10000 episodes, total num timesteps 882800/2000000, FPS 225.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4414/10000 episodes, total num timesteps 883000/2000000, FPS 225.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4415/10000 episodes, total num timesteps 883200/2000000, FPS 225.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4416/10000 episodes, total num timesteps 883400/2000000, FPS 225.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4417/10000 episodes, total num timesteps 883600/2000000, FPS 225.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4418/10000 episodes, total num timesteps 883800/2000000, FPS 225.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4419/10000 episodes, total num timesteps 884000/2000000, FPS 225.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4420/10000 episodes, total num timesteps 884200/2000000, FPS 225.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4421/10000 episodes, total num timesteps 884400/2000000, FPS 225.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4422/10000 episodes, total num timesteps 884600/2000000, FPS 225.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4423/10000 episodes, total num timesteps 884800/2000000, FPS 225.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4424/10000 episodes, total num timesteps 885000/2000000, FPS 225.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4425/10000 episodes, total num timesteps 885200/2000000, FPS 225.

team_policy eval average step individual rewards of agent0: 0.7093213228176238
team_policy eval average team episode rewards of agent0: 87.5
team_policy eval idv catch total num of agent0: 30
team_policy eval team catch total num: 35
team_policy eval average step individual rewards of agent1: 0.6042154049372207
team_policy eval average team episode rewards of agent1: 87.5
team_policy eval idv catch total num of agent1: 26
team_policy eval team catch total num: 35
team_policy eval average step individual rewards of agent2: 0.5317730646641451
team_policy eval average team episode rewards of agent2: 87.5
team_policy eval idv catch total num of agent2: 23
team_policy eval team catch total num: 35
team_policy eval average step individual rewards of agent3: 0.6559151582095285
team_policy eval average team episode rewards of agent3: 87.5
team_policy eval idv catch total num of agent3: 28
team_policy eval team catch total num: 35
team_policy eval average step individual rewards of agent4: 0.8601072062467119
team_policy eval average team episode rewards of agent4: 87.5
team_policy eval idv catch total num of agent4: 36
team_policy eval team catch total num: 35
idv_policy eval average step individual rewards of agent0: 1.2930517848154923
idv_policy eval average team episode rewards of agent0: 155.0
idv_policy eval idv catch total num of agent0: 53
idv_policy eval team catch total num: 62
idv_policy eval average step individual rewards of agent1: 1.1534128994494806
idv_policy eval average team episode rewards of agent1: 155.0
idv_policy eval idv catch total num of agent1: 47
idv_policy eval team catch total num: 62
idv_policy eval average step individual rewards of agent2: 0.9098185207581448
idv_policy eval average team episode rewards of agent2: 155.0
idv_policy eval idv catch total num of agent2: 38
idv_policy eval team catch total num: 62
idv_policy eval average step individual rewards of agent3: 1.1001464932909923
idv_policy eval average team episode rewards of agent3: 155.0
idv_policy eval idv catch total num of agent3: 45
idv_policy eval team catch total num: 62
idv_policy eval average step individual rewards of agent4: 0.8951815363247883
idv_policy eval average team episode rewards of agent4: 155.0
idv_policy eval idv catch total num of agent4: 37
idv_policy eval team catch total num: 62

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4426/10000 episodes, total num timesteps 885400/2000000, FPS 225.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4427/10000 episodes, total num timesteps 885600/2000000, FPS 225.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4428/10000 episodes, total num timesteps 885800/2000000, FPS 225.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4429/10000 episodes, total num timesteps 886000/2000000, FPS 225.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4430/10000 episodes, total num timesteps 886200/2000000, FPS 225.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4431/10000 episodes, total num timesteps 886400/2000000, FPS 225.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4432/10000 episodes, total num timesteps 886600/2000000, FPS 225.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4433/10000 episodes, total num timesteps 886800/2000000, FPS 225.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4434/10000 episodes, total num timesteps 887000/2000000, FPS 225.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4435/10000 episodes, total num timesteps 887200/2000000, FPS 225.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4436/10000 episodes, total num timesteps 887400/2000000, FPS 225.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4437/10000 episodes, total num timesteps 887600/2000000, FPS 225.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4438/10000 episodes, total num timesteps 887800/2000000, FPS 225.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4439/10000 episodes, total num timesteps 888000/2000000, FPS 225.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4440/10000 episodes, total num timesteps 888200/2000000, FPS 225.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4441/10000 episodes, total num timesteps 888400/2000000, FPS 225.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4442/10000 episodes, total num timesteps 888600/2000000, FPS 225.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4443/10000 episodes, total num timesteps 888800/2000000, FPS 225.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4444/10000 episodes, total num timesteps 889000/2000000, FPS 225.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4445/10000 episodes, total num timesteps 889200/2000000, FPS 225.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4446/10000 episodes, total num timesteps 889400/2000000, FPS 225.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4447/10000 episodes, total num timesteps 889600/2000000, FPS 225.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4448/10000 episodes, total num timesteps 889800/2000000, FPS 225.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4449/10000 episodes, total num timesteps 890000/2000000, FPS 225.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4450/10000 episodes, total num timesteps 890200/2000000, FPS 226.

team_policy eval average step individual rewards of agent0: 1.0227761977295815
team_policy eval average team episode rewards of agent0: 122.5
team_policy eval idv catch total num of agent0: 42
team_policy eval team catch total num: 49
team_policy eval average step individual rewards of agent1: 1.0809910373781428
team_policy eval average team episode rewards of agent1: 122.5
team_policy eval idv catch total num of agent1: 45
team_policy eval team catch total num: 49
team_policy eval average step individual rewards of agent2: 0.4999017434198116
team_policy eval average team episode rewards of agent2: 122.5
team_policy eval idv catch total num of agent2: 22
team_policy eval team catch total num: 49
team_policy eval average step individual rewards of agent3: 0.8390424993743326
team_policy eval average team episode rewards of agent3: 122.5
team_policy eval idv catch total num of agent3: 35
team_policy eval team catch total num: 49
team_policy eval average step individual rewards of agent4: 0.7627558257218598
team_policy eval average team episode rewards of agent4: 122.5
team_policy eval idv catch total num of agent4: 32
team_policy eval team catch total num: 49
idv_policy eval average step individual rewards of agent0: 0.610359820005744
idv_policy eval average team episode rewards of agent0: 107.5
idv_policy eval idv catch total num of agent0: 26
idv_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent1: 0.6435650712919561
idv_policy eval average team episode rewards of agent1: 107.5
idv_policy eval idv catch total num of agent1: 27
idv_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent2: 1.0644899926785352
idv_policy eval average team episode rewards of agent2: 107.5
idv_policy eval idv catch total num of agent2: 44
idv_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent3: 0.7853737657241916
idv_policy eval average team episode rewards of agent3: 107.5
idv_policy eval idv catch total num of agent3: 33
idv_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent4: 0.8949117075748626
idv_policy eval average team episode rewards of agent4: 107.5
idv_policy eval idv catch total num of agent4: 37
idv_policy eval team catch total num: 43

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4451/10000 episodes, total num timesteps 890400/2000000, FPS 226.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4452/10000 episodes, total num timesteps 890600/2000000, FPS 226.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4453/10000 episodes, total num timesteps 890800/2000000, FPS 226.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4454/10000 episodes, total num timesteps 891000/2000000, FPS 226.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4455/10000 episodes, total num timesteps 891200/2000000, FPS 226.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4456/10000 episodes, total num timesteps 891400/2000000, FPS 226.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4457/10000 episodes, total num timesteps 891600/2000000, FPS 226.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4458/10000 episodes, total num timesteps 891800/2000000, FPS 226.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4459/10000 episodes, total num timesteps 892000/2000000, FPS 226.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4460/10000 episodes, total num timesteps 892200/2000000, FPS 226.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4461/10000 episodes, total num timesteps 892400/2000000, FPS 226.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4462/10000 episodes, total num timesteps 892600/2000000, FPS 226.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4463/10000 episodes, total num timesteps 892800/2000000, FPS 226.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4464/10000 episodes, total num timesteps 893000/2000000, FPS 226.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4465/10000 episodes, total num timesteps 893200/2000000, FPS 226.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4466/10000 episodes, total num timesteps 893400/2000000, FPS 226.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4467/10000 episodes, total num timesteps 893600/2000000, FPS 226.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4468/10000 episodes, total num timesteps 893800/2000000, FPS 226.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4469/10000 episodes, total num timesteps 894000/2000000, FPS 226.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4470/10000 episodes, total num timesteps 894200/2000000, FPS 226.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4471/10000 episodes, total num timesteps 894400/2000000, FPS 226.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4472/10000 episodes, total num timesteps 894600/2000000, FPS 226.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4473/10000 episodes, total num timesteps 894800/2000000, FPS 226.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4474/10000 episodes, total num timesteps 895000/2000000, FPS 226.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4475/10000 episodes, total num timesteps 895200/2000000, FPS 226.

team_policy eval average step individual rewards of agent0: 1.7220741891722309
team_policy eval average team episode rewards of agent0: 187.5
team_policy eval idv catch total num of agent0: 70
team_policy eval team catch total num: 75
team_policy eval average step individual rewards of agent1: 1.1229150992451216
team_policy eval average team episode rewards of agent1: 187.5
team_policy eval idv catch total num of agent1: 46
team_policy eval team catch total num: 75
team_policy eval average step individual rewards of agent2: 1.147706469058013
team_policy eval average team episode rewards of agent2: 187.5
team_policy eval idv catch total num of agent2: 47
team_policy eval team catch total num: 75
team_policy eval average step individual rewards of agent3: 1.3128920649209983
team_policy eval average team episode rewards of agent3: 187.5
team_policy eval idv catch total num of agent3: 54
team_policy eval team catch total num: 75
team_policy eval average step individual rewards of agent4: 1.1461722598530448
team_policy eval average team episode rewards of agent4: 187.5
team_policy eval idv catch total num of agent4: 47
team_policy eval team catch total num: 75
idv_policy eval average step individual rewards of agent0: 0.6375871398812676
idv_policy eval average team episode rewards of agent0: 100.0
idv_policy eval idv catch total num of agent0: 27
idv_policy eval team catch total num: 40
idv_policy eval average step individual rewards of agent1: 0.5570822001127836
idv_policy eval average team episode rewards of agent1: 100.0
idv_policy eval idv catch total num of agent1: 24
idv_policy eval team catch total num: 40
idv_policy eval average step individual rewards of agent2: 1.2169548483332817
idv_policy eval average team episode rewards of agent2: 100.0
idv_policy eval idv catch total num of agent2: 50
idv_policy eval team catch total num: 40
idv_policy eval average step individual rewards of agent3: 0.5802773136574568
idv_policy eval average team episode rewards of agent3: 100.0
idv_policy eval idv catch total num of agent3: 25
idv_policy eval team catch total num: 40
idv_policy eval average step individual rewards of agent4: 0.44752510411450563
idv_policy eval average team episode rewards of agent4: 100.0
idv_policy eval idv catch total num of agent4: 20
idv_policy eval team catch total num: 40

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4476/10000 episodes, total num timesteps 895400/2000000, FPS 226.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4477/10000 episodes, total num timesteps 895600/2000000, FPS 226.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4478/10000 episodes, total num timesteps 895800/2000000, FPS 226.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4479/10000 episodes, total num timesteps 896000/2000000, FPS 226.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4480/10000 episodes, total num timesteps 896200/2000000, FPS 226.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4481/10000 episodes, total num timesteps 896400/2000000, FPS 226.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4482/10000 episodes, total num timesteps 896600/2000000, FPS 226.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4483/10000 episodes, total num timesteps 896800/2000000, FPS 226.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4484/10000 episodes, total num timesteps 897000/2000000, FPS 226.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4485/10000 episodes, total num timesteps 897200/2000000, FPS 226.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4486/10000 episodes, total num timesteps 897400/2000000, FPS 226.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4487/10000 episodes, total num timesteps 897600/2000000, FPS 226.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4488/10000 episodes, total num timesteps 897800/2000000, FPS 226.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4489/10000 episodes, total num timesteps 898000/2000000, FPS 226.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4490/10000 episodes, total num timesteps 898200/2000000, FPS 226.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4491/10000 episodes, total num timesteps 898400/2000000, FPS 226.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4492/10000 episodes, total num timesteps 898600/2000000, FPS 226.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4493/10000 episodes, total num timesteps 898800/2000000, FPS 226.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4494/10000 episodes, total num timesteps 899000/2000000, FPS 226.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4495/10000 episodes, total num timesteps 899200/2000000, FPS 226.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4496/10000 episodes, total num timesteps 899400/2000000, FPS 226.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4497/10000 episodes, total num timesteps 899600/2000000, FPS 226.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4498/10000 episodes, total num timesteps 899800/2000000, FPS 226.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4499/10000 episodes, total num timesteps 900000/2000000, FPS 226.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4500/10000 episodes, total num timesteps 900200/2000000, FPS 226.

team_policy eval average step individual rewards of agent0: 1.0961449586103642
team_policy eval average team episode rewards of agent0: 115.0
team_policy eval idv catch total num of agent0: 45
team_policy eval team catch total num: 46
team_policy eval average step individual rewards of agent1: 0.5158988739992443
team_policy eval average team episode rewards of agent1: 115.0
team_policy eval idv catch total num of agent1: 22
team_policy eval team catch total num: 46
team_policy eval average step individual rewards of agent2: 1.1926703288449954
team_policy eval average team episode rewards of agent2: 115.0
team_policy eval idv catch total num of agent2: 49
team_policy eval team catch total num: 46
team_policy eval average step individual rewards of agent3: 0.9491834187165274
team_policy eval average team episode rewards of agent3: 115.0
team_policy eval idv catch total num of agent3: 39
team_policy eval team catch total num: 46
team_policy eval average step individual rewards of agent4: 0.48449017988609805
team_policy eval average team episode rewards of agent4: 115.0
team_policy eval idv catch total num of agent4: 21
team_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent0: 0.5367418214850427
idv_policy eval average team episode rewards of agent0: 107.5
idv_policy eval idv catch total num of agent0: 23
idv_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent1: 0.8271576553333
idv_policy eval average team episode rewards of agent1: 107.5
idv_policy eval idv catch total num of agent1: 35
idv_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent2: 0.34261638399946714
idv_policy eval average team episode rewards of agent2: 107.5
idv_policy eval idv catch total num of agent2: 16
idv_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent3: 0.8549051661027375
idv_policy eval average team episode rewards of agent3: 107.5
idv_policy eval idv catch total num of agent3: 36
idv_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent4: 0.91501297614023
idv_policy eval average team episode rewards of agent4: 107.5
idv_policy eval idv catch total num of agent4: 38
idv_policy eval team catch total num: 43

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4501/10000 episodes, total num timesteps 900400/2000000, FPS 226.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4502/10000 episodes, total num timesteps 900600/2000000, FPS 226.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4503/10000 episodes, total num timesteps 900800/2000000, FPS 226.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4504/10000 episodes, total num timesteps 901000/2000000, FPS 226.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4505/10000 episodes, total num timesteps 901200/2000000, FPS 226.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4506/10000 episodes, total num timesteps 901400/2000000, FPS 226.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4507/10000 episodes, total num timesteps 901600/2000000, FPS 226.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4508/10000 episodes, total num timesteps 901800/2000000, FPS 226.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4509/10000 episodes, total num timesteps 902000/2000000, FPS 226.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4510/10000 episodes, total num timesteps 902200/2000000, FPS 226.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4511/10000 episodes, total num timesteps 902400/2000000, FPS 226.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4512/10000 episodes, total num timesteps 902600/2000000, FPS 226.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4513/10000 episodes, total num timesteps 902800/2000000, FPS 226.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4514/10000 episodes, total num timesteps 903000/2000000, FPS 226.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4515/10000 episodes, total num timesteps 903200/2000000, FPS 226.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4516/10000 episodes, total num timesteps 903400/2000000, FPS 226.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4517/10000 episodes, total num timesteps 903600/2000000, FPS 226.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4518/10000 episodes, total num timesteps 903800/2000000, FPS 226.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4519/10000 episodes, total num timesteps 904000/2000000, FPS 226.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4520/10000 episodes, total num timesteps 904200/2000000, FPS 226.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4521/10000 episodes, total num timesteps 904400/2000000, FPS 226.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4522/10000 episodes, total num timesteps 904600/2000000, FPS 226.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4523/10000 episodes, total num timesteps 904800/2000000, FPS 226.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4524/10000 episodes, total num timesteps 905000/2000000, FPS 226.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4525/10000 episodes, total num timesteps 905200/2000000, FPS 226.

team_policy eval average step individual rewards of agent0: 0.6623554132809076
team_policy eval average team episode rewards of agent0: 107.5
team_policy eval idv catch total num of agent0: 28
team_policy eval team catch total num: 43
team_policy eval average step individual rewards of agent1: 0.8562872704016502
team_policy eval average team episode rewards of agent1: 107.5
team_policy eval idv catch total num of agent1: 36
team_policy eval team catch total num: 43
team_policy eval average step individual rewards of agent2: 0.942826437162606
team_policy eval average team episode rewards of agent2: 107.5
team_policy eval idv catch total num of agent2: 39
team_policy eval team catch total num: 43
team_policy eval average step individual rewards of agent3: 0.8344562512146025
team_policy eval average team episode rewards of agent3: 107.5
team_policy eval idv catch total num of agent3: 35
team_policy eval team catch total num: 43
team_policy eval average step individual rewards of agent4: 0.8086488451887494
team_policy eval average team episode rewards of agent4: 107.5
team_policy eval idv catch total num of agent4: 34
team_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent0: 0.5049371319119864
idv_policy eval average team episode rewards of agent0: 62.5
idv_policy eval idv catch total num of agent0: 22
idv_policy eval team catch total num: 25
idv_policy eval average step individual rewards of agent1: 0.6144101557106161
idv_policy eval average team episode rewards of agent1: 62.5
idv_policy eval idv catch total num of agent1: 26
idv_policy eval team catch total num: 25
idv_policy eval average step individual rewards of agent2: 0.3740385062587764
idv_policy eval average team episode rewards of agent2: 62.5
idv_policy eval idv catch total num of agent2: 17
idv_policy eval team catch total num: 25
idv_policy eval average step individual rewards of agent3: 0.6763765903827803
idv_policy eval average team episode rewards of agent3: 62.5
idv_policy eval idv catch total num of agent3: 29
idv_policy eval team catch total num: 25
idv_policy eval average step individual rewards of agent4: 0.5140097502966658
idv_policy eval average team episode rewards of agent4: 62.5
idv_policy eval idv catch total num of agent4: 22
idv_policy eval team catch total num: 25

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4526/10000 episodes, total num timesteps 905400/2000000, FPS 226.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4527/10000 episodes, total num timesteps 905600/2000000, FPS 226.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4528/10000 episodes, total num timesteps 905800/2000000, FPS 226.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4529/10000 episodes, total num timesteps 906000/2000000, FPS 226.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4530/10000 episodes, total num timesteps 906200/2000000, FPS 226.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4531/10000 episodes, total num timesteps 906400/2000000, FPS 226.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4532/10000 episodes, total num timesteps 906600/2000000, FPS 226.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4533/10000 episodes, total num timesteps 906800/2000000, FPS 226.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4534/10000 episodes, total num timesteps 907000/2000000, FPS 226.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4535/10000 episodes, total num timesteps 907200/2000000, FPS 226.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4536/10000 episodes, total num timesteps 907400/2000000, FPS 226.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4537/10000 episodes, total num timesteps 907600/2000000, FPS 226.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4538/10000 episodes, total num timesteps 907800/2000000, FPS 227.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4539/10000 episodes, total num timesteps 908000/2000000, FPS 227.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4540/10000 episodes, total num timesteps 908200/2000000, FPS 227.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4541/10000 episodes, total num timesteps 908400/2000000, FPS 227.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4542/10000 episodes, total num timesteps 908600/2000000, FPS 227.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4543/10000 episodes, total num timesteps 908800/2000000, FPS 227.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4544/10000 episodes, total num timesteps 909000/2000000, FPS 227.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4545/10000 episodes, total num timesteps 909200/2000000, FPS 227.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4546/10000 episodes, total num timesteps 909400/2000000, FPS 227.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4547/10000 episodes, total num timesteps 909600/2000000, FPS 227.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4548/10000 episodes, total num timesteps 909800/2000000, FPS 227.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4549/10000 episodes, total num timesteps 910000/2000000, FPS 227.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4550/10000 episodes, total num timesteps 910200/2000000, FPS 227.

team_policy eval average step individual rewards of agent0: 0.9396468601976008
team_policy eval average team episode rewards of agent0: 105.0
team_policy eval idv catch total num of agent0: 39
team_policy eval team catch total num: 42
team_policy eval average step individual rewards of agent1: 0.8223231062777046
team_policy eval average team episode rewards of agent1: 105.0
team_policy eval idv catch total num of agent1: 34
team_policy eval team catch total num: 42
team_policy eval average step individual rewards of agent2: 0.8689581049595554
team_policy eval average team episode rewards of agent2: 105.0
team_policy eval idv catch total num of agent2: 36
team_policy eval team catch total num: 42
team_policy eval average step individual rewards of agent3: 0.5172240999115162
team_policy eval average team episode rewards of agent3: 105.0
team_policy eval idv catch total num of agent3: 22
team_policy eval team catch total num: 42
team_policy eval average step individual rewards of agent4: 0.7922929069682703
team_policy eval average team episode rewards of agent4: 105.0
team_policy eval idv catch total num of agent4: 33
team_policy eval team catch total num: 42
idv_policy eval average step individual rewards of agent0: 0.603534389972847
idv_policy eval average team episode rewards of agent0: 62.5
idv_policy eval idv catch total num of agent0: 26
idv_policy eval team catch total num: 25
idv_policy eval average step individual rewards of agent1: 0.6516751171968295
idv_policy eval average team episode rewards of agent1: 62.5
idv_policy eval idv catch total num of agent1: 28
idv_policy eval team catch total num: 25
idv_policy eval average step individual rewards of agent2: 0.2522233526414587
idv_policy eval average team episode rewards of agent2: 62.5
idv_policy eval idv catch total num of agent2: 12
idv_policy eval team catch total num: 25
idv_policy eval average step individual rewards of agent3: 0.7560384732382821
idv_policy eval average team episode rewards of agent3: 62.5
idv_policy eval idv catch total num of agent3: 32
idv_policy eval team catch total num: 25
idv_policy eval average step individual rewards of agent4: 0.5075095302860122
idv_policy eval average team episode rewards of agent4: 62.5
idv_policy eval idv catch total num of agent4: 22
idv_policy eval team catch total num: 25

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4551/10000 episodes, total num timesteps 910400/2000000, FPS 227.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4552/10000 episodes, total num timesteps 910600/2000000, FPS 227.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4553/10000 episodes, total num timesteps 910800/2000000, FPS 227.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4554/10000 episodes, total num timesteps 911000/2000000, FPS 227.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4555/10000 episodes, total num timesteps 911200/2000000, FPS 227.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4556/10000 episodes, total num timesteps 911400/2000000, FPS 227.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4557/10000 episodes, total num timesteps 911600/2000000, FPS 227.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4558/10000 episodes, total num timesteps 911800/2000000, FPS 227.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4559/10000 episodes, total num timesteps 912000/2000000, FPS 227.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4560/10000 episodes, total num timesteps 912200/2000000, FPS 227.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4561/10000 episodes, total num timesteps 912400/2000000, FPS 227.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4562/10000 episodes, total num timesteps 912600/2000000, FPS 227.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4563/10000 episodes, total num timesteps 912800/2000000, FPS 227.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4564/10000 episodes, total num timesteps 913000/2000000, FPS 227.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4565/10000 episodes, total num timesteps 913200/2000000, FPS 227.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4566/10000 episodes, total num timesteps 913400/2000000, FPS 227.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4567/10000 episodes, total num timesteps 913600/2000000, FPS 227.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4568/10000 episodes, total num timesteps 913800/2000000, FPS 227.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4569/10000 episodes, total num timesteps 914000/2000000, FPS 227.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4570/10000 episodes, total num timesteps 914200/2000000, FPS 227.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4571/10000 episodes, total num timesteps 914400/2000000, FPS 227.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4572/10000 episodes, total num timesteps 914600/2000000, FPS 227.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4573/10000 episodes, total num timesteps 914800/2000000, FPS 227.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4574/10000 episodes, total num timesteps 915000/2000000, FPS 227.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4575/10000 episodes, total num timesteps 915200/2000000, FPS 227.

team_policy eval average step individual rewards of agent0: 0.66383337423734
team_policy eval average team episode rewards of agent0: 80.0
team_policy eval idv catch total num of agent0: 28
team_policy eval team catch total num: 32
team_policy eval average step individual rewards of agent1: 0.9417938645691993
team_policy eval average team episode rewards of agent1: 80.0
team_policy eval idv catch total num of agent1: 39
team_policy eval team catch total num: 32
team_policy eval average step individual rewards of agent2: 0.5863400991302037
team_policy eval average team episode rewards of agent2: 80.0
team_policy eval idv catch total num of agent2: 25
team_policy eval team catch total num: 32
team_policy eval average step individual rewards of agent3: 0.7180628799987891
team_policy eval average team episode rewards of agent3: 80.0
team_policy eval idv catch total num of agent3: 30
team_policy eval team catch total num: 32
team_policy eval average step individual rewards of agent4: 0.7572543751303502
team_policy eval average team episode rewards of agent4: 80.0
team_policy eval idv catch total num of agent4: 32
team_policy eval team catch total num: 32
idv_policy eval average step individual rewards of agent0: 0.6590419754295143
idv_policy eval average team episode rewards of agent0: 102.5
idv_policy eval idv catch total num of agent0: 28
idv_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent1: 0.5390358929855615
idv_policy eval average team episode rewards of agent1: 102.5
idv_policy eval idv catch total num of agent1: 23
idv_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent2: 0.5335546542127628
idv_policy eval average team episode rewards of agent2: 102.5
idv_policy eval idv catch total num of agent2: 23
idv_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent3: 1.3090925792495958
idv_policy eval average team episode rewards of agent3: 102.5
idv_policy eval idv catch total num of agent3: 54
idv_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent4: 0.7652043552827653
idv_policy eval average team episode rewards of agent4: 102.5
idv_policy eval idv catch total num of agent4: 32
idv_policy eval team catch total num: 41

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4576/10000 episodes, total num timesteps 915400/2000000, FPS 227.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4577/10000 episodes, total num timesteps 915600/2000000, FPS 227.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4578/10000 episodes, total num timesteps 915800/2000000, FPS 227.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4579/10000 episodes, total num timesteps 916000/2000000, FPS 227.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4580/10000 episodes, total num timesteps 916200/2000000, FPS 227.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4581/10000 episodes, total num timesteps 916400/2000000, FPS 227.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4582/10000 episodes, total num timesteps 916600/2000000, FPS 227.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4583/10000 episodes, total num timesteps 916800/2000000, FPS 227.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4584/10000 episodes, total num timesteps 917000/2000000, FPS 227.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4585/10000 episodes, total num timesteps 917200/2000000, FPS 227.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4586/10000 episodes, total num timesteps 917400/2000000, FPS 227.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4587/10000 episodes, total num timesteps 917600/2000000, FPS 227.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4588/10000 episodes, total num timesteps 917800/2000000, FPS 227.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4589/10000 episodes, total num timesteps 918000/2000000, FPS 227.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4590/10000 episodes, total num timesteps 918200/2000000, FPS 227.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4591/10000 episodes, total num timesteps 918400/2000000, FPS 227.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4592/10000 episodes, total num timesteps 918600/2000000, FPS 227.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4593/10000 episodes, total num timesteps 918800/2000000, FPS 227.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4594/10000 episodes, total num timesteps 919000/2000000, FPS 227.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4595/10000 episodes, total num timesteps 919200/2000000, FPS 227.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4596/10000 episodes, total num timesteps 919400/2000000, FPS 227.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4597/10000 episodes, total num timesteps 919600/2000000, FPS 227.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4598/10000 episodes, total num timesteps 919800/2000000, FPS 227.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4599/10000 episodes, total num timesteps 920000/2000000, FPS 227.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4600/10000 episodes, total num timesteps 920200/2000000, FPS 227.

team_policy eval average step individual rewards of agent0: 0.43209309985100824
team_policy eval average team episode rewards of agent0: 90.0
team_policy eval idv catch total num of agent0: 19
team_policy eval team catch total num: 36
team_policy eval average step individual rewards of agent1: 0.5938107471938457
team_policy eval average team episode rewards of agent1: 90.0
team_policy eval idv catch total num of agent1: 25
team_policy eval team catch total num: 36
team_policy eval average step individual rewards of agent2: 0.7406236032026899
team_policy eval average team episode rewards of agent2: 90.0
team_policy eval idv catch total num of agent2: 31
team_policy eval team catch total num: 36
team_policy eval average step individual rewards of agent3: 0.9720390967143081
team_policy eval average team episode rewards of agent3: 90.0
team_policy eval idv catch total num of agent3: 40
team_policy eval team catch total num: 36
team_policy eval average step individual rewards of agent4: 0.6182061544411795
team_policy eval average team episode rewards of agent4: 90.0
team_policy eval idv catch total num of agent4: 26
team_policy eval team catch total num: 36
idv_policy eval average step individual rewards of agent0: 0.9195368746673835
idv_policy eval average team episode rewards of agent0: 100.0
idv_policy eval idv catch total num of agent0: 38
idv_policy eval team catch total num: 40
idv_policy eval average step individual rewards of agent1: 0.4798702502128724
idv_policy eval average team episode rewards of agent1: 100.0
idv_policy eval idv catch total num of agent1: 21
idv_policy eval team catch total num: 40
idv_policy eval average step individual rewards of agent2: 0.5237556362034709
idv_policy eval average team episode rewards of agent2: 100.0
idv_policy eval idv catch total num of agent2: 23
idv_policy eval team catch total num: 40
idv_policy eval average step individual rewards of agent3: 0.7811598688229937
idv_policy eval average team episode rewards of agent3: 100.0
idv_policy eval idv catch total num of agent3: 33
idv_policy eval team catch total num: 40
idv_policy eval average step individual rewards of agent4: 0.7270554024920802
idv_policy eval average team episode rewards of agent4: 100.0
idv_policy eval idv catch total num of agent4: 31
idv_policy eval team catch total num: 40

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4601/10000 episodes, total num timesteps 920400/2000000, FPS 227.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4602/10000 episodes, total num timesteps 920600/2000000, FPS 227.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4603/10000 episodes, total num timesteps 920800/2000000, FPS 227.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4604/10000 episodes, total num timesteps 921000/2000000, FPS 227.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4605/10000 episodes, total num timesteps 921200/2000000, FPS 227.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4606/10000 episodes, total num timesteps 921400/2000000, FPS 227.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4607/10000 episodes, total num timesteps 921600/2000000, FPS 227.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4608/10000 episodes, total num timesteps 921800/2000000, FPS 227.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4609/10000 episodes, total num timesteps 922000/2000000, FPS 227.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4610/10000 episodes, total num timesteps 922200/2000000, FPS 227.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4611/10000 episodes, total num timesteps 922400/2000000, FPS 227.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4612/10000 episodes, total num timesteps 922600/2000000, FPS 227.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4613/10000 episodes, total num timesteps 922800/2000000, FPS 227.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4614/10000 episodes, total num timesteps 923000/2000000, FPS 227.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4615/10000 episodes, total num timesteps 923200/2000000, FPS 227.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4616/10000 episodes, total num timesteps 923400/2000000, FPS 227.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4617/10000 episodes, total num timesteps 923600/2000000, FPS 227.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4618/10000 episodes, total num timesteps 923800/2000000, FPS 227.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4619/10000 episodes, total num timesteps 924000/2000000, FPS 227.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4620/10000 episodes, total num timesteps 924200/2000000, FPS 227.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4621/10000 episodes, total num timesteps 924400/2000000, FPS 227.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4622/10000 episodes, total num timesteps 924600/2000000, FPS 227.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4623/10000 episodes, total num timesteps 924800/2000000, FPS 227.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4624/10000 episodes, total num timesteps 925000/2000000, FPS 227.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4625/10000 episodes, total num timesteps 925200/2000000, FPS 227.

team_policy eval average step individual rewards of agent0: 0.6131017855057996
team_policy eval average team episode rewards of agent0: 77.5
team_policy eval idv catch total num of agent0: 26
team_policy eval team catch total num: 31
team_policy eval average step individual rewards of agent1: 0.6891797169576681
team_policy eval average team episode rewards of agent1: 77.5
team_policy eval idv catch total num of agent1: 29
team_policy eval team catch total num: 31
team_policy eval average step individual rewards of agent2: 0.91807057500611
team_policy eval average team episode rewards of agent2: 77.5
team_policy eval idv catch total num of agent2: 38
team_policy eval team catch total num: 31
team_policy eval average step individual rewards of agent3: 0.31251213203951417
team_policy eval average team episode rewards of agent3: 77.5
team_policy eval idv catch total num of agent3: 14
team_policy eval team catch total num: 31
team_policy eval average step individual rewards of agent4: 0.44925149878983206
team_policy eval average team episode rewards of agent4: 77.5
team_policy eval idv catch total num of agent4: 20
team_policy eval team catch total num: 31
idv_policy eval average step individual rewards of agent0: 0.8621225145251902
idv_policy eval average team episode rewards of agent0: 125.0
idv_policy eval idv catch total num of agent0: 36
idv_policy eval team catch total num: 50
idv_policy eval average step individual rewards of agent1: 1.0823764156603681
idv_policy eval average team episode rewards of agent1: 125.0
idv_policy eval idv catch total num of agent1: 45
idv_policy eval team catch total num: 50
idv_policy eval average step individual rewards of agent2: 1.0394799436938953
idv_policy eval average team episode rewards of agent2: 125.0
idv_policy eval idv catch total num of agent2: 43
idv_policy eval team catch total num: 50
idv_policy eval average step individual rewards of agent3: 0.7584902234322922
idv_policy eval average team episode rewards of agent3: 125.0
idv_policy eval idv catch total num of agent3: 32
idv_policy eval team catch total num: 50
idv_policy eval average step individual rewards of agent4: 0.90450321998884
idv_policy eval average team episode rewards of agent4: 125.0
idv_policy eval idv catch total num of agent4: 38
idv_policy eval team catch total num: 50

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4626/10000 episodes, total num timesteps 925400/2000000, FPS 227.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4627/10000 episodes, total num timesteps 925600/2000000, FPS 227.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4628/10000 episodes, total num timesteps 925800/2000000, FPS 227.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4629/10000 episodes, total num timesteps 926000/2000000, FPS 227.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4630/10000 episodes, total num timesteps 926200/2000000, FPS 227.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4631/10000 episodes, total num timesteps 926400/2000000, FPS 227.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4632/10000 episodes, total num timesteps 926600/2000000, FPS 228.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4633/10000 episodes, total num timesteps 926800/2000000, FPS 228.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4634/10000 episodes, total num timesteps 927000/2000000, FPS 228.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4635/10000 episodes, total num timesteps 927200/2000000, FPS 228.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4636/10000 episodes, total num timesteps 927400/2000000, FPS 228.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4637/10000 episodes, total num timesteps 927600/2000000, FPS 228.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4638/10000 episodes, total num timesteps 927800/2000000, FPS 228.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4639/10000 episodes, total num timesteps 928000/2000000, FPS 228.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4640/10000 episodes, total num timesteps 928200/2000000, FPS 228.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4641/10000 episodes, total num timesteps 928400/2000000, FPS 228.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4642/10000 episodes, total num timesteps 928600/2000000, FPS 228.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4643/10000 episodes, total num timesteps 928800/2000000, FPS 228.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4644/10000 episodes, total num timesteps 929000/2000000, FPS 228.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4645/10000 episodes, total num timesteps 929200/2000000, FPS 228.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4646/10000 episodes, total num timesteps 929400/2000000, FPS 228.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4647/10000 episodes, total num timesteps 929600/2000000, FPS 228.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4648/10000 episodes, total num timesteps 929800/2000000, FPS 228.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4649/10000 episodes, total num timesteps 930000/2000000, FPS 228.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4650/10000 episodes, total num timesteps 930200/2000000, FPS 228.

team_policy eval average step individual rewards of agent0: 0.8904009687831497
team_policy eval average team episode rewards of agent0: 55.0
team_policy eval idv catch total num of agent0: 37
team_policy eval team catch total num: 22
team_policy eval average step individual rewards of agent1: 0.3882473890648819
team_policy eval average team episode rewards of agent1: 55.0
team_policy eval idv catch total num of agent1: 17
team_policy eval team catch total num: 22
team_policy eval average step individual rewards of agent2: 0.40811440386474485
team_policy eval average team episode rewards of agent2: 55.0
team_policy eval idv catch total num of agent2: 18
team_policy eval team catch total num: 22
team_policy eval average step individual rewards of agent3: 0.6618839901562289
team_policy eval average team episode rewards of agent3: 55.0
team_policy eval idv catch total num of agent3: 28
team_policy eval team catch total num: 22
team_policy eval average step individual rewards of agent4: 0.34843305383093326
team_policy eval average team episode rewards of agent4: 55.0
team_policy eval idv catch total num of agent4: 16
team_policy eval team catch total num: 22
idv_policy eval average step individual rewards of agent0: 0.7373629433346122
idv_policy eval average team episode rewards of agent0: 77.5
idv_policy eval idv catch total num of agent0: 31
idv_policy eval team catch total num: 31
idv_policy eval average step individual rewards of agent1: 0.6619263473280594
idv_policy eval average team episode rewards of agent1: 77.5
idv_policy eval idv catch total num of agent1: 28
idv_policy eval team catch total num: 31
idv_policy eval average step individual rewards of agent2: 0.7194676213732661
idv_policy eval average team episode rewards of agent2: 77.5
idv_policy eval idv catch total num of agent2: 30
idv_policy eval team catch total num: 31
idv_policy eval average step individual rewards of agent3: 0.5708578592515932
idv_policy eval average team episode rewards of agent3: 77.5
idv_policy eval idv catch total num of agent3: 25
idv_policy eval team catch total num: 31
idv_policy eval average step individual rewards of agent4: 0.42243236579231935
idv_policy eval average team episode rewards of agent4: 77.5
idv_policy eval idv catch total num of agent4: 19
idv_policy eval team catch total num: 31

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4651/10000 episodes, total num timesteps 930400/2000000, FPS 228.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4652/10000 episodes, total num timesteps 930600/2000000, FPS 228.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4653/10000 episodes, total num timesteps 930800/2000000, FPS 228.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4654/10000 episodes, total num timesteps 931000/2000000, FPS 228.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4655/10000 episodes, total num timesteps 931200/2000000, FPS 228.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4656/10000 episodes, total num timesteps 931400/2000000, FPS 228.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4657/10000 episodes, total num timesteps 931600/2000000, FPS 228.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4658/10000 episodes, total num timesteps 931800/2000000, FPS 228.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4659/10000 episodes, total num timesteps 932000/2000000, FPS 228.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4660/10000 episodes, total num timesteps 932200/2000000, FPS 228.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4661/10000 episodes, total num timesteps 932400/2000000, FPS 228.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4662/10000 episodes, total num timesteps 932600/2000000, FPS 228.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4663/10000 episodes, total num timesteps 932800/2000000, FPS 228.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4664/10000 episodes, total num timesteps 933000/2000000, FPS 228.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4665/10000 episodes, total num timesteps 933200/2000000, FPS 228.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4666/10000 episodes, total num timesteps 933400/2000000, FPS 228.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4667/10000 episodes, total num timesteps 933600/2000000, FPS 228.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4668/10000 episodes, total num timesteps 933800/2000000, FPS 228.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4669/10000 episodes, total num timesteps 934000/2000000, FPS 228.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4670/10000 episodes, total num timesteps 934200/2000000, FPS 228.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4671/10000 episodes, total num timesteps 934400/2000000, FPS 228.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4672/10000 episodes, total num timesteps 934600/2000000, FPS 228.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4673/10000 episodes, total num timesteps 934800/2000000, FPS 228.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4674/10000 episodes, total num timesteps 935000/2000000, FPS 228.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4675/10000 episodes, total num timesteps 935200/2000000, FPS 228.

team_policy eval average step individual rewards of agent0: 0.9946329886331207
team_policy eval average team episode rewards of agent0: 127.5
team_policy eval idv catch total num of agent0: 41
team_policy eval team catch total num: 51
team_policy eval average step individual rewards of agent1: 0.9342853869097587
team_policy eval average team episode rewards of agent1: 127.5
team_policy eval idv catch total num of agent1: 39
team_policy eval team catch total num: 51
team_policy eval average step individual rewards of agent2: 0.8389113099694945
team_policy eval average team episode rewards of agent2: 127.5
team_policy eval idv catch total num of agent2: 35
team_policy eval team catch total num: 51
team_policy eval average step individual rewards of agent3: 0.5521120681297573
team_policy eval average team episode rewards of agent3: 127.5
team_policy eval idv catch total num of agent3: 24
team_policy eval team catch total num: 51
team_policy eval average step individual rewards of agent4: 1.1205828669597535
team_policy eval average team episode rewards of agent4: 127.5
team_policy eval idv catch total num of agent4: 46
team_policy eval team catch total num: 51
idv_policy eval average step individual rewards of agent0: 0.5921884417752978
idv_policy eval average team episode rewards of agent0: 102.5
idv_policy eval idv catch total num of agent0: 25
idv_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent1: 0.5873423378093974
idv_policy eval average team episode rewards of agent1: 102.5
idv_policy eval idv catch total num of agent1: 25
idv_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent2: 1.0417115181446324
idv_policy eval average team episode rewards of agent2: 102.5
idv_policy eval idv catch total num of agent2: 43
idv_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent3: 0.7855526188795633
idv_policy eval average team episode rewards of agent3: 102.5
idv_policy eval idv catch total num of agent3: 33
idv_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent4: 0.943321422439629
idv_policy eval average team episode rewards of agent4: 102.5
idv_policy eval idv catch total num of agent4: 39
idv_policy eval team catch total num: 41

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4676/10000 episodes, total num timesteps 935400/2000000, FPS 228.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4677/10000 episodes, total num timesteps 935600/2000000, FPS 228.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4678/10000 episodes, total num timesteps 935800/2000000, FPS 228.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4679/10000 episodes, total num timesteps 936000/2000000, FPS 228.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4680/10000 episodes, total num timesteps 936200/2000000, FPS 228.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4681/10000 episodes, total num timesteps 936400/2000000, FPS 228.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4682/10000 episodes, total num timesteps 936600/2000000, FPS 228.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4683/10000 episodes, total num timesteps 936800/2000000, FPS 228.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4684/10000 episodes, total num timesteps 937000/2000000, FPS 228.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4685/10000 episodes, total num timesteps 937200/2000000, FPS 228.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4686/10000 episodes, total num timesteps 937400/2000000, FPS 228.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4687/10000 episodes, total num timesteps 937600/2000000, FPS 228.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4688/10000 episodes, total num timesteps 937800/2000000, FPS 228.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4689/10000 episodes, total num timesteps 938000/2000000, FPS 228.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4690/10000 episodes, total num timesteps 938200/2000000, FPS 228.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4691/10000 episodes, total num timesteps 938400/2000000, FPS 228.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4692/10000 episodes, total num timesteps 938600/2000000, FPS 228.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4693/10000 episodes, total num timesteps 938800/2000000, FPS 228.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4694/10000 episodes, total num timesteps 939000/2000000, FPS 228.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4695/10000 episodes, total num timesteps 939200/2000000, FPS 228.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4696/10000 episodes, total num timesteps 939400/2000000, FPS 228.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4697/10000 episodes, total num timesteps 939600/2000000, FPS 228.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4698/10000 episodes, total num timesteps 939800/2000000, FPS 228.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4699/10000 episodes, total num timesteps 940000/2000000, FPS 228.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4700/10000 episodes, total num timesteps 940200/2000000, FPS 228.

team_policy eval average step individual rewards of agent0: 0.5364440553462342
team_policy eval average team episode rewards of agent0: 125.0
team_policy eval idv catch total num of agent0: 23
team_policy eval team catch total num: 50
team_policy eval average step individual rewards of agent1: 0.997304076590494
team_policy eval average team episode rewards of agent1: 125.0
team_policy eval idv catch total num of agent1: 41
team_policy eval team catch total num: 50
team_policy eval average step individual rewards of agent2: 1.7509282194660787
team_policy eval average team episode rewards of agent2: 125.0
team_policy eval idv catch total num of agent2: 71
team_policy eval team catch total num: 50
team_policy eval average step individual rewards of agent3: 0.6804640273139404
team_policy eval average team episode rewards of agent3: 125.0
team_policy eval idv catch total num of agent3: 29
team_policy eval team catch total num: 50
team_policy eval average step individual rewards of agent4: 0.869406199682036
team_policy eval average team episode rewards of agent4: 125.0
team_policy eval idv catch total num of agent4: 36
team_policy eval team catch total num: 50
idv_policy eval average step individual rewards of agent0: 1.2491870991841834
idv_policy eval average team episode rewards of agent0: 130.0
idv_policy eval idv catch total num of agent0: 51
idv_policy eval team catch total num: 52
idv_policy eval average step individual rewards of agent1: 0.586346828439995
idv_policy eval average team episode rewards of agent1: 130.0
idv_policy eval idv catch total num of agent1: 25
idv_policy eval team catch total num: 52
idv_policy eval average step individual rewards of agent2: 0.7392431422418484
idv_policy eval average team episode rewards of agent2: 130.0
idv_policy eval idv catch total num of agent2: 31
idv_policy eval team catch total num: 52
idv_policy eval average step individual rewards of agent3: 0.8861210723097639
idv_policy eval average team episode rewards of agent3: 130.0
idv_policy eval idv catch total num of agent3: 37
idv_policy eval team catch total num: 52
idv_policy eval average step individual rewards of agent4: 1.0650579384384753
idv_policy eval average team episode rewards of agent4: 130.0
idv_policy eval idv catch total num of agent4: 44
idv_policy eval team catch total num: 52

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4701/10000 episodes, total num timesteps 940400/2000000, FPS 228.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4702/10000 episodes, total num timesteps 940600/2000000, FPS 228.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4703/10000 episodes, total num timesteps 940800/2000000, FPS 228.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4704/10000 episodes, total num timesteps 941000/2000000, FPS 228.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4705/10000 episodes, total num timesteps 941200/2000000, FPS 228.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4706/10000 episodes, total num timesteps 941400/2000000, FPS 228.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4707/10000 episodes, total num timesteps 941600/2000000, FPS 228.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4708/10000 episodes, total num timesteps 941800/2000000, FPS 228.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4709/10000 episodes, total num timesteps 942000/2000000, FPS 228.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4710/10000 episodes, total num timesteps 942200/2000000, FPS 228.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4711/10000 episodes, total num timesteps 942400/2000000, FPS 228.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4712/10000 episodes, total num timesteps 942600/2000000, FPS 228.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4713/10000 episodes, total num timesteps 942800/2000000, FPS 228.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4714/10000 episodes, total num timesteps 943000/2000000, FPS 228.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4715/10000 episodes, total num timesteps 943200/2000000, FPS 228.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4716/10000 episodes, total num timesteps 943400/2000000, FPS 228.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4717/10000 episodes, total num timesteps 943600/2000000, FPS 228.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4718/10000 episodes, total num timesteps 943800/2000000, FPS 228.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4719/10000 episodes, total num timesteps 944000/2000000, FPS 228.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4720/10000 episodes, total num timesteps 944200/2000000, FPS 228.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4721/10000 episodes, total num timesteps 944400/2000000, FPS 228.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4722/10000 episodes, total num timesteps 944600/2000000, FPS 228.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4723/10000 episodes, total num timesteps 944800/2000000, FPS 228.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4724/10000 episodes, total num timesteps 945000/2000000, FPS 228.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4725/10000 episodes, total num timesteps 945200/2000000, FPS 228.

team_policy eval average step individual rewards of agent0: 0.2241942671117607
team_policy eval average team episode rewards of agent0: 70.0
team_policy eval idv catch total num of agent0: 11
team_policy eval team catch total num: 28
team_policy eval average step individual rewards of agent1: 0.5876596006385667
team_policy eval average team episode rewards of agent1: 70.0
team_policy eval idv catch total num of agent1: 25
team_policy eval team catch total num: 28
team_policy eval average step individual rewards of agent2: 0.7218371051592483
team_policy eval average team episode rewards of agent2: 70.0
team_policy eval idv catch total num of agent2: 30
team_policy eval team catch total num: 28
team_policy eval average step individual rewards of agent3: 0.7929950866714239
team_policy eval average team episode rewards of agent3: 70.0
team_policy eval idv catch total num of agent3: 33
team_policy eval team catch total num: 28
team_policy eval average step individual rewards of agent4: 0.5888657506679593
team_policy eval average team episode rewards of agent4: 70.0
team_policy eval idv catch total num of agent4: 25
team_policy eval team catch total num: 28
idv_policy eval average step individual rewards of agent0: 0.5912631057127875
idv_policy eval average team episode rewards of agent0: 95.0
idv_policy eval idv catch total num of agent0: 25
idv_policy eval team catch total num: 38
idv_policy eval average step individual rewards of agent1: 0.7929356809757309
idv_policy eval average team episode rewards of agent1: 95.0
idv_policy eval idv catch total num of agent1: 33
idv_policy eval team catch total num: 38
idv_policy eval average step individual rewards of agent2: 0.5909519626160743
idv_policy eval average team episode rewards of agent2: 95.0
idv_policy eval idv catch total num of agent2: 25
idv_policy eval team catch total num: 38
idv_policy eval average step individual rewards of agent3: 0.4390007750270307
idv_policy eval average team episode rewards of agent3: 95.0
idv_policy eval idv catch total num of agent3: 19
idv_policy eval team catch total num: 38
idv_policy eval average step individual rewards of agent4: 0.6663002744340126
idv_policy eval average team episode rewards of agent4: 95.0
idv_policy eval idv catch total num of agent4: 28
idv_policy eval team catch total num: 38

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4726/10000 episodes, total num timesteps 945400/2000000, FPS 228.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4727/10000 episodes, total num timesteps 945600/2000000, FPS 228.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4728/10000 episodes, total num timesteps 945800/2000000, FPS 228.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4729/10000 episodes, total num timesteps 946000/2000000, FPS 228.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4730/10000 episodes, total num timesteps 946200/2000000, FPS 228.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4731/10000 episodes, total num timesteps 946400/2000000, FPS 228.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4732/10000 episodes, total num timesteps 946600/2000000, FPS 228.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4733/10000 episodes, total num timesteps 946800/2000000, FPS 228.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4734/10000 episodes, total num timesteps 947000/2000000, FPS 228.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4735/10000 episodes, total num timesteps 947200/2000000, FPS 228.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4736/10000 episodes, total num timesteps 947400/2000000, FPS 228.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4737/10000 episodes, total num timesteps 947600/2000000, FPS 228.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4738/10000 episodes, total num timesteps 947800/2000000, FPS 228.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4739/10000 episodes, total num timesteps 948000/2000000, FPS 228.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4740/10000 episodes, total num timesteps 948200/2000000, FPS 228.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4741/10000 episodes, total num timesteps 948400/2000000, FPS 228.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4742/10000 episodes, total num timesteps 948600/2000000, FPS 228.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4743/10000 episodes, total num timesteps 948800/2000000, FPS 228.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4744/10000 episodes, total num timesteps 949000/2000000, FPS 228.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4745/10000 episodes, total num timesteps 949200/2000000, FPS 228.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4746/10000 episodes, total num timesteps 949400/2000000, FPS 228.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4747/10000 episodes, total num timesteps 949600/2000000, FPS 228.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4748/10000 episodes, total num timesteps 949800/2000000, FPS 229.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4749/10000 episodes, total num timesteps 950000/2000000, FPS 229.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4750/10000 episodes, total num timesteps 950200/2000000, FPS 229.

team_policy eval average step individual rewards of agent0: 0.6604698265860043
team_policy eval average team episode rewards of agent0: 87.5
team_policy eval idv catch total num of agent0: 28
team_policy eval team catch total num: 35
team_policy eval average step individual rewards of agent1: 0.7948121779632197
team_policy eval average team episode rewards of agent1: 87.5
team_policy eval idv catch total num of agent1: 33
team_policy eval team catch total num: 35
team_policy eval average step individual rewards of agent2: 0.5546080723260587
team_policy eval average team episode rewards of agent2: 87.5
team_policy eval idv catch total num of agent2: 24
team_policy eval team catch total num: 35
team_policy eval average step individual rewards of agent3: 0.6450838017186606
team_policy eval average team episode rewards of agent3: 87.5
team_policy eval idv catch total num of agent3: 27
team_policy eval team catch total num: 35
team_policy eval average step individual rewards of agent4: 0.84288851189016
team_policy eval average team episode rewards of agent4: 87.5
team_policy eval idv catch total num of agent4: 35
team_policy eval team catch total num: 35
idv_policy eval average step individual rewards of agent0: 0.7211337285969361
idv_policy eval average team episode rewards of agent0: 127.5
idv_policy eval idv catch total num of agent0: 30
idv_policy eval team catch total num: 51
idv_policy eval average step individual rewards of agent1: 1.27080579424014
idv_policy eval average team episode rewards of agent1: 127.5
idv_policy eval idv catch total num of agent1: 52
idv_policy eval team catch total num: 51
idv_policy eval average step individual rewards of agent2: 0.8681076995814506
idv_policy eval average team episode rewards of agent2: 127.5
idv_policy eval idv catch total num of agent2: 36
idv_policy eval team catch total num: 51
idv_policy eval average step individual rewards of agent3: 0.7974015757808993
idv_policy eval average team episode rewards of agent3: 127.5
idv_policy eval idv catch total num of agent3: 33
idv_policy eval team catch total num: 51
idv_policy eval average step individual rewards of agent4: 0.9249434297833815
idv_policy eval average team episode rewards of agent4: 127.5
idv_policy eval idv catch total num of agent4: 38
idv_policy eval team catch total num: 51

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4751/10000 episodes, total num timesteps 950400/2000000, FPS 229.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4752/10000 episodes, total num timesteps 950600/2000000, FPS 229.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4753/10000 episodes, total num timesteps 950800/2000000, FPS 229.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4754/10000 episodes, total num timesteps 951000/2000000, FPS 229.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4755/10000 episodes, total num timesteps 951200/2000000, FPS 229.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4756/10000 episodes, total num timesteps 951400/2000000, FPS 229.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4757/10000 episodes, total num timesteps 951600/2000000, FPS 229.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4758/10000 episodes, total num timesteps 951800/2000000, FPS 229.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4759/10000 episodes, total num timesteps 952000/2000000, FPS 229.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4760/10000 episodes, total num timesteps 952200/2000000, FPS 229.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4761/10000 episodes, total num timesteps 952400/2000000, FPS 229.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4762/10000 episodes, total num timesteps 952600/2000000, FPS 229.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4763/10000 episodes, total num timesteps 952800/2000000, FPS 229.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4764/10000 episodes, total num timesteps 953000/2000000, FPS 229.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4765/10000 episodes, total num timesteps 953200/2000000, FPS 229.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4766/10000 episodes, total num timesteps 953400/2000000, FPS 229.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4767/10000 episodes, total num timesteps 953600/2000000, FPS 229.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4768/10000 episodes, total num timesteps 953800/2000000, FPS 229.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4769/10000 episodes, total num timesteps 954000/2000000, FPS 229.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4770/10000 episodes, total num timesteps 954200/2000000, FPS 229.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4771/10000 episodes, total num timesteps 954400/2000000, FPS 229.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4772/10000 episodes, total num timesteps 954600/2000000, FPS 229.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4773/10000 episodes, total num timesteps 954800/2000000, FPS 229.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4774/10000 episodes, total num timesteps 955000/2000000, FPS 229.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4775/10000 episodes, total num timesteps 955200/2000000, FPS 229.

team_policy eval average step individual rewards of agent0: 0.3093671646778117
team_policy eval average team episode rewards of agent0: 70.0
team_policy eval idv catch total num of agent0: 14
team_policy eval team catch total num: 28
team_policy eval average step individual rewards of agent1: 0.5177236553578058
team_policy eval average team episode rewards of agent1: 70.0
team_policy eval idv catch total num of agent1: 22
team_policy eval team catch total num: 28
team_policy eval average step individual rewards of agent2: 0.6422229144245961
team_policy eval average team episode rewards of agent2: 70.0
team_policy eval idv catch total num of agent2: 27
team_policy eval team catch total num: 28
team_policy eval average step individual rewards of agent3: 0.5850209261758317
team_policy eval average team episode rewards of agent3: 70.0
team_policy eval idv catch total num of agent3: 25
team_policy eval team catch total num: 28
team_policy eval average step individual rewards of agent4: 0.7094247812758233
team_policy eval average team episode rewards of agent4: 70.0
team_policy eval idv catch total num of agent4: 30
team_policy eval team catch total num: 28
idv_policy eval average step individual rewards of agent0: 1.162230442726207
idv_policy eval average team episode rewards of agent0: 137.5
idv_policy eval idv catch total num of agent0: 48
idv_policy eval team catch total num: 55
idv_policy eval average step individual rewards of agent1: 1.0224153311501334
idv_policy eval average team episode rewards of agent1: 137.5
idv_policy eval idv catch total num of agent1: 42
idv_policy eval team catch total num: 55
idv_policy eval average step individual rewards of agent2: 0.9908940470925294
idv_policy eval average team episode rewards of agent2: 137.5
idv_policy eval idv catch total num of agent2: 41
idv_policy eval team catch total num: 55
idv_policy eval average step individual rewards of agent3: 0.83300718528398
idv_policy eval average team episode rewards of agent3: 137.5
idv_policy eval idv catch total num of agent3: 35
idv_policy eval team catch total num: 55
idv_policy eval average step individual rewards of agent4: 0.6886965996591209
idv_policy eval average team episode rewards of agent4: 137.5
idv_policy eval idv catch total num of agent4: 29
idv_policy eval team catch total num: 55

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4776/10000 episodes, total num timesteps 955400/2000000, FPS 229.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4777/10000 episodes, total num timesteps 955600/2000000, FPS 229.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4778/10000 episodes, total num timesteps 955800/2000000, FPS 229.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4779/10000 episodes, total num timesteps 956000/2000000, FPS 229.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4780/10000 episodes, total num timesteps 956200/2000000, FPS 229.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4781/10000 episodes, total num timesteps 956400/2000000, FPS 229.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4782/10000 episodes, total num timesteps 956600/2000000, FPS 229.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4783/10000 episodes, total num timesteps 956800/2000000, FPS 229.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4784/10000 episodes, total num timesteps 957000/2000000, FPS 229.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4785/10000 episodes, total num timesteps 957200/2000000, FPS 229.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4786/10000 episodes, total num timesteps 957400/2000000, FPS 229.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4787/10000 episodes, total num timesteps 957600/2000000, FPS 229.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4788/10000 episodes, total num timesteps 957800/2000000, FPS 229.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4789/10000 episodes, total num timesteps 958000/2000000, FPS 229.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4790/10000 episodes, total num timesteps 958200/2000000, FPS 229.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4791/10000 episodes, total num timesteps 958400/2000000, FPS 229.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4792/10000 episodes, total num timesteps 958600/2000000, FPS 229.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4793/10000 episodes, total num timesteps 958800/2000000, FPS 229.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4794/10000 episodes, total num timesteps 959000/2000000, FPS 229.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4795/10000 episodes, total num timesteps 959200/2000000, FPS 229.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4796/10000 episodes, total num timesteps 959400/2000000, FPS 229.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4797/10000 episodes, total num timesteps 959600/2000000, FPS 229.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4798/10000 episodes, total num timesteps 959800/2000000, FPS 229.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4799/10000 episodes, total num timesteps 960000/2000000, FPS 229.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4800/10000 episodes, total num timesteps 960200/2000000, FPS 229.

team_policy eval average step individual rewards of agent0: 0.5022249665639881
team_policy eval average team episode rewards of agent0: 102.5
team_policy eval idv catch total num of agent0: 22
team_policy eval team catch total num: 41
team_policy eval average step individual rewards of agent1: 0.5585187654346369
team_policy eval average team episode rewards of agent1: 102.5
team_policy eval idv catch total num of agent1: 24
team_policy eval team catch total num: 41
team_policy eval average step individual rewards of agent2: 0.5575801959101645
team_policy eval average team episode rewards of agent2: 102.5
team_policy eval idv catch total num of agent2: 24
team_policy eval team catch total num: 41
team_policy eval average step individual rewards of agent3: 0.7780209335169407
team_policy eval average team episode rewards of agent3: 102.5
team_policy eval idv catch total num of agent3: 33
team_policy eval team catch total num: 41
team_policy eval average step individual rewards of agent4: 1.0629863156263266
team_policy eval average team episode rewards of agent4: 102.5
team_policy eval idv catch total num of agent4: 44
team_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent0: 0.7902923481832603
idv_policy eval average team episode rewards of agent0: 120.0
idv_policy eval idv catch total num of agent0: 33
idv_policy eval team catch total num: 48
idv_policy eval average step individual rewards of agent1: 0.7483757911920332
idv_policy eval average team episode rewards of agent1: 120.0
idv_policy eval idv catch total num of agent1: 32
idv_policy eval team catch total num: 48
idv_policy eval average step individual rewards of agent2: 0.8864910708305235
idv_policy eval average team episode rewards of agent2: 120.0
idv_policy eval idv catch total num of agent2: 37
idv_policy eval team catch total num: 48
idv_policy eval average step individual rewards of agent3: 0.7320176574032891
idv_policy eval average team episode rewards of agent3: 120.0
idv_policy eval idv catch total num of agent3: 31
idv_policy eval team catch total num: 48
idv_policy eval average step individual rewards of agent4: 0.9128707600435344
idv_policy eval average team episode rewards of agent4: 120.0
idv_policy eval idv catch total num of agent4: 38
idv_policy eval team catch total num: 48

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4801/10000 episodes, total num timesteps 960400/2000000, FPS 229.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4802/10000 episodes, total num timesteps 960600/2000000, FPS 229.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4803/10000 episodes, total num timesteps 960800/2000000, FPS 229.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4804/10000 episodes, total num timesteps 961000/2000000, FPS 229.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4805/10000 episodes, total num timesteps 961200/2000000, FPS 229.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4806/10000 episodes, total num timesteps 961400/2000000, FPS 229.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4807/10000 episodes, total num timesteps 961600/2000000, FPS 229.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4808/10000 episodes, total num timesteps 961800/2000000, FPS 229.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4809/10000 episodes, total num timesteps 962000/2000000, FPS 229.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4810/10000 episodes, total num timesteps 962200/2000000, FPS 229.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4811/10000 episodes, total num timesteps 962400/2000000, FPS 229.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4812/10000 episodes, total num timesteps 962600/2000000, FPS 229.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4813/10000 episodes, total num timesteps 962800/2000000, FPS 229.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4814/10000 episodes, total num timesteps 963000/2000000, FPS 229.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4815/10000 episodes, total num timesteps 963200/2000000, FPS 229.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4816/10000 episodes, total num timesteps 963400/2000000, FPS 229.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4817/10000 episodes, total num timesteps 963600/2000000, FPS 229.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4818/10000 episodes, total num timesteps 963800/2000000, FPS 229.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4819/10000 episodes, total num timesteps 964000/2000000, FPS 229.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4820/10000 episodes, total num timesteps 964200/2000000, FPS 229.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4821/10000 episodes, total num timesteps 964400/2000000, FPS 229.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4822/10000 episodes, total num timesteps 964600/2000000, FPS 229.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4823/10000 episodes, total num timesteps 964800/2000000, FPS 229.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4824/10000 episodes, total num timesteps 965000/2000000, FPS 229.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4825/10000 episodes, total num timesteps 965200/2000000, FPS 229.

team_policy eval average step individual rewards of agent0: 0.7750140590490642
team_policy eval average team episode rewards of agent0: 115.0
team_policy eval idv catch total num of agent0: 33
team_policy eval team catch total num: 46
team_policy eval average step individual rewards of agent1: 1.2786588850160028
team_policy eval average team episode rewards of agent1: 115.0
team_policy eval idv catch total num of agent1: 52
team_policy eval team catch total num: 46
team_policy eval average step individual rewards of agent2: 0.8326801036648419
team_policy eval average team episode rewards of agent2: 115.0
team_policy eval idv catch total num of agent2: 35
team_policy eval team catch total num: 46
team_policy eval average step individual rewards of agent3: 0.7337993807378763
team_policy eval average team episode rewards of agent3: 115.0
team_policy eval idv catch total num of agent3: 31
team_policy eval team catch total num: 46
team_policy eval average step individual rewards of agent4: 0.690095898983272
team_policy eval average team episode rewards of agent4: 115.0
team_policy eval idv catch total num of agent4: 30
team_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent0: 1.1181116133668823
idv_policy eval average team episode rewards of agent0: 145.0
idv_policy eval idv catch total num of agent0: 46
idv_policy eval team catch total num: 58
idv_policy eval average step individual rewards of agent1: 0.8388691199384678
idv_policy eval average team episode rewards of agent1: 145.0
idv_policy eval idv catch total num of agent1: 35
idv_policy eval team catch total num: 58
idv_policy eval average step individual rewards of agent2: 0.6294044345435454
idv_policy eval average team episode rewards of agent2: 145.0
idv_policy eval idv catch total num of agent2: 27
idv_policy eval team catch total num: 58
idv_policy eval average step individual rewards of agent3: 1.1946873803644313
idv_policy eval average team episode rewards of agent3: 145.0
idv_policy eval idv catch total num of agent3: 49
idv_policy eval team catch total num: 58
idv_policy eval average step individual rewards of agent4: 1.477865617324057
idv_policy eval average team episode rewards of agent4: 145.0
idv_policy eval idv catch total num of agent4: 60
idv_policy eval team catch total num: 58

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4826/10000 episodes, total num timesteps 965400/2000000, FPS 229.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4827/10000 episodes, total num timesteps 965600/2000000, FPS 229.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4828/10000 episodes, total num timesteps 965800/2000000, FPS 229.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4829/10000 episodes, total num timesteps 966000/2000000, FPS 229.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4830/10000 episodes, total num timesteps 966200/2000000, FPS 229.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4831/10000 episodes, total num timesteps 966400/2000000, FPS 229.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4832/10000 episodes, total num timesteps 966600/2000000, FPS 229.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4833/10000 episodes, total num timesteps 966800/2000000, FPS 229.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4834/10000 episodes, total num timesteps 967000/2000000, FPS 229.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4835/10000 episodes, total num timesteps 967200/2000000, FPS 229.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4836/10000 episodes, total num timesteps 967400/2000000, FPS 229.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4837/10000 episodes, total num timesteps 967600/2000000, FPS 229.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4838/10000 episodes, total num timesteps 967800/2000000, FPS 229.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4839/10000 episodes, total num timesteps 968000/2000000, FPS 229.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4840/10000 episodes, total num timesteps 968200/2000000, FPS 229.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4841/10000 episodes, total num timesteps 968400/2000000, FPS 229.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4842/10000 episodes, total num timesteps 968600/2000000, FPS 229.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4843/10000 episodes, total num timesteps 968800/2000000, FPS 229.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4844/10000 episodes, total num timesteps 969000/2000000, FPS 229.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4845/10000 episodes, total num timesteps 969200/2000000, FPS 229.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4846/10000 episodes, total num timesteps 969400/2000000, FPS 229.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4847/10000 episodes, total num timesteps 969600/2000000, FPS 229.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4848/10000 episodes, total num timesteps 969800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4849/10000 episodes, total num timesteps 970000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4850/10000 episodes, total num timesteps 970200/2000000, FPS 230.

team_policy eval average step individual rewards of agent0: 1.065085051648594
team_policy eval average team episode rewards of agent0: 140.0
team_policy eval idv catch total num of agent0: 44
team_policy eval team catch total num: 56
team_policy eval average step individual rewards of agent1: 0.7137915169979843
team_policy eval average team episode rewards of agent1: 140.0
team_policy eval idv catch total num of agent1: 30
team_policy eval team catch total num: 56
team_policy eval average step individual rewards of agent2: 0.8637081980321442
team_policy eval average team episode rewards of agent2: 140.0
team_policy eval idv catch total num of agent2: 36
team_policy eval team catch total num: 56
team_policy eval average step individual rewards of agent3: 0.9694497113711309
team_policy eval average team episode rewards of agent3: 140.0
team_policy eval idv catch total num of agent3: 40
team_policy eval team catch total num: 56
team_policy eval average step individual rewards of agent4: 0.8455752705372305
team_policy eval average team episode rewards of agent4: 140.0
team_policy eval idv catch total num of agent4: 35
team_policy eval team catch total num: 56
idv_policy eval average step individual rewards of agent0: 0.3351296142733833
idv_policy eval average team episode rewards of agent0: 107.5
idv_policy eval idv catch total num of agent0: 15
idv_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent1: 0.8148557780265036
idv_policy eval average team episode rewards of agent1: 107.5
idv_policy eval idv catch total num of agent1: 34
idv_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent2: 1.1722539830696117
idv_policy eval average team episode rewards of agent2: 107.5
idv_policy eval idv catch total num of agent2: 48
idv_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent3: 0.4577406310642059
idv_policy eval average team episode rewards of agent3: 107.5
idv_policy eval idv catch total num of agent3: 20
idv_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent4: 1.3739570032831943
idv_policy eval average team episode rewards of agent4: 107.5
idv_policy eval idv catch total num of agent4: 56
idv_policy eval team catch total num: 43

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4851/10000 episodes, total num timesteps 970400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4852/10000 episodes, total num timesteps 970600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4853/10000 episodes, total num timesteps 970800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4854/10000 episodes, total num timesteps 971000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4855/10000 episodes, total num timesteps 971200/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4856/10000 episodes, total num timesteps 971400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4857/10000 episodes, total num timesteps 971600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4858/10000 episodes, total num timesteps 971800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4859/10000 episodes, total num timesteps 972000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4860/10000 episodes, total num timesteps 972200/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4861/10000 episodes, total num timesteps 972400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4862/10000 episodes, total num timesteps 972600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4863/10000 episodes, total num timesteps 972800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4864/10000 episodes, total num timesteps 973000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4865/10000 episodes, total num timesteps 973200/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4866/10000 episodes, total num timesteps 973400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4867/10000 episodes, total num timesteps 973600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4868/10000 episodes, total num timesteps 973800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4869/10000 episodes, total num timesteps 974000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4870/10000 episodes, total num timesteps 974200/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4871/10000 episodes, total num timesteps 974400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4872/10000 episodes, total num timesteps 974600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4873/10000 episodes, total num timesteps 974800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4874/10000 episodes, total num timesteps 975000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4875/10000 episodes, total num timesteps 975200/2000000, FPS 230.

team_policy eval average step individual rewards of agent0: 0.9669182027842007
team_policy eval average team episode rewards of agent0: 130.0
team_policy eval idv catch total num of agent0: 40
team_policy eval team catch total num: 52
team_policy eval average step individual rewards of agent1: 1.0351634436904087
team_policy eval average team episode rewards of agent1: 130.0
team_policy eval idv catch total num of agent1: 43
team_policy eval team catch total num: 52
team_policy eval average step individual rewards of agent2: 0.7788975963330249
team_policy eval average team episode rewards of agent2: 130.0
team_policy eval idv catch total num of agent2: 33
team_policy eval team catch total num: 52
team_policy eval average step individual rewards of agent3: 1.1708353811927112
team_policy eval average team episode rewards of agent3: 130.0
team_policy eval idv catch total num of agent3: 48
team_policy eval team catch total num: 52
team_policy eval average step individual rewards of agent4: 0.7304694493416335
team_policy eval average team episode rewards of agent4: 130.0
team_policy eval idv catch total num of agent4: 31
team_policy eval team catch total num: 52
idv_policy eval average step individual rewards of agent0: 0.4370870775370402
idv_policy eval average team episode rewards of agent0: 72.5
idv_policy eval idv catch total num of agent0: 19
idv_policy eval team catch total num: 29
idv_policy eval average step individual rewards of agent1: 0.5012483649561897
idv_policy eval average team episode rewards of agent1: 72.5
idv_policy eval idv catch total num of agent1: 22
idv_policy eval team catch total num: 29
idv_policy eval average step individual rewards of agent2: 0.6101905864737115
idv_policy eval average team episode rewards of agent2: 72.5
idv_policy eval idv catch total num of agent2: 26
idv_policy eval team catch total num: 29
idv_policy eval average step individual rewards of agent3: 0.7101538909735449
idv_policy eval average team episode rewards of agent3: 72.5
idv_policy eval idv catch total num of agent3: 30
idv_policy eval team catch total num: 29
idv_policy eval average step individual rewards of agent4: 0.6495548418443394
idv_policy eval average team episode rewards of agent4: 72.5
idv_policy eval idv catch total num of agent4: 27
idv_policy eval team catch total num: 29

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4876/10000 episodes, total num timesteps 975400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4877/10000 episodes, total num timesteps 975600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4878/10000 episodes, total num timesteps 975800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4879/10000 episodes, total num timesteps 976000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4880/10000 episodes, total num timesteps 976200/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4881/10000 episodes, total num timesteps 976400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4882/10000 episodes, total num timesteps 976600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4883/10000 episodes, total num timesteps 976800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4884/10000 episodes, total num timesteps 977000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4885/10000 episodes, total num timesteps 977200/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4886/10000 episodes, total num timesteps 977400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4887/10000 episodes, total num timesteps 977600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4888/10000 episodes, total num timesteps 977800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4889/10000 episodes, total num timesteps 978000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4890/10000 episodes, total num timesteps 978200/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4891/10000 episodes, total num timesteps 978400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4892/10000 episodes, total num timesteps 978600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4893/10000 episodes, total num timesteps 978800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4894/10000 episodes, total num timesteps 979000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4895/10000 episodes, total num timesteps 979200/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4896/10000 episodes, total num timesteps 979400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4897/10000 episodes, total num timesteps 979600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4898/10000 episodes, total num timesteps 979800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4899/10000 episodes, total num timesteps 980000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4900/10000 episodes, total num timesteps 980200/2000000, FPS 230.

team_policy eval average step individual rewards of agent0: 0.9335013090359251
team_policy eval average team episode rewards of agent0: 100.0
team_policy eval idv catch total num of agent0: 39
team_policy eval team catch total num: 40
team_policy eval average step individual rewards of agent1: 0.7680880372164534
team_policy eval average team episode rewards of agent1: 100.0
team_policy eval idv catch total num of agent1: 32
team_policy eval team catch total num: 40
team_policy eval average step individual rewards of agent2: 0.7553617080705064
team_policy eval average team episode rewards of agent2: 100.0
team_policy eval idv catch total num of agent2: 32
team_policy eval team catch total num: 40
team_policy eval average step individual rewards of agent3: 0.6352931632462053
team_policy eval average team episode rewards of agent3: 100.0
team_policy eval idv catch total num of agent3: 27
team_policy eval team catch total num: 40
team_policy eval average step individual rewards of agent4: 0.9031762515669639
team_policy eval average team episode rewards of agent4: 100.0
team_policy eval idv catch total num of agent4: 38
team_policy eval team catch total num: 40
idv_policy eval average step individual rewards of agent0: 0.6793037760717527
idv_policy eval average team episode rewards of agent0: 75.0
idv_policy eval idv catch total num of agent0: 29
idv_policy eval team catch total num: 30
idv_policy eval average step individual rewards of agent1: 0.4112309773216167
idv_policy eval average team episode rewards of agent1: 75.0
idv_policy eval idv catch total num of agent1: 18
idv_policy eval team catch total num: 30
idv_policy eval average step individual rewards of agent2: 0.7107103721214584
idv_policy eval average team episode rewards of agent2: 75.0
idv_policy eval idv catch total num of agent2: 30
idv_policy eval team catch total num: 30
idv_policy eval average step individual rewards of agent3: 0.5340553400606821
idv_policy eval average team episode rewards of agent3: 75.0
idv_policy eval idv catch total num of agent3: 23
idv_policy eval team catch total num: 30
idv_policy eval average step individual rewards of agent4: 0.27605279088035617
idv_policy eval average team episode rewards of agent4: 75.0
idv_policy eval idv catch total num of agent4: 13
idv_policy eval team catch total num: 30

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4901/10000 episodes, total num timesteps 980400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4902/10000 episodes, total num timesteps 980600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4903/10000 episodes, total num timesteps 980800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4904/10000 episodes, total num timesteps 981000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4905/10000 episodes, total num timesteps 981200/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4906/10000 episodes, total num timesteps 981400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4907/10000 episodes, total num timesteps 981600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4908/10000 episodes, total num timesteps 981800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4909/10000 episodes, total num timesteps 982000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4910/10000 episodes, total num timesteps 982200/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4911/10000 episodes, total num timesteps 982400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4912/10000 episodes, total num timesteps 982600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4913/10000 episodes, total num timesteps 982800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4914/10000 episodes, total num timesteps 983000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4915/10000 episodes, total num timesteps 983200/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4916/10000 episodes, total num timesteps 983400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4917/10000 episodes, total num timesteps 983600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4918/10000 episodes, total num timesteps 983800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4919/10000 episodes, total num timesteps 984000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4920/10000 episodes, total num timesteps 984200/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4921/10000 episodes, total num timesteps 984400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4922/10000 episodes, total num timesteps 984600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4923/10000 episodes, total num timesteps 984800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4924/10000 episodes, total num timesteps 985000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4925/10000 episodes, total num timesteps 985200/2000000, FPS 230.

team_policy eval average step individual rewards of agent0: 0.7600083742678072
team_policy eval average team episode rewards of agent0: 75.0
team_policy eval idv catch total num of agent0: 32
team_policy eval team catch total num: 30
team_policy eval average step individual rewards of agent1: 0.44899944704538897
team_policy eval average team episode rewards of agent1: 75.0
team_policy eval idv catch total num of agent1: 20
team_policy eval team catch total num: 30
team_policy eval average step individual rewards of agent2: 0.43843461523998245
team_policy eval average team episode rewards of agent2: 75.0
team_policy eval idv catch total num of agent2: 19
team_policy eval team catch total num: 30
team_policy eval average step individual rewards of agent3: 0.7094889130650351
team_policy eval average team episode rewards of agent3: 75.0
team_policy eval idv catch total num of agent3: 30
team_policy eval team catch total num: 30
team_policy eval average step individual rewards of agent4: 0.6666688033641833
team_policy eval average team episode rewards of agent4: 75.0
team_policy eval idv catch total num of agent4: 28
team_policy eval team catch total num: 30
idv_policy eval average step individual rewards of agent0: 0.45547535207294276
idv_policy eval average team episode rewards of agent0: 112.5
idv_policy eval idv catch total num of agent0: 20
idv_policy eval team catch total num: 45
idv_policy eval average step individual rewards of agent1: 0.323813351962588
idv_policy eval average team episode rewards of agent1: 112.5
idv_policy eval idv catch total num of agent1: 15
idv_policy eval team catch total num: 45
idv_policy eval average step individual rewards of agent2: 1.139971367948326
idv_policy eval average team episode rewards of agent2: 112.5
idv_policy eval idv catch total num of agent2: 47
idv_policy eval team catch total num: 45
idv_policy eval average step individual rewards of agent3: 1.217114086079566
idv_policy eval average team episode rewards of agent3: 112.5
idv_policy eval idv catch total num of agent3: 50
idv_policy eval team catch total num: 45
idv_policy eval average step individual rewards of agent4: 0.9846159070646346
idv_policy eval average team episode rewards of agent4: 112.5
idv_policy eval idv catch total num of agent4: 41
idv_policy eval team catch total num: 45

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4926/10000 episodes, total num timesteps 985400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4927/10000 episodes, total num timesteps 985600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4928/10000 episodes, total num timesteps 985800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4929/10000 episodes, total num timesteps 986000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4930/10000 episodes, total num timesteps 986200/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4931/10000 episodes, total num timesteps 986400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4932/10000 episodes, total num timesteps 986600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4933/10000 episodes, total num timesteps 986800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4934/10000 episodes, total num timesteps 987000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4935/10000 episodes, total num timesteps 987200/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4936/10000 episodes, total num timesteps 987400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4937/10000 episodes, total num timesteps 987600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4938/10000 episodes, total num timesteps 987800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4939/10000 episodes, total num timesteps 988000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4940/10000 episodes, total num timesteps 988200/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4941/10000 episodes, total num timesteps 988400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4942/10000 episodes, total num timesteps 988600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4943/10000 episodes, total num timesteps 988800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4944/10000 episodes, total num timesteps 989000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4945/10000 episodes, total num timesteps 989200/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4946/10000 episodes, total num timesteps 989400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4947/10000 episodes, total num timesteps 989600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4948/10000 episodes, total num timesteps 989800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4949/10000 episodes, total num timesteps 990000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4950/10000 episodes, total num timesteps 990200/2000000, FPS 230.

team_policy eval average step individual rewards of agent0: 0.5608241428060102
team_policy eval average team episode rewards of agent0: 92.5
team_policy eval idv catch total num of agent0: 24
team_policy eval team catch total num: 37
team_policy eval average step individual rewards of agent1: 1.146922924515578
team_policy eval average team episode rewards of agent1: 92.5
team_policy eval idv catch total num of agent1: 47
team_policy eval team catch total num: 37
team_policy eval average step individual rewards of agent2: 0.01965060026577489
team_policy eval average team episode rewards of agent2: 92.5
team_policy eval idv catch total num of agent2: 3
team_policy eval team catch total num: 37
team_policy eval average step individual rewards of agent3: 0.8911570294812445
team_policy eval average team episode rewards of agent3: 92.5
team_policy eval idv catch total num of agent3: 37
team_policy eval team catch total num: 37
team_policy eval average step individual rewards of agent4: 0.7007739870198347
team_policy eval average team episode rewards of agent4: 92.5
team_policy eval idv catch total num of agent4: 30
team_policy eval team catch total num: 37
idv_policy eval average step individual rewards of agent0: 0.5686095567706361
idv_policy eval average team episode rewards of agent0: 80.0
idv_policy eval idv catch total num of agent0: 25
idv_policy eval team catch total num: 32
idv_policy eval average step individual rewards of agent1: 0.7462067496532488
idv_policy eval average team episode rewards of agent1: 80.0
idv_policy eval idv catch total num of agent1: 32
idv_policy eval team catch total num: 32
idv_policy eval average step individual rewards of agent2: 0.24126673527639114
idv_policy eval average team episode rewards of agent2: 80.0
idv_policy eval idv catch total num of agent2: 12
idv_policy eval team catch total num: 32
idv_policy eval average step individual rewards of agent3: 0.7472107917815594
idv_policy eval average team episode rewards of agent3: 80.0
idv_policy eval idv catch total num of agent3: 32
idv_policy eval team catch total num: 32
idv_policy eval average step individual rewards of agent4: 0.6757474964412097
idv_policy eval average team episode rewards of agent4: 80.0
idv_policy eval idv catch total num of agent4: 29
idv_policy eval team catch total num: 32

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4951/10000 episodes, total num timesteps 990400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4952/10000 episodes, total num timesteps 990600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4953/10000 episodes, total num timesteps 990800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4954/10000 episodes, total num timesteps 991000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4955/10000 episodes, total num timesteps 991200/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4956/10000 episodes, total num timesteps 991400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4957/10000 episodes, total num timesteps 991600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4958/10000 episodes, total num timesteps 991800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4959/10000 episodes, total num timesteps 992000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4960/10000 episodes, total num timesteps 992200/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4961/10000 episodes, total num timesteps 992400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4962/10000 episodes, total num timesteps 992600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4963/10000 episodes, total num timesteps 992800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4964/10000 episodes, total num timesteps 993000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4965/10000 episodes, total num timesteps 993200/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4966/10000 episodes, total num timesteps 993400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4967/10000 episodes, total num timesteps 993600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4968/10000 episodes, total num timesteps 993800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4969/10000 episodes, total num timesteps 994000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4970/10000 episodes, total num timesteps 994200/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4971/10000 episodes, total num timesteps 994400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4972/10000 episodes, total num timesteps 994600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4973/10000 episodes, total num timesteps 994800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4974/10000 episodes, total num timesteps 995000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4975/10000 episodes, total num timesteps 995200/2000000, FPS 230.

team_policy eval average step individual rewards of agent0: 0.5654690367496965
team_policy eval average team episode rewards of agent0: 75.0
team_policy eval idv catch total num of agent0: 24
team_policy eval team catch total num: 30
team_policy eval average step individual rewards of agent1: 0.6363652266433751
team_policy eval average team episode rewards of agent1: 75.0
team_policy eval idv catch total num of agent1: 27
team_policy eval team catch total num: 30
team_policy eval average step individual rewards of agent2: 0.9689128863032856
team_policy eval average team episode rewards of agent2: 75.0
team_policy eval idv catch total num of agent2: 40
team_policy eval team catch total num: 30
team_policy eval average step individual rewards of agent3: 0.30622325210989065
team_policy eval average team episode rewards of agent3: 75.0
team_policy eval idv catch total num of agent3: 14
team_policy eval team catch total num: 30
team_policy eval average step individual rewards of agent4: 0.4211675854063193
team_policy eval average team episode rewards of agent4: 75.0
team_policy eval idv catch total num of agent4: 19
team_policy eval team catch total num: 30
idv_policy eval average step individual rewards of agent0: 0.5535583140350957
idv_policy eval average team episode rewards of agent0: 135.0
idv_policy eval idv catch total num of agent0: 24
idv_policy eval team catch total num: 54
idv_policy eval average step individual rewards of agent1: 1.0110336722583686
idv_policy eval average team episode rewards of agent1: 135.0
idv_policy eval idv catch total num of agent1: 42
idv_policy eval team catch total num: 54
idv_policy eval average step individual rewards of agent2: 0.8150826450237951
idv_policy eval average team episode rewards of agent2: 135.0
idv_policy eval idv catch total num of agent2: 34
idv_policy eval team catch total num: 54
idv_policy eval average step individual rewards of agent3: 0.9966294169809308
idv_policy eval average team episode rewards of agent3: 135.0
idv_policy eval idv catch total num of agent3: 41
idv_policy eval team catch total num: 54
idv_policy eval average step individual rewards of agent4: 1.069728850764245
idv_policy eval average team episode rewards of agent4: 135.0
idv_policy eval idv catch total num of agent4: 44
idv_policy eval team catch total num: 54

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4976/10000 episodes, total num timesteps 995400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4977/10000 episodes, total num timesteps 995600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4978/10000 episodes, total num timesteps 995800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4979/10000 episodes, total num timesteps 996000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4980/10000 episodes, total num timesteps 996200/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4981/10000 episodes, total num timesteps 996400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4982/10000 episodes, total num timesteps 996600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4983/10000 episodes, total num timesteps 996800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4984/10000 episodes, total num timesteps 997000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4985/10000 episodes, total num timesteps 997200/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4986/10000 episodes, total num timesteps 997400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4987/10000 episodes, total num timesteps 997600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4988/10000 episodes, total num timesteps 997800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4989/10000 episodes, total num timesteps 998000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4990/10000 episodes, total num timesteps 998200/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4991/10000 episodes, total num timesteps 998400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4992/10000 episodes, total num timesteps 998600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4993/10000 episodes, total num timesteps 998800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4994/10000 episodes, total num timesteps 999000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4995/10000 episodes, total num timesteps 999200/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4996/10000 episodes, total num timesteps 999400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4997/10000 episodes, total num timesteps 999600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4998/10000 episodes, total num timesteps 999800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4999/10000 episodes, total num timesteps 1000000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5000/10000 episodes, total num timesteps 1000200/2000000, FPS 230.

team_policy eval average step individual rewards of agent0: 0.526886474513675
team_policy eval average team episode rewards of agent0: 100.0
team_policy eval idv catch total num of agent0: 23
team_policy eval team catch total num: 40
team_policy eval average step individual rewards of agent1: 0.8301706326995867
team_policy eval average team episode rewards of agent1: 100.0
team_policy eval idv catch total num of agent1: 35
team_policy eval team catch total num: 40
team_policy eval average step individual rewards of agent2: 0.5380367598401733
team_policy eval average team episode rewards of agent2: 100.0
team_policy eval idv catch total num of agent2: 23
team_policy eval team catch total num: 40
team_policy eval average step individual rewards of agent3: 0.9732331766351475
team_policy eval average team episode rewards of agent3: 100.0
team_policy eval idv catch total num of agent3: 41
team_policy eval team catch total num: 40
team_policy eval average step individual rewards of agent4: 0.5897634211053523
team_policy eval average team episode rewards of agent4: 100.0
team_policy eval idv catch total num of agent4: 26
team_policy eval team catch total num: 40
idv_policy eval average step individual rewards of agent0: 0.7322407323111031
idv_policy eval average team episode rewards of agent0: 80.0
idv_policy eval idv catch total num of agent0: 31
idv_policy eval team catch total num: 32
idv_policy eval average step individual rewards of agent1: 0.6794933024324119
idv_policy eval average team episode rewards of agent1: 80.0
idv_policy eval idv catch total num of agent1: 29
idv_policy eval team catch total num: 32
idv_policy eval average step individual rewards of agent2: 0.3958376212024989
idv_policy eval average team episode rewards of agent2: 80.0
idv_policy eval idv catch total num of agent2: 18
idv_policy eval team catch total num: 32
idv_policy eval average step individual rewards of agent3: 0.6405724459436162
idv_policy eval average team episode rewards of agent3: 80.0
idv_policy eval idv catch total num of agent3: 27
idv_policy eval team catch total num: 32
idv_policy eval average step individual rewards of agent4: 0.5541281407081378
idv_policy eval average team episode rewards of agent4: 80.0
idv_policy eval idv catch total num of agent4: 24
idv_policy eval team catch total num: 32

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5001/10000 episodes, total num timesteps 1000400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5002/10000 episodes, total num timesteps 1000600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5003/10000 episodes, total num timesteps 1000800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5004/10000 episodes, total num timesteps 1001000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5005/10000 episodes, total num timesteps 1001200/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5006/10000 episodes, total num timesteps 1001400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5007/10000 episodes, total num timesteps 1001600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5008/10000 episodes, total num timesteps 1001800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5009/10000 episodes, total num timesteps 1002000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5010/10000 episodes, total num timesteps 1002200/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5011/10000 episodes, total num timesteps 1002400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5012/10000 episodes, total num timesteps 1002600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5013/10000 episodes, total num timesteps 1002800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5014/10000 episodes, total num timesteps 1003000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5015/10000 episodes, total num timesteps 1003200/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5016/10000 episodes, total num timesteps 1003400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5017/10000 episodes, total num timesteps 1003600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5018/10000 episodes, total num timesteps 1003800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5019/10000 episodes, total num timesteps 1004000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5020/10000 episodes, total num timesteps 1004200/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5021/10000 episodes, total num timesteps 1004400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5022/10000 episodes, total num timesteps 1004600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5023/10000 episodes, total num timesteps 1004800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5024/10000 episodes, total num timesteps 1005000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5025/10000 episodes, total num timesteps 1005200/2000000, FPS 230.

team_policy eval average step individual rewards of agent0: 1.4774142772821717
team_policy eval average team episode rewards of agent0: 135.0
team_policy eval idv catch total num of agent0: 60
team_policy eval team catch total num: 54
team_policy eval average step individual rewards of agent1: 1.090828396945389
team_policy eval average team episode rewards of agent1: 135.0
team_policy eval idv catch total num of agent1: 45
team_policy eval team catch total num: 54
team_policy eval average step individual rewards of agent2: 0.8933895794923161
team_policy eval average team episode rewards of agent2: 135.0
team_policy eval idv catch total num of agent2: 37
team_policy eval team catch total num: 54
team_policy eval average step individual rewards of agent3: 0.7671787204520518
team_policy eval average team episode rewards of agent3: 135.0
team_policy eval idv catch total num of agent3: 32
team_policy eval team catch total num: 54
team_policy eval average step individual rewards of agent4: 0.843671363631953
team_policy eval average team episode rewards of agent4: 135.0
team_policy eval idv catch total num of agent4: 35
team_policy eval team catch total num: 54
idv_policy eval average step individual rewards of agent0: 0.6318650979876993
idv_policy eval average team episode rewards of agent0: 107.5
idv_policy eval idv catch total num of agent0: 27
idv_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent1: 0.8801942630032816
idv_policy eval average team episode rewards of agent1: 107.5
idv_policy eval idv catch total num of agent1: 37
idv_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent2: 0.9723821493905188
idv_policy eval average team episode rewards of agent2: 107.5
idv_policy eval idv catch total num of agent2: 40
idv_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent3: 0.7050035395483875
idv_policy eval average team episode rewards of agent3: 107.5
idv_policy eval idv catch total num of agent3: 30
idv_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent4: 1.3195459278094126
idv_policy eval average team episode rewards of agent4: 107.5
idv_policy eval idv catch total num of agent4: 54
idv_policy eval team catch total num: 43

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5026/10000 episodes, total num timesteps 1005400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5027/10000 episodes, total num timesteps 1005600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5028/10000 episodes, total num timesteps 1005800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5029/10000 episodes, total num timesteps 1006000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5030/10000 episodes, total num timesteps 1006200/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5031/10000 episodes, total num timesteps 1006400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5032/10000 episodes, total num timesteps 1006600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5033/10000 episodes, total num timesteps 1006800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5034/10000 episodes, total num timesteps 1007000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5035/10000 episodes, total num timesteps 1007200/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5036/10000 episodes, total num timesteps 1007400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5037/10000 episodes, total num timesteps 1007600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5038/10000 episodes, total num timesteps 1007800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5039/10000 episodes, total num timesteps 1008000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5040/10000 episodes, total num timesteps 1008200/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5041/10000 episodes, total num timesteps 1008400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5042/10000 episodes, total num timesteps 1008600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5043/10000 episodes, total num timesteps 1008800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5044/10000 episodes, total num timesteps 1009000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5045/10000 episodes, total num timesteps 1009200/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5046/10000 episodes, total num timesteps 1009400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5047/10000 episodes, total num timesteps 1009600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5048/10000 episodes, total num timesteps 1009800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5049/10000 episodes, total num timesteps 1010000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5050/10000 episodes, total num timesteps 1010200/2000000, FPS 230.

team_policy eval average step individual rewards of agent0: 0.8218873205790311
team_policy eval average team episode rewards of agent0: 80.0
team_policy eval idv catch total num of agent0: 34
team_policy eval team catch total num: 32
team_policy eval average step individual rewards of agent1: 0.7644310591512806
team_policy eval average team episode rewards of agent1: 80.0
team_policy eval idv catch total num of agent1: 32
team_policy eval team catch total num: 32
team_policy eval average step individual rewards of agent2: 0.22914070132006562
team_policy eval average team episode rewards of agent2: 80.0
team_policy eval idv catch total num of agent2: 11
team_policy eval team catch total num: 32
team_policy eval average step individual rewards of agent3: 0.5598023494057676
team_policy eval average team episode rewards of agent3: 80.0
team_policy eval idv catch total num of agent3: 24
team_policy eval team catch total num: 32
team_policy eval average step individual rewards of agent4: 0.65885703294048
team_policy eval average team episode rewards of agent4: 80.0
team_policy eval idv catch total num of agent4: 28
team_policy eval team catch total num: 32
idv_policy eval average step individual rewards of agent0: 0.9419290693105061
idv_policy eval average team episode rewards of agent0: 92.5
idv_policy eval idv catch total num of agent0: 39
idv_policy eval team catch total num: 37
idv_policy eval average step individual rewards of agent1: 0.43258368397771446
idv_policy eval average team episode rewards of agent1: 92.5
idv_policy eval idv catch total num of agent1: 19
idv_policy eval team catch total num: 37
idv_policy eval average step individual rewards of agent2: 0.8137746041479541
idv_policy eval average team episode rewards of agent2: 92.5
idv_policy eval idv catch total num of agent2: 34
idv_policy eval team catch total num: 37
idv_policy eval average step individual rewards of agent3: 0.7381895749866628
idv_policy eval average team episode rewards of agent3: 92.5
idv_policy eval idv catch total num of agent3: 31
idv_policy eval team catch total num: 37
idv_policy eval average step individual rewards of agent4: 0.6349974051004711
idv_policy eval average team episode rewards of agent4: 92.5
idv_policy eval idv catch total num of agent4: 27
idv_policy eval team catch total num: 37

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5051/10000 episodes, total num timesteps 1010400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5052/10000 episodes, total num timesteps 1010600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5053/10000 episodes, total num timesteps 1010800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5054/10000 episodes, total num timesteps 1011000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5055/10000 episodes, total num timesteps 1011200/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5056/10000 episodes, total num timesteps 1011400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5057/10000 episodes, total num timesteps 1011600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5058/10000 episodes, total num timesteps 1011800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5059/10000 episodes, total num timesteps 1012000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5060/10000 episodes, total num timesteps 1012200/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5061/10000 episodes, total num timesteps 1012400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5062/10000 episodes, total num timesteps 1012600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5063/10000 episodes, total num timesteps 1012800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5064/10000 episodes, total num timesteps 1013000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5065/10000 episodes, total num timesteps 1013200/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5066/10000 episodes, total num timesteps 1013400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5067/10000 episodes, total num timesteps 1013600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5068/10000 episodes, total num timesteps 1013800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5069/10000 episodes, total num timesteps 1014000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5070/10000 episodes, total num timesteps 1014200/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5071/10000 episodes, total num timesteps 1014400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5072/10000 episodes, total num timesteps 1014600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5073/10000 episodes, total num timesteps 1014800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5074/10000 episodes, total num timesteps 1015000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5075/10000 episodes, total num timesteps 1015200/2000000, FPS 230.

team_policy eval average step individual rewards of agent0: 0.6376612399666978
team_policy eval average team episode rewards of agent0: 107.5
team_policy eval idv catch total num of agent0: 27
team_policy eval team catch total num: 43
team_policy eval average step individual rewards of agent1: 0.8664337620494209
team_policy eval average team episode rewards of agent1: 107.5
team_policy eval idv catch total num of agent1: 36
team_policy eval team catch total num: 43
team_policy eval average step individual rewards of agent2: 0.8178744694419373
team_policy eval average team episode rewards of agent2: 107.5
team_policy eval idv catch total num of agent2: 34
team_policy eval team catch total num: 43
team_policy eval average step individual rewards of agent3: 0.7312125404791294
team_policy eval average team episode rewards of agent3: 107.5
team_policy eval idv catch total num of agent3: 31
team_policy eval team catch total num: 43
team_policy eval average step individual rewards of agent4: 0.6410605724766983
team_policy eval average team episode rewards of agent4: 107.5
team_policy eval idv catch total num of agent4: 27
team_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent0: 0.6094683335706622
idv_policy eval average team episode rewards of agent0: 82.5
idv_policy eval idv catch total num of agent0: 26
idv_policy eval team catch total num: 33
idv_policy eval average step individual rewards of agent1: 0.8400433115063568
idv_policy eval average team episode rewards of agent1: 82.5
idv_policy eval idv catch total num of agent1: 35
idv_policy eval team catch total num: 33
idv_policy eval average step individual rewards of agent2: 0.3292474119188531
idv_policy eval average team episode rewards of agent2: 82.5
idv_policy eval idv catch total num of agent2: 15
idv_policy eval team catch total num: 33
idv_policy eval average step individual rewards of agent3: 0.8112914364179581
idv_policy eval average team episode rewards of agent3: 82.5
idv_policy eval idv catch total num of agent3: 34
idv_policy eval team catch total num: 33
idv_policy eval average step individual rewards of agent4: 0.7396335781127894
idv_policy eval average team episode rewards of agent4: 82.5
idv_policy eval idv catch total num of agent4: 31
idv_policy eval team catch total num: 33

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5076/10000 episodes, total num timesteps 1015400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5077/10000 episodes, total num timesteps 1015600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5078/10000 episodes, total num timesteps 1015800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5079/10000 episodes, total num timesteps 1016000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5080/10000 episodes, total num timesteps 1016200/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5081/10000 episodes, total num timesteps 1016400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5082/10000 episodes, total num timesteps 1016600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5083/10000 episodes, total num timesteps 1016800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5084/10000 episodes, total num timesteps 1017000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5085/10000 episodes, total num timesteps 1017200/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5086/10000 episodes, total num timesteps 1017400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5087/10000 episodes, total num timesteps 1017600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5088/10000 episodes, total num timesteps 1017800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5089/10000 episodes, total num timesteps 1018000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5090/10000 episodes, total num timesteps 1018200/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5091/10000 episodes, total num timesteps 1018400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5092/10000 episodes, total num timesteps 1018600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5093/10000 episodes, total num timesteps 1018800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5094/10000 episodes, total num timesteps 1019000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5095/10000 episodes, total num timesteps 1019200/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5096/10000 episodes, total num timesteps 1019400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5097/10000 episodes, total num timesteps 1019600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5098/10000 episodes, total num timesteps 1019800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5099/10000 episodes, total num timesteps 1020000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5100/10000 episodes, total num timesteps 1020200/2000000, FPS 230.

team_policy eval average step individual rewards of agent0: 0.8976640081143228
team_policy eval average team episode rewards of agent0: 95.0
team_policy eval idv catch total num of agent0: 37
team_policy eval team catch total num: 38
team_policy eval average step individual rewards of agent1: 0.8394735434769703
team_policy eval average team episode rewards of agent1: 95.0
team_policy eval idv catch total num of agent1: 35
team_policy eval team catch total num: 38
team_policy eval average step individual rewards of agent2: 0.7934032577346072
team_policy eval average team episode rewards of agent2: 95.0
team_policy eval idv catch total num of agent2: 33
team_policy eval team catch total num: 38
team_policy eval average step individual rewards of agent3: 0.6486241439290401
team_policy eval average team episode rewards of agent3: 95.0
team_policy eval idv catch total num of agent3: 27
team_policy eval team catch total num: 38
team_policy eval average step individual rewards of agent4: 0.3100325228965034
team_policy eval average team episode rewards of agent4: 95.0
team_policy eval idv catch total num of agent4: 14
team_policy eval team catch total num: 38
idv_policy eval average step individual rewards of agent0: 0.8663239208856618
idv_policy eval average team episode rewards of agent0: 110.0
idv_policy eval idv catch total num of agent0: 36
idv_policy eval team catch total num: 44
idv_policy eval average step individual rewards of agent1: 0.8378275673995134
idv_policy eval average team episode rewards of agent1: 110.0
idv_policy eval idv catch total num of agent1: 35
idv_policy eval team catch total num: 44
idv_policy eval average step individual rewards of agent2: 0.8895676198559356
idv_policy eval average team episode rewards of agent2: 110.0
idv_policy eval idv catch total num of agent2: 37
idv_policy eval team catch total num: 44
idv_policy eval average step individual rewards of agent3: 0.8088660298045783
idv_policy eval average team episode rewards of agent3: 110.0
idv_policy eval idv catch total num of agent3: 34
idv_policy eval team catch total num: 44
idv_policy eval average step individual rewards of agent4: 1.0985979353584365
idv_policy eval average team episode rewards of agent4: 110.0
idv_policy eval idv catch total num of agent4: 45
idv_policy eval team catch total num: 44

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5101/10000 episodes, total num timesteps 1020400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5102/10000 episodes, total num timesteps 1020600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5103/10000 episodes, total num timesteps 1020800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5104/10000 episodes, total num timesteps 1021000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5105/10000 episodes, total num timesteps 1021200/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5106/10000 episodes, total num timesteps 1021400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5107/10000 episodes, total num timesteps 1021600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5108/10000 episodes, total num timesteps 1021800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5109/10000 episodes, total num timesteps 1022000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5110/10000 episodes, total num timesteps 1022200/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5111/10000 episodes, total num timesteps 1022400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5112/10000 episodes, total num timesteps 1022600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5113/10000 episodes, total num timesteps 1022800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5114/10000 episodes, total num timesteps 1023000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5115/10000 episodes, total num timesteps 1023200/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5116/10000 episodes, total num timesteps 1023400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5117/10000 episodes, total num timesteps 1023600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5118/10000 episodes, total num timesteps 1023800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5119/10000 episodes, total num timesteps 1024000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5120/10000 episodes, total num timesteps 1024200/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5121/10000 episodes, total num timesteps 1024400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5122/10000 episodes, total num timesteps 1024600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5123/10000 episodes, total num timesteps 1024800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5124/10000 episodes, total num timesteps 1025000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5125/10000 episodes, total num timesteps 1025200/2000000, FPS 230.

team_policy eval average step individual rewards of agent0: 0.9700436905492593
team_policy eval average team episode rewards of agent0: 127.5
team_policy eval idv catch total num of agent0: 40
team_policy eval team catch total num: 51
team_policy eval average step individual rewards of agent1: 0.8380314587159209
team_policy eval average team episode rewards of agent1: 127.5
team_policy eval idv catch total num of agent1: 35
team_policy eval team catch total num: 51
team_policy eval average step individual rewards of agent2: 0.6348285635753494
team_policy eval average team episode rewards of agent2: 127.5
team_policy eval idv catch total num of agent2: 27
team_policy eval team catch total num: 51
team_policy eval average step individual rewards of agent3: 1.0310451016706665
team_policy eval average team episode rewards of agent3: 127.5
team_policy eval idv catch total num of agent3: 43
team_policy eval team catch total num: 51
team_policy eval average step individual rewards of agent4: 0.7622803074278939
team_policy eval average team episode rewards of agent4: 127.5
team_policy eval idv catch total num of agent4: 32
team_policy eval team catch total num: 51
idv_policy eval average step individual rewards of agent0: 0.9000849385285554
idv_policy eval average team episode rewards of agent0: 125.0
idv_policy eval idv catch total num of agent0: 37
idv_policy eval team catch total num: 50
idv_policy eval average step individual rewards of agent1: 0.6529315810359119
idv_policy eval average team episode rewards of agent1: 125.0
idv_policy eval idv catch total num of agent1: 28
idv_policy eval team catch total num: 50
idv_policy eval average step individual rewards of agent2: 0.81474553451176
idv_policy eval average team episode rewards of agent2: 125.0
idv_policy eval idv catch total num of agent2: 34
idv_policy eval team catch total num: 50
idv_policy eval average step individual rewards of agent3: 1.1519805067319961
idv_policy eval average team episode rewards of agent3: 125.0
idv_policy eval idv catch total num of agent3: 47
idv_policy eval team catch total num: 50
idv_policy eval average step individual rewards of agent4: 0.6873432269448869
idv_policy eval average team episode rewards of agent4: 125.0
idv_policy eval idv catch total num of agent4: 29
idv_policy eval team catch total num: 50

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5126/10000 episodes, total num timesteps 1025400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5127/10000 episodes, total num timesteps 1025600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5128/10000 episodes, total num timesteps 1025800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5129/10000 episodes, total num timesteps 1026000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5130/10000 episodes, total num timesteps 1026200/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5131/10000 episodes, total num timesteps 1026400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5132/10000 episodes, total num timesteps 1026600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5133/10000 episodes, total num timesteps 1026800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5134/10000 episodes, total num timesteps 1027000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5135/10000 episodes, total num timesteps 1027200/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5136/10000 episodes, total num timesteps 1027400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5137/10000 episodes, total num timesteps 1027600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5138/10000 episodes, total num timesteps 1027800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5139/10000 episodes, total num timesteps 1028000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5140/10000 episodes, total num timesteps 1028200/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5141/10000 episodes, total num timesteps 1028400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5142/10000 episodes, total num timesteps 1028600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5143/10000 episodes, total num timesteps 1028800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5144/10000 episodes, total num timesteps 1029000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5145/10000 episodes, total num timesteps 1029200/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5146/10000 episodes, total num timesteps 1029400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5147/10000 episodes, total num timesteps 1029600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5148/10000 episodes, total num timesteps 1029800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5149/10000 episodes, total num timesteps 1030000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5150/10000 episodes, total num timesteps 1030200/2000000, FPS 230.

team_policy eval average step individual rewards of agent0: 0.4630585531607505
team_policy eval average team episode rewards of agent0: 82.5
team_policy eval idv catch total num of agent0: 20
team_policy eval team catch total num: 33
team_policy eval average step individual rewards of agent1: 0.6663815119602452
team_policy eval average team episode rewards of agent1: 82.5
team_policy eval idv catch total num of agent1: 28
team_policy eval team catch total num: 33
team_policy eval average step individual rewards of agent2: 0.4719369525464534
team_policy eval average team episode rewards of agent2: 82.5
team_policy eval idv catch total num of agent2: 21
team_policy eval team catch total num: 33
team_policy eval average step individual rewards of agent3: 0.8156337588765319
team_policy eval average team episode rewards of agent3: 82.5
team_policy eval idv catch total num of agent3: 34
team_policy eval team catch total num: 33
team_policy eval average step individual rewards of agent4: 0.8346716538148644
team_policy eval average team episode rewards of agent4: 82.5
team_policy eval idv catch total num of agent4: 35
team_policy eval team catch total num: 33
idv_policy eval average step individual rewards of agent0: 1.0334484332915503
idv_policy eval average team episode rewards of agent0: 192.5
idv_policy eval idv catch total num of agent0: 43
idv_policy eval team catch total num: 77
idv_policy eval average step individual rewards of agent1: 1.9480462696109486
idv_policy eval average team episode rewards of agent1: 192.5
idv_policy eval idv catch total num of agent1: 79
idv_policy eval team catch total num: 77
idv_policy eval average step individual rewards of agent2: 1.0450090529236555
idv_policy eval average team episode rewards of agent2: 192.5
idv_policy eval idv catch total num of agent2: 43
idv_policy eval team catch total num: 77
idv_policy eval average step individual rewards of agent3: 1.2377746798562346
idv_policy eval average team episode rewards of agent3: 192.5
idv_policy eval idv catch total num of agent3: 51
idv_policy eval team catch total num: 77
idv_policy eval average step individual rewards of agent4: 1.0690764955566332
idv_policy eval average team episode rewards of agent4: 192.5
idv_policy eval idv catch total num of agent4: 44
idv_policy eval team catch total num: 77

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5151/10000 episodes, total num timesteps 1030400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5152/10000 episodes, total num timesteps 1030600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5153/10000 episodes, total num timesteps 1030800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5154/10000 episodes, total num timesteps 1031000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5155/10000 episodes, total num timesteps 1031200/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5156/10000 episodes, total num timesteps 1031400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5157/10000 episodes, total num timesteps 1031600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5158/10000 episodes, total num timesteps 1031800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5159/10000 episodes, total num timesteps 1032000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5160/10000 episodes, total num timesteps 1032200/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5161/10000 episodes, total num timesteps 1032400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5162/10000 episodes, total num timesteps 1032600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5163/10000 episodes, total num timesteps 1032800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5164/10000 episodes, total num timesteps 1033000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5165/10000 episodes, total num timesteps 1033200/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5166/10000 episodes, total num timesteps 1033400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5167/10000 episodes, total num timesteps 1033600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5168/10000 episodes, total num timesteps 1033800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5169/10000 episodes, total num timesteps 1034000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5170/10000 episodes, total num timesteps 1034200/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5171/10000 episodes, total num timesteps 1034400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5172/10000 episodes, total num timesteps 1034600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5173/10000 episodes, total num timesteps 1034800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5174/10000 episodes, total num timesteps 1035000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5175/10000 episodes, total num timesteps 1035200/2000000, FPS 230.

team_policy eval average step individual rewards of agent0: 0.6956868447521262
team_policy eval average team episode rewards of agent0: 127.5
team_policy eval idv catch total num of agent0: 29
team_policy eval team catch total num: 51
team_policy eval average step individual rewards of agent1: 1.0736561040053827
team_policy eval average team episode rewards of agent1: 127.5
team_policy eval idv catch total num of agent1: 44
team_policy eval team catch total num: 51
team_policy eval average step individual rewards of agent2: 1.098893596278236
team_policy eval average team episode rewards of agent2: 127.5
team_policy eval idv catch total num of agent2: 45
team_policy eval team catch total num: 51
team_policy eval average step individual rewards of agent3: 1.1208923974021883
team_policy eval average team episode rewards of agent3: 127.5
team_policy eval idv catch total num of agent3: 46
team_policy eval team catch total num: 51
team_policy eval average step individual rewards of agent4: 0.8130315167452644
team_policy eval average team episode rewards of agent4: 127.5
team_policy eval idv catch total num of agent4: 34
team_policy eval team catch total num: 51
idv_policy eval average step individual rewards of agent0: 1.2868599845591917
idv_policy eval average team episode rewards of agent0: 122.5
idv_policy eval idv catch total num of agent0: 53
idv_policy eval team catch total num: 49
idv_policy eval average step individual rewards of agent1: 0.7635803436578239
idv_policy eval average team episode rewards of agent1: 122.5
idv_policy eval idv catch total num of agent1: 32
idv_policy eval team catch total num: 49
idv_policy eval average step individual rewards of agent2: 0.6607428801594224
idv_policy eval average team episode rewards of agent2: 122.5
idv_policy eval idv catch total num of agent2: 28
idv_policy eval team catch total num: 49
idv_policy eval average step individual rewards of agent3: 0.9627285066083877
idv_policy eval average team episode rewards of agent3: 122.5
idv_policy eval idv catch total num of agent3: 40
idv_policy eval team catch total num: 49
idv_policy eval average step individual rewards of agent4: 0.9207787905701253
idv_policy eval average team episode rewards of agent4: 122.5
idv_policy eval idv catch total num of agent4: 38
idv_policy eval team catch total num: 49

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5176/10000 episodes, total num timesteps 1035400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5177/10000 episodes, total num timesteps 1035600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5178/10000 episodes, total num timesteps 1035800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5179/10000 episodes, total num timesteps 1036000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5180/10000 episodes, total num timesteps 1036200/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5181/10000 episodes, total num timesteps 1036400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5182/10000 episodes, total num timesteps 1036600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5183/10000 episodes, total num timesteps 1036800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5184/10000 episodes, total num timesteps 1037000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5185/10000 episodes, total num timesteps 1037200/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5186/10000 episodes, total num timesteps 1037400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5187/10000 episodes, total num timesteps 1037600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5188/10000 episodes, total num timesteps 1037800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5189/10000 episodes, total num timesteps 1038000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5190/10000 episodes, total num timesteps 1038200/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5191/10000 episodes, total num timesteps 1038400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5192/10000 episodes, total num timesteps 1038600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5193/10000 episodes, total num timesteps 1038800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5194/10000 episodes, total num timesteps 1039000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5195/10000 episodes, total num timesteps 1039200/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5196/10000 episodes, total num timesteps 1039400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5197/10000 episodes, total num timesteps 1039600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5198/10000 episodes, total num timesteps 1039800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5199/10000 episodes, total num timesteps 1040000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5200/10000 episodes, total num timesteps 1040200/2000000, FPS 230.

team_policy eval average step individual rewards of agent0: 0.42413047703059903
team_policy eval average team episode rewards of agent0: 95.0
team_policy eval idv catch total num of agent0: 19
team_policy eval team catch total num: 38
team_policy eval average step individual rewards of agent1: 0.6150256220542758
team_policy eval average team episode rewards of agent1: 95.0
team_policy eval idv catch total num of agent1: 26
team_policy eval team catch total num: 38
team_policy eval average step individual rewards of agent2: 0.46328126780285606
team_policy eval average team episode rewards of agent2: 95.0
team_policy eval idv catch total num of agent2: 20
team_policy eval team catch total num: 38
team_policy eval average step individual rewards of agent3: 0.994743641595271
team_policy eval average team episode rewards of agent3: 95.0
team_policy eval idv catch total num of agent3: 41
team_policy eval team catch total num: 38
team_policy eval average step individual rewards of agent4: 0.982809579861785
team_policy eval average team episode rewards of agent4: 95.0
team_policy eval idv catch total num of agent4: 41
team_policy eval team catch total num: 38
idv_policy eval average step individual rewards of agent0: 0.8399399353381511
idv_policy eval average team episode rewards of agent0: 132.5
idv_policy eval idv catch total num of agent0: 35
idv_policy eval team catch total num: 53
idv_policy eval average step individual rewards of agent1: 1.0365495009382775
idv_policy eval average team episode rewards of agent1: 132.5
idv_policy eval idv catch total num of agent1: 43
idv_policy eval team catch total num: 53
idv_policy eval average step individual rewards of agent2: 0.7109382680778654
idv_policy eval average team episode rewards of agent2: 132.5
idv_policy eval idv catch total num of agent2: 30
idv_policy eval team catch total num: 53
idv_policy eval average step individual rewards of agent3: 1.0706143834845696
idv_policy eval average team episode rewards of agent3: 132.5
idv_policy eval idv catch total num of agent3: 44
idv_policy eval team catch total num: 53
idv_policy eval average step individual rewards of agent4: 0.8910186207462218
idv_policy eval average team episode rewards of agent4: 132.5
idv_policy eval idv catch total num of agent4: 37
idv_policy eval team catch total num: 53

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5201/10000 episodes, total num timesteps 1040400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5202/10000 episodes, total num timesteps 1040600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5203/10000 episodes, total num timesteps 1040800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5204/10000 episodes, total num timesteps 1041000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5205/10000 episodes, total num timesteps 1041200/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5206/10000 episodes, total num timesteps 1041400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5207/10000 episodes, total num timesteps 1041600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5208/10000 episodes, total num timesteps 1041800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5209/10000 episodes, total num timesteps 1042000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5210/10000 episodes, total num timesteps 1042200/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5211/10000 episodes, total num timesteps 1042400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5212/10000 episodes, total num timesteps 1042600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5213/10000 episodes, total num timesteps 1042800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5214/10000 episodes, total num timesteps 1043000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5215/10000 episodes, total num timesteps 1043200/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5216/10000 episodes, total num timesteps 1043400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5217/10000 episodes, total num timesteps 1043600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5218/10000 episodes, total num timesteps 1043800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5219/10000 episodes, total num timesteps 1044000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5220/10000 episodes, total num timesteps 1044200/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5221/10000 episodes, total num timesteps 1044400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5222/10000 episodes, total num timesteps 1044600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5223/10000 episodes, total num timesteps 1044800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5224/10000 episodes, total num timesteps 1045000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5225/10000 episodes, total num timesteps 1045200/2000000, FPS 230.

team_policy eval average step individual rewards of agent0: 0.9305046483375015
team_policy eval average team episode rewards of agent0: 117.5
team_policy eval idv catch total num of agent0: 39
team_policy eval team catch total num: 47
team_policy eval average step individual rewards of agent1: 0.7670492761952892
team_policy eval average team episode rewards of agent1: 117.5
team_policy eval idv catch total num of agent1: 32
team_policy eval team catch total num: 47
team_policy eval average step individual rewards of agent2: 1.1598040960939349
team_policy eval average team episode rewards of agent2: 117.5
team_policy eval idv catch total num of agent2: 48
team_policy eval team catch total num: 47
team_policy eval average step individual rewards of agent3: 0.7082404829615426
team_policy eval average team episode rewards of agent3: 117.5
team_policy eval idv catch total num of agent3: 30
team_policy eval team catch total num: 47
team_policy eval average step individual rewards of agent4: 0.7422309313897658
team_policy eval average team episode rewards of agent4: 117.5
team_policy eval idv catch total num of agent4: 31
team_policy eval team catch total num: 47
idv_policy eval average step individual rewards of agent0: 0.7873022866073529
idv_policy eval average team episode rewards of agent0: 127.5
idv_policy eval idv catch total num of agent0: 33
idv_policy eval team catch total num: 51
idv_policy eval average step individual rewards of agent1: 1.0483520620569613
idv_policy eval average team episode rewards of agent1: 127.5
idv_policy eval idv catch total num of agent1: 43
idv_policy eval team catch total num: 51
idv_policy eval average step individual rewards of agent2: 1.2439468712751995
idv_policy eval average team episode rewards of agent2: 127.5
idv_policy eval idv catch total num of agent2: 51
idv_policy eval team catch total num: 51
idv_policy eval average step individual rewards of agent3: 0.6567243184343344
idv_policy eval average team episode rewards of agent3: 127.5
idv_policy eval idv catch total num of agent3: 28
idv_policy eval team catch total num: 51
idv_policy eval average step individual rewards of agent4: 0.5335668610701599
idv_policy eval average team episode rewards of agent4: 127.5
idv_policy eval idv catch total num of agent4: 23
idv_policy eval team catch total num: 51

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5226/10000 episodes, total num timesteps 1045400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5227/10000 episodes, total num timesteps 1045600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5228/10000 episodes, total num timesteps 1045800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5229/10000 episodes, total num timesteps 1046000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5230/10000 episodes, total num timesteps 1046200/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5231/10000 episodes, total num timesteps 1046400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5232/10000 episodes, total num timesteps 1046600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5233/10000 episodes, total num timesteps 1046800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5234/10000 episodes, total num timesteps 1047000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5235/10000 episodes, total num timesteps 1047200/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5236/10000 episodes, total num timesteps 1047400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5237/10000 episodes, total num timesteps 1047600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5238/10000 episodes, total num timesteps 1047800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5239/10000 episodes, total num timesteps 1048000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5240/10000 episodes, total num timesteps 1048200/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5241/10000 episodes, total num timesteps 1048400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5242/10000 episodes, total num timesteps 1048600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5243/10000 episodes, total num timesteps 1048800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5244/10000 episodes, total num timesteps 1049000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5245/10000 episodes, total num timesteps 1049200/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5246/10000 episodes, total num timesteps 1049400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5247/10000 episodes, total num timesteps 1049600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5248/10000 episodes, total num timesteps 1049800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5249/10000 episodes, total num timesteps 1050000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5250/10000 episodes, total num timesteps 1050200/2000000, FPS 230.

team_policy eval average step individual rewards of agent0: 0.5770738990796994
team_policy eval average team episode rewards of agent0: 85.0
team_policy eval idv catch total num of agent0: 25
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent1: 0.5780248545302892
team_policy eval average team episode rewards of agent1: 85.0
team_policy eval idv catch total num of agent1: 25
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent2: 0.7114470205142287
team_policy eval average team episode rewards of agent2: 85.0
team_policy eval idv catch total num of agent2: 30
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent3: 0.30079684901845805
team_policy eval average team episode rewards of agent3: 85.0
team_policy eval idv catch total num of agent3: 14
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent4: 0.7707926190525538
team_policy eval average team episode rewards of agent4: 85.0
team_policy eval idv catch total num of agent4: 32
team_policy eval team catch total num: 34
idv_policy eval average step individual rewards of agent0: 1.0740015059366406
idv_policy eval average team episode rewards of agent0: 107.5
idv_policy eval idv catch total num of agent0: 44
idv_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent1: 0.8097402798339377
idv_policy eval average team episode rewards of agent1: 107.5
idv_policy eval idv catch total num of agent1: 34
idv_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent2: 0.9907386035488827
idv_policy eval average team episode rewards of agent2: 107.5
idv_policy eval idv catch total num of agent2: 41
idv_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent3: 0.35524141427686906
idv_policy eval average team episode rewards of agent3: 107.5
idv_policy eval idv catch total num of agent3: 16
idv_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent4: 0.8913978049666906
idv_policy eval average team episode rewards of agent4: 107.5
idv_policy eval idv catch total num of agent4: 37
idv_policy eval team catch total num: 43

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5251/10000 episodes, total num timesteps 1050400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5252/10000 episodes, total num timesteps 1050600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5253/10000 episodes, total num timesteps 1050800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5254/10000 episodes, total num timesteps 1051000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5255/10000 episodes, total num timesteps 1051200/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5256/10000 episodes, total num timesteps 1051400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5257/10000 episodes, total num timesteps 1051600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5258/10000 episodes, total num timesteps 1051800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5259/10000 episodes, total num timesteps 1052000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5260/10000 episodes, total num timesteps 1052200/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5261/10000 episodes, total num timesteps 1052400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5262/10000 episodes, total num timesteps 1052600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5263/10000 episodes, total num timesteps 1052800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5264/10000 episodes, total num timesteps 1053000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5265/10000 episodes, total num timesteps 1053200/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5266/10000 episodes, total num timesteps 1053400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5267/10000 episodes, total num timesteps 1053600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5268/10000 episodes, total num timesteps 1053800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5269/10000 episodes, total num timesteps 1054000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5270/10000 episodes, total num timesteps 1054200/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5271/10000 episodes, total num timesteps 1054400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5272/10000 episodes, total num timesteps 1054600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5273/10000 episodes, total num timesteps 1054800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5274/10000 episodes, total num timesteps 1055000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5275/10000 episodes, total num timesteps 1055200/2000000, FPS 230.

team_policy eval average step individual rewards of agent0: 1.0178892460804452
team_policy eval average team episode rewards of agent0: 85.0
team_policy eval idv catch total num of agent0: 42
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent1: 0.6687163611292262
team_policy eval average team episode rewards of agent1: 85.0
team_policy eval idv catch total num of agent1: 28
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent2: 0.6836044427748038
team_policy eval average team episode rewards of agent2: 85.0
team_policy eval idv catch total num of agent2: 29
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent3: 0.3317347042472046
team_policy eval average team episode rewards of agent3: 85.0
team_policy eval idv catch total num of agent3: 15
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent4: 0.5100452627595523
team_policy eval average team episode rewards of agent4: 85.0
team_policy eval idv catch total num of agent4: 22
team_policy eval team catch total num: 34
idv_policy eval average step individual rewards of agent0: 0.552486475248923
idv_policy eval average team episode rewards of agent0: 120.0
idv_policy eval idv catch total num of agent0: 24
idv_policy eval team catch total num: 48
idv_policy eval average step individual rewards of agent1: 1.1379518364221268
idv_policy eval average team episode rewards of agent1: 120.0
idv_policy eval idv catch total num of agent1: 47
idv_policy eval team catch total num: 48
idv_policy eval average step individual rewards of agent2: 1.032853750274962
idv_policy eval average team episode rewards of agent2: 120.0
idv_policy eval idv catch total num of agent2: 43
idv_policy eval team catch total num: 48
idv_policy eval average step individual rewards of agent3: 0.7913096864852289
idv_policy eval average team episode rewards of agent3: 120.0
idv_policy eval idv catch total num of agent3: 33
idv_policy eval team catch total num: 48
idv_policy eval average step individual rewards of agent4: 0.710587012086659
idv_policy eval average team episode rewards of agent4: 120.0
idv_policy eval idv catch total num of agent4: 30
idv_policy eval team catch total num: 48

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5276/10000 episodes, total num timesteps 1055400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5277/10000 episodes, total num timesteps 1055600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5278/10000 episodes, total num timesteps 1055800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5279/10000 episodes, total num timesteps 1056000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5280/10000 episodes, total num timesteps 1056200/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5281/10000 episodes, total num timesteps 1056400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5282/10000 episodes, total num timesteps 1056600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5283/10000 episodes, total num timesteps 1056800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5284/10000 episodes, total num timesteps 1057000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5285/10000 episodes, total num timesteps 1057200/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5286/10000 episodes, total num timesteps 1057400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5287/10000 episodes, total num timesteps 1057600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5288/10000 episodes, total num timesteps 1057800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5289/10000 episodes, total num timesteps 1058000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5290/10000 episodes, total num timesteps 1058200/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5291/10000 episodes, total num timesteps 1058400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5292/10000 episodes, total num timesteps 1058600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5293/10000 episodes, total num timesteps 1058800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5294/10000 episodes, total num timesteps 1059000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5295/10000 episodes, total num timesteps 1059200/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5296/10000 episodes, total num timesteps 1059400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5297/10000 episodes, total num timesteps 1059600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5298/10000 episodes, total num timesteps 1059800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5299/10000 episodes, total num timesteps 1060000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5300/10000 episodes, total num timesteps 1060200/2000000, FPS 230.

team_policy eval average step individual rewards of agent0: 1.1736153929480384
team_policy eval average team episode rewards of agent0: 172.5
team_policy eval idv catch total num of agent0: 48
team_policy eval team catch total num: 69
team_policy eval average step individual rewards of agent1: 1.3280280163138647
team_policy eval average team episode rewards of agent1: 172.5
team_policy eval idv catch total num of agent1: 54
team_policy eval team catch total num: 69
team_policy eval average step individual rewards of agent2: 1.3015671123426917
team_policy eval average team episode rewards of agent2: 172.5
team_policy eval idv catch total num of agent2: 53
team_policy eval team catch total num: 69
team_policy eval average step individual rewards of agent3: 1.0691451936007375
team_policy eval average team episode rewards of agent3: 172.5
team_policy eval idv catch total num of agent3: 44
team_policy eval team catch total num: 69
team_policy eval average step individual rewards of agent4: 1.5952705984347881
team_policy eval average team episode rewards of agent4: 172.5
team_policy eval idv catch total num of agent4: 65
team_policy eval team catch total num: 69
idv_policy eval average step individual rewards of agent0: 0.6350376167183792
idv_policy eval average team episode rewards of agent0: 92.5
idv_policy eval idv catch total num of agent0: 27
idv_policy eval team catch total num: 37
idv_policy eval average step individual rewards of agent1: 0.3406583942261257
idv_policy eval average team episode rewards of agent1: 92.5
idv_policy eval idv catch total num of agent1: 15
idv_policy eval team catch total num: 37
idv_policy eval average step individual rewards of agent2: 0.8644774367671003
idv_policy eval average team episode rewards of agent2: 92.5
idv_policy eval idv catch total num of agent2: 36
idv_policy eval team catch total num: 37
idv_policy eval average step individual rewards of agent3: 0.5830522546473041
idv_policy eval average team episode rewards of agent3: 92.5
idv_policy eval idv catch total num of agent3: 25
idv_policy eval team catch total num: 37
idv_policy eval average step individual rewards of agent4: 0.7097138945770156
idv_policy eval average team episode rewards of agent4: 92.5
idv_policy eval idv catch total num of agent4: 30
idv_policy eval team catch total num: 37

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5301/10000 episodes, total num timesteps 1060400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5302/10000 episodes, total num timesteps 1060600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5303/10000 episodes, total num timesteps 1060800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5304/10000 episodes, total num timesteps 1061000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5305/10000 episodes, total num timesteps 1061200/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5306/10000 episodes, total num timesteps 1061400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5307/10000 episodes, total num timesteps 1061600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5308/10000 episodes, total num timesteps 1061800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5309/10000 episodes, total num timesteps 1062000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5310/10000 episodes, total num timesteps 1062200/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5311/10000 episodes, total num timesteps 1062400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5312/10000 episodes, total num timesteps 1062600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5313/10000 episodes, total num timesteps 1062800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5314/10000 episodes, total num timesteps 1063000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5315/10000 episodes, total num timesteps 1063200/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5316/10000 episodes, total num timesteps 1063400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5317/10000 episodes, total num timesteps 1063600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5318/10000 episodes, total num timesteps 1063800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5319/10000 episodes, total num timesteps 1064000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5320/10000 episodes, total num timesteps 1064200/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5321/10000 episodes, total num timesteps 1064400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5322/10000 episodes, total num timesteps 1064600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5323/10000 episodes, total num timesteps 1064800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5324/10000 episodes, total num timesteps 1065000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5325/10000 episodes, total num timesteps 1065200/2000000, FPS 230.

team_policy eval average step individual rewards of agent0: 1.0738620766188145
team_policy eval average team episode rewards of agent0: 117.5
team_policy eval idv catch total num of agent0: 44
team_policy eval team catch total num: 47
team_policy eval average step individual rewards of agent1: 0.8664744859344506
team_policy eval average team episode rewards of agent1: 117.5
team_policy eval idv catch total num of agent1: 36
team_policy eval team catch total num: 47
team_policy eval average step individual rewards of agent2: 0.7613504540877318
team_policy eval average team episode rewards of agent2: 117.5
team_policy eval idv catch total num of agent2: 32
team_policy eval team catch total num: 47
team_policy eval average step individual rewards of agent3: 0.9702800818468148
team_policy eval average team episode rewards of agent3: 117.5
team_policy eval idv catch total num of agent3: 40
team_policy eval team catch total num: 47
team_policy eval average step individual rewards of agent4: 0.7565569149484247
team_policy eval average team episode rewards of agent4: 117.5
team_policy eval idv catch total num of agent4: 32
team_policy eval team catch total num: 47
idv_policy eval average step individual rewards of agent0: 0.6571764356562096
idv_policy eval average team episode rewards of agent0: 132.5
idv_policy eval idv catch total num of agent0: 28
idv_policy eval team catch total num: 53
idv_policy eval average step individual rewards of agent1: 1.1010780152101682
idv_policy eval average team episode rewards of agent1: 132.5
idv_policy eval idv catch total num of agent1: 46
idv_policy eval team catch total num: 53
idv_policy eval average step individual rewards of agent2: 1.0882820593397289
idv_policy eval average team episode rewards of agent2: 132.5
idv_policy eval idv catch total num of agent2: 45
idv_policy eval team catch total num: 53
idv_policy eval average step individual rewards of agent3: 0.8837481579927727
idv_policy eval average team episode rewards of agent3: 132.5
idv_policy eval idv catch total num of agent3: 37
idv_policy eval team catch total num: 53
idv_policy eval average step individual rewards of agent4: 0.7629641978478584
idv_policy eval average team episode rewards of agent4: 132.5
idv_policy eval idv catch total num of agent4: 32
idv_policy eval team catch total num: 53

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5326/10000 episodes, total num timesteps 1065400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5327/10000 episodes, total num timesteps 1065600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5328/10000 episodes, total num timesteps 1065800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5329/10000 episodes, total num timesteps 1066000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5330/10000 episodes, total num timesteps 1066200/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5331/10000 episodes, total num timesteps 1066400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5332/10000 episodes, total num timesteps 1066600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5333/10000 episodes, total num timesteps 1066800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5334/10000 episodes, total num timesteps 1067000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5335/10000 episodes, total num timesteps 1067200/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5336/10000 episodes, total num timesteps 1067400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5337/10000 episodes, total num timesteps 1067600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5338/10000 episodes, total num timesteps 1067800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5339/10000 episodes, total num timesteps 1068000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5340/10000 episodes, total num timesteps 1068200/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5341/10000 episodes, total num timesteps 1068400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5342/10000 episodes, total num timesteps 1068600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5343/10000 episodes, total num timesteps 1068800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5344/10000 episodes, total num timesteps 1069000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5345/10000 episodes, total num timesteps 1069200/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5346/10000 episodes, total num timesteps 1069400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5347/10000 episodes, total num timesteps 1069600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5348/10000 episodes, total num timesteps 1069800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5349/10000 episodes, total num timesteps 1070000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5350/10000 episodes, total num timesteps 1070200/2000000, FPS 230.

team_policy eval average step individual rewards of agent0: 0.5772152849652382
team_policy eval average team episode rewards of agent0: 100.0
team_policy eval idv catch total num of agent0: 25
team_policy eval team catch total num: 40
team_policy eval average step individual rewards of agent1: 0.7929813569696426
team_policy eval average team episode rewards of agent1: 100.0
team_policy eval idv catch total num of agent1: 33
team_policy eval team catch total num: 40
team_policy eval average step individual rewards of agent2: 0.5568181922577589
team_policy eval average team episode rewards of agent2: 100.0
team_policy eval idv catch total num of agent2: 24
team_policy eval team catch total num: 40
team_policy eval average step individual rewards of agent3: 0.9646885730220136
team_policy eval average team episode rewards of agent3: 100.0
team_policy eval idv catch total num of agent3: 40
team_policy eval team catch total num: 40
team_policy eval average step individual rewards of agent4: 1.1176847434891974
team_policy eval average team episode rewards of agent4: 100.0
team_policy eval idv catch total num of agent4: 46
team_policy eval team catch total num: 40
idv_policy eval average step individual rewards of agent0: 0.4615395010883343
idv_policy eval average team episode rewards of agent0: 67.5
idv_policy eval idv catch total num of agent0: 20
idv_policy eval team catch total num: 27
idv_policy eval average step individual rewards of agent1: 0.5625411357317399
idv_policy eval average team episode rewards of agent1: 67.5
idv_policy eval idv catch total num of agent1: 24
idv_policy eval team catch total num: 27
idv_policy eval average step individual rewards of agent2: 0.35251224935664077
idv_policy eval average team episode rewards of agent2: 67.5
idv_policy eval idv catch total num of agent2: 16
idv_policy eval team catch total num: 27
idv_policy eval average step individual rewards of agent3: 0.6920395616803079
idv_policy eval average team episode rewards of agent3: 67.5
idv_policy eval idv catch total num of agent3: 29
idv_policy eval team catch total num: 27
idv_policy eval average step individual rewards of agent4: 0.5608496563463622
idv_policy eval average team episode rewards of agent4: 67.5
idv_policy eval idv catch total num of agent4: 24
idv_policy eval team catch total num: 27

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5351/10000 episodes, total num timesteps 1070400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5352/10000 episodes, total num timesteps 1070600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5353/10000 episodes, total num timesteps 1070800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5354/10000 episodes, total num timesteps 1071000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5355/10000 episodes, total num timesteps 1071200/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5356/10000 episodes, total num timesteps 1071400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5357/10000 episodes, total num timesteps 1071600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5358/10000 episodes, total num timesteps 1071800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5359/10000 episodes, total num timesteps 1072000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5360/10000 episodes, total num timesteps 1072200/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5361/10000 episodes, total num timesteps 1072400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5362/10000 episodes, total num timesteps 1072600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5363/10000 episodes, total num timesteps 1072800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5364/10000 episodes, total num timesteps 1073000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5365/10000 episodes, total num timesteps 1073200/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5366/10000 episodes, total num timesteps 1073400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5367/10000 episodes, total num timesteps 1073600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5368/10000 episodes, total num timesteps 1073800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5369/10000 episodes, total num timesteps 1074000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5370/10000 episodes, total num timesteps 1074200/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5371/10000 episodes, total num timesteps 1074400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5372/10000 episodes, total num timesteps 1074600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5373/10000 episodes, total num timesteps 1074800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5374/10000 episodes, total num timesteps 1075000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5375/10000 episodes, total num timesteps 1075200/2000000, FPS 230.

team_policy eval average step individual rewards of agent0: 1.1223126145987343
team_policy eval average team episode rewards of agent0: 140.0
team_policy eval idv catch total num of agent0: 46
team_policy eval team catch total num: 56
team_policy eval average step individual rewards of agent1: 0.7320664675938331
team_policy eval average team episode rewards of agent1: 140.0
team_policy eval idv catch total num of agent1: 31
team_policy eval team catch total num: 56
team_policy eval average step individual rewards of agent2: 1.196845174962893
team_policy eval average team episode rewards of agent2: 140.0
team_policy eval idv catch total num of agent2: 49
team_policy eval team catch total num: 56
team_policy eval average step individual rewards of agent3: 0.8399880848153447
team_policy eval average team episode rewards of agent3: 140.0
team_policy eval idv catch total num of agent3: 35
team_policy eval team catch total num: 56
team_policy eval average step individual rewards of agent4: 1.18934075205075
team_policy eval average team episode rewards of agent4: 140.0
team_policy eval idv catch total num of agent4: 49
team_policy eval team catch total num: 56
idv_policy eval average step individual rewards of agent0: 1.0443058914571832
idv_policy eval average team episode rewards of agent0: 120.0
idv_policy eval idv catch total num of agent0: 43
idv_policy eval team catch total num: 48
idv_policy eval average step individual rewards of agent1: 0.8143565469361751
idv_policy eval average team episode rewards of agent1: 120.0
idv_policy eval idv catch total num of agent1: 34
idv_policy eval team catch total num: 48
idv_policy eval average step individual rewards of agent2: 0.737195451565045
idv_policy eval average team episode rewards of agent2: 120.0
idv_policy eval idv catch total num of agent2: 31
idv_policy eval team catch total num: 48
idv_policy eval average step individual rewards of agent3: 0.8184543389317943
idv_policy eval average team episode rewards of agent3: 120.0
idv_policy eval idv catch total num of agent3: 34
idv_policy eval team catch total num: 48
idv_policy eval average step individual rewards of agent4: 0.9416261835949471
idv_policy eval average team episode rewards of agent4: 120.0
idv_policy eval idv catch total num of agent4: 39
idv_policy eval team catch total num: 48

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5376/10000 episodes, total num timesteps 1075400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5377/10000 episodes, total num timesteps 1075600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5378/10000 episodes, total num timesteps 1075800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5379/10000 episodes, total num timesteps 1076000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5380/10000 episodes, total num timesteps 1076200/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5381/10000 episodes, total num timesteps 1076400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5382/10000 episodes, total num timesteps 1076600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5383/10000 episodes, total num timesteps 1076800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5384/10000 episodes, total num timesteps 1077000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5385/10000 episodes, total num timesteps 1077200/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5386/10000 episodes, total num timesteps 1077400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5387/10000 episodes, total num timesteps 1077600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5388/10000 episodes, total num timesteps 1077800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5389/10000 episodes, total num timesteps 1078000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5390/10000 episodes, total num timesteps 1078200/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5391/10000 episodes, total num timesteps 1078400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5392/10000 episodes, total num timesteps 1078600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5393/10000 episodes, total num timesteps 1078800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5394/10000 episodes, total num timesteps 1079000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5395/10000 episodes, total num timesteps 1079200/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5396/10000 episodes, total num timesteps 1079400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5397/10000 episodes, total num timesteps 1079600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5398/10000 episodes, total num timesteps 1079800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5399/10000 episodes, total num timesteps 1080000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5400/10000 episodes, total num timesteps 1080200/2000000, FPS 230.

team_policy eval average step individual rewards of agent0: 0.7420431223364647
team_policy eval average team episode rewards of agent0: 107.5
team_policy eval idv catch total num of agent0: 31
team_policy eval team catch total num: 43
team_policy eval average step individual rewards of agent1: 0.9355849464932378
team_policy eval average team episode rewards of agent1: 107.5
team_policy eval idv catch total num of agent1: 39
team_policy eval team catch total num: 43
team_policy eval average step individual rewards of agent2: 1.1695358733867078
team_policy eval average team episode rewards of agent2: 107.5
team_policy eval idv catch total num of agent2: 48
team_policy eval team catch total num: 43
team_policy eval average step individual rewards of agent3: 0.8370006510258898
team_policy eval average team episode rewards of agent3: 107.5
team_policy eval idv catch total num of agent3: 35
team_policy eval team catch total num: 43
team_policy eval average step individual rewards of agent4: 0.8248312729031162
team_policy eval average team episode rewards of agent4: 107.5
team_policy eval idv catch total num of agent4: 34
team_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent0: 0.7858026397569228
idv_policy eval average team episode rewards of agent0: 77.5
idv_policy eval idv catch total num of agent0: 33
idv_policy eval team catch total num: 31
idv_policy eval average step individual rewards of agent1: 0.557295546763603
idv_policy eval average team episode rewards of agent1: 77.5
idv_policy eval idv catch total num of agent1: 24
idv_policy eval team catch total num: 31
idv_policy eval average step individual rewards of agent2: 1.0366227935643415
idv_policy eval average team episode rewards of agent2: 77.5
idv_policy eval idv catch total num of agent2: 43
idv_policy eval team catch total num: 31
idv_policy eval average step individual rewards of agent3: 0.2947851053592364
idv_policy eval average team episode rewards of agent3: 77.5
idv_policy eval idv catch total num of agent3: 14
idv_policy eval team catch total num: 31
idv_policy eval average step individual rewards of agent4: 0.2323836344214557
idv_policy eval average team episode rewards of agent4: 77.5
idv_policy eval idv catch total num of agent4: 11
idv_policy eval team catch total num: 31

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5401/10000 episodes, total num timesteps 1080400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5402/10000 episodes, total num timesteps 1080600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5403/10000 episodes, total num timesteps 1080800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5404/10000 episodes, total num timesteps 1081000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5405/10000 episodes, total num timesteps 1081200/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5406/10000 episodes, total num timesteps 1081400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5407/10000 episodes, total num timesteps 1081600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5408/10000 episodes, total num timesteps 1081800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5409/10000 episodes, total num timesteps 1082000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5410/10000 episodes, total num timesteps 1082200/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5411/10000 episodes, total num timesteps 1082400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5412/10000 episodes, total num timesteps 1082600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5413/10000 episodes, total num timesteps 1082800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5414/10000 episodes, total num timesteps 1083000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5415/10000 episodes, total num timesteps 1083200/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5416/10000 episodes, total num timesteps 1083400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5417/10000 episodes, total num timesteps 1083600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5418/10000 episodes, total num timesteps 1083800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5419/10000 episodes, total num timesteps 1084000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5420/10000 episodes, total num timesteps 1084200/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5421/10000 episodes, total num timesteps 1084400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5422/10000 episodes, total num timesteps 1084600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5423/10000 episodes, total num timesteps 1084800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5424/10000 episodes, total num timesteps 1085000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5425/10000 episodes, total num timesteps 1085200/2000000, FPS 230.

team_policy eval average step individual rewards of agent0: 1.1983348763283184
team_policy eval average team episode rewards of agent0: 112.5
team_policy eval idv catch total num of agent0: 49
team_policy eval team catch total num: 45
team_policy eval average step individual rewards of agent1: 0.5925338860908783
team_policy eval average team episode rewards of agent1: 112.5
team_policy eval idv catch total num of agent1: 25
team_policy eval team catch total num: 45
team_policy eval average step individual rewards of agent2: 1.051803112513777
team_policy eval average team episode rewards of agent2: 112.5
team_policy eval idv catch total num of agent2: 43
team_policy eval team catch total num: 45
team_policy eval average step individual rewards of agent3: 0.6641495585254917
team_policy eval average team episode rewards of agent3: 112.5
team_policy eval idv catch total num of agent3: 28
team_policy eval team catch total num: 45
team_policy eval average step individual rewards of agent4: 1.0742894625190988
team_policy eval average team episode rewards of agent4: 112.5
team_policy eval idv catch total num of agent4: 44
team_policy eval team catch total num: 45
idv_policy eval average step individual rewards of agent0: 0.9394935664503948
idv_policy eval average team episode rewards of agent0: 115.0
idv_policy eval idv catch total num of agent0: 39
idv_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent1: 0.8321827327840883
idv_policy eval average team episode rewards of agent1: 115.0
idv_policy eval idv catch total num of agent1: 35
idv_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent2: 0.8817716945086418
idv_policy eval average team episode rewards of agent2: 115.0
idv_policy eval idv catch total num of agent2: 37
idv_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent3: 0.9602848396274419
idv_policy eval average team episode rewards of agent3: 115.0
idv_policy eval idv catch total num of agent3: 40
idv_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent4: 0.5589893382017898
idv_policy eval average team episode rewards of agent4: 115.0
idv_policy eval idv catch total num of agent4: 24
idv_policy eval team catch total num: 46

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5426/10000 episodes, total num timesteps 1085400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5427/10000 episodes, total num timesteps 1085600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5428/10000 episodes, total num timesteps 1085800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5429/10000 episodes, total num timesteps 1086000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5430/10000 episodes, total num timesteps 1086200/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5431/10000 episodes, total num timesteps 1086400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5432/10000 episodes, total num timesteps 1086600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5433/10000 episodes, total num timesteps 1086800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5434/10000 episodes, total num timesteps 1087000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5435/10000 episodes, total num timesteps 1087200/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5436/10000 episodes, total num timesteps 1087400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5437/10000 episodes, total num timesteps 1087600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5438/10000 episodes, total num timesteps 1087800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5439/10000 episodes, total num timesteps 1088000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5440/10000 episodes, total num timesteps 1088200/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5441/10000 episodes, total num timesteps 1088400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5442/10000 episodes, total num timesteps 1088600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5443/10000 episodes, total num timesteps 1088800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5444/10000 episodes, total num timesteps 1089000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5445/10000 episodes, total num timesteps 1089200/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5446/10000 episodes, total num timesteps 1089400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5447/10000 episodes, total num timesteps 1089600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5448/10000 episodes, total num timesteps 1089800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5449/10000 episodes, total num timesteps 1090000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5450/10000 episodes, total num timesteps 1090200/2000000, FPS 230.

team_policy eval average step individual rewards of agent0: 0.8032592225988702
team_policy eval average team episode rewards of agent0: 147.5
team_policy eval idv catch total num of agent0: 34
team_policy eval team catch total num: 59
team_policy eval average step individual rewards of agent1: 0.9018837581856036
team_policy eval average team episode rewards of agent1: 147.5
team_policy eval idv catch total num of agent1: 38
team_policy eval team catch total num: 59
team_policy eval average step individual rewards of agent2: 1.4252011993815268
team_policy eval average team episode rewards of agent2: 147.5
team_policy eval idv catch total num of agent2: 58
team_policy eval team catch total num: 59
team_policy eval average step individual rewards of agent3: 1.2093189817055736
team_policy eval average team episode rewards of agent3: 147.5
team_policy eval idv catch total num of agent3: 50
team_policy eval team catch total num: 59
team_policy eval average step individual rewards of agent4: 0.7893293561998544
team_policy eval average team episode rewards of agent4: 147.5
team_policy eval idv catch total num of agent4: 33
team_policy eval team catch total num: 59
idv_policy eval average step individual rewards of agent0: 0.6495379240179244
idv_policy eval average team episode rewards of agent0: 92.5
idv_policy eval idv catch total num of agent0: 28
idv_policy eval team catch total num: 37
idv_policy eval average step individual rewards of agent1: 0.3988724257167182
idv_policy eval average team episode rewards of agent1: 92.5
idv_policy eval idv catch total num of agent1: 18
idv_policy eval team catch total num: 37
idv_policy eval average step individual rewards of agent2: 0.3721078089244284
idv_policy eval average team episode rewards of agent2: 92.5
idv_policy eval idv catch total num of agent2: 17
idv_policy eval team catch total num: 37
idv_policy eval average step individual rewards of agent3: 0.707133910835418
idv_policy eval average team episode rewards of agent3: 92.5
idv_policy eval idv catch total num of agent3: 30
idv_policy eval team catch total num: 37
idv_policy eval average step individual rewards of agent4: 1.1918755811978634
idv_policy eval average team episode rewards of agent4: 92.5
idv_policy eval idv catch total num of agent4: 49
idv_policy eval team catch total num: 37

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5451/10000 episodes, total num timesteps 1090400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5452/10000 episodes, total num timesteps 1090600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5453/10000 episodes, total num timesteps 1090800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5454/10000 episodes, total num timesteps 1091000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5455/10000 episodes, total num timesteps 1091200/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5456/10000 episodes, total num timesteps 1091400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5457/10000 episodes, total num timesteps 1091600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5458/10000 episodes, total num timesteps 1091800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5459/10000 episodes, total num timesteps 1092000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5460/10000 episodes, total num timesteps 1092200/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5461/10000 episodes, total num timesteps 1092400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5462/10000 episodes, total num timesteps 1092600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5463/10000 episodes, total num timesteps 1092800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5464/10000 episodes, total num timesteps 1093000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5465/10000 episodes, total num timesteps 1093200/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5466/10000 episodes, total num timesteps 1093400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5467/10000 episodes, total num timesteps 1093600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5468/10000 episodes, total num timesteps 1093800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5469/10000 episodes, total num timesteps 1094000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5470/10000 episodes, total num timesteps 1094200/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5471/10000 episodes, total num timesteps 1094400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5472/10000 episodes, total num timesteps 1094600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5473/10000 episodes, total num timesteps 1094800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5474/10000 episodes, total num timesteps 1095000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5475/10000 episodes, total num timesteps 1095200/2000000, FPS 230.

team_policy eval average step individual rewards of agent0: 0.6358995697987305
team_policy eval average team episode rewards of agent0: 92.5
team_policy eval idv catch total num of agent0: 27
team_policy eval team catch total num: 37
team_policy eval average step individual rewards of agent1: 0.959112550651661
team_policy eval average team episode rewards of agent1: 92.5
team_policy eval idv catch total num of agent1: 40
team_policy eval team catch total num: 37
team_policy eval average step individual rewards of agent2: 0.17742056630196856
team_policy eval average team episode rewards of agent2: 92.5
team_policy eval idv catch total num of agent2: 9
team_policy eval team catch total num: 37
team_policy eval average step individual rewards of agent3: 0.8121789572817338
team_policy eval average team episode rewards of agent3: 92.5
team_policy eval idv catch total num of agent3: 34
team_policy eval team catch total num: 37
team_policy eval average step individual rewards of agent4: 0.766940370780502
team_policy eval average team episode rewards of agent4: 92.5
team_policy eval idv catch total num of agent4: 32
team_policy eval team catch total num: 37
idv_policy eval average step individual rewards of agent0: 0.5082742725235668
idv_policy eval average team episode rewards of agent0: 110.0
idv_policy eval idv catch total num of agent0: 22
idv_policy eval team catch total num: 44
idv_policy eval average step individual rewards of agent1: 1.2246161448817516
idv_policy eval average team episode rewards of agent1: 110.0
idv_policy eval idv catch total num of agent1: 50
idv_policy eval team catch total num: 44
idv_policy eval average step individual rewards of agent2: 0.7881213759378286
idv_policy eval average team episode rewards of agent2: 110.0
idv_policy eval idv catch total num of agent2: 33
idv_policy eval team catch total num: 44
idv_policy eval average step individual rewards of agent3: 0.9678312940038396
idv_policy eval average team episode rewards of agent3: 110.0
idv_policy eval idv catch total num of agent3: 40
idv_policy eval team catch total num: 44
idv_policy eval average step individual rewards of agent4: 0.6938751277994644
idv_policy eval average team episode rewards of agent4: 110.0
idv_policy eval idv catch total num of agent4: 29
idv_policy eval team catch total num: 44

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5476/10000 episodes, total num timesteps 1095400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5477/10000 episodes, total num timesteps 1095600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5478/10000 episodes, total num timesteps 1095800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5479/10000 episodes, total num timesteps 1096000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5480/10000 episodes, total num timesteps 1096200/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5481/10000 episodes, total num timesteps 1096400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5482/10000 episodes, total num timesteps 1096600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5483/10000 episodes, total num timesteps 1096800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5484/10000 episodes, total num timesteps 1097000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5485/10000 episodes, total num timesteps 1097200/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5486/10000 episodes, total num timesteps 1097400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5487/10000 episodes, total num timesteps 1097600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5488/10000 episodes, total num timesteps 1097800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5489/10000 episodes, total num timesteps 1098000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5490/10000 episodes, total num timesteps 1098200/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5491/10000 episodes, total num timesteps 1098400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5492/10000 episodes, total num timesteps 1098600/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5493/10000 episodes, total num timesteps 1098800/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5494/10000 episodes, total num timesteps 1099000/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5495/10000 episodes, total num timesteps 1099200/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5496/10000 episodes, total num timesteps 1099400/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5497/10000 episodes, total num timesteps 1099600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5498/10000 episodes, total num timesteps 1099800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5499/10000 episodes, total num timesteps 1100000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5500/10000 episodes, total num timesteps 1100200/2000000, FPS 230.

team_policy eval average step individual rewards of agent0: 0.8634690868982866
team_policy eval average team episode rewards of agent0: 162.5
team_policy eval idv catch total num of agent0: 36
team_policy eval team catch total num: 65
team_policy eval average step individual rewards of agent1: 0.8447402337425212
team_policy eval average team episode rewards of agent1: 162.5
team_policy eval idv catch total num of agent1: 35
team_policy eval team catch total num: 65
team_policy eval average step individual rewards of agent2: 1.6461027285770928
team_policy eval average team episode rewards of agent2: 162.5
team_policy eval idv catch total num of agent2: 67
team_policy eval team catch total num: 65
team_policy eval average step individual rewards of agent3: 0.8868937048538388
team_policy eval average team episode rewards of agent3: 162.5
team_policy eval idv catch total num of agent3: 37
team_policy eval team catch total num: 65
team_policy eval average step individual rewards of agent4: 1.1431585255000252
team_policy eval average team episode rewards of agent4: 162.5
team_policy eval idv catch total num of agent4: 47
team_policy eval team catch total num: 65
idv_policy eval average step individual rewards of agent0: 0.8677330530890219
idv_policy eval average team episode rewards of agent0: 100.0
idv_policy eval idv catch total num of agent0: 36
idv_policy eval team catch total num: 40
idv_policy eval average step individual rewards of agent1: 0.8361056556887312
idv_policy eval average team episode rewards of agent1: 100.0
idv_policy eval idv catch total num of agent1: 35
idv_policy eval team catch total num: 40
idv_policy eval average step individual rewards of agent2: 0.9950936790037807
idv_policy eval average team episode rewards of agent2: 100.0
idv_policy eval idv catch total num of agent2: 41
idv_policy eval team catch total num: 40
idv_policy eval average step individual rewards of agent3: 0.8894458484608393
idv_policy eval average team episode rewards of agent3: 100.0
idv_policy eval idv catch total num of agent3: 37
idv_policy eval team catch total num: 40
idv_policy eval average step individual rewards of agent4: 0.7679629025378892
idv_policy eval average team episode rewards of agent4: 100.0
idv_policy eval idv catch total num of agent4: 32
idv_policy eval team catch total num: 40

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5501/10000 episodes, total num timesteps 1100400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5502/10000 episodes, total num timesteps 1100600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5503/10000 episodes, total num timesteps 1100800/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5504/10000 episodes, total num timesteps 1101000/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5505/10000 episodes, total num timesteps 1101200/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5506/10000 episodes, total num timesteps 1101400/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5507/10000 episodes, total num timesteps 1101600/2000000, FPS 230.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5508/10000 episodes, total num timesteps 1101800/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5509/10000 episodes, total num timesteps 1102000/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5510/10000 episodes, total num timesteps 1102200/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5511/10000 episodes, total num timesteps 1102400/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5512/10000 episodes, total num timesteps 1102600/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5513/10000 episodes, total num timesteps 1102800/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5514/10000 episodes, total num timesteps 1103000/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5515/10000 episodes, total num timesteps 1103200/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5516/10000 episodes, total num timesteps 1103400/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5517/10000 episodes, total num timesteps 1103600/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5518/10000 episodes, total num timesteps 1103800/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5519/10000 episodes, total num timesteps 1104000/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5520/10000 episodes, total num timesteps 1104200/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5521/10000 episodes, total num timesteps 1104400/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5522/10000 episodes, total num timesteps 1104600/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5523/10000 episodes, total num timesteps 1104800/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5524/10000 episodes, total num timesteps 1105000/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5525/10000 episodes, total num timesteps 1105200/2000000, FPS 231.

team_policy eval average step individual rewards of agent0: 0.6270151325584359
team_policy eval average team episode rewards of agent0: 77.5
team_policy eval idv catch total num of agent0: 27
team_policy eval team catch total num: 31
team_policy eval average step individual rewards of agent1: 1.0803881577469268
team_policy eval average team episode rewards of agent1: 77.5
team_policy eval idv catch total num of agent1: 45
team_policy eval team catch total num: 31
team_policy eval average step individual rewards of agent2: 0.5815611910861311
team_policy eval average team episode rewards of agent2: 77.5
team_policy eval idv catch total num of agent2: 25
team_policy eval team catch total num: 31
team_policy eval average step individual rewards of agent3: 0.5997274545466768
team_policy eval average team episode rewards of agent3: 77.5
team_policy eval idv catch total num of agent3: 26
team_policy eval team catch total num: 31
team_policy eval average step individual rewards of agent4: 0.5033605573357604
team_policy eval average team episode rewards of agent4: 77.5
team_policy eval idv catch total num of agent4: 22
team_policy eval team catch total num: 31
idv_policy eval average step individual rewards of agent0: 0.8384676020840304
idv_policy eval average team episode rewards of agent0: 115.0
idv_policy eval idv catch total num of agent0: 35
idv_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent1: 0.96592628006252
idv_policy eval average team episode rewards of agent1: 115.0
idv_policy eval idv catch total num of agent1: 40
idv_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent2: 0.6883148301201433
idv_policy eval average team episode rewards of agent2: 115.0
idv_policy eval idv catch total num of agent2: 29
idv_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent3: 1.046984867200859
idv_policy eval average team episode rewards of agent3: 115.0
idv_policy eval idv catch total num of agent3: 43
idv_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent4: 0.8189564595412918
idv_policy eval average team episode rewards of agent4: 115.0
idv_policy eval idv catch total num of agent4: 34
idv_policy eval team catch total num: 46

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5526/10000 episodes, total num timesteps 1105400/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5527/10000 episodes, total num timesteps 1105600/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5528/10000 episodes, total num timesteps 1105800/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5529/10000 episodes, total num timesteps 1106000/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5530/10000 episodes, total num timesteps 1106200/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5531/10000 episodes, total num timesteps 1106400/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5532/10000 episodes, total num timesteps 1106600/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5533/10000 episodes, total num timesteps 1106800/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5534/10000 episodes, total num timesteps 1107000/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5535/10000 episodes, total num timesteps 1107200/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5536/10000 episodes, total num timesteps 1107400/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5537/10000 episodes, total num timesteps 1107600/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5538/10000 episodes, total num timesteps 1107800/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5539/10000 episodes, total num timesteps 1108000/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5540/10000 episodes, total num timesteps 1108200/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5541/10000 episodes, total num timesteps 1108400/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5542/10000 episodes, total num timesteps 1108600/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5543/10000 episodes, total num timesteps 1108800/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5544/10000 episodes, total num timesteps 1109000/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5545/10000 episodes, total num timesteps 1109200/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5546/10000 episodes, total num timesteps 1109400/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5547/10000 episodes, total num timesteps 1109600/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5548/10000 episodes, total num timesteps 1109800/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5549/10000 episodes, total num timesteps 1110000/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5550/10000 episodes, total num timesteps 1110200/2000000, FPS 231.

team_policy eval average step individual rewards of agent0: 0.7334928971584421
team_policy eval average team episode rewards of agent0: 125.0
team_policy eval idv catch total num of agent0: 31
team_policy eval team catch total num: 50
team_policy eval average step individual rewards of agent1: 0.9423806712464841
team_policy eval average team episode rewards of agent1: 125.0
team_policy eval idv catch total num of agent1: 39
team_policy eval team catch total num: 50
team_policy eval average step individual rewards of agent2: 0.677265773737798
team_policy eval average team episode rewards of agent2: 125.0
team_policy eval idv catch total num of agent2: 29
team_policy eval team catch total num: 50
team_policy eval average step individual rewards of agent3: 1.1164063472462251
team_policy eval average team episode rewards of agent3: 125.0
team_policy eval idv catch total num of agent3: 46
team_policy eval team catch total num: 50
team_policy eval average step individual rewards of agent4: 0.7663420411016446
team_policy eval average team episode rewards of agent4: 125.0
team_policy eval idv catch total num of agent4: 32
team_policy eval team catch total num: 50
idv_policy eval average step individual rewards of agent0: 0.7378404831936297
idv_policy eval average team episode rewards of agent0: 105.0
idv_policy eval idv catch total num of agent0: 31
idv_policy eval team catch total num: 42
idv_policy eval average step individual rewards of agent1: 0.4585347507274413
idv_policy eval average team episode rewards of agent1: 105.0
idv_policy eval idv catch total num of agent1: 20
idv_policy eval team catch total num: 42
idv_policy eval average step individual rewards of agent2: 0.9637961633512288
idv_policy eval average team episode rewards of agent2: 105.0
idv_policy eval idv catch total num of agent2: 40
idv_policy eval team catch total num: 42
idv_policy eval average step individual rewards of agent3: 0.9418311997854738
idv_policy eval average team episode rewards of agent3: 105.0
idv_policy eval idv catch total num of agent3: 39
idv_policy eval team catch total num: 42
idv_policy eval average step individual rewards of agent4: 0.8614270341175227
idv_policy eval average team episode rewards of agent4: 105.0
idv_policy eval idv catch total num of agent4: 36
idv_policy eval team catch total num: 42

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5551/10000 episodes, total num timesteps 1110400/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5552/10000 episodes, total num timesteps 1110600/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5553/10000 episodes, total num timesteps 1110800/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5554/10000 episodes, total num timesteps 1111000/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5555/10000 episodes, total num timesteps 1111200/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5556/10000 episodes, total num timesteps 1111400/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5557/10000 episodes, total num timesteps 1111600/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5558/10000 episodes, total num timesteps 1111800/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5559/10000 episodes, total num timesteps 1112000/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5560/10000 episodes, total num timesteps 1112200/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5561/10000 episodes, total num timesteps 1112400/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5562/10000 episodes, total num timesteps 1112600/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5563/10000 episodes, total num timesteps 1112800/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5564/10000 episodes, total num timesteps 1113000/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5565/10000 episodes, total num timesteps 1113200/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5566/10000 episodes, total num timesteps 1113400/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5567/10000 episodes, total num timesteps 1113600/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5568/10000 episodes, total num timesteps 1113800/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5569/10000 episodes, total num timesteps 1114000/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5570/10000 episodes, total num timesteps 1114200/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5571/10000 episodes, total num timesteps 1114400/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5572/10000 episodes, total num timesteps 1114600/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5573/10000 episodes, total num timesteps 1114800/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5574/10000 episodes, total num timesteps 1115000/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5575/10000 episodes, total num timesteps 1115200/2000000, FPS 231.

team_policy eval average step individual rewards of agent0: 0.777315631546142
team_policy eval average team episode rewards of agent0: 82.5
team_policy eval idv catch total num of agent0: 33
team_policy eval team catch total num: 33
team_policy eval average step individual rewards of agent1: 0.8469383138104907
team_policy eval average team episode rewards of agent1: 82.5
team_policy eval idv catch total num of agent1: 36
team_policy eval team catch total num: 33
team_policy eval average step individual rewards of agent2: 0.8382799042536291
team_policy eval average team episode rewards of agent2: 82.5
team_policy eval idv catch total num of agent2: 35
team_policy eval team catch total num: 33
team_policy eval average step individual rewards of agent3: 0.5854695766142782
team_policy eval average team episode rewards of agent3: 82.5
team_policy eval idv catch total num of agent3: 25
team_policy eval team catch total num: 33
team_policy eval average step individual rewards of agent4: 0.5619283861995745
team_policy eval average team episode rewards of agent4: 82.5
team_policy eval idv catch total num of agent4: 24
team_policy eval team catch total num: 33
idv_policy eval average step individual rewards of agent0: 0.7846759937787658
idv_policy eval average team episode rewards of agent0: 115.0
idv_policy eval idv catch total num of agent0: 33
idv_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent1: 1.064932536874885
idv_policy eval average team episode rewards of agent1: 115.0
idv_policy eval idv catch total num of agent1: 44
idv_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent2: 0.4346080448527495
idv_policy eval average team episode rewards of agent2: 115.0
idv_policy eval idv catch total num of agent2: 19
idv_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent3: 0.7691564673190453
idv_policy eval average team episode rewards of agent3: 115.0
idv_policy eval idv catch total num of agent3: 32
idv_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent4: 0.838093975898184
idv_policy eval average team episode rewards of agent4: 115.0
idv_policy eval idv catch total num of agent4: 35
idv_policy eval team catch total num: 46

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5576/10000 episodes, total num timesteps 1115400/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5577/10000 episodes, total num timesteps 1115600/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5578/10000 episodes, total num timesteps 1115800/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5579/10000 episodes, total num timesteps 1116000/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5580/10000 episodes, total num timesteps 1116200/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5581/10000 episodes, total num timesteps 1116400/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5582/10000 episodes, total num timesteps 1116600/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5583/10000 episodes, total num timesteps 1116800/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5584/10000 episodes, total num timesteps 1117000/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5585/10000 episodes, total num timesteps 1117200/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5586/10000 episodes, total num timesteps 1117400/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5587/10000 episodes, total num timesteps 1117600/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5588/10000 episodes, total num timesteps 1117800/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5589/10000 episodes, total num timesteps 1118000/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5590/10000 episodes, total num timesteps 1118200/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5591/10000 episodes, total num timesteps 1118400/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5592/10000 episodes, total num timesteps 1118600/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5593/10000 episodes, total num timesteps 1118800/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5594/10000 episodes, total num timesteps 1119000/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5595/10000 episodes, total num timesteps 1119200/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5596/10000 episodes, total num timesteps 1119400/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5597/10000 episodes, total num timesteps 1119600/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5598/10000 episodes, total num timesteps 1119800/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5599/10000 episodes, total num timesteps 1120000/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5600/10000 episodes, total num timesteps 1120200/2000000, FPS 231.

team_policy eval average step individual rewards of agent0: 0.9431651975205514
team_policy eval average team episode rewards of agent0: 100.0
team_policy eval idv catch total num of agent0: 39
team_policy eval team catch total num: 40
team_policy eval average step individual rewards of agent1: 0.5628164676552262
team_policy eval average team episode rewards of agent1: 100.0
team_policy eval idv catch total num of agent1: 24
team_policy eval team catch total num: 40
team_policy eval average step individual rewards of agent2: 0.7333759965537829
team_policy eval average team episode rewards of agent2: 100.0
team_policy eval idv catch total num of agent2: 31
team_policy eval team catch total num: 40
team_policy eval average step individual rewards of agent3: 0.5920719765467062
team_policy eval average team episode rewards of agent3: 100.0
team_policy eval idv catch total num of agent3: 25
team_policy eval team catch total num: 40
team_policy eval average step individual rewards of agent4: 0.8465651938994434
team_policy eval average team episode rewards of agent4: 100.0
team_policy eval idv catch total num of agent4: 35
team_policy eval team catch total num: 40
idv_policy eval average step individual rewards of agent0: 1.2412629869424832
idv_policy eval average team episode rewards of agent0: 132.5
idv_policy eval idv catch total num of agent0: 51
idv_policy eval team catch total num: 53
idv_policy eval average step individual rewards of agent1: 1.1095062412875587
idv_policy eval average team episode rewards of agent1: 132.5
idv_policy eval idv catch total num of agent1: 46
idv_policy eval team catch total num: 53
idv_policy eval average step individual rewards of agent2: 0.8383240976300496
idv_policy eval average team episode rewards of agent2: 132.5
idv_policy eval idv catch total num of agent2: 35
idv_policy eval team catch total num: 53
idv_policy eval average step individual rewards of agent3: 0.921097932383583
idv_policy eval average team episode rewards of agent3: 132.5
idv_policy eval idv catch total num of agent3: 38
idv_policy eval team catch total num: 53
idv_policy eval average step individual rewards of agent4: 0.7691719258075281
idv_policy eval average team episode rewards of agent4: 132.5
idv_policy eval idv catch total num of agent4: 32
idv_policy eval team catch total num: 53

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5601/10000 episodes, total num timesteps 1120400/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5602/10000 episodes, total num timesteps 1120600/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5603/10000 episodes, total num timesteps 1120800/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5604/10000 episodes, total num timesteps 1121000/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5605/10000 episodes, total num timesteps 1121200/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5606/10000 episodes, total num timesteps 1121400/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5607/10000 episodes, total num timesteps 1121600/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5608/10000 episodes, total num timesteps 1121800/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5609/10000 episodes, total num timesteps 1122000/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5610/10000 episodes, total num timesteps 1122200/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5611/10000 episodes, total num timesteps 1122400/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5612/10000 episodes, total num timesteps 1122600/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5613/10000 episodes, total num timesteps 1122800/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5614/10000 episodes, total num timesteps 1123000/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5615/10000 episodes, total num timesteps 1123200/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5616/10000 episodes, total num timesteps 1123400/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5617/10000 episodes, total num timesteps 1123600/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5618/10000 episodes, total num timesteps 1123800/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5619/10000 episodes, total num timesteps 1124000/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5620/10000 episodes, total num timesteps 1124200/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5621/10000 episodes, total num timesteps 1124400/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5622/10000 episodes, total num timesteps 1124600/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5623/10000 episodes, total num timesteps 1124800/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5624/10000 episodes, total num timesteps 1125000/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5625/10000 episodes, total num timesteps 1125200/2000000, FPS 231.

team_policy eval average step individual rewards of agent0: 0.6068904827441203
team_policy eval average team episode rewards of agent0: 107.5
team_policy eval idv catch total num of agent0: 26
team_policy eval team catch total num: 43
team_policy eval average step individual rewards of agent1: 1.1666598857132806
team_policy eval average team episode rewards of agent1: 107.5
team_policy eval idv catch total num of agent1: 48
team_policy eval team catch total num: 43
team_policy eval average step individual rewards of agent2: 0.3807736060957581
team_policy eval average team episode rewards of agent2: 107.5
team_policy eval idv catch total num of agent2: 17
team_policy eval team catch total num: 43
team_policy eval average step individual rewards of agent3: 0.7834661284963534
team_policy eval average team episode rewards of agent3: 107.5
team_policy eval idv catch total num of agent3: 33
team_policy eval team catch total num: 43
team_policy eval average step individual rewards of agent4: 0.657160007471755
team_policy eval average team episode rewards of agent4: 107.5
team_policy eval idv catch total num of agent4: 28
team_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent0: 0.5161287600494596
idv_policy eval average team episode rewards of agent0: 97.5
idv_policy eval idv catch total num of agent0: 22
idv_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent1: 0.834836408100593
idv_policy eval average team episode rewards of agent1: 97.5
idv_policy eval idv catch total num of agent1: 35
idv_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent2: 0.9212054627025488
idv_policy eval average team episode rewards of agent2: 97.5
idv_policy eval idv catch total num of agent2: 38
idv_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent3: 0.4043143955576896
idv_policy eval average team episode rewards of agent3: 97.5
idv_policy eval idv catch total num of agent3: 18
idv_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent4: 0.9412918118526618
idv_policy eval average team episode rewards of agent4: 97.5
idv_policy eval idv catch total num of agent4: 39
idv_policy eval team catch total num: 39

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5626/10000 episodes, total num timesteps 1125400/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5627/10000 episodes, total num timesteps 1125600/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5628/10000 episodes, total num timesteps 1125800/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5629/10000 episodes, total num timesteps 1126000/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5630/10000 episodes, total num timesteps 1126200/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5631/10000 episodes, total num timesteps 1126400/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5632/10000 episodes, total num timesteps 1126600/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5633/10000 episodes, total num timesteps 1126800/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5634/10000 episodes, total num timesteps 1127000/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5635/10000 episodes, total num timesteps 1127200/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5636/10000 episodes, total num timesteps 1127400/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5637/10000 episodes, total num timesteps 1127600/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5638/10000 episodes, total num timesteps 1127800/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5639/10000 episodes, total num timesteps 1128000/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5640/10000 episodes, total num timesteps 1128200/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5641/10000 episodes, total num timesteps 1128400/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5642/10000 episodes, total num timesteps 1128600/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5643/10000 episodes, total num timesteps 1128800/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5644/10000 episodes, total num timesteps 1129000/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5645/10000 episodes, total num timesteps 1129200/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5646/10000 episodes, total num timesteps 1129400/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5647/10000 episodes, total num timesteps 1129600/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5648/10000 episodes, total num timesteps 1129800/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5649/10000 episodes, total num timesteps 1130000/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5650/10000 episodes, total num timesteps 1130200/2000000, FPS 231.

team_policy eval average step individual rewards of agent0: 0.8326002099960244
team_policy eval average team episode rewards of agent0: 87.5
team_policy eval idv catch total num of agent0: 35
team_policy eval team catch total num: 35
team_policy eval average step individual rewards of agent1: 0.9365336648010748
team_policy eval average team episode rewards of agent1: 87.5
team_policy eval idv catch total num of agent1: 39
team_policy eval team catch total num: 35
team_policy eval average step individual rewards of agent2: 0.5311137533481544
team_policy eval average team episode rewards of agent2: 87.5
team_policy eval idv catch total num of agent2: 23
team_policy eval team catch total num: 35
team_policy eval average step individual rewards of agent3: 0.604596141155146
team_policy eval average team episode rewards of agent3: 87.5
team_policy eval idv catch total num of agent3: 26
team_policy eval team catch total num: 35
team_policy eval average step individual rewards of agent4: 0.4241013164925137
team_policy eval average team episode rewards of agent4: 87.5
team_policy eval idv catch total num of agent4: 19
team_policy eval team catch total num: 35
idv_policy eval average step individual rewards of agent0: 0.6894341324690467
idv_policy eval average team episode rewards of agent0: 107.5
idv_policy eval idv catch total num of agent0: 29
idv_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent1: 0.5359819382026048
idv_policy eval average team episode rewards of agent1: 107.5
idv_policy eval idv catch total num of agent1: 23
idv_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent2: 1.0226896021326048
idv_policy eval average team episode rewards of agent2: 107.5
idv_policy eval idv catch total num of agent2: 42
idv_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent3: 0.7087395900562433
idv_policy eval average team episode rewards of agent3: 107.5
idv_policy eval idv catch total num of agent3: 30
idv_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent4: 0.6570005396631682
idv_policy eval average team episode rewards of agent4: 107.5
idv_policy eval idv catch total num of agent4: 28
idv_policy eval team catch total num: 43

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5651/10000 episodes, total num timesteps 1130400/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5652/10000 episodes, total num timesteps 1130600/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5653/10000 episodes, total num timesteps 1130800/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5654/10000 episodes, total num timesteps 1131000/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5655/10000 episodes, total num timesteps 1131200/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5656/10000 episodes, total num timesteps 1131400/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5657/10000 episodes, total num timesteps 1131600/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5658/10000 episodes, total num timesteps 1131800/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5659/10000 episodes, total num timesteps 1132000/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5660/10000 episodes, total num timesteps 1132200/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5661/10000 episodes, total num timesteps 1132400/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5662/10000 episodes, total num timesteps 1132600/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5663/10000 episodes, total num timesteps 1132800/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5664/10000 episodes, total num timesteps 1133000/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5665/10000 episodes, total num timesteps 1133200/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5666/10000 episodes, total num timesteps 1133400/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5667/10000 episodes, total num timesteps 1133600/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5668/10000 episodes, total num timesteps 1133800/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5669/10000 episodes, total num timesteps 1134000/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5670/10000 episodes, total num timesteps 1134200/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5671/10000 episodes, total num timesteps 1134400/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5672/10000 episodes, total num timesteps 1134600/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5673/10000 episodes, total num timesteps 1134800/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5674/10000 episodes, total num timesteps 1135000/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5675/10000 episodes, total num timesteps 1135200/2000000, FPS 231.

team_policy eval average step individual rewards of agent0: 0.6855281904895548
team_policy eval average team episode rewards of agent0: 95.0
team_policy eval idv catch total num of agent0: 29
team_policy eval team catch total num: 38
team_policy eval average step individual rewards of agent1: 0.940382213451698
team_policy eval average team episode rewards of agent1: 95.0
team_policy eval idv catch total num of agent1: 39
team_policy eval team catch total num: 38
team_policy eval average step individual rewards of agent2: 0.663165622650566
team_policy eval average team episode rewards of agent2: 95.0
team_policy eval idv catch total num of agent2: 28
team_policy eval team catch total num: 38
team_policy eval average step individual rewards of agent3: 1.0160730151928694
team_policy eval average team episode rewards of agent3: 95.0
team_policy eval idv catch total num of agent3: 42
team_policy eval team catch total num: 38
team_policy eval average step individual rewards of agent4: 0.8128132223844945
team_policy eval average team episode rewards of agent4: 95.0
team_policy eval idv catch total num of agent4: 34
team_policy eval team catch total num: 38
idv_policy eval average step individual rewards of agent0: 0.8981526765520943
idv_policy eval average team episode rewards of agent0: 117.5
idv_policy eval idv catch total num of agent0: 37
idv_policy eval team catch total num: 47
idv_policy eval average step individual rewards of agent1: 0.8218458912298432
idv_policy eval average team episode rewards of agent1: 117.5
idv_policy eval idv catch total num of agent1: 34
idv_policy eval team catch total num: 47
idv_policy eval average step individual rewards of agent2: 0.7458465026680716
idv_policy eval average team episode rewards of agent2: 117.5
idv_policy eval idv catch total num of agent2: 31
idv_policy eval team catch total num: 47
idv_policy eval average step individual rewards of agent3: 0.7291516814600594
idv_policy eval average team episode rewards of agent3: 117.5
idv_policy eval idv catch total num of agent3: 31
idv_policy eval team catch total num: 47
idv_policy eval average step individual rewards of agent4: 0.5619740731285018
idv_policy eval average team episode rewards of agent4: 117.5
idv_policy eval idv catch total num of agent4: 24
idv_policy eval team catch total num: 47

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5676/10000 episodes, total num timesteps 1135400/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5677/10000 episodes, total num timesteps 1135600/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5678/10000 episodes, total num timesteps 1135800/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5679/10000 episodes, total num timesteps 1136000/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5680/10000 episodes, total num timesteps 1136200/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5681/10000 episodes, total num timesteps 1136400/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5682/10000 episodes, total num timesteps 1136600/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5683/10000 episodes, total num timesteps 1136800/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5684/10000 episodes, total num timesteps 1137000/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5685/10000 episodes, total num timesteps 1137200/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5686/10000 episodes, total num timesteps 1137400/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5687/10000 episodes, total num timesteps 1137600/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5688/10000 episodes, total num timesteps 1137800/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5689/10000 episodes, total num timesteps 1138000/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5690/10000 episodes, total num timesteps 1138200/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5691/10000 episodes, total num timesteps 1138400/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5692/10000 episodes, total num timesteps 1138600/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5693/10000 episodes, total num timesteps 1138800/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5694/10000 episodes, total num timesteps 1139000/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5695/10000 episodes, total num timesteps 1139200/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5696/10000 episodes, total num timesteps 1139400/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5697/10000 episodes, total num timesteps 1139600/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5698/10000 episodes, total num timesteps 1139800/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5699/10000 episodes, total num timesteps 1140000/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5700/10000 episodes, total num timesteps 1140200/2000000, FPS 231.

team_policy eval average step individual rewards of agent0: 1.2685728082249472
team_policy eval average team episode rewards of agent0: 130.0
team_policy eval idv catch total num of agent0: 52
team_policy eval team catch total num: 52
team_policy eval average step individual rewards of agent1: 0.7623015925290213
team_policy eval average team episode rewards of agent1: 130.0
team_policy eval idv catch total num of agent1: 32
team_policy eval team catch total num: 52
team_policy eval average step individual rewards of agent2: 1.0460693148631592
team_policy eval average team episode rewards of agent2: 130.0
team_policy eval idv catch total num of agent2: 43
team_policy eval team catch total num: 52
team_policy eval average step individual rewards of agent3: 0.8648184476841135
team_policy eval average team episode rewards of agent3: 130.0
team_policy eval idv catch total num of agent3: 36
team_policy eval team catch total num: 52
team_policy eval average step individual rewards of agent4: 0.8619666246588753
team_policy eval average team episode rewards of agent4: 130.0
team_policy eval idv catch total num of agent4: 36
team_policy eval team catch total num: 52
idv_policy eval average step individual rewards of agent0: 1.4971742569755333
idv_policy eval average team episode rewards of agent0: 155.0
idv_policy eval idv catch total num of agent0: 61
idv_policy eval team catch total num: 62
idv_policy eval average step individual rewards of agent1: 0.9607143894606183
idv_policy eval average team episode rewards of agent1: 155.0
idv_policy eval idv catch total num of agent1: 40
idv_policy eval team catch total num: 62
idv_policy eval average step individual rewards of agent2: 1.494789659023059
idv_policy eval average team episode rewards of agent2: 155.0
idv_policy eval idv catch total num of agent2: 61
idv_policy eval team catch total num: 62
idv_policy eval average step individual rewards of agent3: 0.7272410225200998
idv_policy eval average team episode rewards of agent3: 155.0
idv_policy eval idv catch total num of agent3: 31
idv_policy eval team catch total num: 62
idv_policy eval average step individual rewards of agent4: 0.6864873128227593
idv_policy eval average team episode rewards of agent4: 155.0
idv_policy eval idv catch total num of agent4: 29
idv_policy eval team catch total num: 62

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5701/10000 episodes, total num timesteps 1140400/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5702/10000 episodes, total num timesteps 1140600/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5703/10000 episodes, total num timesteps 1140800/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5704/10000 episodes, total num timesteps 1141000/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5705/10000 episodes, total num timesteps 1141200/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5706/10000 episodes, total num timesteps 1141400/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5707/10000 episodes, total num timesteps 1141600/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5708/10000 episodes, total num timesteps 1141800/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5709/10000 episodes, total num timesteps 1142000/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5710/10000 episodes, total num timesteps 1142200/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5711/10000 episodes, total num timesteps 1142400/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5712/10000 episodes, total num timesteps 1142600/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5713/10000 episodes, total num timesteps 1142800/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5714/10000 episodes, total num timesteps 1143000/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5715/10000 episodes, total num timesteps 1143200/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5716/10000 episodes, total num timesteps 1143400/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5717/10000 episodes, total num timesteps 1143600/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5718/10000 episodes, total num timesteps 1143800/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5719/10000 episodes, total num timesteps 1144000/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5720/10000 episodes, total num timesteps 1144200/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5721/10000 episodes, total num timesteps 1144400/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5722/10000 episodes, total num timesteps 1144600/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5723/10000 episodes, total num timesteps 1144800/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5724/10000 episodes, total num timesteps 1145000/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5725/10000 episodes, total num timesteps 1145200/2000000, FPS 231.

team_policy eval average step individual rewards of agent0: 0.8076744587880946
team_policy eval average team episode rewards of agent0: 105.0
team_policy eval idv catch total num of agent0: 34
team_policy eval team catch total num: 42
team_policy eval average step individual rewards of agent1: 0.4876276464050776
team_policy eval average team episode rewards of agent1: 105.0
team_policy eval idv catch total num of agent1: 21
team_policy eval team catch total num: 42
team_policy eval average step individual rewards of agent2: 1.0097321985392762
team_policy eval average team episode rewards of agent2: 105.0
team_policy eval idv catch total num of agent2: 42
team_policy eval team catch total num: 42
team_policy eval average step individual rewards of agent3: 0.6112935154491148
team_policy eval average team episode rewards of agent3: 105.0
team_policy eval idv catch total num of agent3: 26
team_policy eval team catch total num: 42
team_policy eval average step individual rewards of agent4: 0.6338956170370413
team_policy eval average team episode rewards of agent4: 105.0
team_policy eval idv catch total num of agent4: 27
team_policy eval team catch total num: 42
idv_policy eval average step individual rewards of agent0: 0.43095815163430096
idv_policy eval average team episode rewards of agent0: 87.5
idv_policy eval idv catch total num of agent0: 19
idv_policy eval team catch total num: 35
idv_policy eval average step individual rewards of agent1: 0.6111665819343411
idv_policy eval average team episode rewards of agent1: 87.5
idv_policy eval idv catch total num of agent1: 26
idv_policy eval team catch total num: 35
idv_policy eval average step individual rewards of agent2: 0.7842801246486527
idv_policy eval average team episode rewards of agent2: 87.5
idv_policy eval idv catch total num of agent2: 33
idv_policy eval team catch total num: 35
idv_policy eval average step individual rewards of agent3: 0.6329691169251289
idv_policy eval average team episode rewards of agent3: 87.5
idv_policy eval idv catch total num of agent3: 27
idv_policy eval team catch total num: 35
idv_policy eval average step individual rewards of agent4: 0.6846225445126233
idv_policy eval average team episode rewards of agent4: 87.5
idv_policy eval idv catch total num of agent4: 29
idv_policy eval team catch total num: 35

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5726/10000 episodes, total num timesteps 1145400/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5727/10000 episodes, total num timesteps 1145600/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5728/10000 episodes, total num timesteps 1145800/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5729/10000 episodes, total num timesteps 1146000/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5730/10000 episodes, total num timesteps 1146200/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5731/10000 episodes, total num timesteps 1146400/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5732/10000 episodes, total num timesteps 1146600/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5733/10000 episodes, total num timesteps 1146800/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5734/10000 episodes, total num timesteps 1147000/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5735/10000 episodes, total num timesteps 1147200/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5736/10000 episodes, total num timesteps 1147400/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5737/10000 episodes, total num timesteps 1147600/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5738/10000 episodes, total num timesteps 1147800/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5739/10000 episodes, total num timesteps 1148000/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5740/10000 episodes, total num timesteps 1148200/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5741/10000 episodes, total num timesteps 1148400/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5742/10000 episodes, total num timesteps 1148600/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5743/10000 episodes, total num timesteps 1148800/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5744/10000 episodes, total num timesteps 1149000/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5745/10000 episodes, total num timesteps 1149200/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5746/10000 episodes, total num timesteps 1149400/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5747/10000 episodes, total num timesteps 1149600/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5748/10000 episodes, total num timesteps 1149800/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5749/10000 episodes, total num timesteps 1150000/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5750/10000 episodes, total num timesteps 1150200/2000000, FPS 231.

team_policy eval average step individual rewards of agent0: 0.6436472936990425
team_policy eval average team episode rewards of agent0: 110.0
team_policy eval idv catch total num of agent0: 27
team_policy eval team catch total num: 44
team_policy eval average step individual rewards of agent1: 0.5906975391805003
team_policy eval average team episode rewards of agent1: 110.0
team_policy eval idv catch total num of agent1: 25
team_policy eval team catch total num: 44
team_policy eval average step individual rewards of agent2: 0.7692364877823457
team_policy eval average team episode rewards of agent2: 110.0
team_policy eval idv catch total num of agent2: 32
team_policy eval team catch total num: 44
team_policy eval average step individual rewards of agent3: 0.961976465409378
team_policy eval average team episode rewards of agent3: 110.0
team_policy eval idv catch total num of agent3: 40
team_policy eval team catch total num: 44
team_policy eval average step individual rewards of agent4: 0.6853846550877455
team_policy eval average team episode rewards of agent4: 110.0
team_policy eval idv catch total num of agent4: 29
team_policy eval team catch total num: 44
idv_policy eval average step individual rewards of agent0: 1.014943210550323
idv_policy eval average team episode rewards of agent0: 97.5
idv_policy eval idv catch total num of agent0: 42
idv_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent1: 0.9165509765103234
idv_policy eval average team episode rewards of agent1: 97.5
idv_policy eval idv catch total num of agent1: 38
idv_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent2: 0.5867141459581117
idv_policy eval average team episode rewards of agent2: 97.5
idv_policy eval idv catch total num of agent2: 25
idv_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent3: 0.8882736862965797
idv_policy eval average team episode rewards of agent3: 97.5
idv_policy eval idv catch total num of agent3: 37
idv_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent4: 0.4046053527447947
idv_policy eval average team episode rewards of agent4: 97.5
idv_policy eval idv catch total num of agent4: 18
idv_policy eval team catch total num: 39

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5751/10000 episodes, total num timesteps 1150400/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5752/10000 episodes, total num timesteps 1150600/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5753/10000 episodes, total num timesteps 1150800/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5754/10000 episodes, total num timesteps 1151000/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5755/10000 episodes, total num timesteps 1151200/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5756/10000 episodes, total num timesteps 1151400/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5757/10000 episodes, total num timesteps 1151600/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5758/10000 episodes, total num timesteps 1151800/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5759/10000 episodes, total num timesteps 1152000/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5760/10000 episodes, total num timesteps 1152200/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5761/10000 episodes, total num timesteps 1152400/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5762/10000 episodes, total num timesteps 1152600/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5763/10000 episodes, total num timesteps 1152800/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5764/10000 episodes, total num timesteps 1153000/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5765/10000 episodes, total num timesteps 1153200/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5766/10000 episodes, total num timesteps 1153400/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5767/10000 episodes, total num timesteps 1153600/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5768/10000 episodes, total num timesteps 1153800/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5769/10000 episodes, total num timesteps 1154000/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5770/10000 episodes, total num timesteps 1154200/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5771/10000 episodes, total num timesteps 1154400/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5772/10000 episodes, total num timesteps 1154600/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5773/10000 episodes, total num timesteps 1154800/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5774/10000 episodes, total num timesteps 1155000/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5775/10000 episodes, total num timesteps 1155200/2000000, FPS 231.

team_policy eval average step individual rewards of agent0: 0.893128642995716
team_policy eval average team episode rewards of agent0: 87.5
team_policy eval idv catch total num of agent0: 37
team_policy eval team catch total num: 35
team_policy eval average step individual rewards of agent1: 0.6159794355701848
team_policy eval average team episode rewards of agent1: 87.5
team_policy eval idv catch total num of agent1: 26
team_policy eval team catch total num: 35
team_policy eval average step individual rewards of agent2: 0.714885999015987
team_policy eval average team episode rewards of agent2: 87.5
team_policy eval idv catch total num of agent2: 30
team_policy eval team catch total num: 35
team_policy eval average step individual rewards of agent3: 0.9476908879636095
team_policy eval average team episode rewards of agent3: 87.5
team_policy eval idv catch total num of agent3: 39
team_policy eval team catch total num: 35
team_policy eval average step individual rewards of agent4: 0.4127480109733149
team_policy eval average team episode rewards of agent4: 87.5
team_policy eval idv catch total num of agent4: 18
team_policy eval team catch total num: 35
idv_policy eval average step individual rewards of agent0: 0.6083720006540109
idv_policy eval average team episode rewards of agent0: 82.5
idv_policy eval idv catch total num of agent0: 26
idv_policy eval team catch total num: 33
idv_policy eval average step individual rewards of agent1: 0.2536393434104429
idv_policy eval average team episode rewards of agent1: 82.5
idv_policy eval idv catch total num of agent1: 12
idv_policy eval team catch total num: 33
idv_policy eval average step individual rewards of agent2: 0.836323806880745
idv_policy eval average team episode rewards of agent2: 82.5
idv_policy eval idv catch total num of agent2: 35
idv_policy eval team catch total num: 33
idv_policy eval average step individual rewards of agent3: 0.5310986115026188
idv_policy eval average team episode rewards of agent3: 82.5
idv_policy eval idv catch total num of agent3: 23
idv_policy eval team catch total num: 33
idv_policy eval average step individual rewards of agent4: 1.030881530202085
idv_policy eval average team episode rewards of agent4: 82.5
idv_policy eval idv catch total num of agent4: 43
idv_policy eval team catch total num: 33

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5776/10000 episodes, total num timesteps 1155400/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5777/10000 episodes, total num timesteps 1155600/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5778/10000 episodes, total num timesteps 1155800/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5779/10000 episodes, total num timesteps 1156000/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5780/10000 episodes, total num timesteps 1156200/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5781/10000 episodes, total num timesteps 1156400/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5782/10000 episodes, total num timesteps 1156600/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5783/10000 episodes, total num timesteps 1156800/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5784/10000 episodes, total num timesteps 1157000/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5785/10000 episodes, total num timesteps 1157200/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5786/10000 episodes, total num timesteps 1157400/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5787/10000 episodes, total num timesteps 1157600/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5788/10000 episodes, total num timesteps 1157800/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5789/10000 episodes, total num timesteps 1158000/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5790/10000 episodes, total num timesteps 1158200/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5791/10000 episodes, total num timesteps 1158400/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5792/10000 episodes, total num timesteps 1158600/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5793/10000 episodes, total num timesteps 1158800/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5794/10000 episodes, total num timesteps 1159000/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5795/10000 episodes, total num timesteps 1159200/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5796/10000 episodes, total num timesteps 1159400/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5797/10000 episodes, total num timesteps 1159600/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5798/10000 episodes, total num timesteps 1159800/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5799/10000 episodes, total num timesteps 1160000/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5800/10000 episodes, total num timesteps 1160200/2000000, FPS 231.

team_policy eval average step individual rewards of agent0: 0.6402996940043424
team_policy eval average team episode rewards of agent0: 147.5
team_policy eval idv catch total num of agent0: 27
team_policy eval team catch total num: 59
team_policy eval average step individual rewards of agent1: 0.6330602666037423
team_policy eval average team episode rewards of agent1: 147.5
team_policy eval idv catch total num of agent1: 27
team_policy eval team catch total num: 59
team_policy eval average step individual rewards of agent2: 1.0500849816000828
team_policy eval average team episode rewards of agent2: 147.5
team_policy eval idv catch total num of agent2: 43
team_policy eval team catch total num: 59
team_policy eval average step individual rewards of agent3: 0.9661850038721487
team_policy eval average team episode rewards of agent3: 147.5
team_policy eval idv catch total num of agent3: 40
team_policy eval team catch total num: 59
team_policy eval average step individual rewards of agent4: 1.4966389675740674
team_policy eval average team episode rewards of agent4: 147.5
team_policy eval idv catch total num of agent4: 61
team_policy eval team catch total num: 59
idv_policy eval average step individual rewards of agent0: 0.47938271491547774
idv_policy eval average team episode rewards of agent0: 85.0
idv_policy eval idv catch total num of agent0: 21
idv_policy eval team catch total num: 34
idv_policy eval average step individual rewards of agent1: 0.8924076076191975
idv_policy eval average team episode rewards of agent1: 85.0
idv_policy eval idv catch total num of agent1: 37
idv_policy eval team catch total num: 34
idv_policy eval average step individual rewards of agent2: 0.514614260529853
idv_policy eval average team episode rewards of agent2: 85.0
idv_policy eval idv catch total num of agent2: 22
idv_policy eval team catch total num: 34
idv_policy eval average step individual rewards of agent3: 0.6594864491783686
idv_policy eval average team episode rewards of agent3: 85.0
idv_policy eval idv catch total num of agent3: 28
idv_policy eval team catch total num: 34
idv_policy eval average step individual rewards of agent4: 0.7141072762639533
idv_policy eval average team episode rewards of agent4: 85.0
idv_policy eval idv catch total num of agent4: 30
idv_policy eval team catch total num: 34

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5801/10000 episodes, total num timesteps 1160400/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5802/10000 episodes, total num timesteps 1160600/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5803/10000 episodes, total num timesteps 1160800/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5804/10000 episodes, total num timesteps 1161000/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5805/10000 episodes, total num timesteps 1161200/2000000, FPS 231.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5806/10000 episodes, total num timesteps 1161400/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5807/10000 episodes, total num timesteps 1161600/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5808/10000 episodes, total num timesteps 1161800/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5809/10000 episodes, total num timesteps 1162000/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5810/10000 episodes, total num timesteps 1162200/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5811/10000 episodes, total num timesteps 1162400/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5812/10000 episodes, total num timesteps 1162600/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5813/10000 episodes, total num timesteps 1162800/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5814/10000 episodes, total num timesteps 1163000/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5815/10000 episodes, total num timesteps 1163200/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5816/10000 episodes, total num timesteps 1163400/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5817/10000 episodes, total num timesteps 1163600/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5818/10000 episodes, total num timesteps 1163800/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5819/10000 episodes, total num timesteps 1164000/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5820/10000 episodes, total num timesteps 1164200/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5821/10000 episodes, total num timesteps 1164400/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5822/10000 episodes, total num timesteps 1164600/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5823/10000 episodes, total num timesteps 1164800/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5824/10000 episodes, total num timesteps 1165000/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5825/10000 episodes, total num timesteps 1165200/2000000, FPS 232.

team_policy eval average step individual rewards of agent0: 0.4931372567232741
team_policy eval average team episode rewards of agent0: 127.5
team_policy eval idv catch total num of agent0: 21
team_policy eval team catch total num: 51
team_policy eval average step individual rewards of agent1: 1.0681609741794897
team_policy eval average team episode rewards of agent1: 127.5
team_policy eval idv catch total num of agent1: 44
team_policy eval team catch total num: 51
team_policy eval average step individual rewards of agent2: 0.5602448302727285
team_policy eval average team episode rewards of agent2: 127.5
team_policy eval idv catch total num of agent2: 24
team_policy eval team catch total num: 51
team_policy eval average step individual rewards of agent3: 1.3013776368171697
team_policy eval average team episode rewards of agent3: 127.5
team_policy eval idv catch total num of agent3: 53
team_policy eval team catch total num: 51
team_policy eval average step individual rewards of agent4: 0.764442217507549
team_policy eval average team episode rewards of agent4: 127.5
team_policy eval idv catch total num of agent4: 32
team_policy eval team catch total num: 51
idv_policy eval average step individual rewards of agent0: 0.9922372501566308
idv_policy eval average team episode rewards of agent0: 147.5
idv_policy eval idv catch total num of agent0: 41
idv_policy eval team catch total num: 59
idv_policy eval average step individual rewards of agent1: 1.1962407274505609
idv_policy eval average team episode rewards of agent1: 147.5
idv_policy eval idv catch total num of agent1: 49
idv_policy eval team catch total num: 59
idv_policy eval average step individual rewards of agent2: 0.8879028685852245
idv_policy eval average team episode rewards of agent2: 147.5
idv_policy eval idv catch total num of agent2: 37
idv_policy eval team catch total num: 59
idv_policy eval average step individual rewards of agent3: 0.9883861860621783
idv_policy eval average team episode rewards of agent3: 147.5
idv_policy eval idv catch total num of agent3: 41
idv_policy eval team catch total num: 59
idv_policy eval average step individual rewards of agent4: 1.3245284495696896
idv_policy eval average team episode rewards of agent4: 147.5
idv_policy eval idv catch total num of agent4: 54
idv_policy eval team catch total num: 59

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5826/10000 episodes, total num timesteps 1165400/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5827/10000 episodes, total num timesteps 1165600/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5828/10000 episodes, total num timesteps 1165800/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5829/10000 episodes, total num timesteps 1166000/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5830/10000 episodes, total num timesteps 1166200/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5831/10000 episodes, total num timesteps 1166400/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5832/10000 episodes, total num timesteps 1166600/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5833/10000 episodes, total num timesteps 1166800/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5834/10000 episodes, total num timesteps 1167000/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5835/10000 episodes, total num timesteps 1167200/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5836/10000 episodes, total num timesteps 1167400/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5837/10000 episodes, total num timesteps 1167600/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5838/10000 episodes, total num timesteps 1167800/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5839/10000 episodes, total num timesteps 1168000/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5840/10000 episodes, total num timesteps 1168200/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5841/10000 episodes, total num timesteps 1168400/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5842/10000 episodes, total num timesteps 1168600/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5843/10000 episodes, total num timesteps 1168800/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5844/10000 episodes, total num timesteps 1169000/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5845/10000 episodes, total num timesteps 1169200/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5846/10000 episodes, total num timesteps 1169400/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5847/10000 episodes, total num timesteps 1169600/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5848/10000 episodes, total num timesteps 1169800/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5849/10000 episodes, total num timesteps 1170000/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5850/10000 episodes, total num timesteps 1170200/2000000, FPS 232.

team_policy eval average step individual rewards of agent0: 0.39194053529049094
team_policy eval average team episode rewards of agent0: 77.5
team_policy eval idv catch total num of agent0: 18
team_policy eval team catch total num: 31
team_policy eval average step individual rewards of agent1: 0.857312131688029
team_policy eval average team episode rewards of agent1: 77.5
team_policy eval idv catch total num of agent1: 36
team_policy eval team catch total num: 31
team_policy eval average step individual rewards of agent2: 0.6325309917871277
team_policy eval average team episode rewards of agent2: 77.5
team_policy eval idv catch total num of agent2: 27
team_policy eval team catch total num: 31
team_policy eval average step individual rewards of agent3: 0.9126226519763856
team_policy eval average team episode rewards of agent3: 77.5
team_policy eval idv catch total num of agent3: 38
team_policy eval team catch total num: 31
team_policy eval average step individual rewards of agent4: 0.502888885411592
team_policy eval average team episode rewards of agent4: 77.5
team_policy eval idv catch total num of agent4: 22
team_policy eval team catch total num: 31
idv_policy eval average step individual rewards of agent0: 0.5586852828062417
idv_policy eval average team episode rewards of agent0: 120.0
idv_policy eval idv catch total num of agent0: 24
idv_policy eval team catch total num: 48
idv_policy eval average step individual rewards of agent1: 1.094262375455912
idv_policy eval average team episode rewards of agent1: 120.0
idv_policy eval idv catch total num of agent1: 45
idv_policy eval team catch total num: 48
idv_policy eval average step individual rewards of agent2: 0.762974627207556
idv_policy eval average team episode rewards of agent2: 120.0
idv_policy eval idv catch total num of agent2: 32
idv_policy eval team catch total num: 48
idv_policy eval average step individual rewards of agent3: 1.242231917949764
idv_policy eval average team episode rewards of agent3: 120.0
idv_policy eval idv catch total num of agent3: 51
idv_policy eval team catch total num: 48
idv_policy eval average step individual rewards of agent4: 0.7315669723679484
idv_policy eval average team episode rewards of agent4: 120.0
idv_policy eval idv catch total num of agent4: 31
idv_policy eval team catch total num: 48

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5851/10000 episodes, total num timesteps 1170400/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5852/10000 episodes, total num timesteps 1170600/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5853/10000 episodes, total num timesteps 1170800/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5854/10000 episodes, total num timesteps 1171000/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5855/10000 episodes, total num timesteps 1171200/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5856/10000 episodes, total num timesteps 1171400/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5857/10000 episodes, total num timesteps 1171600/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5858/10000 episodes, total num timesteps 1171800/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5859/10000 episodes, total num timesteps 1172000/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5860/10000 episodes, total num timesteps 1172200/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5861/10000 episodes, total num timesteps 1172400/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5862/10000 episodes, total num timesteps 1172600/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5863/10000 episodes, total num timesteps 1172800/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5864/10000 episodes, total num timesteps 1173000/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5865/10000 episodes, total num timesteps 1173200/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5866/10000 episodes, total num timesteps 1173400/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5867/10000 episodes, total num timesteps 1173600/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5868/10000 episodes, total num timesteps 1173800/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5869/10000 episodes, total num timesteps 1174000/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5870/10000 episodes, total num timesteps 1174200/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5871/10000 episodes, total num timesteps 1174400/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5872/10000 episodes, total num timesteps 1174600/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5873/10000 episodes, total num timesteps 1174800/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5874/10000 episodes, total num timesteps 1175000/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5875/10000 episodes, total num timesteps 1175200/2000000, FPS 232.

team_policy eval average step individual rewards of agent0: 0.8282606808197085
team_policy eval average team episode rewards of agent0: 102.5
team_policy eval idv catch total num of agent0: 35
team_policy eval team catch total num: 41
team_policy eval average step individual rewards of agent1: 0.5822002266842783
team_policy eval average team episode rewards of agent1: 102.5
team_policy eval idv catch total num of agent1: 25
team_policy eval team catch total num: 41
team_policy eval average step individual rewards of agent2: 0.7171630912456621
team_policy eval average team episode rewards of agent2: 102.5
team_policy eval idv catch total num of agent2: 30
team_policy eval team catch total num: 41
team_policy eval average step individual rewards of agent3: 0.7374646948119313
team_policy eval average team episode rewards of agent3: 102.5
team_policy eval idv catch total num of agent3: 31
team_policy eval team catch total num: 41
team_policy eval average step individual rewards of agent4: 0.9605784148535962
team_policy eval average team episode rewards of agent4: 102.5
team_policy eval idv catch total num of agent4: 40
team_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent0: 0.302018138688764
idv_policy eval average team episode rewards of agent0: 112.5
idv_policy eval idv catch total num of agent0: 14
idv_policy eval team catch total num: 45
idv_policy eval average step individual rewards of agent1: 0.81689496776515
idv_policy eval average team episode rewards of agent1: 112.5
idv_policy eval idv catch total num of agent1: 34
idv_policy eval team catch total num: 45
idv_policy eval average step individual rewards of agent2: 1.1867687538403466
idv_policy eval average team episode rewards of agent2: 112.5
idv_policy eval idv catch total num of agent2: 49
idv_policy eval team catch total num: 45
idv_policy eval average step individual rewards of agent3: 0.7871753305865705
idv_policy eval average team episode rewards of agent3: 112.5
idv_policy eval idv catch total num of agent3: 33
idv_policy eval team catch total num: 45
idv_policy eval average step individual rewards of agent4: 0.6163479016845224
idv_policy eval average team episode rewards of agent4: 112.5
idv_policy eval idv catch total num of agent4: 27
idv_policy eval team catch total num: 45

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5876/10000 episodes, total num timesteps 1175400/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5877/10000 episodes, total num timesteps 1175600/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5878/10000 episodes, total num timesteps 1175800/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5879/10000 episodes, total num timesteps 1176000/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5880/10000 episodes, total num timesteps 1176200/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5881/10000 episodes, total num timesteps 1176400/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5882/10000 episodes, total num timesteps 1176600/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5883/10000 episodes, total num timesteps 1176800/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5884/10000 episodes, total num timesteps 1177000/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5885/10000 episodes, total num timesteps 1177200/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5886/10000 episodes, total num timesteps 1177400/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5887/10000 episodes, total num timesteps 1177600/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5888/10000 episodes, total num timesteps 1177800/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5889/10000 episodes, total num timesteps 1178000/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5890/10000 episodes, total num timesteps 1178200/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5891/10000 episodes, total num timesteps 1178400/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5892/10000 episodes, total num timesteps 1178600/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5893/10000 episodes, total num timesteps 1178800/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5894/10000 episodes, total num timesteps 1179000/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5895/10000 episodes, total num timesteps 1179200/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5896/10000 episodes, total num timesteps 1179400/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5897/10000 episodes, total num timesteps 1179600/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5898/10000 episodes, total num timesteps 1179800/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5899/10000 episodes, total num timesteps 1180000/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5900/10000 episodes, total num timesteps 1180200/2000000, FPS 232.

team_policy eval average step individual rewards of agent0: 0.32943755472952574
team_policy eval average team episode rewards of agent0: 52.5
team_policy eval idv catch total num of agent0: 15
team_policy eval team catch total num: 21
team_policy eval average step individual rewards of agent1: 0.8891199736126058
team_policy eval average team episode rewards of agent1: 52.5
team_policy eval idv catch total num of agent1: 37
team_policy eval team catch total num: 21
team_policy eval average step individual rewards of agent2: 0.17351519204334143
team_policy eval average team episode rewards of agent2: 52.5
team_policy eval idv catch total num of agent2: 9
team_policy eval team catch total num: 21
team_policy eval average step individual rewards of agent3: 0.5582716735843393
team_policy eval average team episode rewards of agent3: 52.5
team_policy eval idv catch total num of agent3: 24
team_policy eval team catch total num: 21
team_policy eval average step individual rewards of agent4: 0.5833635219150748
team_policy eval average team episode rewards of agent4: 52.5
team_policy eval idv catch total num of agent4: 25
team_policy eval team catch total num: 21
idv_policy eval average step individual rewards of agent0: 1.2683346251165737
idv_policy eval average team episode rewards of agent0: 137.5
idv_policy eval idv catch total num of agent0: 52
idv_policy eval team catch total num: 55
idv_policy eval average step individual rewards of agent1: 1.0896964775390852
idv_policy eval average team episode rewards of agent1: 137.5
idv_policy eval idv catch total num of agent1: 45
idv_policy eval team catch total num: 55
idv_policy eval average step individual rewards of agent2: 0.8939762837327774
idv_policy eval average team episode rewards of agent2: 137.5
idv_policy eval idv catch total num of agent2: 37
idv_policy eval team catch total num: 55
idv_policy eval average step individual rewards of agent3: 1.0219930735159433
idv_policy eval average team episode rewards of agent3: 137.5
idv_policy eval idv catch total num of agent3: 42
idv_policy eval team catch total num: 55
idv_policy eval average step individual rewards of agent4: 0.9409121398956667
idv_policy eval average team episode rewards of agent4: 137.5
idv_policy eval idv catch total num of agent4: 39
idv_policy eval team catch total num: 55

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5901/10000 episodes, total num timesteps 1180400/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5902/10000 episodes, total num timesteps 1180600/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5903/10000 episodes, total num timesteps 1180800/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5904/10000 episodes, total num timesteps 1181000/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5905/10000 episodes, total num timesteps 1181200/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5906/10000 episodes, total num timesteps 1181400/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5907/10000 episodes, total num timesteps 1181600/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5908/10000 episodes, total num timesteps 1181800/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5909/10000 episodes, total num timesteps 1182000/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5910/10000 episodes, total num timesteps 1182200/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5911/10000 episodes, total num timesteps 1182400/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5912/10000 episodes, total num timesteps 1182600/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5913/10000 episodes, total num timesteps 1182800/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5914/10000 episodes, total num timesteps 1183000/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5915/10000 episodes, total num timesteps 1183200/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5916/10000 episodes, total num timesteps 1183400/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5917/10000 episodes, total num timesteps 1183600/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5918/10000 episodes, total num timesteps 1183800/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5919/10000 episodes, total num timesteps 1184000/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5920/10000 episodes, total num timesteps 1184200/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5921/10000 episodes, total num timesteps 1184400/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5922/10000 episodes, total num timesteps 1184600/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5923/10000 episodes, total num timesteps 1184800/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5924/10000 episodes, total num timesteps 1185000/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5925/10000 episodes, total num timesteps 1185200/2000000, FPS 232.

team_policy eval average step individual rewards of agent0: 0.4563944424242162
team_policy eval average team episode rewards of agent0: 122.5
team_policy eval idv catch total num of agent0: 20
team_policy eval team catch total num: 49
team_policy eval average step individual rewards of agent1: 0.8412714948823306
team_policy eval average team episode rewards of agent1: 122.5
team_policy eval idv catch total num of agent1: 35
team_policy eval team catch total num: 49
team_policy eval average step individual rewards of agent2: 1.1180262867481
team_policy eval average team episode rewards of agent2: 122.5
team_policy eval idv catch total num of agent2: 46
team_policy eval team catch total num: 49
team_policy eval average step individual rewards of agent3: 1.0145111960678863
team_policy eval average team episode rewards of agent3: 122.5
team_policy eval idv catch total num of agent3: 42
team_policy eval team catch total num: 49
team_policy eval average step individual rewards of agent4: 0.9864831401710552
team_policy eval average team episode rewards of agent4: 122.5
team_policy eval idv catch total num of agent4: 41
team_policy eval team catch total num: 49
idv_policy eval average step individual rewards of agent0: 0.7376250672667541
idv_policy eval average team episode rewards of agent0: 97.5
idv_policy eval idv catch total num of agent0: 31
idv_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent1: 0.38008746237184865
idv_policy eval average team episode rewards of agent1: 97.5
idv_policy eval idv catch total num of agent1: 17
idv_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent2: 1.1142682552441854
idv_policy eval average team episode rewards of agent2: 97.5
idv_policy eval idv catch total num of agent2: 46
idv_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent3: 0.8553760044120609
idv_policy eval average team episode rewards of agent3: 97.5
idv_policy eval idv catch total num of agent3: 36
idv_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent4: 0.35073939777836854
idv_policy eval average team episode rewards of agent4: 97.5
idv_policy eval idv catch total num of agent4: 16
idv_policy eval team catch total num: 39

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5926/10000 episodes, total num timesteps 1185400/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5927/10000 episodes, total num timesteps 1185600/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5928/10000 episodes, total num timesteps 1185800/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5929/10000 episodes, total num timesteps 1186000/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5930/10000 episodes, total num timesteps 1186200/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5931/10000 episodes, total num timesteps 1186400/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5932/10000 episodes, total num timesteps 1186600/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5933/10000 episodes, total num timesteps 1186800/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5934/10000 episodes, total num timesteps 1187000/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5935/10000 episodes, total num timesteps 1187200/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5936/10000 episodes, total num timesteps 1187400/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5937/10000 episodes, total num timesteps 1187600/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5938/10000 episodes, total num timesteps 1187800/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5939/10000 episodes, total num timesteps 1188000/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5940/10000 episodes, total num timesteps 1188200/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5941/10000 episodes, total num timesteps 1188400/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5942/10000 episodes, total num timesteps 1188600/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5943/10000 episodes, total num timesteps 1188800/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5944/10000 episodes, total num timesteps 1189000/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5945/10000 episodes, total num timesteps 1189200/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5946/10000 episodes, total num timesteps 1189400/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5947/10000 episodes, total num timesteps 1189600/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5948/10000 episodes, total num timesteps 1189800/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5949/10000 episodes, total num timesteps 1190000/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5950/10000 episodes, total num timesteps 1190200/2000000, FPS 232.

team_policy eval average step individual rewards of agent0: 0.3539374992714487
team_policy eval average team episode rewards of agent0: 112.5
team_policy eval idv catch total num of agent0: 16
team_policy eval team catch total num: 45
team_policy eval average step individual rewards of agent1: 0.8166609558620129
team_policy eval average team episode rewards of agent1: 112.5
team_policy eval idv catch total num of agent1: 34
team_policy eval team catch total num: 45
team_policy eval average step individual rewards of agent2: 0.8610959362170402
team_policy eval average team episode rewards of agent2: 112.5
team_policy eval idv catch total num of agent2: 36
team_policy eval team catch total num: 45
team_policy eval average step individual rewards of agent3: 0.8613354383398668
team_policy eval average team episode rewards of agent3: 112.5
team_policy eval idv catch total num of agent3: 36
team_policy eval team catch total num: 45
team_policy eval average step individual rewards of agent4: 0.6608900323590524
team_policy eval average team episode rewards of agent4: 112.5
team_policy eval idv catch total num of agent4: 28
team_policy eval team catch total num: 45
idv_policy eval average step individual rewards of agent0: 0.7316929009085866
idv_policy eval average team episode rewards of agent0: 107.5
idv_policy eval idv catch total num of agent0: 31
idv_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent1: 0.6819513289042927
idv_policy eval average team episode rewards of agent1: 107.5
idv_policy eval idv catch total num of agent1: 29
idv_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent2: 0.7792009427840441
idv_policy eval average team episode rewards of agent2: 107.5
idv_policy eval idv catch total num of agent2: 33
idv_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent3: 0.7538997565749816
idv_policy eval average team episode rewards of agent3: 107.5
idv_policy eval idv catch total num of agent3: 32
idv_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent4: 0.577065048586077
idv_policy eval average team episode rewards of agent4: 107.5
idv_policy eval idv catch total num of agent4: 25
idv_policy eval team catch total num: 43

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5951/10000 episodes, total num timesteps 1190400/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5952/10000 episodes, total num timesteps 1190600/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5953/10000 episodes, total num timesteps 1190800/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5954/10000 episodes, total num timesteps 1191000/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5955/10000 episodes, total num timesteps 1191200/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5956/10000 episodes, total num timesteps 1191400/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5957/10000 episodes, total num timesteps 1191600/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5958/10000 episodes, total num timesteps 1191800/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5959/10000 episodes, total num timesteps 1192000/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5960/10000 episodes, total num timesteps 1192200/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5961/10000 episodes, total num timesteps 1192400/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5962/10000 episodes, total num timesteps 1192600/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5963/10000 episodes, total num timesteps 1192800/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5964/10000 episodes, total num timesteps 1193000/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5965/10000 episodes, total num timesteps 1193200/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5966/10000 episodes, total num timesteps 1193400/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5967/10000 episodes, total num timesteps 1193600/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5968/10000 episodes, total num timesteps 1193800/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5969/10000 episodes, total num timesteps 1194000/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5970/10000 episodes, total num timesteps 1194200/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5971/10000 episodes, total num timesteps 1194400/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5972/10000 episodes, total num timesteps 1194600/2000000, FPS 232.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5973/10000 episodes, total num timesteps 1194800/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5974/10000 episodes, total num timesteps 1195000/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5975/10000 episodes, total num timesteps 1195200/2000000, FPS 233.

team_policy eval average step individual rewards of agent0: 0.825306012304194
team_policy eval average team episode rewards of agent0: 75.0
team_policy eval idv catch total num of agent0: 35
team_policy eval team catch total num: 30
team_policy eval average step individual rewards of agent1: 0.6816450507696225
team_policy eval average team episode rewards of agent1: 75.0
team_policy eval idv catch total num of agent1: 29
team_policy eval team catch total num: 30
team_policy eval average step individual rewards of agent2: 0.6026790257974508
team_policy eval average team episode rewards of agent2: 75.0
team_policy eval idv catch total num of agent2: 26
team_policy eval team catch total num: 30
team_policy eval average step individual rewards of agent3: 0.8068044525369404
team_policy eval average team episode rewards of agent3: 75.0
team_policy eval idv catch total num of agent3: 34
team_policy eval team catch total num: 30
team_policy eval average step individual rewards of agent4: 0.7068197107864753
team_policy eval average team episode rewards of agent4: 75.0
team_policy eval idv catch total num of agent4: 30
team_policy eval team catch total num: 30
idv_policy eval average step individual rewards of agent0: 1.005230293827195
idv_policy eval average team episode rewards of agent0: 115.0
idv_policy eval idv catch total num of agent0: 42
idv_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent1: 0.5968758138596021
idv_policy eval average team episode rewards of agent1: 115.0
idv_policy eval idv catch total num of agent1: 26
idv_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent2: 1.1665865490862042
idv_policy eval average team episode rewards of agent2: 115.0
idv_policy eval idv catch total num of agent2: 49
idv_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent3: 0.5552948525191317
idv_policy eval average team episode rewards of agent3: 115.0
idv_policy eval idv catch total num of agent3: 24
idv_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent4: 0.790415850020798
idv_policy eval average team episode rewards of agent4: 115.0
idv_policy eval idv catch total num of agent4: 33
idv_policy eval team catch total num: 46

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5976/10000 episodes, total num timesteps 1195400/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5977/10000 episodes, total num timesteps 1195600/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5978/10000 episodes, total num timesteps 1195800/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5979/10000 episodes, total num timesteps 1196000/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5980/10000 episodes, total num timesteps 1196200/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5981/10000 episodes, total num timesteps 1196400/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5982/10000 episodes, total num timesteps 1196600/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5983/10000 episodes, total num timesteps 1196800/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5984/10000 episodes, total num timesteps 1197000/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5985/10000 episodes, total num timesteps 1197200/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5986/10000 episodes, total num timesteps 1197400/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5987/10000 episodes, total num timesteps 1197600/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5988/10000 episodes, total num timesteps 1197800/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5989/10000 episodes, total num timesteps 1198000/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5990/10000 episodes, total num timesteps 1198200/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5991/10000 episodes, total num timesteps 1198400/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5992/10000 episodes, total num timesteps 1198600/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5993/10000 episodes, total num timesteps 1198800/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5994/10000 episodes, total num timesteps 1199000/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5995/10000 episodes, total num timesteps 1199200/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5996/10000 episodes, total num timesteps 1199400/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5997/10000 episodes, total num timesteps 1199600/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5998/10000 episodes, total num timesteps 1199800/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5999/10000 episodes, total num timesteps 1200000/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6000/10000 episodes, total num timesteps 1200200/2000000, FPS 233.

team_policy eval average step individual rewards of agent0: 0.42674949208277524
team_policy eval average team episode rewards of agent0: 55.0
team_policy eval idv catch total num of agent0: 19
team_policy eval team catch total num: 22
team_policy eval average step individual rewards of agent1: 0.6066153582354659
team_policy eval average team episode rewards of agent1: 55.0
team_policy eval idv catch total num of agent1: 26
team_policy eval team catch total num: 22
team_policy eval average step individual rewards of agent2: 0.27840518334022435
team_policy eval average team episode rewards of agent2: 55.0
team_policy eval idv catch total num of agent2: 13
team_policy eval team catch total num: 22
team_policy eval average step individual rewards of agent3: 0.8708594116412347
team_policy eval average team episode rewards of agent3: 55.0
team_policy eval idv catch total num of agent3: 36
team_policy eval team catch total num: 22
team_policy eval average step individual rewards of agent4: 0.5060684876711571
team_policy eval average team episode rewards of agent4: 55.0
team_policy eval idv catch total num of agent4: 22
team_policy eval team catch total num: 22
idv_policy eval average step individual rewards of agent0: 1.0201471897372125
idv_policy eval average team episode rewards of agent0: 150.0
idv_policy eval idv catch total num of agent0: 42
idv_policy eval team catch total num: 60
idv_policy eval average step individual rewards of agent1: 0.7684030262209537
idv_policy eval average team episode rewards of agent1: 150.0
idv_policy eval idv catch total num of agent1: 32
idv_policy eval team catch total num: 60
idv_policy eval average step individual rewards of agent2: 1.1677835100497034
idv_policy eval average team episode rewards of agent2: 150.0
idv_policy eval idv catch total num of agent2: 48
idv_policy eval team catch total num: 60
idv_policy eval average step individual rewards of agent3: 0.9928179887280489
idv_policy eval average team episode rewards of agent3: 150.0
idv_policy eval idv catch total num of agent3: 41
idv_policy eval team catch total num: 60
idv_policy eval average step individual rewards of agent4: 0.9194564036460809
idv_policy eval average team episode rewards of agent4: 150.0
idv_policy eval idv catch total num of agent4: 38
idv_policy eval team catch total num: 60

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6001/10000 episodes, total num timesteps 1200400/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6002/10000 episodes, total num timesteps 1200600/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6003/10000 episodes, total num timesteps 1200800/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6004/10000 episodes, total num timesteps 1201000/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6005/10000 episodes, total num timesteps 1201200/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6006/10000 episodes, total num timesteps 1201400/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6007/10000 episodes, total num timesteps 1201600/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6008/10000 episodes, total num timesteps 1201800/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6009/10000 episodes, total num timesteps 1202000/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6010/10000 episodes, total num timesteps 1202200/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6011/10000 episodes, total num timesteps 1202400/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6012/10000 episodes, total num timesteps 1202600/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6013/10000 episodes, total num timesteps 1202800/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6014/10000 episodes, total num timesteps 1203000/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6015/10000 episodes, total num timesteps 1203200/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6016/10000 episodes, total num timesteps 1203400/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6017/10000 episodes, total num timesteps 1203600/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6018/10000 episodes, total num timesteps 1203800/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6019/10000 episodes, total num timesteps 1204000/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6020/10000 episodes, total num timesteps 1204200/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6021/10000 episodes, total num timesteps 1204400/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6022/10000 episodes, total num timesteps 1204600/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6023/10000 episodes, total num timesteps 1204800/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6024/10000 episodes, total num timesteps 1205000/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6025/10000 episodes, total num timesteps 1205200/2000000, FPS 233.

team_policy eval average step individual rewards of agent0: 0.5096077891446563
team_policy eval average team episode rewards of agent0: 110.0
team_policy eval idv catch total num of agent0: 22
team_policy eval team catch total num: 44
team_policy eval average step individual rewards of agent1: 0.9123317893374774
team_policy eval average team episode rewards of agent1: 110.0
team_policy eval idv catch total num of agent1: 38
team_policy eval team catch total num: 44
team_policy eval average step individual rewards of agent2: 0.8888231212814801
team_policy eval average team episode rewards of agent2: 110.0
team_policy eval idv catch total num of agent2: 37
team_policy eval team catch total num: 44
team_policy eval average step individual rewards of agent3: 0.5847533269382313
team_policy eval average team episode rewards of agent3: 110.0
team_policy eval idv catch total num of agent3: 25
team_policy eval team catch total num: 44
team_policy eval average step individual rewards of agent4: 1.0897555152875964
team_policy eval average team episode rewards of agent4: 110.0
team_policy eval idv catch total num of agent4: 45
team_policy eval team catch total num: 44
idv_policy eval average step individual rewards of agent0: 0.9643883432618727
idv_policy eval average team episode rewards of agent0: 172.5
idv_policy eval idv catch total num of agent0: 40
idv_policy eval team catch total num: 69
idv_policy eval average step individual rewards of agent1: 1.2438544804785008
idv_policy eval average team episode rewards of agent1: 172.5
idv_policy eval idv catch total num of agent1: 51
idv_policy eval team catch total num: 69
idv_policy eval average step individual rewards of agent2: 1.388685957369611
idv_policy eval average team episode rewards of agent2: 172.5
idv_policy eval idv catch total num of agent2: 57
idv_policy eval team catch total num: 69
idv_policy eval average step individual rewards of agent3: 1.3171752481857228
idv_policy eval average team episode rewards of agent3: 172.5
idv_policy eval idv catch total num of agent3: 54
idv_policy eval team catch total num: 69
idv_policy eval average step individual rewards of agent4: 0.7071255807877138
idv_policy eval average team episode rewards of agent4: 172.5
idv_policy eval idv catch total num of agent4: 30
idv_policy eval team catch total num: 69

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6026/10000 episodes, total num timesteps 1205400/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6027/10000 episodes, total num timesteps 1205600/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6028/10000 episodes, total num timesteps 1205800/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6029/10000 episodes, total num timesteps 1206000/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6030/10000 episodes, total num timesteps 1206200/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6031/10000 episodes, total num timesteps 1206400/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6032/10000 episodes, total num timesteps 1206600/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6033/10000 episodes, total num timesteps 1206800/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6034/10000 episodes, total num timesteps 1207000/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6035/10000 episodes, total num timesteps 1207200/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6036/10000 episodes, total num timesteps 1207400/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6037/10000 episodes, total num timesteps 1207600/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6038/10000 episodes, total num timesteps 1207800/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6039/10000 episodes, total num timesteps 1208000/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6040/10000 episodes, total num timesteps 1208200/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6041/10000 episodes, total num timesteps 1208400/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6042/10000 episodes, total num timesteps 1208600/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6043/10000 episodes, total num timesteps 1208800/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6044/10000 episodes, total num timesteps 1209000/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6045/10000 episodes, total num timesteps 1209200/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6046/10000 episodes, total num timesteps 1209400/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6047/10000 episodes, total num timesteps 1209600/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6048/10000 episodes, total num timesteps 1209800/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6049/10000 episodes, total num timesteps 1210000/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6050/10000 episodes, total num timesteps 1210200/2000000, FPS 233.

team_policy eval average step individual rewards of agent0: 1.116497301411965
team_policy eval average team episode rewards of agent0: 122.5
team_policy eval idv catch total num of agent0: 46
team_policy eval team catch total num: 49
team_policy eval average step individual rewards of agent1: 0.7147032894103027
team_policy eval average team episode rewards of agent1: 122.5
team_policy eval idv catch total num of agent1: 30
team_policy eval team catch total num: 49
team_policy eval average step individual rewards of agent2: 0.9127789536644352
team_policy eval average team episode rewards of agent2: 122.5
team_policy eval idv catch total num of agent2: 38
team_policy eval team catch total num: 49
team_policy eval average step individual rewards of agent3: 0.8092098359364388
team_policy eval average team episode rewards of agent3: 122.5
team_policy eval idv catch total num of agent3: 34
team_policy eval team catch total num: 49
team_policy eval average step individual rewards of agent4: 0.7329129068070577
team_policy eval average team episode rewards of agent4: 122.5
team_policy eval idv catch total num of agent4: 31
team_policy eval team catch total num: 49
idv_policy eval average step individual rewards of agent0: 1.120899019511194
idv_policy eval average team episode rewards of agent0: 122.5
idv_policy eval idv catch total num of agent0: 46
idv_policy eval team catch total num: 49
idv_policy eval average step individual rewards of agent1: 0.8109103073580344
idv_policy eval average team episode rewards of agent1: 122.5
idv_policy eval idv catch total num of agent1: 34
idv_policy eval team catch total num: 49
idv_policy eval average step individual rewards of agent2: 0.8101355483079437
idv_policy eval average team episode rewards of agent2: 122.5
idv_policy eval idv catch total num of agent2: 34
idv_policy eval team catch total num: 49
idv_policy eval average step individual rewards of agent3: 0.8878350738974637
idv_policy eval average team episode rewards of agent3: 122.5
idv_policy eval idv catch total num of agent3: 37
idv_policy eval team catch total num: 49
idv_policy eval average step individual rewards of agent4: 0.6373331047184617
idv_policy eval average team episode rewards of agent4: 122.5
idv_policy eval idv catch total num of agent4: 27
idv_policy eval team catch total num: 49

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6051/10000 episodes, total num timesteps 1210400/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6052/10000 episodes, total num timesteps 1210600/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6053/10000 episodes, total num timesteps 1210800/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6054/10000 episodes, total num timesteps 1211000/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6055/10000 episodes, total num timesteps 1211200/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6056/10000 episodes, total num timesteps 1211400/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6057/10000 episodes, total num timesteps 1211600/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6058/10000 episodes, total num timesteps 1211800/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6059/10000 episodes, total num timesteps 1212000/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6060/10000 episodes, total num timesteps 1212200/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6061/10000 episodes, total num timesteps 1212400/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6062/10000 episodes, total num timesteps 1212600/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6063/10000 episodes, total num timesteps 1212800/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6064/10000 episodes, total num timesteps 1213000/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6065/10000 episodes, total num timesteps 1213200/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6066/10000 episodes, total num timesteps 1213400/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6067/10000 episodes, total num timesteps 1213600/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6068/10000 episodes, total num timesteps 1213800/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6069/10000 episodes, total num timesteps 1214000/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6070/10000 episodes, total num timesteps 1214200/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6071/10000 episodes, total num timesteps 1214400/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6072/10000 episodes, total num timesteps 1214600/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6073/10000 episodes, total num timesteps 1214800/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6074/10000 episodes, total num timesteps 1215000/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6075/10000 episodes, total num timesteps 1215200/2000000, FPS 233.

team_policy eval average step individual rewards of agent0: 1.01962432798701
team_policy eval average team episode rewards of agent0: 120.0
team_policy eval idv catch total num of agent0: 42
team_policy eval team catch total num: 48
team_policy eval average step individual rewards of agent1: 1.0901383978480643
team_policy eval average team episode rewards of agent1: 120.0
team_policy eval idv catch total num of agent1: 45
team_policy eval team catch total num: 48
team_policy eval average step individual rewards of agent2: 0.6716733090000778
team_policy eval average team episode rewards of agent2: 120.0
team_policy eval idv catch total num of agent2: 28
team_policy eval team catch total num: 48
team_policy eval average step individual rewards of agent3: 0.43722850042819744
team_policy eval average team episode rewards of agent3: 120.0
team_policy eval idv catch total num of agent3: 19
team_policy eval team catch total num: 48
team_policy eval average step individual rewards of agent4: 0.9238877227019233
team_policy eval average team episode rewards of agent4: 120.0
team_policy eval idv catch total num of agent4: 38
team_policy eval team catch total num: 48
idv_policy eval average step individual rewards of agent0: 0.6263317313075139
idv_policy eval average team episode rewards of agent0: 80.0
idv_policy eval idv catch total num of agent0: 27
idv_policy eval team catch total num: 32
idv_policy eval average step individual rewards of agent1: 0.5316118302562184
idv_policy eval average team episode rewards of agent1: 80.0
idv_policy eval idv catch total num of agent1: 23
idv_policy eval team catch total num: 32
idv_policy eval average step individual rewards of agent2: 0.684349202125557
idv_policy eval average team episode rewards of agent2: 80.0
idv_policy eval idv catch total num of agent2: 29
idv_policy eval team catch total num: 32
idv_policy eval average step individual rewards of agent3: 0.5340819497938771
idv_policy eval average team episode rewards of agent3: 80.0
idv_policy eval idv catch total num of agent3: 23
idv_policy eval team catch total num: 32
idv_policy eval average step individual rewards of agent4: 0.960251685451142
idv_policy eval average team episode rewards of agent4: 80.0
idv_policy eval idv catch total num of agent4: 40
idv_policy eval team catch total num: 32

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6076/10000 episodes, total num timesteps 1215400/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6077/10000 episodes, total num timesteps 1215600/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6078/10000 episodes, total num timesteps 1215800/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6079/10000 episodes, total num timesteps 1216000/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6080/10000 episodes, total num timesteps 1216200/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6081/10000 episodes, total num timesteps 1216400/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6082/10000 episodes, total num timesteps 1216600/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6083/10000 episodes, total num timesteps 1216800/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6084/10000 episodes, total num timesteps 1217000/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6085/10000 episodes, total num timesteps 1217200/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6086/10000 episodes, total num timesteps 1217400/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6087/10000 episodes, total num timesteps 1217600/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6088/10000 episodes, total num timesteps 1217800/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6089/10000 episodes, total num timesteps 1218000/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6090/10000 episodes, total num timesteps 1218200/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6091/10000 episodes, total num timesteps 1218400/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6092/10000 episodes, total num timesteps 1218600/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6093/10000 episodes, total num timesteps 1218800/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6094/10000 episodes, total num timesteps 1219000/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6095/10000 episodes, total num timesteps 1219200/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6096/10000 episodes, total num timesteps 1219400/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6097/10000 episodes, total num timesteps 1219600/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6098/10000 episodes, total num timesteps 1219800/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6099/10000 episodes, total num timesteps 1220000/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6100/10000 episodes, total num timesteps 1220200/2000000, FPS 233.

team_policy eval average step individual rewards of agent0: 0.6641703100654418
team_policy eval average team episode rewards of agent0: 95.0
team_policy eval idv catch total num of agent0: 28
team_policy eval team catch total num: 38
team_policy eval average step individual rewards of agent1: 0.7272733696633243
team_policy eval average team episode rewards of agent1: 95.0
team_policy eval idv catch total num of agent1: 31
team_policy eval team catch total num: 38
team_policy eval average step individual rewards of agent2: 0.5102947696631076
team_policy eval average team episode rewards of agent2: 95.0
team_policy eval idv catch total num of agent2: 22
team_policy eval team catch total num: 38
team_policy eval average step individual rewards of agent3: 0.9680364231770553
team_policy eval average team episode rewards of agent3: 95.0
team_policy eval idv catch total num of agent3: 40
team_policy eval team catch total num: 38
team_policy eval average step individual rewards of agent4: 0.9225308270571361
team_policy eval average team episode rewards of agent4: 95.0
team_policy eval idv catch total num of agent4: 38
team_policy eval team catch total num: 38
idv_policy eval average step individual rewards of agent0: 0.8370910946088145
idv_policy eval average team episode rewards of agent0: 115.0
idv_policy eval idv catch total num of agent0: 35
idv_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent1: 0.5330968237590564
idv_policy eval average team episode rewards of agent1: 115.0
idv_policy eval idv catch total num of agent1: 23
idv_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent2: 0.6573021885632104
idv_policy eval average team episode rewards of agent2: 115.0
idv_policy eval idv catch total num of agent2: 28
idv_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent3: 0.9336359018974477
idv_policy eval average team episode rewards of agent3: 115.0
idv_policy eval idv catch total num of agent3: 39
idv_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent4: 1.0657369460809119
idv_policy eval average team episode rewards of agent4: 115.0
idv_policy eval idv catch total num of agent4: 44
idv_policy eval team catch total num: 46

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6101/10000 episodes, total num timesteps 1220400/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6102/10000 episodes, total num timesteps 1220600/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6103/10000 episodes, total num timesteps 1220800/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6104/10000 episodes, total num timesteps 1221000/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6105/10000 episodes, total num timesteps 1221200/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6106/10000 episodes, total num timesteps 1221400/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6107/10000 episodes, total num timesteps 1221600/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6108/10000 episodes, total num timesteps 1221800/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6109/10000 episodes, total num timesteps 1222000/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6110/10000 episodes, total num timesteps 1222200/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6111/10000 episodes, total num timesteps 1222400/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6112/10000 episodes, total num timesteps 1222600/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6113/10000 episodes, total num timesteps 1222800/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6114/10000 episodes, total num timesteps 1223000/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6115/10000 episodes, total num timesteps 1223200/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6116/10000 episodes, total num timesteps 1223400/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6117/10000 episodes, total num timesteps 1223600/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6118/10000 episodes, total num timesteps 1223800/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6119/10000 episodes, total num timesteps 1224000/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6120/10000 episodes, total num timesteps 1224200/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6121/10000 episodes, total num timesteps 1224400/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6122/10000 episodes, total num timesteps 1224600/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6123/10000 episodes, total num timesteps 1224800/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6124/10000 episodes, total num timesteps 1225000/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6125/10000 episodes, total num timesteps 1225200/2000000, FPS 233.

team_policy eval average step individual rewards of agent0: 0.5804938695466442
team_policy eval average team episode rewards of agent0: 72.5
team_policy eval idv catch total num of agent0: 25
team_policy eval team catch total num: 29
team_policy eval average step individual rewards of agent1: 0.9120304053011671
team_policy eval average team episode rewards of agent1: 72.5
team_policy eval idv catch total num of agent1: 38
team_policy eval team catch total num: 29
team_policy eval average step individual rewards of agent2: 0.5756420416135718
team_policy eval average team episode rewards of agent2: 72.5
team_policy eval idv catch total num of agent2: 25
team_policy eval team catch total num: 29
team_policy eval average step individual rewards of agent3: 0.9319918459347208
team_policy eval average team episode rewards of agent3: 72.5
team_policy eval idv catch total num of agent3: 39
team_policy eval team catch total num: 29
team_policy eval average step individual rewards of agent4: 0.19702203963283577
team_policy eval average team episode rewards of agent4: 72.5
team_policy eval idv catch total num of agent4: 10
team_policy eval team catch total num: 29
idv_policy eval average step individual rewards of agent0: 0.42718471239081823
idv_policy eval average team episode rewards of agent0: 100.0
idv_policy eval idv catch total num of agent0: 19
idv_policy eval team catch total num: 40
idv_policy eval average step individual rewards of agent1: 0.45525137624348977
idv_policy eval average team episode rewards of agent1: 100.0
idv_policy eval idv catch total num of agent1: 20
idv_policy eval team catch total num: 40
idv_policy eval average step individual rewards of agent2: 0.8398725784300288
idv_policy eval average team episode rewards of agent2: 100.0
idv_policy eval idv catch total num of agent2: 35
idv_policy eval team catch total num: 40
idv_policy eval average step individual rewards of agent3: 0.8630201348665342
idv_policy eval average team episode rewards of agent3: 100.0
idv_policy eval idv catch total num of agent3: 36
idv_policy eval team catch total num: 40
idv_policy eval average step individual rewards of agent4: 1.146012707617794
idv_policy eval average team episode rewards of agent4: 100.0
idv_policy eval idv catch total num of agent4: 47
idv_policy eval team catch total num: 40

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6126/10000 episodes, total num timesteps 1225400/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6127/10000 episodes, total num timesteps 1225600/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6128/10000 episodes, total num timesteps 1225800/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6129/10000 episodes, total num timesteps 1226000/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6130/10000 episodes, total num timesteps 1226200/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6131/10000 episodes, total num timesteps 1226400/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6132/10000 episodes, total num timesteps 1226600/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6133/10000 episodes, total num timesteps 1226800/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6134/10000 episodes, total num timesteps 1227000/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6135/10000 episodes, total num timesteps 1227200/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6136/10000 episodes, total num timesteps 1227400/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6137/10000 episodes, total num timesteps 1227600/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6138/10000 episodes, total num timesteps 1227800/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6139/10000 episodes, total num timesteps 1228000/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6140/10000 episodes, total num timesteps 1228200/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6141/10000 episodes, total num timesteps 1228400/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6142/10000 episodes, total num timesteps 1228600/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6143/10000 episodes, total num timesteps 1228800/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6144/10000 episodes, total num timesteps 1229000/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6145/10000 episodes, total num timesteps 1229200/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6146/10000 episodes, total num timesteps 1229400/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6147/10000 episodes, total num timesteps 1229600/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6148/10000 episodes, total num timesteps 1229800/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6149/10000 episodes, total num timesteps 1230000/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6150/10000 episodes, total num timesteps 1230200/2000000, FPS 233.

team_policy eval average step individual rewards of agent0: 0.5192442657532713
team_policy eval average team episode rewards of agent0: 132.5
team_policy eval idv catch total num of agent0: 22
team_policy eval team catch total num: 53
team_policy eval average step individual rewards of agent1: 1.1224796028945452
team_policy eval average team episode rewards of agent1: 132.5
team_policy eval idv catch total num of agent1: 46
team_policy eval team catch total num: 53
team_policy eval average step individual rewards of agent2: 0.7700068870634914
team_policy eval average team episode rewards of agent2: 132.5
team_policy eval idv catch total num of agent2: 32
team_policy eval team catch total num: 53
team_policy eval average step individual rewards of agent3: 1.170002593515652
team_policy eval average team episode rewards of agent3: 132.5
team_policy eval idv catch total num of agent3: 48
team_policy eval team catch total num: 53
team_policy eval average step individual rewards of agent4: 0.7904068063024827
team_policy eval average team episode rewards of agent4: 132.5
team_policy eval idv catch total num of agent4: 33
team_policy eval team catch total num: 53
idv_policy eval average step individual rewards of agent0: 1.1409210991966234
idv_policy eval average team episode rewards of agent0: 127.5
idv_policy eval idv catch total num of agent0: 47
idv_policy eval team catch total num: 51
idv_policy eval average step individual rewards of agent1: 1.1878522452418252
idv_policy eval average team episode rewards of agent1: 127.5
idv_policy eval idv catch total num of agent1: 49
idv_policy eval team catch total num: 51
idv_policy eval average step individual rewards of agent2: 0.7887001062168602
idv_policy eval average team episode rewards of agent2: 127.5
idv_policy eval idv catch total num of agent2: 33
idv_policy eval team catch total num: 51
idv_policy eval average step individual rewards of agent3: 1.0954044602564408
idv_policy eval average team episode rewards of agent3: 127.5
idv_policy eval idv catch total num of agent3: 45
idv_policy eval team catch total num: 51
idv_policy eval average step individual rewards of agent4: 0.5851942084210296
idv_policy eval average team episode rewards of agent4: 127.5
idv_policy eval idv catch total num of agent4: 25
idv_policy eval team catch total num: 51

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6151/10000 episodes, total num timesteps 1230400/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6152/10000 episodes, total num timesteps 1230600/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6153/10000 episodes, total num timesteps 1230800/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6154/10000 episodes, total num timesteps 1231000/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6155/10000 episodes, total num timesteps 1231200/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6156/10000 episodes, total num timesteps 1231400/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6157/10000 episodes, total num timesteps 1231600/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6158/10000 episodes, total num timesteps 1231800/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6159/10000 episodes, total num timesteps 1232000/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6160/10000 episodes, total num timesteps 1232200/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6161/10000 episodes, total num timesteps 1232400/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6162/10000 episodes, total num timesteps 1232600/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6163/10000 episodes, total num timesteps 1232800/2000000, FPS 233.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6164/10000 episodes, total num timesteps 1233000/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6165/10000 episodes, total num timesteps 1233200/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6166/10000 episodes, total num timesteps 1233400/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6167/10000 episodes, total num timesteps 1233600/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6168/10000 episodes, total num timesteps 1233800/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6169/10000 episodes, total num timesteps 1234000/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6170/10000 episodes, total num timesteps 1234200/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6171/10000 episodes, total num timesteps 1234400/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6172/10000 episodes, total num timesteps 1234600/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6173/10000 episodes, total num timesteps 1234800/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6174/10000 episodes, total num timesteps 1235000/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6175/10000 episodes, total num timesteps 1235200/2000000, FPS 234.

team_policy eval average step individual rewards of agent0: 0.7825508109147967
team_policy eval average team episode rewards of agent0: 135.0
team_policy eval idv catch total num of agent0: 33
team_policy eval team catch total num: 54
team_policy eval average step individual rewards of agent1: 1.1204462746510766
team_policy eval average team episode rewards of agent1: 135.0
team_policy eval idv catch total num of agent1: 46
team_policy eval team catch total num: 54
team_policy eval average step individual rewards of agent2: 0.8134401272374712
team_policy eval average team episode rewards of agent2: 135.0
team_policy eval idv catch total num of agent2: 34
team_policy eval team catch total num: 54
team_policy eval average step individual rewards of agent3: 0.9658727387338208
team_policy eval average team episode rewards of agent3: 135.0
team_policy eval idv catch total num of agent3: 40
team_policy eval team catch total num: 54
team_policy eval average step individual rewards of agent4: 0.9934213371492112
team_policy eval average team episode rewards of agent4: 135.0
team_policy eval idv catch total num of agent4: 41
team_policy eval team catch total num: 54
idv_policy eval average step individual rewards of agent0: 1.0253979604142978
idv_policy eval average team episode rewards of agent0: 150.0
idv_policy eval idv catch total num of agent0: 42
idv_policy eval team catch total num: 60
idv_policy eval average step individual rewards of agent1: 1.17199943389287
idv_policy eval average team episode rewards of agent1: 150.0
idv_policy eval idv catch total num of agent1: 48
idv_policy eval team catch total num: 60
idv_policy eval average step individual rewards of agent2: 0.9914058657554006
idv_policy eval average team episode rewards of agent2: 150.0
idv_policy eval idv catch total num of agent2: 41
idv_policy eval team catch total num: 60
idv_policy eval average step individual rewards of agent3: 0.8670738086142586
idv_policy eval average team episode rewards of agent3: 150.0
idv_policy eval idv catch total num of agent3: 36
idv_policy eval team catch total num: 60
idv_policy eval average step individual rewards of agent4: 0.8935853915949376
idv_policy eval average team episode rewards of agent4: 150.0
idv_policy eval idv catch total num of agent4: 37
idv_policy eval team catch total num: 60

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6176/10000 episodes, total num timesteps 1235400/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6177/10000 episodes, total num timesteps 1235600/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6178/10000 episodes, total num timesteps 1235800/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6179/10000 episodes, total num timesteps 1236000/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6180/10000 episodes, total num timesteps 1236200/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6181/10000 episodes, total num timesteps 1236400/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6182/10000 episodes, total num timesteps 1236600/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6183/10000 episodes, total num timesteps 1236800/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6184/10000 episodes, total num timesteps 1237000/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6185/10000 episodes, total num timesteps 1237200/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6186/10000 episodes, total num timesteps 1237400/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6187/10000 episodes, total num timesteps 1237600/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6188/10000 episodes, total num timesteps 1237800/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6189/10000 episodes, total num timesteps 1238000/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6190/10000 episodes, total num timesteps 1238200/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6191/10000 episodes, total num timesteps 1238400/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6192/10000 episodes, total num timesteps 1238600/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6193/10000 episodes, total num timesteps 1238800/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6194/10000 episodes, total num timesteps 1239000/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6195/10000 episodes, total num timesteps 1239200/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6196/10000 episodes, total num timesteps 1239400/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6197/10000 episodes, total num timesteps 1239600/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6198/10000 episodes, total num timesteps 1239800/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6199/10000 episodes, total num timesteps 1240000/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6200/10000 episodes, total num timesteps 1240200/2000000, FPS 234.

team_policy eval average step individual rewards of agent0: 1.122190985769933
team_policy eval average team episode rewards of agent0: 132.5
team_policy eval idv catch total num of agent0: 46
team_policy eval team catch total num: 53
team_policy eval average step individual rewards of agent1: 0.6141145994356273
team_policy eval average team episode rewards of agent1: 132.5
team_policy eval idv catch total num of agent1: 26
team_policy eval team catch total num: 53
team_policy eval average step individual rewards of agent2: 0.9147852111874292
team_policy eval average team episode rewards of agent2: 132.5
team_policy eval idv catch total num of agent2: 38
team_policy eval team catch total num: 53
team_policy eval average step individual rewards of agent3: 1.0178673546897137
team_policy eval average team episode rewards of agent3: 132.5
team_policy eval idv catch total num of agent3: 42
team_policy eval team catch total num: 53
team_policy eval average step individual rewards of agent4: 0.9846359264697016
team_policy eval average team episode rewards of agent4: 132.5
team_policy eval idv catch total num of agent4: 41
team_policy eval team catch total num: 53
idv_policy eval average step individual rewards of agent0: 0.7876282621739611
idv_policy eval average team episode rewards of agent0: 92.5
idv_policy eval idv catch total num of agent0: 33
idv_policy eval team catch total num: 37
idv_policy eval average step individual rewards of agent1: 0.8860115478493055
idv_policy eval average team episode rewards of agent1: 92.5
idv_policy eval idv catch total num of agent1: 37
idv_policy eval team catch total num: 37
idv_policy eval average step individual rewards of agent2: 0.5314902955204646
idv_policy eval average team episode rewards of agent2: 92.5
idv_policy eval idv catch total num of agent2: 23
idv_policy eval team catch total num: 37
idv_policy eval average step individual rewards of agent3: 1.09066361760207
idv_policy eval average team episode rewards of agent3: 92.5
idv_policy eval idv catch total num of agent3: 45
idv_policy eval team catch total num: 37
idv_policy eval average step individual rewards of agent4: 0.49860093000191463
idv_policy eval average team episode rewards of agent4: 92.5
idv_policy eval idv catch total num of agent4: 22
idv_policy eval team catch total num: 37

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6201/10000 episodes, total num timesteps 1240400/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6202/10000 episodes, total num timesteps 1240600/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6203/10000 episodes, total num timesteps 1240800/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6204/10000 episodes, total num timesteps 1241000/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6205/10000 episodes, total num timesteps 1241200/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6206/10000 episodes, total num timesteps 1241400/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6207/10000 episodes, total num timesteps 1241600/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6208/10000 episodes, total num timesteps 1241800/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6209/10000 episodes, total num timesteps 1242000/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6210/10000 episodes, total num timesteps 1242200/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6211/10000 episodes, total num timesteps 1242400/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6212/10000 episodes, total num timesteps 1242600/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6213/10000 episodes, total num timesteps 1242800/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6214/10000 episodes, total num timesteps 1243000/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6215/10000 episodes, total num timesteps 1243200/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6216/10000 episodes, total num timesteps 1243400/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6217/10000 episodes, total num timesteps 1243600/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6218/10000 episodes, total num timesteps 1243800/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6219/10000 episodes, total num timesteps 1244000/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6220/10000 episodes, total num timesteps 1244200/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6221/10000 episodes, total num timesteps 1244400/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6222/10000 episodes, total num timesteps 1244600/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6223/10000 episodes, total num timesteps 1244800/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6224/10000 episodes, total num timesteps 1245000/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6225/10000 episodes, total num timesteps 1245200/2000000, FPS 234.

team_policy eval average step individual rewards of agent0: 1.3741120476616064
team_policy eval average team episode rewards of agent0: 165.0
team_policy eval idv catch total num of agent0: 56
team_policy eval team catch total num: 66
team_policy eval average step individual rewards of agent1: 1.1478978299796885
team_policy eval average team episode rewards of agent1: 165.0
team_policy eval idv catch total num of agent1: 47
team_policy eval team catch total num: 66
team_policy eval average step individual rewards of agent2: 1.1774133504978184
team_policy eval average team episode rewards of agent2: 165.0
team_policy eval idv catch total num of agent2: 48
team_policy eval team catch total num: 66
team_policy eval average step individual rewards of agent3: 1.0173416167912923
team_policy eval average team episode rewards of agent3: 165.0
team_policy eval idv catch total num of agent3: 42
team_policy eval team catch total num: 66
team_policy eval average step individual rewards of agent4: 0.8986349559316017
team_policy eval average team episode rewards of agent4: 165.0
team_policy eval idv catch total num of agent4: 37
team_policy eval team catch total num: 66
idv_policy eval average step individual rewards of agent0: 0.7819509273725817
idv_policy eval average team episode rewards of agent0: 152.5
idv_policy eval idv catch total num of agent0: 33
idv_policy eval team catch total num: 61
idv_policy eval average step individual rewards of agent1: 1.2488233573697736
idv_policy eval average team episode rewards of agent1: 152.5
idv_policy eval idv catch total num of agent1: 51
idv_policy eval team catch total num: 61
idv_policy eval average step individual rewards of agent2: 1.0999411624633613
idv_policy eval average team episode rewards of agent2: 152.5
idv_policy eval idv catch total num of agent2: 46
idv_policy eval team catch total num: 61
idv_policy eval average step individual rewards of agent3: 1.1941490612269252
idv_policy eval average team episode rewards of agent3: 152.5
idv_policy eval idv catch total num of agent3: 49
idv_policy eval team catch total num: 61
idv_policy eval average step individual rewards of agent4: 0.5641100896475207
idv_policy eval average team episode rewards of agent4: 152.5
idv_policy eval idv catch total num of agent4: 24
idv_policy eval team catch total num: 61

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6226/10000 episodes, total num timesteps 1245400/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6227/10000 episodes, total num timesteps 1245600/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6228/10000 episodes, total num timesteps 1245800/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6229/10000 episodes, total num timesteps 1246000/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6230/10000 episodes, total num timesteps 1246200/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6231/10000 episodes, total num timesteps 1246400/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6232/10000 episodes, total num timesteps 1246600/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6233/10000 episodes, total num timesteps 1246800/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6234/10000 episodes, total num timesteps 1247000/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6235/10000 episodes, total num timesteps 1247200/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6236/10000 episodes, total num timesteps 1247400/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6237/10000 episodes, total num timesteps 1247600/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6238/10000 episodes, total num timesteps 1247800/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6239/10000 episodes, total num timesteps 1248000/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6240/10000 episodes, total num timesteps 1248200/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6241/10000 episodes, total num timesteps 1248400/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6242/10000 episodes, total num timesteps 1248600/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6243/10000 episodes, total num timesteps 1248800/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6244/10000 episodes, total num timesteps 1249000/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6245/10000 episodes, total num timesteps 1249200/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6246/10000 episodes, total num timesteps 1249400/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6247/10000 episodes, total num timesteps 1249600/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6248/10000 episodes, total num timesteps 1249800/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6249/10000 episodes, total num timesteps 1250000/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6250/10000 episodes, total num timesteps 1250200/2000000, FPS 234.

team_policy eval average step individual rewards of agent0: 1.2461241068771578
team_policy eval average team episode rewards of agent0: 152.5
team_policy eval idv catch total num of agent0: 51
team_policy eval team catch total num: 61
team_policy eval average step individual rewards of agent1: 1.4228791753514
team_policy eval average team episode rewards of agent1: 152.5
team_policy eval idv catch total num of agent1: 58
team_policy eval team catch total num: 61
team_policy eval average step individual rewards of agent2: 1.2436294365661162
team_policy eval average team episode rewards of agent2: 152.5
team_policy eval idv catch total num of agent2: 51
team_policy eval team catch total num: 61
team_policy eval average step individual rewards of agent3: 1.0664488583227159
team_policy eval average team episode rewards of agent3: 152.5
team_policy eval idv catch total num of agent3: 44
team_policy eval team catch total num: 61
team_policy eval average step individual rewards of agent4: 0.503941685263117
team_policy eval average team episode rewards of agent4: 152.5
team_policy eval idv catch total num of agent4: 22
team_policy eval team catch total num: 61
idv_policy eval average step individual rewards of agent0: 1.006833921835401
idv_policy eval average team episode rewards of agent0: 115.0
idv_policy eval idv catch total num of agent0: 42
idv_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent1: 0.8388447433001134
idv_policy eval average team episode rewards of agent1: 115.0
idv_policy eval idv catch total num of agent1: 35
idv_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent2: 0.9210447341755703
idv_policy eval average team episode rewards of agent2: 115.0
idv_policy eval idv catch total num of agent2: 38
idv_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent3: 0.7862710844300351
idv_policy eval average team episode rewards of agent3: 115.0
idv_policy eval idv catch total num of agent3: 33
idv_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent4: 0.663922739119653
idv_policy eval average team episode rewards of agent4: 115.0
idv_policy eval idv catch total num of agent4: 28
idv_policy eval team catch total num: 46

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6251/10000 episodes, total num timesteps 1250400/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6252/10000 episodes, total num timesteps 1250600/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6253/10000 episodes, total num timesteps 1250800/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6254/10000 episodes, total num timesteps 1251000/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6255/10000 episodes, total num timesteps 1251200/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6256/10000 episodes, total num timesteps 1251400/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6257/10000 episodes, total num timesteps 1251600/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6258/10000 episodes, total num timesteps 1251800/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6259/10000 episodes, total num timesteps 1252000/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6260/10000 episodes, total num timesteps 1252200/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6261/10000 episodes, total num timesteps 1252400/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6262/10000 episodes, total num timesteps 1252600/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6263/10000 episodes, total num timesteps 1252800/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6264/10000 episodes, total num timesteps 1253000/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6265/10000 episodes, total num timesteps 1253200/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6266/10000 episodes, total num timesteps 1253400/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6267/10000 episodes, total num timesteps 1253600/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6268/10000 episodes, total num timesteps 1253800/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6269/10000 episodes, total num timesteps 1254000/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6270/10000 episodes, total num timesteps 1254200/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6271/10000 episodes, total num timesteps 1254400/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6272/10000 episodes, total num timesteps 1254600/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6273/10000 episodes, total num timesteps 1254800/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6274/10000 episodes, total num timesteps 1255000/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6275/10000 episodes, total num timesteps 1255200/2000000, FPS 234.

team_policy eval average step individual rewards of agent0: 0.73743811090332
team_policy eval average team episode rewards of agent0: 167.5
team_policy eval idv catch total num of agent0: 31
team_policy eval team catch total num: 67
team_policy eval average step individual rewards of agent1: 1.3190403749617883
team_policy eval average team episode rewards of agent1: 167.5
team_policy eval idv catch total num of agent1: 54
team_policy eval team catch total num: 67
team_policy eval average step individual rewards of agent2: 1.094096022880853
team_policy eval average team episode rewards of agent2: 167.5
team_policy eval idv catch total num of agent2: 45
team_policy eval team catch total num: 67
team_policy eval average step individual rewards of agent3: 1.1409153682993725
team_policy eval average team episode rewards of agent3: 167.5
team_policy eval idv catch total num of agent3: 47
team_policy eval team catch total num: 67
team_policy eval average step individual rewards of agent4: 1.348080435484066
team_policy eval average team episode rewards of agent4: 167.5
team_policy eval idv catch total num of agent4: 55
team_policy eval team catch total num: 67
idv_policy eval average step individual rewards of agent0: 1.2696155268737959
idv_policy eval average team episode rewards of agent0: 85.0
idv_policy eval idv catch total num of agent0: 52
idv_policy eval team catch total num: 34
idv_policy eval average step individual rewards of agent1: 0.5496136560193521
idv_policy eval average team episode rewards of agent1: 85.0
idv_policy eval idv catch total num of agent1: 24
idv_policy eval team catch total num: 34
idv_policy eval average step individual rewards of agent2: 0.5897988965739783
idv_policy eval average team episode rewards of agent2: 85.0
idv_policy eval idv catch total num of agent2: 25
idv_policy eval team catch total num: 34
idv_policy eval average step individual rewards of agent3: 0.6088056503481509
idv_policy eval average team episode rewards of agent3: 85.0
idv_policy eval idv catch total num of agent3: 26
idv_policy eval team catch total num: 34
idv_policy eval average step individual rewards of agent4: 0.42733038697404246
idv_policy eval average team episode rewards of agent4: 85.0
idv_policy eval idv catch total num of agent4: 19
idv_policy eval team catch total num: 34

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6276/10000 episodes, total num timesteps 1255400/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6277/10000 episodes, total num timesteps 1255600/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6278/10000 episodes, total num timesteps 1255800/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6279/10000 episodes, total num timesteps 1256000/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6280/10000 episodes, total num timesteps 1256200/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6281/10000 episodes, total num timesteps 1256400/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6282/10000 episodes, total num timesteps 1256600/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6283/10000 episodes, total num timesteps 1256800/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6284/10000 episodes, total num timesteps 1257000/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6285/10000 episodes, total num timesteps 1257200/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6286/10000 episodes, total num timesteps 1257400/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6287/10000 episodes, total num timesteps 1257600/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6288/10000 episodes, total num timesteps 1257800/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6289/10000 episodes, total num timesteps 1258000/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6290/10000 episodes, total num timesteps 1258200/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6291/10000 episodes, total num timesteps 1258400/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6292/10000 episodes, total num timesteps 1258600/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6293/10000 episodes, total num timesteps 1258800/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6294/10000 episodes, total num timesteps 1259000/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6295/10000 episodes, total num timesteps 1259200/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6296/10000 episodes, total num timesteps 1259400/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6297/10000 episodes, total num timesteps 1259600/2000000, FPS 234.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6298/10000 episodes, total num timesteps 1259800/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6299/10000 episodes, total num timesteps 1260000/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6300/10000 episodes, total num timesteps 1260200/2000000, FPS 235.

team_policy eval average step individual rewards of agent0: 0.6620597423297118
team_policy eval average team episode rewards of agent0: 105.0
team_policy eval idv catch total num of agent0: 28
team_policy eval team catch total num: 42
team_policy eval average step individual rewards of agent1: 0.8624996855082208
team_policy eval average team episode rewards of agent1: 105.0
team_policy eval idv catch total num of agent1: 36
team_policy eval team catch total num: 42
team_policy eval average step individual rewards of agent2: 0.8132846851257806
team_policy eval average team episode rewards of agent2: 105.0
team_policy eval idv catch total num of agent2: 34
team_policy eval team catch total num: 42
team_policy eval average step individual rewards of agent3: 0.642659588120253
team_policy eval average team episode rewards of agent3: 105.0
team_policy eval idv catch total num of agent3: 27
team_policy eval team catch total num: 42
team_policy eval average step individual rewards of agent4: 0.6876699770479473
team_policy eval average team episode rewards of agent4: 105.0
team_policy eval idv catch total num of agent4: 29
team_policy eval team catch total num: 42
idv_policy eval average step individual rewards of agent0: 0.5939848751136764
idv_policy eval average team episode rewards of agent0: 80.0
idv_policy eval idv catch total num of agent0: 25
idv_policy eval team catch total num: 32
idv_policy eval average step individual rewards of agent1: 0.48371324950134886
idv_policy eval average team episode rewards of agent1: 80.0
idv_policy eval idv catch total num of agent1: 21
idv_policy eval team catch total num: 32
idv_policy eval average step individual rewards of agent2: 0.7035181120492091
idv_policy eval average team episode rewards of agent2: 80.0
idv_policy eval idv catch total num of agent2: 30
idv_policy eval team catch total num: 32
idv_policy eval average step individual rewards of agent3: 0.4305908137366186
idv_policy eval average team episode rewards of agent3: 80.0
idv_policy eval idv catch total num of agent3: 19
idv_policy eval team catch total num: 32
idv_policy eval average step individual rewards of agent4: 0.9438975680350584
idv_policy eval average team episode rewards of agent4: 80.0
idv_policy eval idv catch total num of agent4: 39
idv_policy eval team catch total num: 32

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6301/10000 episodes, total num timesteps 1260400/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6302/10000 episodes, total num timesteps 1260600/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6303/10000 episodes, total num timesteps 1260800/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6304/10000 episodes, total num timesteps 1261000/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6305/10000 episodes, total num timesteps 1261200/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6306/10000 episodes, total num timesteps 1261400/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6307/10000 episodes, total num timesteps 1261600/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6308/10000 episodes, total num timesteps 1261800/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6309/10000 episodes, total num timesteps 1262000/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6310/10000 episodes, total num timesteps 1262200/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6311/10000 episodes, total num timesteps 1262400/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6312/10000 episodes, total num timesteps 1262600/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6313/10000 episodes, total num timesteps 1262800/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6314/10000 episodes, total num timesteps 1263000/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6315/10000 episodes, total num timesteps 1263200/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6316/10000 episodes, total num timesteps 1263400/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6317/10000 episodes, total num timesteps 1263600/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6318/10000 episodes, total num timesteps 1263800/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6319/10000 episodes, total num timesteps 1264000/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6320/10000 episodes, total num timesteps 1264200/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6321/10000 episodes, total num timesteps 1264400/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6322/10000 episodes, total num timesteps 1264600/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6323/10000 episodes, total num timesteps 1264800/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6324/10000 episodes, total num timesteps 1265000/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6325/10000 episodes, total num timesteps 1265200/2000000, FPS 235.

team_policy eval average step individual rewards of agent0: 1.5002010149726914
team_policy eval average team episode rewards of agent0: 162.5
team_policy eval idv catch total num of agent0: 61
team_policy eval team catch total num: 65
team_policy eval average step individual rewards of agent1: 0.7426761143294701
team_policy eval average team episode rewards of agent1: 162.5
team_policy eval idv catch total num of agent1: 31
team_policy eval team catch total num: 65
team_policy eval average step individual rewards of agent2: 1.3736946841357593
team_policy eval average team episode rewards of agent2: 162.5
team_policy eval idv catch total num of agent2: 56
team_policy eval team catch total num: 65
team_policy eval average step individual rewards of agent3: 1.0122479721380007
team_policy eval average team episode rewards of agent3: 162.5
team_policy eval idv catch total num of agent3: 42
team_policy eval team catch total num: 65
team_policy eval average step individual rewards of agent4: 0.8886297221314599
team_policy eval average team episode rewards of agent4: 162.5
team_policy eval idv catch total num of agent4: 37
team_policy eval team catch total num: 65
idv_policy eval average step individual rewards of agent0: 1.0697300035216528
idv_policy eval average team episode rewards of agent0: 145.0
idv_policy eval idv catch total num of agent0: 44
idv_policy eval team catch total num: 58
idv_policy eval average step individual rewards of agent1: 1.0840544749232561
idv_policy eval average team episode rewards of agent1: 145.0
idv_policy eval idv catch total num of agent1: 45
idv_policy eval team catch total num: 58
idv_policy eval average step individual rewards of agent2: 1.3658404858524613
idv_policy eval average team episode rewards of agent2: 145.0
idv_policy eval idv catch total num of agent2: 56
idv_policy eval team catch total num: 58
idv_policy eval average step individual rewards of agent3: 0.5598259208558304
idv_policy eval average team episode rewards of agent3: 145.0
idv_policy eval idv catch total num of agent3: 24
idv_policy eval team catch total num: 58
idv_policy eval average step individual rewards of agent4: 0.9821664751874922
idv_policy eval average team episode rewards of agent4: 145.0
idv_policy eval idv catch total num of agent4: 41
idv_policy eval team catch total num: 58

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6326/10000 episodes, total num timesteps 1265400/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6327/10000 episodes, total num timesteps 1265600/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6328/10000 episodes, total num timesteps 1265800/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6329/10000 episodes, total num timesteps 1266000/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6330/10000 episodes, total num timesteps 1266200/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6331/10000 episodes, total num timesteps 1266400/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6332/10000 episodes, total num timesteps 1266600/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6333/10000 episodes, total num timesteps 1266800/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6334/10000 episodes, total num timesteps 1267000/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6335/10000 episodes, total num timesteps 1267200/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6336/10000 episodes, total num timesteps 1267400/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6337/10000 episodes, total num timesteps 1267600/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6338/10000 episodes, total num timesteps 1267800/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6339/10000 episodes, total num timesteps 1268000/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6340/10000 episodes, total num timesteps 1268200/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6341/10000 episodes, total num timesteps 1268400/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6342/10000 episodes, total num timesteps 1268600/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6343/10000 episodes, total num timesteps 1268800/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6344/10000 episodes, total num timesteps 1269000/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6345/10000 episodes, total num timesteps 1269200/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6346/10000 episodes, total num timesteps 1269400/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6347/10000 episodes, total num timesteps 1269600/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6348/10000 episodes, total num timesteps 1269800/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6349/10000 episodes, total num timesteps 1270000/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6350/10000 episodes, total num timesteps 1270200/2000000, FPS 235.

team_policy eval average step individual rewards of agent0: 0.9143417767189973
team_policy eval average team episode rewards of agent0: 112.5
team_policy eval idv catch total num of agent0: 38
team_policy eval team catch total num: 45
team_policy eval average step individual rewards of agent1: 0.8898300294599011
team_policy eval average team episode rewards of agent1: 112.5
team_policy eval idv catch total num of agent1: 37
team_policy eval team catch total num: 45
team_policy eval average step individual rewards of agent2: 0.556005947056932
team_policy eval average team episode rewards of agent2: 112.5
team_policy eval idv catch total num of agent2: 24
team_policy eval team catch total num: 45
team_policy eval average step individual rewards of agent3: 0.5740648350534127
team_policy eval average team episode rewards of agent3: 112.5
team_policy eval idv catch total num of agent3: 25
team_policy eval team catch total num: 45
team_policy eval average step individual rewards of agent4: 0.9878391786193056
team_policy eval average team episode rewards of agent4: 112.5
team_policy eval idv catch total num of agent4: 41
team_policy eval team catch total num: 45
idv_policy eval average step individual rewards of agent0: 1.0400064302239675
idv_policy eval average team episode rewards of agent0: 122.5
idv_policy eval idv catch total num of agent0: 43
idv_policy eval team catch total num: 49
idv_policy eval average step individual rewards of agent1: 0.7399463297706654
idv_policy eval average team episode rewards of agent1: 122.5
idv_policy eval idv catch total num of agent1: 31
idv_policy eval team catch total num: 49
idv_policy eval average step individual rewards of agent2: 0.8916034532013893
idv_policy eval average team episode rewards of agent2: 122.5
idv_policy eval idv catch total num of agent2: 37
idv_policy eval team catch total num: 49
idv_policy eval average step individual rewards of agent3: 0.6333783201761983
idv_policy eval average team episode rewards of agent3: 122.5
idv_policy eval idv catch total num of agent3: 27
idv_policy eval team catch total num: 49
idv_policy eval average step individual rewards of agent4: 0.6162085303293781
idv_policy eval average team episode rewards of agent4: 122.5
idv_policy eval idv catch total num of agent4: 26
idv_policy eval team catch total num: 49

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6351/10000 episodes, total num timesteps 1270400/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6352/10000 episodes, total num timesteps 1270600/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6353/10000 episodes, total num timesteps 1270800/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6354/10000 episodes, total num timesteps 1271000/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6355/10000 episodes, total num timesteps 1271200/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6356/10000 episodes, total num timesteps 1271400/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6357/10000 episodes, total num timesteps 1271600/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6358/10000 episodes, total num timesteps 1271800/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6359/10000 episodes, total num timesteps 1272000/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6360/10000 episodes, total num timesteps 1272200/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6361/10000 episodes, total num timesteps 1272400/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6362/10000 episodes, total num timesteps 1272600/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6363/10000 episodes, total num timesteps 1272800/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6364/10000 episodes, total num timesteps 1273000/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6365/10000 episodes, total num timesteps 1273200/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6366/10000 episodes, total num timesteps 1273400/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6367/10000 episodes, total num timesteps 1273600/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6368/10000 episodes, total num timesteps 1273800/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6369/10000 episodes, total num timesteps 1274000/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6370/10000 episodes, total num timesteps 1274200/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6371/10000 episodes, total num timesteps 1274400/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6372/10000 episodes, total num timesteps 1274600/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6373/10000 episodes, total num timesteps 1274800/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6374/10000 episodes, total num timesteps 1275000/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6375/10000 episodes, total num timesteps 1275200/2000000, FPS 235.

team_policy eval average step individual rewards of agent0: 1.2676858705026577
team_policy eval average team episode rewards of agent0: 127.5
team_policy eval idv catch total num of agent0: 52
team_policy eval team catch total num: 51
team_policy eval average step individual rewards of agent1: 1.1497873764820645
team_policy eval average team episode rewards of agent1: 127.5
team_policy eval idv catch total num of agent1: 47
team_policy eval team catch total num: 51
team_policy eval average step individual rewards of agent2: 0.5615728561573872
team_policy eval average team episode rewards of agent2: 127.5
team_policy eval idv catch total num of agent2: 24
team_policy eval team catch total num: 51
team_policy eval average step individual rewards of agent3: 0.5629874680473916
team_policy eval average team episode rewards of agent3: 127.5
team_policy eval idv catch total num of agent3: 24
team_policy eval team catch total num: 51
team_policy eval average step individual rewards of agent4: 0.8164545894537523
team_policy eval average team episode rewards of agent4: 127.5
team_policy eval idv catch total num of agent4: 34
team_policy eval team catch total num: 51
idv_policy eval average step individual rewards of agent0: 1.3005605484899592
idv_policy eval average team episode rewards of agent0: 125.0
idv_policy eval idv catch total num of agent0: 53
idv_policy eval team catch total num: 50
idv_policy eval average step individual rewards of agent1: 0.9425804809861889
idv_policy eval average team episode rewards of agent1: 125.0
idv_policy eval idv catch total num of agent1: 39
idv_policy eval team catch total num: 50
idv_policy eval average step individual rewards of agent2: 0.7239082164697779
idv_policy eval average team episode rewards of agent2: 125.0
idv_policy eval idv catch total num of agent2: 31
idv_policy eval team catch total num: 50
idv_policy eval average step individual rewards of agent3: 0.7310649906776134
idv_policy eval average team episode rewards of agent3: 125.0
idv_policy eval idv catch total num of agent3: 31
idv_policy eval team catch total num: 50
idv_policy eval average step individual rewards of agent4: 0.6106958402134207
idv_policy eval average team episode rewards of agent4: 125.0
idv_policy eval idv catch total num of agent4: 26
idv_policy eval team catch total num: 50

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6376/10000 episodes, total num timesteps 1275400/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6377/10000 episodes, total num timesteps 1275600/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6378/10000 episodes, total num timesteps 1275800/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6379/10000 episodes, total num timesteps 1276000/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6380/10000 episodes, total num timesteps 1276200/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6381/10000 episodes, total num timesteps 1276400/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6382/10000 episodes, total num timesteps 1276600/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6383/10000 episodes, total num timesteps 1276800/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6384/10000 episodes, total num timesteps 1277000/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6385/10000 episodes, total num timesteps 1277200/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6386/10000 episodes, total num timesteps 1277400/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6387/10000 episodes, total num timesteps 1277600/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6388/10000 episodes, total num timesteps 1277800/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6389/10000 episodes, total num timesteps 1278000/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6390/10000 episodes, total num timesteps 1278200/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6391/10000 episodes, total num timesteps 1278400/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6392/10000 episodes, total num timesteps 1278600/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6393/10000 episodes, total num timesteps 1278800/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6394/10000 episodes, total num timesteps 1279000/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6395/10000 episodes, total num timesteps 1279200/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6396/10000 episodes, total num timesteps 1279400/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6397/10000 episodes, total num timesteps 1279600/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6398/10000 episodes, total num timesteps 1279800/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6399/10000 episodes, total num timesteps 1280000/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6400/10000 episodes, total num timesteps 1280200/2000000, FPS 235.

team_policy eval average step individual rewards of agent0: 1.0927231587686972
team_policy eval average team episode rewards of agent0: 125.0
team_policy eval idv catch total num of agent0: 45
team_policy eval team catch total num: 50
team_policy eval average step individual rewards of agent1: 0.7142535605456382
team_policy eval average team episode rewards of agent1: 125.0
team_policy eval idv catch total num of agent1: 30
team_policy eval team catch total num: 50
team_policy eval average step individual rewards of agent2: 0.7334472808340367
team_policy eval average team episode rewards of agent2: 125.0
team_policy eval idv catch total num of agent2: 31
team_policy eval team catch total num: 50
team_policy eval average step individual rewards of agent3: 0.8174822731123106
team_policy eval average team episode rewards of agent3: 125.0
team_policy eval idv catch total num of agent3: 34
team_policy eval team catch total num: 50
team_policy eval average step individual rewards of agent4: 1.1963426011588363
team_policy eval average team episode rewards of agent4: 125.0
team_policy eval idv catch total num of agent4: 49
team_policy eval team catch total num: 50
idv_policy eval average step individual rewards of agent0: 0.4984345625868977
idv_policy eval average team episode rewards of agent0: 70.0
idv_policy eval idv catch total num of agent0: 22
idv_policy eval team catch total num: 28
idv_policy eval average step individual rewards of agent1: 0.3975123938416168
idv_policy eval average team episode rewards of agent1: 70.0
idv_policy eval idv catch total num of agent1: 18
idv_policy eval team catch total num: 28
idv_policy eval average step individual rewards of agent2: 0.6714420990341106
idv_policy eval average team episode rewards of agent2: 70.0
idv_policy eval idv catch total num of agent2: 28
idv_policy eval team catch total num: 28
idv_policy eval average step individual rewards of agent3: 0.8071686673864309
idv_policy eval average team episode rewards of agent3: 70.0
idv_policy eval idv catch total num of agent3: 34
idv_policy eval team catch total num: 28
idv_policy eval average step individual rewards of agent4: 0.6935921898544034
idv_policy eval average team episode rewards of agent4: 70.0
idv_policy eval idv catch total num of agent4: 29
idv_policy eval team catch total num: 28

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6401/10000 episodes, total num timesteps 1280400/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6402/10000 episodes, total num timesteps 1280600/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6403/10000 episodes, total num timesteps 1280800/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6404/10000 episodes, total num timesteps 1281000/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6405/10000 episodes, total num timesteps 1281200/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6406/10000 episodes, total num timesteps 1281400/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6407/10000 episodes, total num timesteps 1281600/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6408/10000 episodes, total num timesteps 1281800/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6409/10000 episodes, total num timesteps 1282000/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6410/10000 episodes, total num timesteps 1282200/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6411/10000 episodes, total num timesteps 1282400/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6412/10000 episodes, total num timesteps 1282600/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6413/10000 episodes, total num timesteps 1282800/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6414/10000 episodes, total num timesteps 1283000/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6415/10000 episodes, total num timesteps 1283200/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6416/10000 episodes, total num timesteps 1283400/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6417/10000 episodes, total num timesteps 1283600/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6418/10000 episodes, total num timesteps 1283800/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6419/10000 episodes, total num timesteps 1284000/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6420/10000 episodes, total num timesteps 1284200/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6421/10000 episodes, total num timesteps 1284400/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6422/10000 episodes, total num timesteps 1284600/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6423/10000 episodes, total num timesteps 1284800/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6424/10000 episodes, total num timesteps 1285000/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6425/10000 episodes, total num timesteps 1285200/2000000, FPS 235.

team_policy eval average step individual rewards of agent0: 0.7347533453747647
team_policy eval average team episode rewards of agent0: 110.0
team_policy eval idv catch total num of agent0: 31
team_policy eval team catch total num: 44
team_policy eval average step individual rewards of agent1: 0.6497142763209495
team_policy eval average team episode rewards of agent1: 110.0
team_policy eval idv catch total num of agent1: 28
team_policy eval team catch total num: 44
team_policy eval average step individual rewards of agent2: 0.5595029339966778
team_policy eval average team episode rewards of agent2: 110.0
team_policy eval idv catch total num of agent2: 24
team_policy eval team catch total num: 44
team_policy eval average step individual rewards of agent3: 0.8805886018557756
team_policy eval average team episode rewards of agent3: 110.0
team_policy eval idv catch total num of agent3: 37
team_policy eval team catch total num: 44
team_policy eval average step individual rewards of agent4: 0.7615438490948836
team_policy eval average team episode rewards of agent4: 110.0
team_policy eval idv catch total num of agent4: 32
team_policy eval team catch total num: 44
idv_policy eval average step individual rewards of agent0: 1.4987713866160866
idv_policy eval average team episode rewards of agent0: 157.5
idv_policy eval idv catch total num of agent0: 61
idv_policy eval team catch total num: 63
idv_policy eval average step individual rewards of agent1: 0.7739174769918954
idv_policy eval average team episode rewards of agent1: 157.5
idv_policy eval idv catch total num of agent1: 32
idv_policy eval team catch total num: 63
idv_policy eval average step individual rewards of agent2: 1.240558931741906
idv_policy eval average team episode rewards of agent2: 157.5
idv_policy eval idv catch total num of agent2: 51
idv_policy eval team catch total num: 63
idv_policy eval average step individual rewards of agent3: 0.41146602894145945
idv_policy eval average team episode rewards of agent3: 157.5
idv_policy eval idv catch total num of agent3: 18
idv_policy eval team catch total num: 63
idv_policy eval average step individual rewards of agent4: 1.0239761465283757
idv_policy eval average team episode rewards of agent4: 157.5
idv_policy eval idv catch total num of agent4: 42
idv_policy eval team catch total num: 63

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6426/10000 episodes, total num timesteps 1285400/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6427/10000 episodes, total num timesteps 1285600/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6428/10000 episodes, total num timesteps 1285800/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6429/10000 episodes, total num timesteps 1286000/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6430/10000 episodes, total num timesteps 1286200/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6431/10000 episodes, total num timesteps 1286400/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6432/10000 episodes, total num timesteps 1286600/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6433/10000 episodes, total num timesteps 1286800/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6434/10000 episodes, total num timesteps 1287000/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6435/10000 episodes, total num timesteps 1287200/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6436/10000 episodes, total num timesteps 1287400/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6437/10000 episodes, total num timesteps 1287600/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6438/10000 episodes, total num timesteps 1287800/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6439/10000 episodes, total num timesteps 1288000/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6440/10000 episodes, total num timesteps 1288200/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6441/10000 episodes, total num timesteps 1288400/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6442/10000 episodes, total num timesteps 1288600/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6443/10000 episodes, total num timesteps 1288800/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6444/10000 episodes, total num timesteps 1289000/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6445/10000 episodes, total num timesteps 1289200/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6446/10000 episodes, total num timesteps 1289400/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6447/10000 episodes, total num timesteps 1289600/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6448/10000 episodes, total num timesteps 1289800/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6449/10000 episodes, total num timesteps 1290000/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6450/10000 episodes, total num timesteps 1290200/2000000, FPS 235.

team_policy eval average step individual rewards of agent0: 0.7394655756662496
team_policy eval average team episode rewards of agent0: 137.5
team_policy eval idv catch total num of agent0: 31
team_policy eval team catch total num: 55
team_policy eval average step individual rewards of agent1: 0.8384044627182087
team_policy eval average team episode rewards of agent1: 137.5
team_policy eval idv catch total num of agent1: 35
team_policy eval team catch total num: 55
team_policy eval average step individual rewards of agent2: 0.8853319534367451
team_policy eval average team episode rewards of agent2: 137.5
team_policy eval idv catch total num of agent2: 37
team_policy eval team catch total num: 55
team_policy eval average step individual rewards of agent3: 0.7297187657134194
team_policy eval average team episode rewards of agent3: 137.5
team_policy eval idv catch total num of agent3: 31
team_policy eval team catch total num: 55
team_policy eval average step individual rewards of agent4: 1.112640662387491
team_policy eval average team episode rewards of agent4: 137.5
team_policy eval idv catch total num of agent4: 46
team_policy eval team catch total num: 55
idv_policy eval average step individual rewards of agent0: 0.8092950202150527
idv_policy eval average team episode rewards of agent0: 112.5
idv_policy eval idv catch total num of agent0: 34
idv_policy eval team catch total num: 45
idv_policy eval average step individual rewards of agent1: 0.7201434940154345
idv_policy eval average team episode rewards of agent1: 112.5
idv_policy eval idv catch total num of agent1: 30
idv_policy eval team catch total num: 45
idv_policy eval average step individual rewards of agent2: 1.1214962240848838
idv_policy eval average team episode rewards of agent2: 112.5
idv_policy eval idv catch total num of agent2: 46
idv_policy eval team catch total num: 45
idv_policy eval average step individual rewards of agent3: 0.7097008892645396
idv_policy eval average team episode rewards of agent3: 112.5
idv_policy eval idv catch total num of agent3: 30
idv_policy eval team catch total num: 45
idv_policy eval average step individual rewards of agent4: 1.1154157358519172
idv_policy eval average team episode rewards of agent4: 112.5
idv_policy eval idv catch total num of agent4: 46
idv_policy eval team catch total num: 45

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6451/10000 episodes, total num timesteps 1290400/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6452/10000 episodes, total num timesteps 1290600/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6453/10000 episodes, total num timesteps 1290800/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6454/10000 episodes, total num timesteps 1291000/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6455/10000 episodes, total num timesteps 1291200/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6456/10000 episodes, total num timesteps 1291400/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6457/10000 episodes, total num timesteps 1291600/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6458/10000 episodes, total num timesteps 1291800/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6459/10000 episodes, total num timesteps 1292000/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6460/10000 episodes, total num timesteps 1292200/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6461/10000 episodes, total num timesteps 1292400/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6462/10000 episodes, total num timesteps 1292600/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6463/10000 episodes, total num timesteps 1292800/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6464/10000 episodes, total num timesteps 1293000/2000000, FPS 235.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6465/10000 episodes, total num timesteps 1293200/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6466/10000 episodes, total num timesteps 1293400/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6467/10000 episodes, total num timesteps 1293600/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6468/10000 episodes, total num timesteps 1293800/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6469/10000 episodes, total num timesteps 1294000/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6470/10000 episodes, total num timesteps 1294200/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6471/10000 episodes, total num timesteps 1294400/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6472/10000 episodes, total num timesteps 1294600/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6473/10000 episodes, total num timesteps 1294800/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6474/10000 episodes, total num timesteps 1295000/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6475/10000 episodes, total num timesteps 1295200/2000000, FPS 236.

team_policy eval average step individual rewards of agent0: 0.4408252523053515
team_policy eval average team episode rewards of agent0: 72.5
team_policy eval idv catch total num of agent0: 20
team_policy eval team catch total num: 29
team_policy eval average step individual rewards of agent1: 0.48179128561533935
team_policy eval average team episode rewards of agent1: 72.5
team_policy eval idv catch total num of agent1: 21
team_policy eval team catch total num: 29
team_policy eval average step individual rewards of agent2: 0.948614441116292
team_policy eval average team episode rewards of agent2: 72.5
team_policy eval idv catch total num of agent2: 40
team_policy eval team catch total num: 29
team_policy eval average step individual rewards of agent3: 0.37968071631929917
team_policy eval average team episode rewards of agent3: 72.5
team_policy eval idv catch total num of agent3: 17
team_policy eval team catch total num: 29
team_policy eval average step individual rewards of agent4: 0.8379299619794988
team_policy eval average team episode rewards of agent4: 72.5
team_policy eval idv catch total num of agent4: 35
team_policy eval team catch total num: 29
idv_policy eval average step individual rewards of agent0: 0.43310185842931714
idv_policy eval average team episode rewards of agent0: 72.5
idv_policy eval idv catch total num of agent0: 19
idv_policy eval team catch total num: 29
idv_policy eval average step individual rewards of agent1: 0.4867616109105298
idv_policy eval average team episode rewards of agent1: 72.5
idv_policy eval idv catch total num of agent1: 21
idv_policy eval team catch total num: 29
idv_policy eval average step individual rewards of agent2: 0.6857244219874371
idv_policy eval average team episode rewards of agent2: 72.5
idv_policy eval idv catch total num of agent2: 29
idv_policy eval team catch total num: 29
idv_policy eval average step individual rewards of agent3: 0.7646310142530662
idv_policy eval average team episode rewards of agent3: 72.5
idv_policy eval idv catch total num of agent3: 32
idv_policy eval team catch total num: 29
idv_policy eval average step individual rewards of agent4: 0.30135211853650257
idv_policy eval average team episode rewards of agent4: 72.5
idv_policy eval idv catch total num of agent4: 14
idv_policy eval team catch total num: 29

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6476/10000 episodes, total num timesteps 1295400/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6477/10000 episodes, total num timesteps 1295600/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6478/10000 episodes, total num timesteps 1295800/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6479/10000 episodes, total num timesteps 1296000/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6480/10000 episodes, total num timesteps 1296200/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6481/10000 episodes, total num timesteps 1296400/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6482/10000 episodes, total num timesteps 1296600/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6483/10000 episodes, total num timesteps 1296800/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6484/10000 episodes, total num timesteps 1297000/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6485/10000 episodes, total num timesteps 1297200/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6486/10000 episodes, total num timesteps 1297400/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6487/10000 episodes, total num timesteps 1297600/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6488/10000 episodes, total num timesteps 1297800/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6489/10000 episodes, total num timesteps 1298000/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6490/10000 episodes, total num timesteps 1298200/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6491/10000 episodes, total num timesteps 1298400/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6492/10000 episodes, total num timesteps 1298600/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6493/10000 episodes, total num timesteps 1298800/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6494/10000 episodes, total num timesteps 1299000/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6495/10000 episodes, total num timesteps 1299200/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6496/10000 episodes, total num timesteps 1299400/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6497/10000 episodes, total num timesteps 1299600/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6498/10000 episodes, total num timesteps 1299800/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6499/10000 episodes, total num timesteps 1300000/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6500/10000 episodes, total num timesteps 1300200/2000000, FPS 236.

team_policy eval average step individual rewards of agent0: 1.4714435566806532
team_policy eval average team episode rewards of agent0: 160.0
team_policy eval idv catch total num of agent0: 60
team_policy eval team catch total num: 64
team_policy eval average step individual rewards of agent1: 0.941266344112427
team_policy eval average team episode rewards of agent1: 160.0
team_policy eval idv catch total num of agent1: 39
team_policy eval team catch total num: 64
team_policy eval average step individual rewards of agent2: 1.4977626321193225
team_policy eval average team episode rewards of agent2: 160.0
team_policy eval idv catch total num of agent2: 61
team_policy eval team catch total num: 64
team_policy eval average step individual rewards of agent3: 0.6389588895602261
team_policy eval average team episode rewards of agent3: 160.0
team_policy eval idv catch total num of agent3: 27
team_policy eval team catch total num: 64
team_policy eval average step individual rewards of agent4: 0.8122622558099941
team_policy eval average team episode rewards of agent4: 160.0
team_policy eval idv catch total num of agent4: 34
team_policy eval team catch total num: 64
idv_policy eval average step individual rewards of agent0: 0.8370128958503866
idv_policy eval average team episode rewards of agent0: 135.0
idv_policy eval idv catch total num of agent0: 35
idv_policy eval team catch total num: 54
idv_policy eval average step individual rewards of agent1: 0.969924766080188
idv_policy eval average team episode rewards of agent1: 135.0
idv_policy eval idv catch total num of agent1: 40
idv_policy eval team catch total num: 54
idv_policy eval average step individual rewards of agent2: 1.066483382624405
idv_policy eval average team episode rewards of agent2: 135.0
idv_policy eval idv catch total num of agent2: 44
idv_policy eval team catch total num: 54
idv_policy eval average step individual rewards of agent3: 0.7901141260560207
idv_policy eval average team episode rewards of agent3: 135.0
idv_policy eval idv catch total num of agent3: 33
idv_policy eval team catch total num: 54
idv_policy eval average step individual rewards of agent4: 0.912901667892644
idv_policy eval average team episode rewards of agent4: 135.0
idv_policy eval idv catch total num of agent4: 38
idv_policy eval team catch total num: 54

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6501/10000 episodes, total num timesteps 1300400/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6502/10000 episodes, total num timesteps 1300600/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6503/10000 episodes, total num timesteps 1300800/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6504/10000 episodes, total num timesteps 1301000/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6505/10000 episodes, total num timesteps 1301200/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6506/10000 episodes, total num timesteps 1301400/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6507/10000 episodes, total num timesteps 1301600/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6508/10000 episodes, total num timesteps 1301800/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6509/10000 episodes, total num timesteps 1302000/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6510/10000 episodes, total num timesteps 1302200/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6511/10000 episodes, total num timesteps 1302400/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6512/10000 episodes, total num timesteps 1302600/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6513/10000 episodes, total num timesteps 1302800/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6514/10000 episodes, total num timesteps 1303000/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6515/10000 episodes, total num timesteps 1303200/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6516/10000 episodes, total num timesteps 1303400/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6517/10000 episodes, total num timesteps 1303600/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6518/10000 episodes, total num timesteps 1303800/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6519/10000 episodes, total num timesteps 1304000/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6520/10000 episodes, total num timesteps 1304200/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6521/10000 episodes, total num timesteps 1304400/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6522/10000 episodes, total num timesteps 1304600/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6523/10000 episodes, total num timesteps 1304800/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6524/10000 episodes, total num timesteps 1305000/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6525/10000 episodes, total num timesteps 1305200/2000000, FPS 236.

team_policy eval average step individual rewards of agent0: 0.4227992473807572
team_policy eval average team episode rewards of agent0: 95.0
team_policy eval idv catch total num of agent0: 19
team_policy eval team catch total num: 38
team_policy eval average step individual rewards of agent1: 0.6828359231499374
team_policy eval average team episode rewards of agent1: 95.0
team_policy eval idv catch total num of agent1: 29
team_policy eval team catch total num: 38
team_policy eval average step individual rewards of agent2: 0.6510716129466804
team_policy eval average team episode rewards of agent2: 95.0
team_policy eval idv catch total num of agent2: 28
team_policy eval team catch total num: 38
team_policy eval average step individual rewards of agent3: 1.1689320119525002
team_policy eval average team episode rewards of agent3: 95.0
team_policy eval idv catch total num of agent3: 48
team_policy eval team catch total num: 38
team_policy eval average step individual rewards of agent4: 0.4269580066077381
team_policy eval average team episode rewards of agent4: 95.0
team_policy eval idv catch total num of agent4: 19
team_policy eval team catch total num: 38
idv_policy eval average step individual rewards of agent0: 0.6731754297021569
idv_policy eval average team episode rewards of agent0: 110.0
idv_policy eval idv catch total num of agent0: 29
idv_policy eval team catch total num: 44
idv_policy eval average step individual rewards of agent1: 0.7587433718506967
idv_policy eval average team episode rewards of agent1: 110.0
idv_policy eval idv catch total num of agent1: 32
idv_policy eval team catch total num: 44
idv_policy eval average step individual rewards of agent2: 0.5923392561157733
idv_policy eval average team episode rewards of agent2: 110.0
idv_policy eval idv catch total num of agent2: 25
idv_policy eval team catch total num: 44
idv_policy eval average step individual rewards of agent3: 0.5750305314693008
idv_policy eval average team episode rewards of agent3: 110.0
idv_policy eval idv catch total num of agent3: 25
idv_policy eval team catch total num: 44
idv_policy eval average step individual rewards of agent4: 1.001504078237953
idv_policy eval average team episode rewards of agent4: 110.0
idv_policy eval idv catch total num of agent4: 42
idv_policy eval team catch total num: 44

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6526/10000 episodes, total num timesteps 1305400/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6527/10000 episodes, total num timesteps 1305600/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6528/10000 episodes, total num timesteps 1305800/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6529/10000 episodes, total num timesteps 1306000/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6530/10000 episodes, total num timesteps 1306200/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6531/10000 episodes, total num timesteps 1306400/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6532/10000 episodes, total num timesteps 1306600/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6533/10000 episodes, total num timesteps 1306800/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6534/10000 episodes, total num timesteps 1307000/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6535/10000 episodes, total num timesteps 1307200/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6536/10000 episodes, total num timesteps 1307400/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6537/10000 episodes, total num timesteps 1307600/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6538/10000 episodes, total num timesteps 1307800/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6539/10000 episodes, total num timesteps 1308000/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6540/10000 episodes, total num timesteps 1308200/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6541/10000 episodes, total num timesteps 1308400/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6542/10000 episodes, total num timesteps 1308600/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6543/10000 episodes, total num timesteps 1308800/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6544/10000 episodes, total num timesteps 1309000/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6545/10000 episodes, total num timesteps 1309200/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6546/10000 episodes, total num timesteps 1309400/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6547/10000 episodes, total num timesteps 1309600/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6548/10000 episodes, total num timesteps 1309800/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6549/10000 episodes, total num timesteps 1310000/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6550/10000 episodes, total num timesteps 1310200/2000000, FPS 236.

team_policy eval average step individual rewards of agent0: 0.46015094869953443
team_policy eval average team episode rewards of agent0: 75.0
team_policy eval idv catch total num of agent0: 20
team_policy eval team catch total num: 30
team_policy eval average step individual rewards of agent1: 0.3221460357739489
team_policy eval average team episode rewards of agent1: 75.0
team_policy eval idv catch total num of agent1: 15
team_policy eval team catch total num: 30
team_policy eval average step individual rewards of agent2: 0.35001665170752616
team_policy eval average team episode rewards of agent2: 75.0
team_policy eval idv catch total num of agent2: 16
team_policy eval team catch total num: 30
team_policy eval average step individual rewards of agent3: 0.6824621483756337
team_policy eval average team episode rewards of agent3: 75.0
team_policy eval idv catch total num of agent3: 29
team_policy eval team catch total num: 30
team_policy eval average step individual rewards of agent4: 0.8359960936031123
team_policy eval average team episode rewards of agent4: 75.0
team_policy eval idv catch total num of agent4: 35
team_policy eval team catch total num: 30
idv_policy eval average step individual rewards of agent0: 0.48702541137319244
idv_policy eval average team episode rewards of agent0: 65.0
idv_policy eval idv catch total num of agent0: 21
idv_policy eval team catch total num: 26
idv_policy eval average step individual rewards of agent1: 0.8427827743254914
idv_policy eval average team episode rewards of agent1: 65.0
idv_policy eval idv catch total num of agent1: 35
idv_policy eval team catch total num: 26
idv_policy eval average step individual rewards of agent2: 0.5613598575272525
idv_policy eval average team episode rewards of agent2: 65.0
idv_policy eval idv catch total num of agent2: 24
idv_policy eval team catch total num: 26
idv_policy eval average step individual rewards of agent3: 0.4085030586024341
idv_policy eval average team episode rewards of agent3: 65.0
idv_policy eval idv catch total num of agent3: 18
idv_policy eval team catch total num: 26
idv_policy eval average step individual rewards of agent4: 0.6599862275485332
idv_policy eval average team episode rewards of agent4: 65.0
idv_policy eval idv catch total num of agent4: 28
idv_policy eval team catch total num: 26

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6551/10000 episodes, total num timesteps 1310400/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6552/10000 episodes, total num timesteps 1310600/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6553/10000 episodes, total num timesteps 1310800/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6554/10000 episodes, total num timesteps 1311000/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6555/10000 episodes, total num timesteps 1311200/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6556/10000 episodes, total num timesteps 1311400/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6557/10000 episodes, total num timesteps 1311600/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6558/10000 episodes, total num timesteps 1311800/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6559/10000 episodes, total num timesteps 1312000/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6560/10000 episodes, total num timesteps 1312200/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6561/10000 episodes, total num timesteps 1312400/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6562/10000 episodes, total num timesteps 1312600/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6563/10000 episodes, total num timesteps 1312800/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6564/10000 episodes, total num timesteps 1313000/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6565/10000 episodes, total num timesteps 1313200/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6566/10000 episodes, total num timesteps 1313400/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6567/10000 episodes, total num timesteps 1313600/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6568/10000 episodes, total num timesteps 1313800/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6569/10000 episodes, total num timesteps 1314000/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6570/10000 episodes, total num timesteps 1314200/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6571/10000 episodes, total num timesteps 1314400/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6572/10000 episodes, total num timesteps 1314600/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6573/10000 episodes, total num timesteps 1314800/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6574/10000 episodes, total num timesteps 1315000/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6575/10000 episodes, total num timesteps 1315200/2000000, FPS 236.

team_policy eval average step individual rewards of agent0: 0.8064358194886444
team_policy eval average team episode rewards of agent0: 125.0
team_policy eval idv catch total num of agent0: 34
team_policy eval team catch total num: 50
team_policy eval average step individual rewards of agent1: 0.8576764745307989
team_policy eval average team episode rewards of agent1: 125.0
team_policy eval idv catch total num of agent1: 36
team_policy eval team catch total num: 50
team_policy eval average step individual rewards of agent2: 1.2670316266374948
team_policy eval average team episode rewards of agent2: 125.0
team_policy eval idv catch total num of agent2: 52
team_policy eval team catch total num: 50
team_policy eval average step individual rewards of agent3: 0.8815034852080845
team_policy eval average team episode rewards of agent3: 125.0
team_policy eval idv catch total num of agent3: 37
team_policy eval team catch total num: 50
team_policy eval average step individual rewards of agent4: 0.6305126593545283
team_policy eval average team episode rewards of agent4: 125.0
team_policy eval idv catch total num of agent4: 27
team_policy eval team catch total num: 50
idv_policy eval average step individual rewards of agent0: 0.7882342600825454
idv_policy eval average team episode rewards of agent0: 155.0
idv_policy eval idv catch total num of agent0: 33
idv_policy eval team catch total num: 62
idv_policy eval average step individual rewards of agent1: 0.6903212295341487
idv_policy eval average team episode rewards of agent1: 155.0
idv_policy eval idv catch total num of agent1: 29
idv_policy eval team catch total num: 62
idv_policy eval average step individual rewards of agent2: 1.0398343585008458
idv_policy eval average team episode rewards of agent2: 155.0
idv_policy eval idv catch total num of agent2: 43
idv_policy eval team catch total num: 62
idv_policy eval average step individual rewards of agent3: 1.06644130716003
idv_policy eval average team episode rewards of agent3: 155.0
idv_policy eval idv catch total num of agent3: 44
idv_policy eval team catch total num: 62
idv_policy eval average step individual rewards of agent4: 1.2253524059690823
idv_policy eval average team episode rewards of agent4: 155.0
idv_policy eval idv catch total num of agent4: 50
idv_policy eval team catch total num: 62

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6576/10000 episodes, total num timesteps 1315400/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6577/10000 episodes, total num timesteps 1315600/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6578/10000 episodes, total num timesteps 1315800/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6579/10000 episodes, total num timesteps 1316000/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6580/10000 episodes, total num timesteps 1316200/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6581/10000 episodes, total num timesteps 1316400/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6582/10000 episodes, total num timesteps 1316600/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6583/10000 episodes, total num timesteps 1316800/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6584/10000 episodes, total num timesteps 1317000/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6585/10000 episodes, total num timesteps 1317200/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6586/10000 episodes, total num timesteps 1317400/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6587/10000 episodes, total num timesteps 1317600/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6588/10000 episodes, total num timesteps 1317800/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6589/10000 episodes, total num timesteps 1318000/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6590/10000 episodes, total num timesteps 1318200/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6591/10000 episodes, total num timesteps 1318400/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6592/10000 episodes, total num timesteps 1318600/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6593/10000 episodes, total num timesteps 1318800/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6594/10000 episodes, total num timesteps 1319000/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6595/10000 episodes, total num timesteps 1319200/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6596/10000 episodes, total num timesteps 1319400/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6597/10000 episodes, total num timesteps 1319600/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6598/10000 episodes, total num timesteps 1319800/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6599/10000 episodes, total num timesteps 1320000/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6600/10000 episodes, total num timesteps 1320200/2000000, FPS 236.

team_policy eval average step individual rewards of agent0: 0.8160470234172134
team_policy eval average team episode rewards of agent0: 120.0
team_policy eval idv catch total num of agent0: 34
team_policy eval team catch total num: 48
team_policy eval average step individual rewards of agent1: 0.9863904755105835
team_policy eval average team episode rewards of agent1: 120.0
team_policy eval idv catch total num of agent1: 41
team_policy eval team catch total num: 48
team_policy eval average step individual rewards of agent2: 0.8593994120009034
team_policy eval average team episode rewards of agent2: 120.0
team_policy eval idv catch total num of agent2: 36
team_policy eval team catch total num: 48
team_policy eval average step individual rewards of agent3: 0.7825326665095542
team_policy eval average team episode rewards of agent3: 120.0
team_policy eval idv catch total num of agent3: 33
team_policy eval team catch total num: 48
team_policy eval average step individual rewards of agent4: 0.6081777250368868
team_policy eval average team episode rewards of agent4: 120.0
team_policy eval idv catch total num of agent4: 26
team_policy eval team catch total num: 48
idv_policy eval average step individual rewards of agent0: 0.6633918706277394
idv_policy eval average team episode rewards of agent0: 137.5
idv_policy eval idv catch total num of agent0: 28
idv_policy eval team catch total num: 55
idv_policy eval average step individual rewards of agent1: 1.4239345334155251
idv_policy eval average team episode rewards of agent1: 137.5
idv_policy eval idv catch total num of agent1: 58
idv_policy eval team catch total num: 55
idv_policy eval average step individual rewards of agent2: 1.2067819383213956
idv_policy eval average team episode rewards of agent2: 137.5
idv_policy eval idv catch total num of agent2: 50
idv_policy eval team catch total num: 55
idv_policy eval average step individual rewards of agent3: 0.8665838843505804
idv_policy eval average team episode rewards of agent3: 137.5
idv_policy eval idv catch total num of agent3: 36
idv_policy eval team catch total num: 55
idv_policy eval average step individual rewards of agent4: 0.8664786564826863
idv_policy eval average team episode rewards of agent4: 137.5
idv_policy eval idv catch total num of agent4: 36
idv_policy eval team catch total num: 55

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6601/10000 episodes, total num timesteps 1320400/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6602/10000 episodes, total num timesteps 1320600/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6603/10000 episodes, total num timesteps 1320800/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6604/10000 episodes, total num timesteps 1321000/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6605/10000 episodes, total num timesteps 1321200/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6606/10000 episodes, total num timesteps 1321400/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6607/10000 episodes, total num timesteps 1321600/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6608/10000 episodes, total num timesteps 1321800/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6609/10000 episodes, total num timesteps 1322000/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6610/10000 episodes, total num timesteps 1322200/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6611/10000 episodes, total num timesteps 1322400/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6612/10000 episodes, total num timesteps 1322600/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6613/10000 episodes, total num timesteps 1322800/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6614/10000 episodes, total num timesteps 1323000/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6615/10000 episodes, total num timesteps 1323200/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6616/10000 episodes, total num timesteps 1323400/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6617/10000 episodes, total num timesteps 1323600/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6618/10000 episodes, total num timesteps 1323800/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6619/10000 episodes, total num timesteps 1324000/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6620/10000 episodes, total num timesteps 1324200/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6621/10000 episodes, total num timesteps 1324400/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6622/10000 episodes, total num timesteps 1324600/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6623/10000 episodes, total num timesteps 1324800/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6624/10000 episodes, total num timesteps 1325000/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6625/10000 episodes, total num timesteps 1325200/2000000, FPS 236.

team_policy eval average step individual rewards of agent0: 1.191710269499164
team_policy eval average team episode rewards of agent0: 112.5
team_policy eval idv catch total num of agent0: 49
team_policy eval team catch total num: 45
team_policy eval average step individual rewards of agent1: 0.7092138232376022
team_policy eval average team episode rewards of agent1: 112.5
team_policy eval idv catch total num of agent1: 30
team_policy eval team catch total num: 45
team_policy eval average step individual rewards of agent2: 0.7379078557492218
team_policy eval average team episode rewards of agent2: 112.5
team_policy eval idv catch total num of agent2: 31
team_policy eval team catch total num: 45
team_policy eval average step individual rewards of agent3: 0.7198819215574371
team_policy eval average team episode rewards of agent3: 112.5
team_policy eval idv catch total num of agent3: 30
team_policy eval team catch total num: 45
team_policy eval average step individual rewards of agent4: 1.0169889764438071
team_policy eval average team episode rewards of agent4: 112.5
team_policy eval idv catch total num of agent4: 42
team_policy eval team catch total num: 45
idv_policy eval average step individual rewards of agent0: 0.7419723127985925
idv_policy eval average team episode rewards of agent0: 112.5
idv_policy eval idv catch total num of agent0: 31
idv_policy eval team catch total num: 45
idv_policy eval average step individual rewards of agent1: 1.1103610877817796
idv_policy eval average team episode rewards of agent1: 112.5
idv_policy eval idv catch total num of agent1: 46
idv_policy eval team catch total num: 45
idv_policy eval average step individual rewards of agent2: 0.5113501318388421
idv_policy eval average team episode rewards of agent2: 112.5
idv_policy eval idv catch total num of agent2: 22
idv_policy eval team catch total num: 45
idv_policy eval average step individual rewards of agent3: 0.9469663938949628
idv_policy eval average team episode rewards of agent3: 112.5
idv_policy eval idv catch total num of agent3: 39
idv_policy eval team catch total num: 45
idv_policy eval average step individual rewards of agent4: 0.4532153268375768
idv_policy eval average team episode rewards of agent4: 112.5
idv_policy eval idv catch total num of agent4: 20
idv_policy eval team catch total num: 45

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6626/10000 episodes, total num timesteps 1325400/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6627/10000 episodes, total num timesteps 1325600/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6628/10000 episodes, total num timesteps 1325800/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6629/10000 episodes, total num timesteps 1326000/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6630/10000 episodes, total num timesteps 1326200/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6631/10000 episodes, total num timesteps 1326400/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6632/10000 episodes, total num timesteps 1326600/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6633/10000 episodes, total num timesteps 1326800/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6634/10000 episodes, total num timesteps 1327000/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6635/10000 episodes, total num timesteps 1327200/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6636/10000 episodes, total num timesteps 1327400/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6637/10000 episodes, total num timesteps 1327600/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6638/10000 episodes, total num timesteps 1327800/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6639/10000 episodes, total num timesteps 1328000/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6640/10000 episodes, total num timesteps 1328200/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6641/10000 episodes, total num timesteps 1328400/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6642/10000 episodes, total num timesteps 1328600/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6643/10000 episodes, total num timesteps 1328800/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6644/10000 episodes, total num timesteps 1329000/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6645/10000 episodes, total num timesteps 1329200/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6646/10000 episodes, total num timesteps 1329400/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6647/10000 episodes, total num timesteps 1329600/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6648/10000 episodes, total num timesteps 1329800/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6649/10000 episodes, total num timesteps 1330000/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6650/10000 episodes, total num timesteps 1330200/2000000, FPS 237.

team_policy eval average step individual rewards of agent0: 0.5615397386250798
team_policy eval average team episode rewards of agent0: 87.5
team_policy eval idv catch total num of agent0: 24
team_policy eval team catch total num: 35
team_policy eval average step individual rewards of agent1: 0.7650709439384902
team_policy eval average team episode rewards of agent1: 87.5
team_policy eval idv catch total num of agent1: 32
team_policy eval team catch total num: 35
team_policy eval average step individual rewards of agent2: 0.7119984621947816
team_policy eval average team episode rewards of agent2: 87.5
team_policy eval idv catch total num of agent2: 30
team_policy eval team catch total num: 35
team_policy eval average step individual rewards of agent3: 0.6600466091201479
team_policy eval average team episode rewards of agent3: 87.5
team_policy eval idv catch total num of agent3: 28
team_policy eval team catch total num: 35
team_policy eval average step individual rewards of agent4: 0.8107524113616901
team_policy eval average team episode rewards of agent4: 87.5
team_policy eval idv catch total num of agent4: 34
team_policy eval team catch total num: 35
idv_policy eval average step individual rewards of agent0: 0.9485350370278492
idv_policy eval average team episode rewards of agent0: 95.0
idv_policy eval idv catch total num of agent0: 39
idv_policy eval team catch total num: 38
idv_policy eval average step individual rewards of agent1: 0.7838237331056355
idv_policy eval average team episode rewards of agent1: 95.0
idv_policy eval idv catch total num of agent1: 33
idv_policy eval team catch total num: 38
idv_policy eval average step individual rewards of agent2: 0.7396721203136626
idv_policy eval average team episode rewards of agent2: 95.0
idv_policy eval idv catch total num of agent2: 31
idv_policy eval team catch total num: 38
idv_policy eval average step individual rewards of agent3: 1.091191996519367
idv_policy eval average team episode rewards of agent3: 95.0
idv_policy eval idv catch total num of agent3: 45
idv_policy eval team catch total num: 38
idv_policy eval average step individual rewards of agent4: 0.6835524639431577
idv_policy eval average team episode rewards of agent4: 95.0
idv_policy eval idv catch total num of agent4: 29
idv_policy eval team catch total num: 38

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6651/10000 episodes, total num timesteps 1330400/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6652/10000 episodes, total num timesteps 1330600/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6653/10000 episodes, total num timesteps 1330800/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6654/10000 episodes, total num timesteps 1331000/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6655/10000 episodes, total num timesteps 1331200/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6656/10000 episodes, total num timesteps 1331400/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6657/10000 episodes, total num timesteps 1331600/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6658/10000 episodes, total num timesteps 1331800/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6659/10000 episodes, total num timesteps 1332000/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6660/10000 episodes, total num timesteps 1332200/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6661/10000 episodes, total num timesteps 1332400/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6662/10000 episodes, total num timesteps 1332600/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6663/10000 episodes, total num timesteps 1332800/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6664/10000 episodes, total num timesteps 1333000/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6665/10000 episodes, total num timesteps 1333200/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6666/10000 episodes, total num timesteps 1333400/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6667/10000 episodes, total num timesteps 1333600/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6668/10000 episodes, total num timesteps 1333800/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6669/10000 episodes, total num timesteps 1334000/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6670/10000 episodes, total num timesteps 1334200/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6671/10000 episodes, total num timesteps 1334400/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6672/10000 episodes, total num timesteps 1334600/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6673/10000 episodes, total num timesteps 1334800/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6674/10000 episodes, total num timesteps 1335000/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6675/10000 episodes, total num timesteps 1335200/2000000, FPS 237.

team_policy eval average step individual rewards of agent0: 0.8387677920904418
team_policy eval average team episode rewards of agent0: 125.0
team_policy eval idv catch total num of agent0: 35
team_policy eval team catch total num: 50
team_policy eval average step individual rewards of agent1: 0.8188324996022428
team_policy eval average team episode rewards of agent1: 125.0
team_policy eval idv catch total num of agent1: 34
team_policy eval team catch total num: 50
team_policy eval average step individual rewards of agent2: 0.9656448386221405
team_policy eval average team episode rewards of agent2: 125.0
team_policy eval idv catch total num of agent2: 40
team_policy eval team catch total num: 50
team_policy eval average step individual rewards of agent3: 1.064824885861074
team_policy eval average team episode rewards of agent3: 125.0
team_policy eval idv catch total num of agent3: 44
team_policy eval team catch total num: 50
team_policy eval average step individual rewards of agent4: 1.0080911859411676
team_policy eval average team episode rewards of agent4: 125.0
team_policy eval idv catch total num of agent4: 42
team_policy eval team catch total num: 50
idv_policy eval average step individual rewards of agent0: 1.1644925554895698
idv_policy eval average team episode rewards of agent0: 102.5
idv_policy eval idv catch total num of agent0: 48
idv_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent1: 0.3265155083114189
idv_policy eval average team episode rewards of agent1: 102.5
idv_policy eval idv catch total num of agent1: 15
idv_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent2: 0.8859114369333327
idv_policy eval average team episode rewards of agent2: 102.5
idv_policy eval idv catch total num of agent2: 37
idv_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent3: 0.9968859160033722
idv_policy eval average team episode rewards of agent3: 102.5
idv_policy eval idv catch total num of agent3: 41
idv_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent4: 0.8062402233682613
idv_policy eval average team episode rewards of agent4: 102.5
idv_policy eval idv catch total num of agent4: 34
idv_policy eval team catch total num: 41

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6676/10000 episodes, total num timesteps 1335400/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6677/10000 episodes, total num timesteps 1335600/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6678/10000 episodes, total num timesteps 1335800/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6679/10000 episodes, total num timesteps 1336000/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6680/10000 episodes, total num timesteps 1336200/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6681/10000 episodes, total num timesteps 1336400/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6682/10000 episodes, total num timesteps 1336600/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6683/10000 episodes, total num timesteps 1336800/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6684/10000 episodes, total num timesteps 1337000/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6685/10000 episodes, total num timesteps 1337200/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6686/10000 episodes, total num timesteps 1337400/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6687/10000 episodes, total num timesteps 1337600/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6688/10000 episodes, total num timesteps 1337800/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6689/10000 episodes, total num timesteps 1338000/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6690/10000 episodes, total num timesteps 1338200/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6691/10000 episodes, total num timesteps 1338400/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6692/10000 episodes, total num timesteps 1338600/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6693/10000 episodes, total num timesteps 1338800/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6694/10000 episodes, total num timesteps 1339000/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6695/10000 episodes, total num timesteps 1339200/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6696/10000 episodes, total num timesteps 1339400/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6697/10000 episodes, total num timesteps 1339600/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6698/10000 episodes, total num timesteps 1339800/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6699/10000 episodes, total num timesteps 1340000/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6700/10000 episodes, total num timesteps 1340200/2000000, FPS 237.

team_policy eval average step individual rewards of agent0: 0.6869002318224041
team_policy eval average team episode rewards of agent0: 142.5
team_policy eval idv catch total num of agent0: 29
team_policy eval team catch total num: 57
team_policy eval average step individual rewards of agent1: 1.6207340745508747
team_policy eval average team episode rewards of agent1: 142.5
team_policy eval idv catch total num of agent1: 66
team_policy eval team catch total num: 57
team_policy eval average step individual rewards of agent2: 0.8915968081570336
team_policy eval average team episode rewards of agent2: 142.5
team_policy eval idv catch total num of agent2: 37
team_policy eval team catch total num: 57
team_policy eval average step individual rewards of agent3: 0.40769683519856853
team_policy eval average team episode rewards of agent3: 142.5
team_policy eval idv catch total num of agent3: 18
team_policy eval team catch total num: 57
team_policy eval average step individual rewards of agent4: 1.1977526900599051
team_policy eval average team episode rewards of agent4: 142.5
team_policy eval idv catch total num of agent4: 49
team_policy eval team catch total num: 57
idv_policy eval average step individual rewards of agent0: 0.6012138932974467
idv_policy eval average team episode rewards of agent0: 105.0
idv_policy eval idv catch total num of agent0: 26
idv_policy eval team catch total num: 42
idv_policy eval average step individual rewards of agent1: 0.8036093989370734
idv_policy eval average team episode rewards of agent1: 105.0
idv_policy eval idv catch total num of agent1: 34
idv_policy eval team catch total num: 42
idv_policy eval average step individual rewards of agent2: 0.9906815643155398
idv_policy eval average team episode rewards of agent2: 105.0
idv_policy eval idv catch total num of agent2: 41
idv_policy eval team catch total num: 42
idv_policy eval average step individual rewards of agent3: 0.7149796257968964
idv_policy eval average team episode rewards of agent3: 105.0
idv_policy eval idv catch total num of agent3: 30
idv_policy eval team catch total num: 42
idv_policy eval average step individual rewards of agent4: 0.8167381157195439
idv_policy eval average team episode rewards of agent4: 105.0
idv_policy eval idv catch total num of agent4: 34
idv_policy eval team catch total num: 42

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6701/10000 episodes, total num timesteps 1340400/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6702/10000 episodes, total num timesteps 1340600/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6703/10000 episodes, total num timesteps 1340800/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6704/10000 episodes, total num timesteps 1341000/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6705/10000 episodes, total num timesteps 1341200/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6706/10000 episodes, total num timesteps 1341400/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6707/10000 episodes, total num timesteps 1341600/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6708/10000 episodes, total num timesteps 1341800/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6709/10000 episodes, total num timesteps 1342000/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6710/10000 episodes, total num timesteps 1342200/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6711/10000 episodes, total num timesteps 1342400/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6712/10000 episodes, total num timesteps 1342600/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6713/10000 episodes, total num timesteps 1342800/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6714/10000 episodes, total num timesteps 1343000/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6715/10000 episodes, total num timesteps 1343200/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6716/10000 episodes, total num timesteps 1343400/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6717/10000 episodes, total num timesteps 1343600/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6718/10000 episodes, total num timesteps 1343800/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6719/10000 episodes, total num timesteps 1344000/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6720/10000 episodes, total num timesteps 1344200/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6721/10000 episodes, total num timesteps 1344400/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6722/10000 episodes, total num timesteps 1344600/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6723/10000 episodes, total num timesteps 1344800/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6724/10000 episodes, total num timesteps 1345000/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6725/10000 episodes, total num timesteps 1345200/2000000, FPS 237.

team_policy eval average step individual rewards of agent0: 0.780574592623536
team_policy eval average team episode rewards of agent0: 100.0
team_policy eval idv catch total num of agent0: 33
team_policy eval team catch total num: 40
team_policy eval average step individual rewards of agent1: 0.673410447265008
team_policy eval average team episode rewards of agent1: 100.0
team_policy eval idv catch total num of agent1: 29
team_policy eval team catch total num: 40
team_policy eval average step individual rewards of agent2: 0.8958296558815309
team_policy eval average team episode rewards of agent2: 100.0
team_policy eval idv catch total num of agent2: 37
team_policy eval team catch total num: 40
team_policy eval average step individual rewards of agent3: 0.886297705853027
team_policy eval average team episode rewards of agent3: 100.0
team_policy eval idv catch total num of agent3: 37
team_policy eval team catch total num: 40
team_policy eval average step individual rewards of agent4: 0.7207109284075999
team_policy eval average team episode rewards of agent4: 100.0
team_policy eval idv catch total num of agent4: 30
team_policy eval team catch total num: 40
idv_policy eval average step individual rewards of agent0: 0.9396502930533503
idv_policy eval average team episode rewards of agent0: 125.0
idv_policy eval idv catch total num of agent0: 39
idv_policy eval team catch total num: 50
idv_policy eval average step individual rewards of agent1: 0.726841971230069
idv_policy eval average team episode rewards of agent1: 125.0
idv_policy eval idv catch total num of agent1: 31
idv_policy eval team catch total num: 50
idv_policy eval average step individual rewards of agent2: 1.2165078721627536
idv_policy eval average team episode rewards of agent2: 125.0
idv_policy eval idv catch total num of agent2: 50
idv_policy eval team catch total num: 50
idv_policy eval average step individual rewards of agent3: 0.629372853941746
idv_policy eval average team episode rewards of agent3: 125.0
idv_policy eval idv catch total num of agent3: 27
idv_policy eval team catch total num: 50
idv_policy eval average step individual rewards of agent4: 0.8680221253572921
idv_policy eval average team episode rewards of agent4: 125.0
idv_policy eval idv catch total num of agent4: 36
idv_policy eval team catch total num: 50

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6726/10000 episodes, total num timesteps 1345400/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6727/10000 episodes, total num timesteps 1345600/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6728/10000 episodes, total num timesteps 1345800/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6729/10000 episodes, total num timesteps 1346000/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6730/10000 episodes, total num timesteps 1346200/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6731/10000 episodes, total num timesteps 1346400/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6732/10000 episodes, total num timesteps 1346600/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6733/10000 episodes, total num timesteps 1346800/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6734/10000 episodes, total num timesteps 1347000/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6735/10000 episodes, total num timesteps 1347200/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6736/10000 episodes, total num timesteps 1347400/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6737/10000 episodes, total num timesteps 1347600/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6738/10000 episodes, total num timesteps 1347800/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6739/10000 episodes, total num timesteps 1348000/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6740/10000 episodes, total num timesteps 1348200/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6741/10000 episodes, total num timesteps 1348400/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6742/10000 episodes, total num timesteps 1348600/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6743/10000 episodes, total num timesteps 1348800/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6744/10000 episodes, total num timesteps 1349000/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6745/10000 episodes, total num timesteps 1349200/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6746/10000 episodes, total num timesteps 1349400/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6747/10000 episodes, total num timesteps 1349600/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6748/10000 episodes, total num timesteps 1349800/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6749/10000 episodes, total num timesteps 1350000/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6750/10000 episodes, total num timesteps 1350200/2000000, FPS 237.

team_policy eval average step individual rewards of agent0: 0.8650743932851732
team_policy eval average team episode rewards of agent0: 115.0
team_policy eval idv catch total num of agent0: 36
team_policy eval team catch total num: 46
team_policy eval average step individual rewards of agent1: 0.7416842993176602
team_policy eval average team episode rewards of agent1: 115.0
team_policy eval idv catch total num of agent1: 31
team_policy eval team catch total num: 46
team_policy eval average step individual rewards of agent2: 0.6638820998994862
team_policy eval average team episode rewards of agent2: 115.0
team_policy eval idv catch total num of agent2: 28
team_policy eval team catch total num: 46
team_policy eval average step individual rewards of agent3: 1.1229525355248695
team_policy eval average team episode rewards of agent3: 115.0
team_policy eval idv catch total num of agent3: 46
team_policy eval team catch total num: 46
team_policy eval average step individual rewards of agent4: 0.6074964202712795
team_policy eval average team episode rewards of agent4: 115.0
team_policy eval idv catch total num of agent4: 26
team_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent0: 0.5107825801002704
idv_policy eval average team episode rewards of agent0: 90.0
idv_policy eval idv catch total num of agent0: 22
idv_policy eval team catch total num: 36
idv_policy eval average step individual rewards of agent1: 0.4796342778854752
idv_policy eval average team episode rewards of agent1: 90.0
idv_policy eval idv catch total num of agent1: 21
idv_policy eval team catch total num: 36
idv_policy eval average step individual rewards of agent2: 0.14324696160092576
idv_policy eval average team episode rewards of agent2: 90.0
idv_policy eval idv catch total num of agent2: 8
idv_policy eval team catch total num: 36
idv_policy eval average step individual rewards of agent3: 0.7547033647942162
idv_policy eval average team episode rewards of agent3: 90.0
idv_policy eval idv catch total num of agent3: 32
idv_policy eval team catch total num: 36
idv_policy eval average step individual rewards of agent4: 0.9585867525636296
idv_policy eval average team episode rewards of agent4: 90.0
idv_policy eval idv catch total num of agent4: 40
idv_policy eval team catch total num: 36

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6751/10000 episodes, total num timesteps 1350400/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6752/10000 episodes, total num timesteps 1350600/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6753/10000 episodes, total num timesteps 1350800/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6754/10000 episodes, total num timesteps 1351000/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6755/10000 episodes, total num timesteps 1351200/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6756/10000 episodes, total num timesteps 1351400/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6757/10000 episodes, total num timesteps 1351600/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6758/10000 episodes, total num timesteps 1351800/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6759/10000 episodes, total num timesteps 1352000/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6760/10000 episodes, total num timesteps 1352200/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6761/10000 episodes, total num timesteps 1352400/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6762/10000 episodes, total num timesteps 1352600/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6763/10000 episodes, total num timesteps 1352800/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6764/10000 episodes, total num timesteps 1353000/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6765/10000 episodes, total num timesteps 1353200/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6766/10000 episodes, total num timesteps 1353400/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6767/10000 episodes, total num timesteps 1353600/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6768/10000 episodes, total num timesteps 1353800/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6769/10000 episodes, total num timesteps 1354000/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6770/10000 episodes, total num timesteps 1354200/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6771/10000 episodes, total num timesteps 1354400/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6772/10000 episodes, total num timesteps 1354600/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6773/10000 episodes, total num timesteps 1354800/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6774/10000 episodes, total num timesteps 1355000/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6775/10000 episodes, total num timesteps 1355200/2000000, FPS 237.

team_policy eval average step individual rewards of agent0: 0.5857845427455919
team_policy eval average team episode rewards of agent0: 90.0
team_policy eval idv catch total num of agent0: 25
team_policy eval team catch total num: 36
team_policy eval average step individual rewards of agent1: 0.9361726072060046
team_policy eval average team episode rewards of agent1: 90.0
team_policy eval idv catch total num of agent1: 39
team_policy eval team catch total num: 36
team_policy eval average step individual rewards of agent2: 0.6812664662940451
team_policy eval average team episode rewards of agent2: 90.0
team_policy eval idv catch total num of agent2: 29
team_policy eval team catch total num: 36
team_policy eval average step individual rewards of agent3: 0.6400606989075925
team_policy eval average team episode rewards of agent3: 90.0
team_policy eval idv catch total num of agent3: 27
team_policy eval team catch total num: 36
team_policy eval average step individual rewards of agent4: 0.6461229121522689
team_policy eval average team episode rewards of agent4: 90.0
team_policy eval idv catch total num of agent4: 28
team_policy eval team catch total num: 36
idv_policy eval average step individual rewards of agent0: 1.2991101181171119
idv_policy eval average team episode rewards of agent0: 137.5
idv_policy eval idv catch total num of agent0: 53
idv_policy eval team catch total num: 55
idv_policy eval average step individual rewards of agent1: 0.6456062054030943
idv_policy eval average team episode rewards of agent1: 137.5
idv_policy eval idv catch total num of agent1: 27
idv_policy eval team catch total num: 55
idv_policy eval average step individual rewards of agent2: 0.9749850983327423
idv_policy eval average team episode rewards of agent2: 137.5
idv_policy eval idv catch total num of agent2: 40
idv_policy eval team catch total num: 55
idv_policy eval average step individual rewards of agent3: 0.7857690492072283
idv_policy eval average team episode rewards of agent3: 137.5
idv_policy eval idv catch total num of agent3: 33
idv_policy eval team catch total num: 55
idv_policy eval average step individual rewards of agent4: 0.97013856125511
idv_policy eval average team episode rewards of agent4: 137.5
idv_policy eval idv catch total num of agent4: 40
idv_policy eval team catch total num: 55

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6776/10000 episodes, total num timesteps 1355400/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6777/10000 episodes, total num timesteps 1355600/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6778/10000 episodes, total num timesteps 1355800/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6779/10000 episodes, total num timesteps 1356000/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6780/10000 episodes, total num timesteps 1356200/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6781/10000 episodes, total num timesteps 1356400/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6782/10000 episodes, total num timesteps 1356600/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6783/10000 episodes, total num timesteps 1356800/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6784/10000 episodes, total num timesteps 1357000/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6785/10000 episodes, total num timesteps 1357200/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6786/10000 episodes, total num timesteps 1357400/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6787/10000 episodes, total num timesteps 1357600/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6788/10000 episodes, total num timesteps 1357800/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6789/10000 episodes, total num timesteps 1358000/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6790/10000 episodes, total num timesteps 1358200/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6791/10000 episodes, total num timesteps 1358400/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6792/10000 episodes, total num timesteps 1358600/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6793/10000 episodes, total num timesteps 1358800/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6794/10000 episodes, total num timesteps 1359000/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6795/10000 episodes, total num timesteps 1359200/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6796/10000 episodes, total num timesteps 1359400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6797/10000 episodes, total num timesteps 1359600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6798/10000 episodes, total num timesteps 1359800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6799/10000 episodes, total num timesteps 1360000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6800/10000 episodes, total num timesteps 1360200/2000000, FPS 238.

team_policy eval average step individual rewards of agent0: 0.6706691261472257
team_policy eval average team episode rewards of agent0: 120.0
team_policy eval idv catch total num of agent0: 28
team_policy eval team catch total num: 48
team_policy eval average step individual rewards of agent1: 1.326559825206229
team_policy eval average team episode rewards of agent1: 120.0
team_policy eval idv catch total num of agent1: 54
team_policy eval team catch total num: 48
team_policy eval average step individual rewards of agent2: 0.8186452531296223
team_policy eval average team episode rewards of agent2: 120.0
team_policy eval idv catch total num of agent2: 34
team_policy eval team catch total num: 48
team_policy eval average step individual rewards of agent3: 0.7057296871656922
team_policy eval average team episode rewards of agent3: 120.0
team_policy eval idv catch total num of agent3: 30
team_policy eval team catch total num: 48
team_policy eval average step individual rewards of agent4: 0.8421434132968801
team_policy eval average team episode rewards of agent4: 120.0
team_policy eval idv catch total num of agent4: 35
team_policy eval team catch total num: 48
idv_policy eval average step individual rewards of agent0: 0.935849978896796
idv_policy eval average team episode rewards of agent0: 97.5
idv_policy eval idv catch total num of agent0: 39
idv_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent1: 0.8909906036229294
idv_policy eval average team episode rewards of agent1: 97.5
idv_policy eval idv catch total num of agent1: 37
idv_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent2: 0.6168500530371723
idv_policy eval average team episode rewards of agent2: 97.5
idv_policy eval idv catch total num of agent2: 26
idv_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent3: 0.7720919950655362
idv_policy eval average team episode rewards of agent3: 97.5
idv_policy eval idv catch total num of agent3: 32
idv_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent4: 0.5344725287743389
idv_policy eval average team episode rewards of agent4: 97.5
idv_policy eval idv catch total num of agent4: 23
idv_policy eval team catch total num: 39

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6801/10000 episodes, total num timesteps 1360400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6802/10000 episodes, total num timesteps 1360600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6803/10000 episodes, total num timesteps 1360800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6804/10000 episodes, total num timesteps 1361000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6805/10000 episodes, total num timesteps 1361200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6806/10000 episodes, total num timesteps 1361400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6807/10000 episodes, total num timesteps 1361600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6808/10000 episodes, total num timesteps 1361800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6809/10000 episodes, total num timesteps 1362000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6810/10000 episodes, total num timesteps 1362200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6811/10000 episodes, total num timesteps 1362400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6812/10000 episodes, total num timesteps 1362600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6813/10000 episodes, total num timesteps 1362800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6814/10000 episodes, total num timesteps 1363000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6815/10000 episodes, total num timesteps 1363200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6816/10000 episodes, total num timesteps 1363400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6817/10000 episodes, total num timesteps 1363600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6818/10000 episodes, total num timesteps 1363800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6819/10000 episodes, total num timesteps 1364000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6820/10000 episodes, total num timesteps 1364200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6821/10000 episodes, total num timesteps 1364400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6822/10000 episodes, total num timesteps 1364600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6823/10000 episodes, total num timesteps 1364800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6824/10000 episodes, total num timesteps 1365000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6825/10000 episodes, total num timesteps 1365200/2000000, FPS 238.

team_policy eval average step individual rewards of agent0: 1.2184310696087919
team_policy eval average team episode rewards of agent0: 130.0
team_policy eval idv catch total num of agent0: 50
team_policy eval team catch total num: 52
team_policy eval average step individual rewards of agent1: 1.0157563264076304
team_policy eval average team episode rewards of agent1: 130.0
team_policy eval idv catch total num of agent1: 42
team_policy eval team catch total num: 52
team_policy eval average step individual rewards of agent2: 0.7546494147340193
team_policy eval average team episode rewards of agent2: 130.0
team_policy eval idv catch total num of agent2: 32
team_policy eval team catch total num: 52
team_policy eval average step individual rewards of agent3: 0.8610422385015005
team_policy eval average team episode rewards of agent3: 130.0
team_policy eval idv catch total num of agent3: 36
team_policy eval team catch total num: 52
team_policy eval average step individual rewards of agent4: 0.8346742914195936
team_policy eval average team episode rewards of agent4: 130.0
team_policy eval idv catch total num of agent4: 35
team_policy eval team catch total num: 52
idv_policy eval average step individual rewards of agent0: 0.6854796592697994
idv_policy eval average team episode rewards of agent0: 92.5
idv_policy eval idv catch total num of agent0: 29
idv_policy eval team catch total num: 37
idv_policy eval average step individual rewards of agent1: 0.5607918082250852
idv_policy eval average team episode rewards of agent1: 92.5
idv_policy eval idv catch total num of agent1: 24
idv_policy eval team catch total num: 37
idv_policy eval average step individual rewards of agent2: 0.6132466865915829
idv_policy eval average team episode rewards of agent2: 92.5
idv_policy eval idv catch total num of agent2: 26
idv_policy eval team catch total num: 37
idv_policy eval average step individual rewards of agent3: 0.7644226842870269
idv_policy eval average team episode rewards of agent3: 92.5
idv_policy eval idv catch total num of agent3: 32
idv_policy eval team catch total num: 37
idv_policy eval average step individual rewards of agent4: 0.452239971709047
idv_policy eval average team episode rewards of agent4: 92.5
idv_policy eval idv catch total num of agent4: 20
idv_policy eval team catch total num: 37

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6826/10000 episodes, total num timesteps 1365400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6827/10000 episodes, total num timesteps 1365600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6828/10000 episodes, total num timesteps 1365800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6829/10000 episodes, total num timesteps 1366000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6830/10000 episodes, total num timesteps 1366200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6831/10000 episodes, total num timesteps 1366400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6832/10000 episodes, total num timesteps 1366600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6833/10000 episodes, total num timesteps 1366800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6834/10000 episodes, total num timesteps 1367000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6835/10000 episodes, total num timesteps 1367200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6836/10000 episodes, total num timesteps 1367400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6837/10000 episodes, total num timesteps 1367600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6838/10000 episodes, total num timesteps 1367800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6839/10000 episodes, total num timesteps 1368000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6840/10000 episodes, total num timesteps 1368200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6841/10000 episodes, total num timesteps 1368400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6842/10000 episodes, total num timesteps 1368600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6843/10000 episodes, total num timesteps 1368800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6844/10000 episodes, total num timesteps 1369000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6845/10000 episodes, total num timesteps 1369200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6846/10000 episodes, total num timesteps 1369400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6847/10000 episodes, total num timesteps 1369600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6848/10000 episodes, total num timesteps 1369800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6849/10000 episodes, total num timesteps 1370000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6850/10000 episodes, total num timesteps 1370200/2000000, FPS 238.

team_policy eval average step individual rewards of agent0: 1.3751443381411923
team_policy eval average team episode rewards of agent0: 135.0
team_policy eval idv catch total num of agent0: 56
team_policy eval team catch total num: 54
team_policy eval average step individual rewards of agent1: 0.767090764196841
team_policy eval average team episode rewards of agent1: 135.0
team_policy eval idv catch total num of agent1: 32
team_policy eval team catch total num: 54
team_policy eval average step individual rewards of agent2: 0.9155943793877834
team_policy eval average team episode rewards of agent2: 135.0
team_policy eval idv catch total num of agent2: 38
team_policy eval team catch total num: 54
team_policy eval average step individual rewards of agent3: 0.8890578834991285
team_policy eval average team episode rewards of agent3: 135.0
team_policy eval idv catch total num of agent3: 37
team_policy eval team catch total num: 54
team_policy eval average step individual rewards of agent4: 1.1395867282268877
team_policy eval average team episode rewards of agent4: 135.0
team_policy eval idv catch total num of agent4: 47
team_policy eval team catch total num: 54
idv_policy eval average step individual rewards of agent0: 0.7984115594385363
idv_policy eval average team episode rewards of agent0: 125.0
idv_policy eval idv catch total num of agent0: 33
idv_policy eval team catch total num: 50
idv_policy eval average step individual rewards of agent1: 0.731385656192774
idv_policy eval average team episode rewards of agent1: 125.0
idv_policy eval idv catch total num of agent1: 31
idv_policy eval team catch total num: 50
idv_policy eval average step individual rewards of agent2: 0.7087325187737897
idv_policy eval average team episode rewards of agent2: 125.0
idv_policy eval idv catch total num of agent2: 30
idv_policy eval team catch total num: 50
idv_policy eval average step individual rewards of agent3: 0.5370589083504138
idv_policy eval average team episode rewards of agent3: 125.0
idv_policy eval idv catch total num of agent3: 23
idv_policy eval team catch total num: 50
idv_policy eval average step individual rewards of agent4: 1.04473261534466
idv_policy eval average team episode rewards of agent4: 125.0
idv_policy eval idv catch total num of agent4: 43
idv_policy eval team catch total num: 50

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6851/10000 episodes, total num timesteps 1370400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6852/10000 episodes, total num timesteps 1370600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6853/10000 episodes, total num timesteps 1370800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6854/10000 episodes, total num timesteps 1371000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6855/10000 episodes, total num timesteps 1371200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6856/10000 episodes, total num timesteps 1371400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6857/10000 episodes, total num timesteps 1371600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6858/10000 episodes, total num timesteps 1371800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6859/10000 episodes, total num timesteps 1372000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6860/10000 episodes, total num timesteps 1372200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6861/10000 episodes, total num timesteps 1372400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6862/10000 episodes, total num timesteps 1372600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6863/10000 episodes, total num timesteps 1372800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6864/10000 episodes, total num timesteps 1373000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6865/10000 episodes, total num timesteps 1373200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6866/10000 episodes, total num timesteps 1373400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6867/10000 episodes, total num timesteps 1373600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6868/10000 episodes, total num timesteps 1373800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6869/10000 episodes, total num timesteps 1374000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6870/10000 episodes, total num timesteps 1374200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6871/10000 episodes, total num timesteps 1374400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6872/10000 episodes, total num timesteps 1374600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6873/10000 episodes, total num timesteps 1374800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6874/10000 episodes, total num timesteps 1375000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6875/10000 episodes, total num timesteps 1375200/2000000, FPS 238.

team_policy eval average step individual rewards of agent0: 1.0394913160360482
team_policy eval average team episode rewards of agent0: 107.5
team_policy eval idv catch total num of agent0: 43
team_policy eval team catch total num: 43
team_policy eval average step individual rewards of agent1: 1.2191138325573836
team_policy eval average team episode rewards of agent1: 107.5
team_policy eval idv catch total num of agent1: 50
team_policy eval team catch total num: 43
team_policy eval average step individual rewards of agent2: 0.511531916923794
team_policy eval average team episode rewards of agent2: 107.5
team_policy eval idv catch total num of agent2: 22
team_policy eval team catch total num: 43
team_policy eval average step individual rewards of agent3: 0.5123874653118254
team_policy eval average team episode rewards of agent3: 107.5
team_policy eval idv catch total num of agent3: 22
team_policy eval team catch total num: 43
team_policy eval average step individual rewards of agent4: 0.8077088462603464
team_policy eval average team episode rewards of agent4: 107.5
team_policy eval idv catch total num of agent4: 34
team_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent0: 0.4045301070015445
idv_policy eval average team episode rewards of agent0: 45.0
idv_policy eval idv catch total num of agent0: 18
idv_policy eval team catch total num: 18
idv_policy eval average step individual rewards of agent1: 0.46185746732430055
idv_policy eval average team episode rewards of agent1: 45.0
idv_policy eval idv catch total num of agent1: 20
idv_policy eval team catch total num: 18
idv_policy eval average step individual rewards of agent2: 0.4044787159412202
idv_policy eval average team episode rewards of agent2: 45.0
idv_policy eval idv catch total num of agent2: 18
idv_policy eval team catch total num: 18
idv_policy eval average step individual rewards of agent3: 0.2583812513794405
idv_policy eval average team episode rewards of agent3: 45.0
idv_policy eval idv catch total num of agent3: 12
idv_policy eval team catch total num: 18
idv_policy eval average step individual rewards of agent4: 0.3283568564263254
idv_policy eval average team episode rewards of agent4: 45.0
idv_policy eval idv catch total num of agent4: 15
idv_policy eval team catch total num: 18

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6876/10000 episodes, total num timesteps 1375400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6877/10000 episodes, total num timesteps 1375600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6878/10000 episodes, total num timesteps 1375800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6879/10000 episodes, total num timesteps 1376000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6880/10000 episodes, total num timesteps 1376200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6881/10000 episodes, total num timesteps 1376400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6882/10000 episodes, total num timesteps 1376600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6883/10000 episodes, total num timesteps 1376800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6884/10000 episodes, total num timesteps 1377000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6885/10000 episodes, total num timesteps 1377200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6886/10000 episodes, total num timesteps 1377400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6887/10000 episodes, total num timesteps 1377600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6888/10000 episodes, total num timesteps 1377800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6889/10000 episodes, total num timesteps 1378000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6890/10000 episodes, total num timesteps 1378200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6891/10000 episodes, total num timesteps 1378400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6892/10000 episodes, total num timesteps 1378600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6893/10000 episodes, total num timesteps 1378800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6894/10000 episodes, total num timesteps 1379000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6895/10000 episodes, total num timesteps 1379200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6896/10000 episodes, total num timesteps 1379400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6897/10000 episodes, total num timesteps 1379600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6898/10000 episodes, total num timesteps 1379800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6899/10000 episodes, total num timesteps 1380000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6900/10000 episodes, total num timesteps 1380200/2000000, FPS 238.

team_policy eval average step individual rewards of agent0: 1.1162545911603703
team_policy eval average team episode rewards of agent0: 125.0
team_policy eval idv catch total num of agent0: 46
team_policy eval team catch total num: 50
team_policy eval average step individual rewards of agent1: 0.5989072184327217
team_policy eval average team episode rewards of agent1: 125.0
team_policy eval idv catch total num of agent1: 26
team_policy eval team catch total num: 50
team_policy eval average step individual rewards of agent2: 0.8361196996030634
team_policy eval average team episode rewards of agent2: 125.0
team_policy eval idv catch total num of agent2: 35
team_policy eval team catch total num: 50
team_policy eval average step individual rewards of agent3: 0.9413852311989857
team_policy eval average team episode rewards of agent3: 125.0
team_policy eval idv catch total num of agent3: 39
team_policy eval team catch total num: 50
team_policy eval average step individual rewards of agent4: 0.9621931180603314
team_policy eval average team episode rewards of agent4: 125.0
team_policy eval idv catch total num of agent4: 40
team_policy eval team catch total num: 50
idv_policy eval average step individual rewards of agent0: 0.9915806569084676
idv_policy eval average team episode rewards of agent0: 107.5
idv_policy eval idv catch total num of agent0: 41
idv_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent1: 0.6651183650126501
idv_policy eval average team episode rewards of agent1: 107.5
idv_policy eval idv catch total num of agent1: 28
idv_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent2: 0.8617043985816426
idv_policy eval average team episode rewards of agent2: 107.5
idv_policy eval idv catch total num of agent2: 36
idv_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent3: 0.7598363980860438
idv_policy eval average team episode rewards of agent3: 107.5
idv_policy eval idv catch total num of agent3: 32
idv_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent4: 0.4074393665933617
idv_policy eval average team episode rewards of agent4: 107.5
idv_policy eval idv catch total num of agent4: 18
idv_policy eval team catch total num: 43

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6901/10000 episodes, total num timesteps 1380400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6902/10000 episodes, total num timesteps 1380600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6903/10000 episodes, total num timesteps 1380800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6904/10000 episodes, total num timesteps 1381000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6905/10000 episodes, total num timesteps 1381200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6906/10000 episodes, total num timesteps 1381400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6907/10000 episodes, total num timesteps 1381600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6908/10000 episodes, total num timesteps 1381800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6909/10000 episodes, total num timesteps 1382000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6910/10000 episodes, total num timesteps 1382200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6911/10000 episodes, total num timesteps 1382400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6912/10000 episodes, total num timesteps 1382600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6913/10000 episodes, total num timesteps 1382800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6914/10000 episodes, total num timesteps 1383000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6915/10000 episodes, total num timesteps 1383200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6916/10000 episodes, total num timesteps 1383400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6917/10000 episodes, total num timesteps 1383600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6918/10000 episodes, total num timesteps 1383800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6919/10000 episodes, total num timesteps 1384000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6920/10000 episodes, total num timesteps 1384200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6921/10000 episodes, total num timesteps 1384400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6922/10000 episodes, total num timesteps 1384600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6923/10000 episodes, total num timesteps 1384800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6924/10000 episodes, total num timesteps 1385000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6925/10000 episodes, total num timesteps 1385200/2000000, FPS 238.

team_policy eval average step individual rewards of agent0: 0.5605332859351251
team_policy eval average team episode rewards of agent0: 115.0
team_policy eval idv catch total num of agent0: 24
team_policy eval team catch total num: 46
team_policy eval average step individual rewards of agent1: 0.6101946023026699
team_policy eval average team episode rewards of agent1: 115.0
team_policy eval idv catch total num of agent1: 26
team_policy eval team catch total num: 46
team_policy eval average step individual rewards of agent2: 0.7873845782529737
team_policy eval average team episode rewards of agent2: 115.0
team_policy eval idv catch total num of agent2: 33
team_policy eval team catch total num: 46
team_policy eval average step individual rewards of agent3: 0.6110368867229891
team_policy eval average team episode rewards of agent3: 115.0
team_policy eval idv catch total num of agent3: 26
team_policy eval team catch total num: 46
team_policy eval average step individual rewards of agent4: 0.740079672370171
team_policy eval average team episode rewards of agent4: 115.0
team_policy eval idv catch total num of agent4: 31
team_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent0: 0.608697105981772
idv_policy eval average team episode rewards of agent0: 92.5
idv_policy eval idv catch total num of agent0: 26
idv_policy eval team catch total num: 37
idv_policy eval average step individual rewards of agent1: 0.8047161945645424
idv_policy eval average team episode rewards of agent1: 92.5
idv_policy eval idv catch total num of agent1: 34
idv_policy eval team catch total num: 37
idv_policy eval average step individual rewards of agent2: 0.6396284218661257
idv_policy eval average team episode rewards of agent2: 92.5
idv_policy eval idv catch total num of agent2: 27
idv_policy eval team catch total num: 37
idv_policy eval average step individual rewards of agent3: 1.0159719678117727
idv_policy eval average team episode rewards of agent3: 92.5
idv_policy eval idv catch total num of agent3: 42
idv_policy eval team catch total num: 37
idv_policy eval average step individual rewards of agent4: 1.0654798129325092
idv_policy eval average team episode rewards of agent4: 92.5
idv_policy eval idv catch total num of agent4: 44
idv_policy eval team catch total num: 37

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6926/10000 episodes, total num timesteps 1385400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6927/10000 episodes, total num timesteps 1385600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6928/10000 episodes, total num timesteps 1385800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6929/10000 episodes, total num timesteps 1386000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6930/10000 episodes, total num timesteps 1386200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6931/10000 episodes, total num timesteps 1386400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6932/10000 episodes, total num timesteps 1386600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6933/10000 episodes, total num timesteps 1386800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6934/10000 episodes, total num timesteps 1387000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6935/10000 episodes, total num timesteps 1387200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6936/10000 episodes, total num timesteps 1387400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6937/10000 episodes, total num timesteps 1387600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6938/10000 episodes, total num timesteps 1387800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6939/10000 episodes, total num timesteps 1388000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6940/10000 episodes, total num timesteps 1388200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6941/10000 episodes, total num timesteps 1388400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6942/10000 episodes, total num timesteps 1388600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6943/10000 episodes, total num timesteps 1388800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6944/10000 episodes, total num timesteps 1389000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6945/10000 episodes, total num timesteps 1389200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6946/10000 episodes, total num timesteps 1389400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6947/10000 episodes, total num timesteps 1389600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6948/10000 episodes, total num timesteps 1389800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6949/10000 episodes, total num timesteps 1390000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6950/10000 episodes, total num timesteps 1390200/2000000, FPS 238.

team_policy eval average step individual rewards of agent0: 0.8695996726207642
team_policy eval average team episode rewards of agent0: 120.0
team_policy eval idv catch total num of agent0: 36
team_policy eval team catch total num: 48
team_policy eval average step individual rewards of agent1: 0.6153350390883089
team_policy eval average team episode rewards of agent1: 120.0
team_policy eval idv catch total num of agent1: 26
team_policy eval team catch total num: 48
team_policy eval average step individual rewards of agent2: 0.8561996556503518
team_policy eval average team episode rewards of agent2: 120.0
team_policy eval idv catch total num of agent2: 36
team_policy eval team catch total num: 48
team_policy eval average step individual rewards of agent3: 0.6524565158137631
team_policy eval average team episode rewards of agent3: 120.0
team_policy eval idv catch total num of agent3: 28
team_policy eval team catch total num: 48
team_policy eval average step individual rewards of agent4: 0.9396399754860437
team_policy eval average team episode rewards of agent4: 120.0
team_policy eval idv catch total num of agent4: 39
team_policy eval team catch total num: 48
idv_policy eval average step individual rewards of agent0: 0.9662539287428227
idv_policy eval average team episode rewards of agent0: 142.5
idv_policy eval idv catch total num of agent0: 40
idv_policy eval team catch total num: 57
idv_policy eval average step individual rewards of agent1: 0.9215593031170092
idv_policy eval average team episode rewards of agent1: 142.5
idv_policy eval idv catch total num of agent1: 38
idv_policy eval team catch total num: 57
idv_policy eval average step individual rewards of agent2: 0.6369342455385884
idv_policy eval average team episode rewards of agent2: 142.5
idv_policy eval idv catch total num of agent2: 27
idv_policy eval team catch total num: 57
idv_policy eval average step individual rewards of agent3: 1.5522895225479845
idv_policy eval average team episode rewards of agent3: 142.5
idv_policy eval idv catch total num of agent3: 63
idv_policy eval team catch total num: 57
idv_policy eval average step individual rewards of agent4: 1.367813306732465
idv_policy eval average team episode rewards of agent4: 142.5
idv_policy eval idv catch total num of agent4: 56
idv_policy eval team catch total num: 57

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6951/10000 episodes, total num timesteps 1390400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6952/10000 episodes, total num timesteps 1390600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6953/10000 episodes, total num timesteps 1390800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6954/10000 episodes, total num timesteps 1391000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6955/10000 episodes, total num timesteps 1391200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6956/10000 episodes, total num timesteps 1391400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6957/10000 episodes, total num timesteps 1391600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6958/10000 episodes, total num timesteps 1391800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6959/10000 episodes, total num timesteps 1392000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6960/10000 episodes, total num timesteps 1392200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6961/10000 episodes, total num timesteps 1392400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6962/10000 episodes, total num timesteps 1392600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6963/10000 episodes, total num timesteps 1392800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6964/10000 episodes, total num timesteps 1393000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6965/10000 episodes, total num timesteps 1393200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6966/10000 episodes, total num timesteps 1393400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6967/10000 episodes, total num timesteps 1393600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6968/10000 episodes, total num timesteps 1393800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6969/10000 episodes, total num timesteps 1394000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6970/10000 episodes, total num timesteps 1394200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6971/10000 episodes, total num timesteps 1394400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6972/10000 episodes, total num timesteps 1394600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6973/10000 episodes, total num timesteps 1394800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6974/10000 episodes, total num timesteps 1395000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6975/10000 episodes, total num timesteps 1395200/2000000, FPS 238.

team_policy eval average step individual rewards of agent0: 0.7862844367661349
team_policy eval average team episode rewards of agent0: 125.0
team_policy eval idv catch total num of agent0: 33
team_policy eval team catch total num: 50
team_policy eval average step individual rewards of agent1: 0.7351282264166018
team_policy eval average team episode rewards of agent1: 125.0
team_policy eval idv catch total num of agent1: 31
team_policy eval team catch total num: 50
team_policy eval average step individual rewards of agent2: 0.9279532548326866
team_policy eval average team episode rewards of agent2: 125.0
team_policy eval idv catch total num of agent2: 39
team_policy eval team catch total num: 50
team_policy eval average step individual rewards of agent3: 1.0575350521506974
team_policy eval average team episode rewards of agent3: 125.0
team_policy eval idv catch total num of agent3: 44
team_policy eval team catch total num: 50
team_policy eval average step individual rewards of agent4: 1.1707125312516296
team_policy eval average team episode rewards of agent4: 125.0
team_policy eval idv catch total num of agent4: 48
team_policy eval team catch total num: 50
idv_policy eval average step individual rewards of agent0: 0.9876547065813626
idv_policy eval average team episode rewards of agent0: 95.0
idv_policy eval idv catch total num of agent0: 41
idv_policy eval team catch total num: 38
idv_policy eval average step individual rewards of agent1: 0.8134840470824467
idv_policy eval average team episode rewards of agent1: 95.0
idv_policy eval idv catch total num of agent1: 34
idv_policy eval team catch total num: 38
idv_policy eval average step individual rewards of agent2: 0.8174090810044866
idv_policy eval average team episode rewards of agent2: 95.0
idv_policy eval idv catch total num of agent2: 34
idv_policy eval team catch total num: 38
idv_policy eval average step individual rewards of agent3: 0.40531967231782245
idv_policy eval average team episode rewards of agent3: 95.0
idv_policy eval idv catch total num of agent3: 18
idv_policy eval team catch total num: 38
idv_policy eval average step individual rewards of agent4: 0.6356787686469721
idv_policy eval average team episode rewards of agent4: 95.0
idv_policy eval idv catch total num of agent4: 27
idv_policy eval team catch total num: 38

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6976/10000 episodes, total num timesteps 1395400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6977/10000 episodes, total num timesteps 1395600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6978/10000 episodes, total num timesteps 1395800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6979/10000 episodes, total num timesteps 1396000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6980/10000 episodes, total num timesteps 1396200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6981/10000 episodes, total num timesteps 1396400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6982/10000 episodes, total num timesteps 1396600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6983/10000 episodes, total num timesteps 1396800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6984/10000 episodes, total num timesteps 1397000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6985/10000 episodes, total num timesteps 1397200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6986/10000 episodes, total num timesteps 1397400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6987/10000 episodes, total num timesteps 1397600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6988/10000 episodes, total num timesteps 1397800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6989/10000 episodes, total num timesteps 1398000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6990/10000 episodes, total num timesteps 1398200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6991/10000 episodes, total num timesteps 1398400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6992/10000 episodes, total num timesteps 1398600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6993/10000 episodes, total num timesteps 1398800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6994/10000 episodes, total num timesteps 1399000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6995/10000 episodes, total num timesteps 1399200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6996/10000 episodes, total num timesteps 1399400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6997/10000 episodes, total num timesteps 1399600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6998/10000 episodes, total num timesteps 1399800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6999/10000 episodes, total num timesteps 1400000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7000/10000 episodes, total num timesteps 1400200/2000000, FPS 238.

team_policy eval average step individual rewards of agent0: 0.7424049154560944
team_policy eval average team episode rewards of agent0: 85.0
team_policy eval idv catch total num of agent0: 31
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent1: 0.45347940666121495
team_policy eval average team episode rewards of agent1: 85.0
team_policy eval idv catch total num of agent1: 20
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent2: 0.799421352479084
team_policy eval average team episode rewards of agent2: 85.0
team_policy eval idv catch total num of agent2: 34
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent3: 0.7268755486334697
team_policy eval average team episode rewards of agent3: 85.0
team_policy eval idv catch total num of agent3: 31
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent4: 0.9435862594131439
team_policy eval average team episode rewards of agent4: 85.0
team_policy eval idv catch total num of agent4: 39
team_policy eval team catch total num: 34
idv_policy eval average step individual rewards of agent0: 0.9953334078023084
idv_policy eval average team episode rewards of agent0: 160.0
idv_policy eval idv catch total num of agent0: 41
idv_policy eval team catch total num: 64
idv_policy eval average step individual rewards of agent1: 0.7457826938280534
idv_policy eval average team episode rewards of agent1: 160.0
idv_policy eval idv catch total num of agent1: 31
idv_policy eval team catch total num: 64
idv_policy eval average step individual rewards of agent2: 1.0960322486535337
idv_policy eval average team episode rewards of agent2: 160.0
idv_policy eval idv catch total num of agent2: 45
idv_policy eval team catch total num: 64
idv_policy eval average step individual rewards of agent3: 1.0363484614076075
idv_policy eval average team episode rewards of agent3: 160.0
idv_policy eval idv catch total num of agent3: 43
idv_policy eval team catch total num: 64
idv_policy eval average step individual rewards of agent4: 1.2417272108394943
idv_policy eval average team episode rewards of agent4: 160.0
idv_policy eval idv catch total num of agent4: 51
idv_policy eval team catch total num: 64

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7001/10000 episodes, total num timesteps 1400400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7002/10000 episodes, total num timesteps 1400600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7003/10000 episodes, total num timesteps 1400800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7004/10000 episodes, total num timesteps 1401000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7005/10000 episodes, total num timesteps 1401200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7006/10000 episodes, total num timesteps 1401400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7007/10000 episodes, total num timesteps 1401600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7008/10000 episodes, total num timesteps 1401800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7009/10000 episodes, total num timesteps 1402000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7010/10000 episodes, total num timesteps 1402200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7011/10000 episodes, total num timesteps 1402400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7012/10000 episodes, total num timesteps 1402600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7013/10000 episodes, total num timesteps 1402800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7014/10000 episodes, total num timesteps 1403000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7015/10000 episodes, total num timesteps 1403200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7016/10000 episodes, total num timesteps 1403400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7017/10000 episodes, total num timesteps 1403600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7018/10000 episodes, total num timesteps 1403800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7019/10000 episodes, total num timesteps 1404000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7020/10000 episodes, total num timesteps 1404200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7021/10000 episodes, total num timesteps 1404400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7022/10000 episodes, total num timesteps 1404600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7023/10000 episodes, total num timesteps 1404800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7024/10000 episodes, total num timesteps 1405000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7025/10000 episodes, total num timesteps 1405200/2000000, FPS 238.

team_policy eval average step individual rewards of agent0: 0.6528489397986881
team_policy eval average team episode rewards of agent0: 85.0
team_policy eval idv catch total num of agent0: 28
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent1: 0.4932555110799349
team_policy eval average team episode rewards of agent1: 85.0
team_policy eval idv catch total num of agent1: 22
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent2: 0.3549109914118015
team_policy eval average team episode rewards of agent2: 85.0
team_policy eval idv catch total num of agent2: 16
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent3: 0.7367015929455414
team_policy eval average team episode rewards of agent3: 85.0
team_policy eval idv catch total num of agent3: 31
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent4: 0.860202751332498
team_policy eval average team episode rewards of agent4: 85.0
team_policy eval idv catch total num of agent4: 36
team_policy eval team catch total num: 34
idv_policy eval average step individual rewards of agent0: 0.7658145383309936
idv_policy eval average team episode rewards of agent0: 102.5
idv_policy eval idv catch total num of agent0: 32
idv_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent1: 1.0789783198924991
idv_policy eval average team episode rewards of agent1: 102.5
idv_policy eval idv catch total num of agent1: 45
idv_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent2: 0.7038913880887284
idv_policy eval average team episode rewards of agent2: 102.5
idv_policy eval idv catch total num of agent2: 30
idv_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent3: 0.7650100588411337
idv_policy eval average team episode rewards of agent3: 102.5
idv_policy eval idv catch total num of agent3: 32
idv_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent4: 0.613638886400928
idv_policy eval average team episode rewards of agent4: 102.5
idv_policy eval idv catch total num of agent4: 26
idv_policy eval team catch total num: 41

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7026/10000 episodes, total num timesteps 1405400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7027/10000 episodes, total num timesteps 1405600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7028/10000 episodes, total num timesteps 1405800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7029/10000 episodes, total num timesteps 1406000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7030/10000 episodes, total num timesteps 1406200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7031/10000 episodes, total num timesteps 1406400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7032/10000 episodes, total num timesteps 1406600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7033/10000 episodes, total num timesteps 1406800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7034/10000 episodes, total num timesteps 1407000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7035/10000 episodes, total num timesteps 1407200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7036/10000 episodes, total num timesteps 1407400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7037/10000 episodes, total num timesteps 1407600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7038/10000 episodes, total num timesteps 1407800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7039/10000 episodes, total num timesteps 1408000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7040/10000 episodes, total num timesteps 1408200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7041/10000 episodes, total num timesteps 1408400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7042/10000 episodes, total num timesteps 1408600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7043/10000 episodes, total num timesteps 1408800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7044/10000 episodes, total num timesteps 1409000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7045/10000 episodes, total num timesteps 1409200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7046/10000 episodes, total num timesteps 1409400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7047/10000 episodes, total num timesteps 1409600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7048/10000 episodes, total num timesteps 1409800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7049/10000 episodes, total num timesteps 1410000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7050/10000 episodes, total num timesteps 1410200/2000000, FPS 238.

team_policy eval average step individual rewards of agent0: 0.865055057231283
team_policy eval average team episode rewards of agent0: 100.0
team_policy eval idv catch total num of agent0: 36
team_policy eval team catch total num: 40
team_policy eval average step individual rewards of agent1: 0.4339400512371313
team_policy eval average team episode rewards of agent1: 100.0
team_policy eval idv catch total num of agent1: 19
team_policy eval team catch total num: 40
team_policy eval average step individual rewards of agent2: 0.7438321614486182
team_policy eval average team episode rewards of agent2: 100.0
team_policy eval idv catch total num of agent2: 31
team_policy eval team catch total num: 40
team_policy eval average step individual rewards of agent3: 0.6016047307507655
team_policy eval average team episode rewards of agent3: 100.0
team_policy eval idv catch total num of agent3: 26
team_policy eval team catch total num: 40
team_policy eval average step individual rewards of agent4: 0.7832324840592233
team_policy eval average team episode rewards of agent4: 100.0
team_policy eval idv catch total num of agent4: 33
team_policy eval team catch total num: 40
idv_policy eval average step individual rewards of agent0: 0.7524873398613511
idv_policy eval average team episode rewards of agent0: 100.0
idv_policy eval idv catch total num of agent0: 32
idv_policy eval team catch total num: 40
idv_policy eval average step individual rewards of agent1: 0.6326634742290481
idv_policy eval average team episode rewards of agent1: 100.0
idv_policy eval idv catch total num of agent1: 27
idv_policy eval team catch total num: 40
idv_policy eval average step individual rewards of agent2: 0.3744800285779922
idv_policy eval average team episode rewards of agent2: 100.0
idv_policy eval idv catch total num of agent2: 17
idv_policy eval team catch total num: 40
idv_policy eval average step individual rewards of agent3: 0.8557483375184372
idv_policy eval average team episode rewards of agent3: 100.0
idv_policy eval idv catch total num of agent3: 36
idv_policy eval team catch total num: 40
idv_policy eval average step individual rewards of agent4: 1.1205941278685376
idv_policy eval average team episode rewards of agent4: 100.0
idv_policy eval idv catch total num of agent4: 46
idv_policy eval team catch total num: 40

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7051/10000 episodes, total num timesteps 1410400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7052/10000 episodes, total num timesteps 1410600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7053/10000 episodes, total num timesteps 1410800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7054/10000 episodes, total num timesteps 1411000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7055/10000 episodes, total num timesteps 1411200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7056/10000 episodes, total num timesteps 1411400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7057/10000 episodes, total num timesteps 1411600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7058/10000 episodes, total num timesteps 1411800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7059/10000 episodes, total num timesteps 1412000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7060/10000 episodes, total num timesteps 1412200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7061/10000 episodes, total num timesteps 1412400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7062/10000 episodes, total num timesteps 1412600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7063/10000 episodes, total num timesteps 1412800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7064/10000 episodes, total num timesteps 1413000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7065/10000 episodes, total num timesteps 1413200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7066/10000 episodes, total num timesteps 1413400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7067/10000 episodes, total num timesteps 1413600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7068/10000 episodes, total num timesteps 1413800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7069/10000 episodes, total num timesteps 1414000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7070/10000 episodes, total num timesteps 1414200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7071/10000 episodes, total num timesteps 1414400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7072/10000 episodes, total num timesteps 1414600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7073/10000 episodes, total num timesteps 1414800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7074/10000 episodes, total num timesteps 1415000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7075/10000 episodes, total num timesteps 1415200/2000000, FPS 238.

team_policy eval average step individual rewards of agent0: 0.8899465311617746
team_policy eval average team episode rewards of agent0: 100.0
team_policy eval idv catch total num of agent0: 37
team_policy eval team catch total num: 40
team_policy eval average step individual rewards of agent1: 0.8107183161743009
team_policy eval average team episode rewards of agent1: 100.0
team_policy eval idv catch total num of agent1: 34
team_policy eval team catch total num: 40
team_policy eval average step individual rewards of agent2: 0.6900110113045445
team_policy eval average team episode rewards of agent2: 100.0
team_policy eval idv catch total num of agent2: 29
team_policy eval team catch total num: 40
team_policy eval average step individual rewards of agent3: 0.5142901425684601
team_policy eval average team episode rewards of agent3: 100.0
team_policy eval idv catch total num of agent3: 22
team_policy eval team catch total num: 40
team_policy eval average step individual rewards of agent4: 0.8359459924095249
team_policy eval average team episode rewards of agent4: 100.0
team_policy eval idv catch total num of agent4: 35
team_policy eval team catch total num: 40
idv_policy eval average step individual rewards of agent0: 1.1208513612723083
idv_policy eval average team episode rewards of agent0: 110.0
idv_policy eval idv catch total num of agent0: 46
idv_policy eval team catch total num: 44
idv_policy eval average step individual rewards of agent1: 0.7047884545844232
idv_policy eval average team episode rewards of agent1: 110.0
idv_policy eval idv catch total num of agent1: 30
idv_policy eval team catch total num: 44
idv_policy eval average step individual rewards of agent2: 0.922513890994017
idv_policy eval average team episode rewards of agent2: 110.0
idv_policy eval idv catch total num of agent2: 38
idv_policy eval team catch total num: 44
idv_policy eval average step individual rewards of agent3: 0.6142334026132272
idv_policy eval average team episode rewards of agent3: 110.0
idv_policy eval idv catch total num of agent3: 26
idv_policy eval team catch total num: 44
idv_policy eval average step individual rewards of agent4: 0.5106523267975515
idv_policy eval average team episode rewards of agent4: 110.0
idv_policy eval idv catch total num of agent4: 22
idv_policy eval team catch total num: 44

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7076/10000 episodes, total num timesteps 1415400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7077/10000 episodes, total num timesteps 1415600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7078/10000 episodes, total num timesteps 1415800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7079/10000 episodes, total num timesteps 1416000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7080/10000 episodes, total num timesteps 1416200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7081/10000 episodes, total num timesteps 1416400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7082/10000 episodes, total num timesteps 1416600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7083/10000 episodes, total num timesteps 1416800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7084/10000 episodes, total num timesteps 1417000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7085/10000 episodes, total num timesteps 1417200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7086/10000 episodes, total num timesteps 1417400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7087/10000 episodes, total num timesteps 1417600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7088/10000 episodes, total num timesteps 1417800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7089/10000 episodes, total num timesteps 1418000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7090/10000 episodes, total num timesteps 1418200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7091/10000 episodes, total num timesteps 1418400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7092/10000 episodes, total num timesteps 1418600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7093/10000 episodes, total num timesteps 1418800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7094/10000 episodes, total num timesteps 1419000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7095/10000 episodes, total num timesteps 1419200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7096/10000 episodes, total num timesteps 1419400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7097/10000 episodes, total num timesteps 1419600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7098/10000 episodes, total num timesteps 1419800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7099/10000 episodes, total num timesteps 1420000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7100/10000 episodes, total num timesteps 1420200/2000000, FPS 238.

team_policy eval average step individual rewards of agent0: 0.796354257138141
team_policy eval average team episode rewards of agent0: 97.5
team_policy eval idv catch total num of agent0: 33
team_policy eval team catch total num: 39
team_policy eval average step individual rewards of agent1: 0.7345922084159213
team_policy eval average team episode rewards of agent1: 97.5
team_policy eval idv catch total num of agent1: 31
team_policy eval team catch total num: 39
team_policy eval average step individual rewards of agent2: 0.6638046851509413
team_policy eval average team episode rewards of agent2: 97.5
team_policy eval idv catch total num of agent2: 28
team_policy eval team catch total num: 39
team_policy eval average step individual rewards of agent3: 0.703075352702327
team_policy eval average team episode rewards of agent3: 97.5
team_policy eval idv catch total num of agent3: 30
team_policy eval team catch total num: 39
team_policy eval average step individual rewards of agent4: 0.759628226807929
team_policy eval average team episode rewards of agent4: 97.5
team_policy eval idv catch total num of agent4: 32
team_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent0: 1.0193294327972136
idv_policy eval average team episode rewards of agent0: 132.5
idv_policy eval idv catch total num of agent0: 42
idv_policy eval team catch total num: 53
idv_policy eval average step individual rewards of agent1: 0.8130023039183644
idv_policy eval average team episode rewards of agent1: 132.5
idv_policy eval idv catch total num of agent1: 34
idv_policy eval team catch total num: 53
idv_policy eval average step individual rewards of agent2: 0.9534521855369472
idv_policy eval average team episode rewards of agent2: 132.5
idv_policy eval idv catch total num of agent2: 40
idv_policy eval team catch total num: 53
idv_policy eval average step individual rewards of agent3: 0.698133046914856
idv_policy eval average team episode rewards of agent3: 132.5
idv_policy eval idv catch total num of agent3: 29
idv_policy eval team catch total num: 53
idv_policy eval average step individual rewards of agent4: 1.018166919177035
idv_policy eval average team episode rewards of agent4: 132.5
idv_policy eval idv catch total num of agent4: 42
idv_policy eval team catch total num: 53

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7101/10000 episodes, total num timesteps 1420400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7102/10000 episodes, total num timesteps 1420600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7103/10000 episodes, total num timesteps 1420800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7104/10000 episodes, total num timesteps 1421000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7105/10000 episodes, total num timesteps 1421200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7106/10000 episodes, total num timesteps 1421400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7107/10000 episodes, total num timesteps 1421600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7108/10000 episodes, total num timesteps 1421800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7109/10000 episodes, total num timesteps 1422000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7110/10000 episodes, total num timesteps 1422200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7111/10000 episodes, total num timesteps 1422400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7112/10000 episodes, total num timesteps 1422600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7113/10000 episodes, total num timesteps 1422800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7114/10000 episodes, total num timesteps 1423000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7115/10000 episodes, total num timesteps 1423200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7116/10000 episodes, total num timesteps 1423400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7117/10000 episodes, total num timesteps 1423600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7118/10000 episodes, total num timesteps 1423800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7119/10000 episodes, total num timesteps 1424000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7120/10000 episodes, total num timesteps 1424200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7121/10000 episodes, total num timesteps 1424400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7122/10000 episodes, total num timesteps 1424600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7123/10000 episodes, total num timesteps 1424800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7124/10000 episodes, total num timesteps 1425000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7125/10000 episodes, total num timesteps 1425200/2000000, FPS 238.

team_policy eval average step individual rewards of agent0: 0.7418415629748851
team_policy eval average team episode rewards of agent0: 122.5
team_policy eval idv catch total num of agent0: 31
team_policy eval team catch total num: 49
team_policy eval average step individual rewards of agent1: 0.5549047158136619
team_policy eval average team episode rewards of agent1: 122.5
team_policy eval idv catch total num of agent1: 24
team_policy eval team catch total num: 49
team_policy eval average step individual rewards of agent2: 0.7613974270444163
team_policy eval average team episode rewards of agent2: 122.5
team_policy eval idv catch total num of agent2: 32
team_policy eval team catch total num: 49
team_policy eval average step individual rewards of agent3: 1.1624290501801302
team_policy eval average team episode rewards of agent3: 122.5
team_policy eval idv catch total num of agent3: 48
team_policy eval team catch total num: 49
team_policy eval average step individual rewards of agent4: 1.1854085142290207
team_policy eval average team episode rewards of agent4: 122.5
team_policy eval idv catch total num of agent4: 49
team_policy eval team catch total num: 49
idv_policy eval average step individual rewards of agent0: 1.034472595165103
idv_policy eval average team episode rewards of agent0: 92.5
idv_policy eval idv catch total num of agent0: 43
idv_policy eval team catch total num: 37
idv_policy eval average step individual rewards of agent1: 0.5040778806925265
idv_policy eval average team episode rewards of agent1: 92.5
idv_policy eval idv catch total num of agent1: 22
idv_policy eval team catch total num: 37
idv_policy eval average step individual rewards of agent2: 0.6230165338104242
idv_policy eval average team episode rewards of agent2: 92.5
idv_policy eval idv catch total num of agent2: 27
idv_policy eval team catch total num: 37
idv_policy eval average step individual rewards of agent3: 0.6626269111590338
idv_policy eval average team episode rewards of agent3: 92.5
idv_policy eval idv catch total num of agent3: 28
idv_policy eval team catch total num: 37
idv_policy eval average step individual rewards of agent4: 0.6572064775692046
idv_policy eval average team episode rewards of agent4: 92.5
idv_policy eval idv catch total num of agent4: 28
idv_policy eval team catch total num: 37

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7126/10000 episodes, total num timesteps 1425400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7127/10000 episodes, total num timesteps 1425600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7128/10000 episodes, total num timesteps 1425800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7129/10000 episodes, total num timesteps 1426000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7130/10000 episodes, total num timesteps 1426200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7131/10000 episodes, total num timesteps 1426400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7132/10000 episodes, total num timesteps 1426600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7133/10000 episodes, total num timesteps 1426800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7134/10000 episodes, total num timesteps 1427000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7135/10000 episodes, total num timesteps 1427200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7136/10000 episodes, total num timesteps 1427400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7137/10000 episodes, total num timesteps 1427600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7138/10000 episodes, total num timesteps 1427800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7139/10000 episodes, total num timesteps 1428000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7140/10000 episodes, total num timesteps 1428200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7141/10000 episodes, total num timesteps 1428400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7142/10000 episodes, total num timesteps 1428600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7143/10000 episodes, total num timesteps 1428800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7144/10000 episodes, total num timesteps 1429000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7145/10000 episodes, total num timesteps 1429200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7146/10000 episodes, total num timesteps 1429400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7147/10000 episodes, total num timesteps 1429600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7148/10000 episodes, total num timesteps 1429800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7149/10000 episodes, total num timesteps 1430000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7150/10000 episodes, total num timesteps 1430200/2000000, FPS 238.

team_policy eval average step individual rewards of agent0: 0.7656557721437386
team_policy eval average team episode rewards of agent0: 70.0
team_policy eval idv catch total num of agent0: 32
team_policy eval team catch total num: 28
team_policy eval average step individual rewards of agent1: 0.4890901861633881
team_policy eval average team episode rewards of agent1: 70.0
team_policy eval idv catch total num of agent1: 21
team_policy eval team catch total num: 28
team_policy eval average step individual rewards of agent2: 1.3163424389044969
team_policy eval average team episode rewards of agent2: 70.0
team_policy eval idv catch total num of agent2: 54
team_policy eval team catch total num: 28
team_policy eval average step individual rewards of agent3: 0.5526603148937432
team_policy eval average team episode rewards of agent3: 70.0
team_policy eval idv catch total num of agent3: 24
team_policy eval team catch total num: 28
team_policy eval average step individual rewards of agent4: 0.3548953111642523
team_policy eval average team episode rewards of agent4: 70.0
team_policy eval idv catch total num of agent4: 16
team_policy eval team catch total num: 28
idv_policy eval average step individual rewards of agent0: 0.7625659850038172
idv_policy eval average team episode rewards of agent0: 92.5
idv_policy eval idv catch total num of agent0: 32
idv_policy eval team catch total num: 37
idv_policy eval average step individual rewards of agent1: 0.7677986495374131
idv_policy eval average team episode rewards of agent1: 92.5
idv_policy eval idv catch total num of agent1: 33
idv_policy eval team catch total num: 37
idv_policy eval average step individual rewards of agent2: 0.7171156106521163
idv_policy eval average team episode rewards of agent2: 92.5
idv_policy eval idv catch total num of agent2: 30
idv_policy eval team catch total num: 37
idv_policy eval average step individual rewards of agent3: 0.8976516100581254
idv_policy eval average team episode rewards of agent3: 92.5
idv_policy eval idv catch total num of agent3: 37
idv_policy eval team catch total num: 37
idv_policy eval average step individual rewards of agent4: 1.0201065120865511
idv_policy eval average team episode rewards of agent4: 92.5
idv_policy eval idv catch total num of agent4: 42
idv_policy eval team catch total num: 37

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7151/10000 episodes, total num timesteps 1430400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7152/10000 episodes, total num timesteps 1430600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7153/10000 episodes, total num timesteps 1430800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7154/10000 episodes, total num timesteps 1431000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7155/10000 episodes, total num timesteps 1431200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7156/10000 episodes, total num timesteps 1431400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7157/10000 episodes, total num timesteps 1431600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7158/10000 episodes, total num timesteps 1431800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7159/10000 episodes, total num timesteps 1432000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7160/10000 episodes, total num timesteps 1432200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7161/10000 episodes, total num timesteps 1432400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7162/10000 episodes, total num timesteps 1432600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7163/10000 episodes, total num timesteps 1432800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7164/10000 episodes, total num timesteps 1433000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7165/10000 episodes, total num timesteps 1433200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7166/10000 episodes, total num timesteps 1433400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7167/10000 episodes, total num timesteps 1433600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7168/10000 episodes, total num timesteps 1433800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7169/10000 episodes, total num timesteps 1434000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7170/10000 episodes, total num timesteps 1434200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7171/10000 episodes, total num timesteps 1434400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7172/10000 episodes, total num timesteps 1434600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7173/10000 episodes, total num timesteps 1434800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7174/10000 episodes, total num timesteps 1435000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7175/10000 episodes, total num timesteps 1435200/2000000, FPS 238.

team_policy eval average step individual rewards of agent0: 0.9313132000290966
team_policy eval average team episode rewards of agent0: 125.0
team_policy eval idv catch total num of agent0: 39
team_policy eval team catch total num: 50
team_policy eval average step individual rewards of agent1: 1.346524944902344
team_policy eval average team episode rewards of agent1: 125.0
team_policy eval idv catch total num of agent1: 55
team_policy eval team catch total num: 50
team_policy eval average step individual rewards of agent2: 0.7911569884862198
team_policy eval average team episode rewards of agent2: 125.0
team_policy eval idv catch total num of agent2: 33
team_policy eval team catch total num: 50
team_policy eval average step individual rewards of agent3: 0.91739528580951
team_policy eval average team episode rewards of agent3: 125.0
team_policy eval idv catch total num of agent3: 38
team_policy eval team catch total num: 50
team_policy eval average step individual rewards of agent4: 0.6083800184496347
team_policy eval average team episode rewards of agent4: 125.0
team_policy eval idv catch total num of agent4: 26
team_policy eval team catch total num: 50
idv_policy eval average step individual rewards of agent0: 0.452818479292274
idv_policy eval average team episode rewards of agent0: 67.5
idv_policy eval idv catch total num of agent0: 20
idv_policy eval team catch total num: 27
idv_policy eval average step individual rewards of agent1: 0.38225877421137744
idv_policy eval average team episode rewards of agent1: 67.5
idv_policy eval idv catch total num of agent1: 17
idv_policy eval team catch total num: 27
idv_policy eval average step individual rewards of agent2: 0.2461334044257347
idv_policy eval average team episode rewards of agent2: 67.5
idv_policy eval idv catch total num of agent2: 12
idv_policy eval team catch total num: 27
idv_policy eval average step individual rewards of agent3: 0.5814097363940961
idv_policy eval average team episode rewards of agent3: 67.5
idv_policy eval idv catch total num of agent3: 25
idv_policy eval team catch total num: 27
idv_policy eval average step individual rewards of agent4: 0.8603413607374388
idv_policy eval average team episode rewards of agent4: 67.5
idv_policy eval idv catch total num of agent4: 36
idv_policy eval team catch total num: 27

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7176/10000 episodes, total num timesteps 1435400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7177/10000 episodes, total num timesteps 1435600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7178/10000 episodes, total num timesteps 1435800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7179/10000 episodes, total num timesteps 1436000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7180/10000 episodes, total num timesteps 1436200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7181/10000 episodes, total num timesteps 1436400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7182/10000 episodes, total num timesteps 1436600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7183/10000 episodes, total num timesteps 1436800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7184/10000 episodes, total num timesteps 1437000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7185/10000 episodes, total num timesteps 1437200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7186/10000 episodes, total num timesteps 1437400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7187/10000 episodes, total num timesteps 1437600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7188/10000 episodes, total num timesteps 1437800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7189/10000 episodes, total num timesteps 1438000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7190/10000 episodes, total num timesteps 1438200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7191/10000 episodes, total num timesteps 1438400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7192/10000 episodes, total num timesteps 1438600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7193/10000 episodes, total num timesteps 1438800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7194/10000 episodes, total num timesteps 1439000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7195/10000 episodes, total num timesteps 1439200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7196/10000 episodes, total num timesteps 1439400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7197/10000 episodes, total num timesteps 1439600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7198/10000 episodes, total num timesteps 1439800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7199/10000 episodes, total num timesteps 1440000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7200/10000 episodes, total num timesteps 1440200/2000000, FPS 238.

team_policy eval average step individual rewards of agent0: 0.40552647103620454
team_policy eval average team episode rewards of agent0: 70.0
team_policy eval idv catch total num of agent0: 18
team_policy eval team catch total num: 28
team_policy eval average step individual rewards of agent1: 0.5058264605258674
team_policy eval average team episode rewards of agent1: 70.0
team_policy eval idv catch total num of agent1: 22
team_policy eval team catch total num: 28
team_policy eval average step individual rewards of agent2: 0.701394282707458
team_policy eval average team episode rewards of agent2: 70.0
team_policy eval idv catch total num of agent2: 30
team_policy eval team catch total num: 28
team_policy eval average step individual rewards of agent3: 0.6802907407922111
team_policy eval average team episode rewards of agent3: 70.0
team_policy eval idv catch total num of agent3: 29
team_policy eval team catch total num: 28
team_policy eval average step individual rewards of agent4: 0.49072217136944785
team_policy eval average team episode rewards of agent4: 70.0
team_policy eval idv catch total num of agent4: 21
team_policy eval team catch total num: 28
idv_policy eval average step individual rewards of agent0: 0.9673143138400851
idv_policy eval average team episode rewards of agent0: 140.0
idv_policy eval idv catch total num of agent0: 40
idv_policy eval team catch total num: 56
idv_policy eval average step individual rewards of agent1: 1.1124130412417874
idv_policy eval average team episode rewards of agent1: 140.0
idv_policy eval idv catch total num of agent1: 46
idv_policy eval team catch total num: 56
idv_policy eval average step individual rewards of agent2: 0.9423940484418196
idv_policy eval average team episode rewards of agent2: 140.0
idv_policy eval idv catch total num of agent2: 39
idv_policy eval team catch total num: 56
idv_policy eval average step individual rewards of agent3: 1.0403818276682224
idv_policy eval average team episode rewards of agent3: 140.0
idv_policy eval idv catch total num of agent3: 43
idv_policy eval team catch total num: 56
idv_policy eval average step individual rewards of agent4: 1.09135213206285
idv_policy eval average team episode rewards of agent4: 140.0
idv_policy eval idv catch total num of agent4: 45
idv_policy eval team catch total num: 56

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7201/10000 episodes, total num timesteps 1440400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7202/10000 episodes, total num timesteps 1440600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7203/10000 episodes, total num timesteps 1440800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7204/10000 episodes, total num timesteps 1441000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7205/10000 episodes, total num timesteps 1441200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7206/10000 episodes, total num timesteps 1441400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7207/10000 episodes, total num timesteps 1441600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7208/10000 episodes, total num timesteps 1441800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7209/10000 episodes, total num timesteps 1442000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7210/10000 episodes, total num timesteps 1442200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7211/10000 episodes, total num timesteps 1442400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7212/10000 episodes, total num timesteps 1442600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7213/10000 episodes, total num timesteps 1442800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7214/10000 episodes, total num timesteps 1443000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7215/10000 episodes, total num timesteps 1443200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7216/10000 episodes, total num timesteps 1443400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7217/10000 episodes, total num timesteps 1443600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7218/10000 episodes, total num timesteps 1443800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7219/10000 episodes, total num timesteps 1444000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7220/10000 episodes, total num timesteps 1444200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7221/10000 episodes, total num timesteps 1444400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7222/10000 episodes, total num timesteps 1444600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7223/10000 episodes, total num timesteps 1444800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7224/10000 episodes, total num timesteps 1445000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7225/10000 episodes, total num timesteps 1445200/2000000, FPS 238.

team_policy eval average step individual rewards of agent0: 0.7253043894376969
team_policy eval average team episode rewards of agent0: 132.5
team_policy eval idv catch total num of agent0: 31
team_policy eval team catch total num: 53
team_policy eval average step individual rewards of agent1: 0.7608921214934079
team_policy eval average team episode rewards of agent1: 132.5
team_policy eval idv catch total num of agent1: 32
team_policy eval team catch total num: 53
team_policy eval average step individual rewards of agent2: 0.7073354434562702
team_policy eval average team episode rewards of agent2: 132.5
team_policy eval idv catch total num of agent2: 30
team_policy eval team catch total num: 53
team_policy eval average step individual rewards of agent3: 1.2487090610129181
team_policy eval average team episode rewards of agent3: 132.5
team_policy eval idv catch total num of agent3: 51
team_policy eval team catch total num: 53
team_policy eval average step individual rewards of agent4: 0.9405353528472467
team_policy eval average team episode rewards of agent4: 132.5
team_policy eval idv catch total num of agent4: 39
team_policy eval team catch total num: 53
idv_policy eval average step individual rewards of agent0: 0.7425172754134992
idv_policy eval average team episode rewards of agent0: 115.0
idv_policy eval idv catch total num of agent0: 31
idv_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent1: 0.6429479519932692
idv_policy eval average team episode rewards of agent1: 115.0
idv_policy eval idv catch total num of agent1: 27
idv_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent2: 0.8338722533845843
idv_policy eval average team episode rewards of agent2: 115.0
idv_policy eval idv catch total num of agent2: 35
idv_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent3: 0.7126840340552815
idv_policy eval average team episode rewards of agent3: 115.0
idv_policy eval idv catch total num of agent3: 30
idv_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent4: 0.817327035798775
idv_policy eval average team episode rewards of agent4: 115.0
idv_policy eval idv catch total num of agent4: 34
idv_policy eval team catch total num: 46

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7226/10000 episodes, total num timesteps 1445400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7227/10000 episodes, total num timesteps 1445600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7228/10000 episodes, total num timesteps 1445800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7229/10000 episodes, total num timesteps 1446000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7230/10000 episodes, total num timesteps 1446200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7231/10000 episodes, total num timesteps 1446400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7232/10000 episodes, total num timesteps 1446600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7233/10000 episodes, total num timesteps 1446800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7234/10000 episodes, total num timesteps 1447000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7235/10000 episodes, total num timesteps 1447200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7236/10000 episodes, total num timesteps 1447400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7237/10000 episodes, total num timesteps 1447600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7238/10000 episodes, total num timesteps 1447800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7239/10000 episodes, total num timesteps 1448000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7240/10000 episodes, total num timesteps 1448200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7241/10000 episodes, total num timesteps 1448400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7242/10000 episodes, total num timesteps 1448600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7243/10000 episodes, total num timesteps 1448800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7244/10000 episodes, total num timesteps 1449000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7245/10000 episodes, total num timesteps 1449200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7246/10000 episodes, total num timesteps 1449400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7247/10000 episodes, total num timesteps 1449600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7248/10000 episodes, total num timesteps 1449800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7249/10000 episodes, total num timesteps 1450000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7250/10000 episodes, total num timesteps 1450200/2000000, FPS 238.

team_policy eval average step individual rewards of agent0: 0.9154148406628445
team_policy eval average team episode rewards of agent0: 105.0
team_policy eval idv catch total num of agent0: 38
team_policy eval team catch total num: 42
team_policy eval average step individual rewards of agent1: 0.8415081931066465
team_policy eval average team episode rewards of agent1: 105.0
team_policy eval idv catch total num of agent1: 35
team_policy eval team catch total num: 42
team_policy eval average step individual rewards of agent2: 0.8178081754045522
team_policy eval average team episode rewards of agent2: 105.0
team_policy eval idv catch total num of agent2: 34
team_policy eval team catch total num: 42
team_policy eval average step individual rewards of agent3: 0.40339144739695487
team_policy eval average team episode rewards of agent3: 105.0
team_policy eval idv catch total num of agent3: 18
team_policy eval team catch total num: 42
team_policy eval average step individual rewards of agent4: 1.094810719475651
team_policy eval average team episode rewards of agent4: 105.0
team_policy eval idv catch total num of agent4: 45
team_policy eval team catch total num: 42
idv_policy eval average step individual rewards of agent0: 0.5043389047742736
idv_policy eval average team episode rewards of agent0: 75.0
idv_policy eval idv catch total num of agent0: 22
idv_policy eval team catch total num: 30
idv_policy eval average step individual rewards of agent1: 0.8194930637460268
idv_policy eval average team episode rewards of agent1: 75.0
idv_policy eval idv catch total num of agent1: 34
idv_policy eval team catch total num: 30
idv_policy eval average step individual rewards of agent2: 0.4174175240553006
idv_policy eval average team episode rewards of agent2: 75.0
idv_policy eval idv catch total num of agent2: 18
idv_policy eval team catch total num: 30
idv_policy eval average step individual rewards of agent3: 0.7609003326469869
idv_policy eval average team episode rewards of agent3: 75.0
idv_policy eval idv catch total num of agent3: 32
idv_policy eval team catch total num: 30
idv_policy eval average step individual rewards of agent4: 0.4136160294751786
idv_policy eval average team episode rewards of agent4: 75.0
idv_policy eval idv catch total num of agent4: 18
idv_policy eval team catch total num: 30

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7251/10000 episodes, total num timesteps 1450400/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7252/10000 episodes, total num timesteps 1450600/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7253/10000 episodes, total num timesteps 1450800/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7254/10000 episodes, total num timesteps 1451000/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7255/10000 episodes, total num timesteps 1451200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7256/10000 episodes, total num timesteps 1451400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7257/10000 episodes, total num timesteps 1451600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7258/10000 episodes, total num timesteps 1451800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7259/10000 episodes, total num timesteps 1452000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7260/10000 episodes, total num timesteps 1452200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7261/10000 episodes, total num timesteps 1452400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7262/10000 episodes, total num timesteps 1452600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7263/10000 episodes, total num timesteps 1452800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7264/10000 episodes, total num timesteps 1453000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7265/10000 episodes, total num timesteps 1453200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7266/10000 episodes, total num timesteps 1453400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7267/10000 episodes, total num timesteps 1453600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7268/10000 episodes, total num timesteps 1453800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7269/10000 episodes, total num timesteps 1454000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7270/10000 episodes, total num timesteps 1454200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7271/10000 episodes, total num timesteps 1454400/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7272/10000 episodes, total num timesteps 1454600/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7273/10000 episodes, total num timesteps 1454800/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7274/10000 episodes, total num timesteps 1455000/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7275/10000 episodes, total num timesteps 1455200/2000000, FPS 237.

team_policy eval average step individual rewards of agent0: 0.8452729250407213
team_policy eval average team episode rewards of agent0: 142.5
team_policy eval idv catch total num of agent0: 35
team_policy eval team catch total num: 57
team_policy eval average step individual rewards of agent1: 1.2153098072026018
team_policy eval average team episode rewards of agent1: 142.5
team_policy eval idv catch total num of agent1: 50
team_policy eval team catch total num: 57
team_policy eval average step individual rewards of agent2: 0.8628608594926035
team_policy eval average team episode rewards of agent2: 142.5
team_policy eval idv catch total num of agent2: 36
team_policy eval team catch total num: 57
team_policy eval average step individual rewards of agent3: 0.7306116698106897
team_policy eval average team episode rewards of agent3: 142.5
team_policy eval idv catch total num of agent3: 31
team_policy eval team catch total num: 57
team_policy eval average step individual rewards of agent4: 1.1823668746448062
team_policy eval average team episode rewards of agent4: 142.5
team_policy eval idv catch total num of agent4: 49
team_policy eval team catch total num: 57
idv_policy eval average step individual rewards of agent0: 0.7926136401977198
idv_policy eval average team episode rewards of agent0: 107.5
idv_policy eval idv catch total num of agent0: 33
idv_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent1: 1.1910116079743494
idv_policy eval average team episode rewards of agent1: 107.5
idv_policy eval idv catch total num of agent1: 49
idv_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent2: 0.7429345389814631
idv_policy eval average team episode rewards of agent2: 107.5
idv_policy eval idv catch total num of agent2: 31
idv_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent3: 0.8110060732392202
idv_policy eval average team episode rewards of agent3: 107.5
idv_policy eval idv catch total num of agent3: 34
idv_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent4: 0.9738192857042267
idv_policy eval average team episode rewards of agent4: 107.5
idv_policy eval idv catch total num of agent4: 40
idv_policy eval team catch total num: 43

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7276/10000 episodes, total num timesteps 1455400/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7277/10000 episodes, total num timesteps 1455600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7278/10000 episodes, total num timesteps 1455800/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7279/10000 episodes, total num timesteps 1456000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7280/10000 episodes, total num timesteps 1456200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7281/10000 episodes, total num timesteps 1456400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7282/10000 episodes, total num timesteps 1456600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7283/10000 episodes, total num timesteps 1456800/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7284/10000 episodes, total num timesteps 1457000/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7285/10000 episodes, total num timesteps 1457200/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7286/10000 episodes, total num timesteps 1457400/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7287/10000 episodes, total num timesteps 1457600/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7288/10000 episodes, total num timesteps 1457800/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7289/10000 episodes, total num timesteps 1458000/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7290/10000 episodes, total num timesteps 1458200/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7291/10000 episodes, total num timesteps 1458400/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7292/10000 episodes, total num timesteps 1458600/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7293/10000 episodes, total num timesteps 1458800/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7294/10000 episodes, total num timesteps 1459000/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7295/10000 episodes, total num timesteps 1459200/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7296/10000 episodes, total num timesteps 1459400/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7297/10000 episodes, total num timesteps 1459600/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7298/10000 episodes, total num timesteps 1459800/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7299/10000 episodes, total num timesteps 1460000/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7300/10000 episodes, total num timesteps 1460200/2000000, FPS 237.

team_policy eval average step individual rewards of agent0: 0.25402435392397
team_policy eval average team episode rewards of agent0: 67.5
team_policy eval idv catch total num of agent0: 12
team_policy eval team catch total num: 27
team_policy eval average step individual rewards of agent1: 0.758665733097472
team_policy eval average team episode rewards of agent1: 67.5
team_policy eval idv catch total num of agent1: 32
team_policy eval team catch total num: 27
team_policy eval average step individual rewards of agent2: 0.3166521681972505
team_policy eval average team episode rewards of agent2: 67.5
team_policy eval idv catch total num of agent2: 15
team_policy eval team catch total num: 27
team_policy eval average step individual rewards of agent3: 0.583337910573337
team_policy eval average team episode rewards of agent3: 67.5
team_policy eval idv catch total num of agent3: 25
team_policy eval team catch total num: 27
team_policy eval average step individual rewards of agent4: 0.5812986152027313
team_policy eval average team episode rewards of agent4: 67.5
team_policy eval idv catch total num of agent4: 25
team_policy eval team catch total num: 27
idv_policy eval average step individual rewards of agent0: 0.867236816383591
idv_policy eval average team episode rewards of agent0: 105.0
idv_policy eval idv catch total num of agent0: 36
idv_policy eval team catch total num: 42
idv_policy eval average step individual rewards of agent1: 0.6872321194148987
idv_policy eval average team episode rewards of agent1: 105.0
idv_policy eval idv catch total num of agent1: 29
idv_policy eval team catch total num: 42
idv_policy eval average step individual rewards of agent2: 0.7916863839724472
idv_policy eval average team episode rewards of agent2: 105.0
idv_policy eval idv catch total num of agent2: 33
idv_policy eval team catch total num: 42
idv_policy eval average step individual rewards of agent3: 0.6891378597409488
idv_policy eval average team episode rewards of agent3: 105.0
idv_policy eval idv catch total num of agent3: 29
idv_policy eval team catch total num: 42
idv_policy eval average step individual rewards of agent4: 0.8906842331297057
idv_policy eval average team episode rewards of agent4: 105.0
idv_policy eval idv catch total num of agent4: 37
idv_policy eval team catch total num: 42

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7301/10000 episodes, total num timesteps 1460400/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7302/10000 episodes, total num timesteps 1460600/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7303/10000 episodes, total num timesteps 1460800/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7304/10000 episodes, total num timesteps 1461000/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7305/10000 episodes, total num timesteps 1461200/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7306/10000 episodes, total num timesteps 1461400/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7307/10000 episodes, total num timesteps 1461600/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7308/10000 episodes, total num timesteps 1461800/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7309/10000 episodes, total num timesteps 1462000/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7310/10000 episodes, total num timesteps 1462200/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7311/10000 episodes, total num timesteps 1462400/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7312/10000 episodes, total num timesteps 1462600/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7313/10000 episodes, total num timesteps 1462800/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7314/10000 episodes, total num timesteps 1463000/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7315/10000 episodes, total num timesteps 1463200/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7316/10000 episodes, total num timesteps 1463400/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7317/10000 episodes, total num timesteps 1463600/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7318/10000 episodes, total num timesteps 1463800/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7319/10000 episodes, total num timesteps 1464000/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7320/10000 episodes, total num timesteps 1464200/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7321/10000 episodes, total num timesteps 1464400/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7322/10000 episodes, total num timesteps 1464600/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7323/10000 episodes, total num timesteps 1464800/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7324/10000 episodes, total num timesteps 1465000/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7325/10000 episodes, total num timesteps 1465200/2000000, FPS 237.

team_policy eval average step individual rewards of agent0: 0.5884387608223648
team_policy eval average team episode rewards of agent0: 107.5
team_policy eval idv catch total num of agent0: 25
team_policy eval team catch total num: 43
team_policy eval average step individual rewards of agent1: 0.8931937505709278
team_policy eval average team episode rewards of agent1: 107.5
team_policy eval idv catch total num of agent1: 37
team_policy eval team catch total num: 43
team_policy eval average step individual rewards of agent2: 0.6866496052114768
team_policy eval average team episode rewards of agent2: 107.5
team_policy eval idv catch total num of agent2: 29
team_policy eval team catch total num: 43
team_policy eval average step individual rewards of agent3: 0.814272655995663
team_policy eval average team episode rewards of agent3: 107.5
team_policy eval idv catch total num of agent3: 34
team_policy eval team catch total num: 43
team_policy eval average step individual rewards of agent4: 0.9192242153050999
team_policy eval average team episode rewards of agent4: 107.5
team_policy eval idv catch total num of agent4: 38
team_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent0: 0.8871763227527799
idv_policy eval average team episode rewards of agent0: 117.5
idv_policy eval idv catch total num of agent0: 37
idv_policy eval team catch total num: 47
idv_policy eval average step individual rewards of agent1: 1.243560539692936
idv_policy eval average team episode rewards of agent1: 117.5
idv_policy eval idv catch total num of agent1: 51
idv_policy eval team catch total num: 47
idv_policy eval average step individual rewards of agent2: 0.7089710989224587
idv_policy eval average team episode rewards of agent2: 117.5
idv_policy eval idv catch total num of agent2: 30
idv_policy eval team catch total num: 47
idv_policy eval average step individual rewards of agent3: 0.9120712192812798
idv_policy eval average team episode rewards of agent3: 117.5
idv_policy eval idv catch total num of agent3: 38
idv_policy eval team catch total num: 47
idv_policy eval average step individual rewards of agent4: 0.6341846495635479
idv_policy eval average team episode rewards of agent4: 117.5
idv_policy eval idv catch total num of agent4: 27
idv_policy eval team catch total num: 47

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7326/10000 episodes, total num timesteps 1465400/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7327/10000 episodes, total num timesteps 1465600/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7328/10000 episodes, total num timesteps 1465800/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7329/10000 episodes, total num timesteps 1466000/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7330/10000 episodes, total num timesteps 1466200/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7331/10000 episodes, total num timesteps 1466400/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7332/10000 episodes, total num timesteps 1466600/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7333/10000 episodes, total num timesteps 1466800/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7334/10000 episodes, total num timesteps 1467000/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7335/10000 episodes, total num timesteps 1467200/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7336/10000 episodes, total num timesteps 1467400/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7337/10000 episodes, total num timesteps 1467600/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7338/10000 episodes, total num timesteps 1467800/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7339/10000 episodes, total num timesteps 1468000/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7340/10000 episodes, total num timesteps 1468200/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7341/10000 episodes, total num timesteps 1468400/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7342/10000 episodes, total num timesteps 1468600/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7343/10000 episodes, total num timesteps 1468800/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7344/10000 episodes, total num timesteps 1469000/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7345/10000 episodes, total num timesteps 1469200/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7346/10000 episodes, total num timesteps 1469400/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7347/10000 episodes, total num timesteps 1469600/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7348/10000 episodes, total num timesteps 1469800/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7349/10000 episodes, total num timesteps 1470000/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7350/10000 episodes, total num timesteps 1470200/2000000, FPS 237.

team_policy eval average step individual rewards of agent0: 1.1660929846598882
team_policy eval average team episode rewards of agent0: 132.5
team_policy eval idv catch total num of agent0: 48
team_policy eval team catch total num: 53
team_policy eval average step individual rewards of agent1: 1.194241864780627
team_policy eval average team episode rewards of agent1: 132.5
team_policy eval idv catch total num of agent1: 49
team_policy eval team catch total num: 53
team_policy eval average step individual rewards of agent2: 0.9428175866911035
team_policy eval average team episode rewards of agent2: 132.5
team_policy eval idv catch total num of agent2: 39
team_policy eval team catch total num: 53
team_policy eval average step individual rewards of agent3: 0.6535858670590975
team_policy eval average team episode rewards of agent3: 132.5
team_policy eval idv catch total num of agent3: 28
team_policy eval team catch total num: 53
team_policy eval average step individual rewards of agent4: 0.8909273166659918
team_policy eval average team episode rewards of agent4: 132.5
team_policy eval idv catch total num of agent4: 37
team_policy eval team catch total num: 53
idv_policy eval average step individual rewards of agent0: 0.9829056084845605
idv_policy eval average team episode rewards of agent0: 115.0
idv_policy eval idv catch total num of agent0: 41
idv_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent1: 0.39342044270334114
idv_policy eval average team episode rewards of agent1: 115.0
idv_policy eval idv catch total num of agent1: 18
idv_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent2: 0.8049245827059579
idv_policy eval average team episode rewards of agent2: 115.0
idv_policy eval idv catch total num of agent2: 34
idv_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent3: 0.9863362853160286
idv_policy eval average team episode rewards of agent3: 115.0
idv_policy eval idv catch total num of agent3: 41
idv_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent4: 0.8097752065899846
idv_policy eval average team episode rewards of agent4: 115.0
idv_policy eval idv catch total num of agent4: 34
idv_policy eval team catch total num: 46

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7351/10000 episodes, total num timesteps 1470400/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7352/10000 episodes, total num timesteps 1470600/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7353/10000 episodes, total num timesteps 1470800/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7354/10000 episodes, total num timesteps 1471000/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7355/10000 episodes, total num timesteps 1471200/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7356/10000 episodes, total num timesteps 1471400/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7357/10000 episodes, total num timesteps 1471600/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7358/10000 episodes, total num timesteps 1471800/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7359/10000 episodes, total num timesteps 1472000/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7360/10000 episodes, total num timesteps 1472200/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7361/10000 episodes, total num timesteps 1472400/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7362/10000 episodes, total num timesteps 1472600/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7363/10000 episodes, total num timesteps 1472800/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7364/10000 episodes, total num timesteps 1473000/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7365/10000 episodes, total num timesteps 1473200/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7366/10000 episodes, total num timesteps 1473400/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7367/10000 episodes, total num timesteps 1473600/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7368/10000 episodes, total num timesteps 1473800/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7369/10000 episodes, total num timesteps 1474000/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7370/10000 episodes, total num timesteps 1474200/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7371/10000 episodes, total num timesteps 1474400/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7372/10000 episodes, total num timesteps 1474600/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7373/10000 episodes, total num timesteps 1474800/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7374/10000 episodes, total num timesteps 1475000/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7375/10000 episodes, total num timesteps 1475200/2000000, FPS 237.

team_policy eval average step individual rewards of agent0: 0.5249037535472931
team_policy eval average team episode rewards of agent0: 135.0
team_policy eval idv catch total num of agent0: 23
team_policy eval team catch total num: 54
team_policy eval average step individual rewards of agent1: 0.8435085977273453
team_policy eval average team episode rewards of agent1: 135.0
team_policy eval idv catch total num of agent1: 35
team_policy eval team catch total num: 54
team_policy eval average step individual rewards of agent2: 1.146201621391847
team_policy eval average team episode rewards of agent2: 135.0
team_policy eval idv catch total num of agent2: 47
team_policy eval team catch total num: 54
team_policy eval average step individual rewards of agent3: 1.2465020679086605
team_policy eval average team episode rewards of agent3: 135.0
team_policy eval idv catch total num of agent3: 51
team_policy eval team catch total num: 54
team_policy eval average step individual rewards of agent4: 1.031947480108104
team_policy eval average team episode rewards of agent4: 135.0
team_policy eval idv catch total num of agent4: 43
team_policy eval team catch total num: 54
idv_policy eval average step individual rewards of agent0: 1.365777896229468
idv_policy eval average team episode rewards of agent0: 167.5
idv_policy eval idv catch total num of agent0: 56
idv_policy eval team catch total num: 67
idv_policy eval average step individual rewards of agent1: 1.0452495691997217
idv_policy eval average team episode rewards of agent1: 167.5
idv_policy eval idv catch total num of agent1: 43
idv_policy eval team catch total num: 67
idv_policy eval average step individual rewards of agent2: 1.4728873386267352
idv_policy eval average team episode rewards of agent2: 167.5
idv_policy eval idv catch total num of agent2: 60
idv_policy eval team catch total num: 67
idv_policy eval average step individual rewards of agent3: 1.0583830084722505
idv_policy eval average team episode rewards of agent3: 167.5
idv_policy eval idv catch total num of agent3: 44
idv_policy eval team catch total num: 67
idv_policy eval average step individual rewards of agent4: 1.0197232857722736
idv_policy eval average team episode rewards of agent4: 167.5
idv_policy eval idv catch total num of agent4: 42
idv_policy eval team catch total num: 67

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7376/10000 episodes, total num timesteps 1475400/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7377/10000 episodes, total num timesteps 1475600/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7378/10000 episodes, total num timesteps 1475800/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7379/10000 episodes, total num timesteps 1476000/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7380/10000 episodes, total num timesteps 1476200/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7381/10000 episodes, total num timesteps 1476400/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7382/10000 episodes, total num timesteps 1476600/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7383/10000 episodes, total num timesteps 1476800/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7384/10000 episodes, total num timesteps 1477000/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7385/10000 episodes, total num timesteps 1477200/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7386/10000 episodes, total num timesteps 1477400/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7387/10000 episodes, total num timesteps 1477600/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7388/10000 episodes, total num timesteps 1477800/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7389/10000 episodes, total num timesteps 1478000/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7390/10000 episodes, total num timesteps 1478200/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7391/10000 episodes, total num timesteps 1478400/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7392/10000 episodes, total num timesteps 1478600/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7393/10000 episodes, total num timesteps 1478800/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7394/10000 episodes, total num timesteps 1479000/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7395/10000 episodes, total num timesteps 1479200/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7396/10000 episodes, total num timesteps 1479400/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7397/10000 episodes, total num timesteps 1479600/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7398/10000 episodes, total num timesteps 1479800/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7399/10000 episodes, total num timesteps 1480000/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7400/10000 episodes, total num timesteps 1480200/2000000, FPS 237.

team_policy eval average step individual rewards of agent0: 0.7459736724750675
team_policy eval average team episode rewards of agent0: 107.5
team_policy eval idv catch total num of agent0: 31
team_policy eval team catch total num: 43
team_policy eval average step individual rewards of agent1: 0.7421738649952822
team_policy eval average team episode rewards of agent1: 107.5
team_policy eval idv catch total num of agent1: 31
team_policy eval team catch total num: 43
team_policy eval average step individual rewards of agent2: 0.6935005853132563
team_policy eval average team episode rewards of agent2: 107.5
team_policy eval idv catch total num of agent2: 29
team_policy eval team catch total num: 43
team_policy eval average step individual rewards of agent3: 0.6312477667900002
team_policy eval average team episode rewards of agent3: 107.5
team_policy eval idv catch total num of agent3: 27
team_policy eval team catch total num: 43
team_policy eval average step individual rewards of agent4: 1.1948227228444355
team_policy eval average team episode rewards of agent4: 107.5
team_policy eval idv catch total num of agent4: 49
team_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent0: 0.7418087022076052
idv_policy eval average team episode rewards of agent0: 115.0
idv_policy eval idv catch total num of agent0: 31
idv_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent1: 0.6031666243976802
idv_policy eval average team episode rewards of agent1: 115.0
idv_policy eval idv catch total num of agent1: 26
idv_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent2: 1.096645976386033
idv_policy eval average team episode rewards of agent2: 115.0
idv_policy eval idv catch total num of agent2: 45
idv_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent3: 0.9092040440092262
idv_policy eval average team episode rewards of agent3: 115.0
idv_policy eval idv catch total num of agent3: 38
idv_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent4: 1.1203195469602742
idv_policy eval average team episode rewards of agent4: 115.0
idv_policy eval idv catch total num of agent4: 46
idv_policy eval team catch total num: 46

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7401/10000 episodes, total num timesteps 1480400/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7402/10000 episodes, total num timesteps 1480600/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7403/10000 episodes, total num timesteps 1480800/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7404/10000 episodes, total num timesteps 1481000/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7405/10000 episodes, total num timesteps 1481200/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7406/10000 episodes, total num timesteps 1481400/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7407/10000 episodes, total num timesteps 1481600/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7408/10000 episodes, total num timesteps 1481800/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7409/10000 episodes, total num timesteps 1482000/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7410/10000 episodes, total num timesteps 1482200/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7411/10000 episodes, total num timesteps 1482400/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7412/10000 episodes, total num timesteps 1482600/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7413/10000 episodes, total num timesteps 1482800/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7414/10000 episodes, total num timesteps 1483000/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7415/10000 episodes, total num timesteps 1483200/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7416/10000 episodes, total num timesteps 1483400/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7417/10000 episodes, total num timesteps 1483600/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7418/10000 episodes, total num timesteps 1483800/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7419/10000 episodes, total num timesteps 1484000/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7420/10000 episodes, total num timesteps 1484200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7421/10000 episodes, total num timesteps 1484400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7422/10000 episodes, total num timesteps 1484600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7423/10000 episodes, total num timesteps 1484800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7424/10000 episodes, total num timesteps 1485000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7425/10000 episodes, total num timesteps 1485200/2000000, FPS 238.

team_policy eval average step individual rewards of agent0: 0.9421676651068094
team_policy eval average team episode rewards of agent0: 105.0
team_policy eval idv catch total num of agent0: 39
team_policy eval team catch total num: 42
team_policy eval average step individual rewards of agent1: 0.5326045689384086
team_policy eval average team episode rewards of agent1: 105.0
team_policy eval idv catch total num of agent1: 23
team_policy eval team catch total num: 42
team_policy eval average step individual rewards of agent2: 0.8175271317410142
team_policy eval average team episode rewards of agent2: 105.0
team_policy eval idv catch total num of agent2: 34
team_policy eval team catch total num: 42
team_policy eval average step individual rewards of agent3: 0.5254791901232063
team_policy eval average team episode rewards of agent3: 105.0
team_policy eval idv catch total num of agent3: 23
team_policy eval team catch total num: 42
team_policy eval average step individual rewards of agent4: 1.0605760732543181
team_policy eval average team episode rewards of agent4: 105.0
team_policy eval idv catch total num of agent4: 44
team_policy eval team catch total num: 42
idv_policy eval average step individual rewards of agent0: 0.9940146216376365
idv_policy eval average team episode rewards of agent0: 122.5
idv_policy eval idv catch total num of agent0: 41
idv_policy eval team catch total num: 49
idv_policy eval average step individual rewards of agent1: 0.6069725109516539
idv_policy eval average team episode rewards of agent1: 122.5
idv_policy eval idv catch total num of agent1: 26
idv_policy eval team catch total num: 49
idv_policy eval average step individual rewards of agent2: 0.8661126485027356
idv_policy eval average team episode rewards of agent2: 122.5
idv_policy eval idv catch total num of agent2: 36
idv_policy eval team catch total num: 49
idv_policy eval average step individual rewards of agent3: 0.6801274790994999
idv_policy eval average team episode rewards of agent3: 122.5
idv_policy eval idv catch total num of agent3: 29
idv_policy eval team catch total num: 49
idv_policy eval average step individual rewards of agent4: 1.1145069862936057
idv_policy eval average team episode rewards of agent4: 122.5
idv_policy eval idv catch total num of agent4: 46
idv_policy eval team catch total num: 49

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7426/10000 episodes, total num timesteps 1485400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7427/10000 episodes, total num timesteps 1485600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7428/10000 episodes, total num timesteps 1485800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7429/10000 episodes, total num timesteps 1486000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7430/10000 episodes, total num timesteps 1486200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7431/10000 episodes, total num timesteps 1486400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7432/10000 episodes, total num timesteps 1486600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7433/10000 episodes, total num timesteps 1486800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7434/10000 episodes, total num timesteps 1487000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7435/10000 episodes, total num timesteps 1487200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7436/10000 episodes, total num timesteps 1487400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7437/10000 episodes, total num timesteps 1487600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7438/10000 episodes, total num timesteps 1487800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7439/10000 episodes, total num timesteps 1488000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7440/10000 episodes, total num timesteps 1488200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7441/10000 episodes, total num timesteps 1488400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7442/10000 episodes, total num timesteps 1488600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7443/10000 episodes, total num timesteps 1488800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7444/10000 episodes, total num timesteps 1489000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7445/10000 episodes, total num timesteps 1489200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7446/10000 episodes, total num timesteps 1489400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7447/10000 episodes, total num timesteps 1489600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7448/10000 episodes, total num timesteps 1489800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7449/10000 episodes, total num timesteps 1490000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7450/10000 episodes, total num timesteps 1490200/2000000, FPS 238.

team_policy eval average step individual rewards of agent0: 0.9602122000475793
team_policy eval average team episode rewards of agent0: 105.0
team_policy eval idv catch total num of agent0: 40
team_policy eval team catch total num: 42
team_policy eval average step individual rewards of agent1: 0.8611231213935384
team_policy eval average team episode rewards of agent1: 105.0
team_policy eval idv catch total num of agent1: 36
team_policy eval team catch total num: 42
team_policy eval average step individual rewards of agent2: 0.6235651722716945
team_policy eval average team episode rewards of agent2: 105.0
team_policy eval idv catch total num of agent2: 27
team_policy eval team catch total num: 42
team_policy eval average step individual rewards of agent3: 1.3452442654453591
team_policy eval average team episode rewards of agent3: 105.0
team_policy eval idv catch total num of agent3: 55
team_policy eval team catch total num: 42
team_policy eval average step individual rewards of agent4: 0.5661380441881726
team_policy eval average team episode rewards of agent4: 105.0
team_policy eval idv catch total num of agent4: 24
team_policy eval team catch total num: 42
idv_policy eval average step individual rewards of agent0: 1.2409995261315985
idv_policy eval average team episode rewards of agent0: 145.0
idv_policy eval idv catch total num of agent0: 51
idv_policy eval team catch total num: 58
idv_policy eval average step individual rewards of agent1: 0.8699065662645566
idv_policy eval average team episode rewards of agent1: 145.0
idv_policy eval idv catch total num of agent1: 36
idv_policy eval team catch total num: 58
idv_policy eval average step individual rewards of agent2: 1.168542704716734
idv_policy eval average team episode rewards of agent2: 145.0
idv_policy eval idv catch total num of agent2: 48
idv_policy eval team catch total num: 58
idv_policy eval average step individual rewards of agent3: 0.8382725977164429
idv_policy eval average team episode rewards of agent3: 145.0
idv_policy eval idv catch total num of agent3: 35
idv_policy eval team catch total num: 58
idv_policy eval average step individual rewards of agent4: 0.716262752016776
idv_policy eval average team episode rewards of agent4: 145.0
idv_policy eval idv catch total num of agent4: 30
idv_policy eval team catch total num: 58

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7451/10000 episodes, total num timesteps 1490400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7452/10000 episodes, total num timesteps 1490600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7453/10000 episodes, total num timesteps 1490800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7454/10000 episodes, total num timesteps 1491000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7455/10000 episodes, total num timesteps 1491200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7456/10000 episodes, total num timesteps 1491400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7457/10000 episodes, total num timesteps 1491600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7458/10000 episodes, total num timesteps 1491800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7459/10000 episodes, total num timesteps 1492000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7460/10000 episodes, total num timesteps 1492200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7461/10000 episodes, total num timesteps 1492400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7462/10000 episodes, total num timesteps 1492600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7463/10000 episodes, total num timesteps 1492800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7464/10000 episodes, total num timesteps 1493000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7465/10000 episodes, total num timesteps 1493200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7466/10000 episodes, total num timesteps 1493400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7467/10000 episodes, total num timesteps 1493600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7468/10000 episodes, total num timesteps 1493800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7469/10000 episodes, total num timesteps 1494000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7470/10000 episodes, total num timesteps 1494200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7471/10000 episodes, total num timesteps 1494400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7472/10000 episodes, total num timesteps 1494600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7473/10000 episodes, total num timesteps 1494800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7474/10000 episodes, total num timesteps 1495000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7475/10000 episodes, total num timesteps 1495200/2000000, FPS 238.

team_policy eval average step individual rewards of agent0: 0.6441659663061
team_policy eval average team episode rewards of agent0: 80.0
team_policy eval idv catch total num of agent0: 27
team_policy eval team catch total num: 32
team_policy eval average step individual rewards of agent1: 0.7467562259983432
team_policy eval average team episode rewards of agent1: 80.0
team_policy eval idv catch total num of agent1: 31
team_policy eval team catch total num: 32
team_policy eval average step individual rewards of agent2: 0.5095594764249001
team_policy eval average team episode rewards of agent2: 80.0
team_policy eval idv catch total num of agent2: 22
team_policy eval team catch total num: 32
team_policy eval average step individual rewards of agent3: 0.5545246081672786
team_policy eval average team episode rewards of agent3: 80.0
team_policy eval idv catch total num of agent3: 24
team_policy eval team catch total num: 32
team_policy eval average step individual rewards of agent4: 0.6921658562143663
team_policy eval average team episode rewards of agent4: 80.0
team_policy eval idv catch total num of agent4: 29
team_policy eval team catch total num: 32
idv_policy eval average step individual rewards of agent0: 0.7832458619928835
idv_policy eval average team episode rewards of agent0: 57.5
idv_policy eval idv catch total num of agent0: 33
idv_policy eval team catch total num: 23
idv_policy eval average step individual rewards of agent1: 0.4319853627017545
idv_policy eval average team episode rewards of agent1: 57.5
idv_policy eval idv catch total num of agent1: 19
idv_policy eval team catch total num: 23
idv_policy eval average step individual rewards of agent2: 0.44124063095134103
idv_policy eval average team episode rewards of agent2: 57.5
idv_policy eval idv catch total num of agent2: 20
idv_policy eval team catch total num: 23
idv_policy eval average step individual rewards of agent3: 0.15618513638458104
idv_policy eval average team episode rewards of agent3: 57.5
idv_policy eval idv catch total num of agent3: 8
idv_policy eval team catch total num: 23
idv_policy eval average step individual rewards of agent4: 0.6869042855066894
idv_policy eval average team episode rewards of agent4: 57.5
idv_policy eval idv catch total num of agent4: 29
idv_policy eval team catch total num: 23

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7476/10000 episodes, total num timesteps 1495400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7477/10000 episodes, total num timesteps 1495600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7478/10000 episodes, total num timesteps 1495800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7479/10000 episodes, total num timesteps 1496000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7480/10000 episodes, total num timesteps 1496200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7481/10000 episodes, total num timesteps 1496400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7482/10000 episodes, total num timesteps 1496600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7483/10000 episodes, total num timesteps 1496800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7484/10000 episodes, total num timesteps 1497000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7485/10000 episodes, total num timesteps 1497200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7486/10000 episodes, total num timesteps 1497400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7487/10000 episodes, total num timesteps 1497600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7488/10000 episodes, total num timesteps 1497800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7489/10000 episodes, total num timesteps 1498000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7490/10000 episodes, total num timesteps 1498200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7491/10000 episodes, total num timesteps 1498400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7492/10000 episodes, total num timesteps 1498600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7493/10000 episodes, total num timesteps 1498800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7494/10000 episodes, total num timesteps 1499000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7495/10000 episodes, total num timesteps 1499200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7496/10000 episodes, total num timesteps 1499400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7497/10000 episodes, total num timesteps 1499600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7498/10000 episodes, total num timesteps 1499800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7499/10000 episodes, total num timesteps 1500000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7500/10000 episodes, total num timesteps 1500200/2000000, FPS 238.

team_policy eval average step individual rewards of agent0: 0.5042509680213761
team_policy eval average team episode rewards of agent0: 87.5
team_policy eval idv catch total num of agent0: 22
team_policy eval team catch total num: 35
team_policy eval average step individual rewards of agent1: 0.7307803680480047
team_policy eval average team episode rewards of agent1: 87.5
team_policy eval idv catch total num of agent1: 31
team_policy eval team catch total num: 35
team_policy eval average step individual rewards of agent2: 0.6054067884870536
team_policy eval average team episode rewards of agent2: 87.5
team_policy eval idv catch total num of agent2: 26
team_policy eval team catch total num: 35
team_policy eval average step individual rewards of agent3: 0.576533510181465
team_policy eval average team episode rewards of agent3: 87.5
team_policy eval idv catch total num of agent3: 25
team_policy eval team catch total num: 35
team_policy eval average step individual rewards of agent4: 0.7107779280738222
team_policy eval average team episode rewards of agent4: 87.5
team_policy eval idv catch total num of agent4: 30
team_policy eval team catch total num: 35
idv_policy eval average step individual rewards of agent0: 1.0450843682615065
idv_policy eval average team episode rewards of agent0: 107.5
idv_policy eval idv catch total num of agent0: 43
idv_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent1: 0.3761943819590268
idv_policy eval average team episode rewards of agent1: 107.5
idv_policy eval idv catch total num of agent1: 17
idv_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent2: 1.0046473525862512
idv_policy eval average team episode rewards of agent2: 107.5
idv_policy eval idv catch total num of agent2: 42
idv_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent3: 0.5116766382616273
idv_policy eval average team episode rewards of agent3: 107.5
idv_policy eval idv catch total num of agent3: 22
idv_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent4: 0.7407226663141188
idv_policy eval average team episode rewards of agent4: 107.5
idv_policy eval idv catch total num of agent4: 31
idv_policy eval team catch total num: 43

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7501/10000 episodes, total num timesteps 1500400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7502/10000 episodes, total num timesteps 1500600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7503/10000 episodes, total num timesteps 1500800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7504/10000 episodes, total num timesteps 1501000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7505/10000 episodes, total num timesteps 1501200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7506/10000 episodes, total num timesteps 1501400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7507/10000 episodes, total num timesteps 1501600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7508/10000 episodes, total num timesteps 1501800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7509/10000 episodes, total num timesteps 1502000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7510/10000 episodes, total num timesteps 1502200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7511/10000 episodes, total num timesteps 1502400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7512/10000 episodes, total num timesteps 1502600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7513/10000 episodes, total num timesteps 1502800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7514/10000 episodes, total num timesteps 1503000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7515/10000 episodes, total num timesteps 1503200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7516/10000 episodes, total num timesteps 1503400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7517/10000 episodes, total num timesteps 1503600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7518/10000 episodes, total num timesteps 1503800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7519/10000 episodes, total num timesteps 1504000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7520/10000 episodes, total num timesteps 1504200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7521/10000 episodes, total num timesteps 1504400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7522/10000 episodes, total num timesteps 1504600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7523/10000 episodes, total num timesteps 1504800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7524/10000 episodes, total num timesteps 1505000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7525/10000 episodes, total num timesteps 1505200/2000000, FPS 238.

team_policy eval average step individual rewards of agent0: 1.4240298748824671
team_policy eval average team episode rewards of agent0: 142.5
team_policy eval idv catch total num of agent0: 58
team_policy eval team catch total num: 57
team_policy eval average step individual rewards of agent1: 1.0191421742849684
team_policy eval average team episode rewards of agent1: 142.5
team_policy eval idv catch total num of agent1: 42
team_policy eval team catch total num: 57
team_policy eval average step individual rewards of agent2: 0.8390023059205087
team_policy eval average team episode rewards of agent2: 142.5
team_policy eval idv catch total num of agent2: 35
team_policy eval team catch total num: 57
team_policy eval average step individual rewards of agent3: 1.1251110851571238
team_policy eval average team episode rewards of agent3: 142.5
team_policy eval idv catch total num of agent3: 46
team_policy eval team catch total num: 57
team_policy eval average step individual rewards of agent4: 0.9362239711349196
team_policy eval average team episode rewards of agent4: 142.5
team_policy eval idv catch total num of agent4: 39
team_policy eval team catch total num: 57
idv_policy eval average step individual rewards of agent0: 0.8316800584048969
idv_policy eval average team episode rewards of agent0: 90.0
idv_policy eval idv catch total num of agent0: 35
idv_policy eval team catch total num: 36
idv_policy eval average step individual rewards of agent1: 0.4975337828512286
idv_policy eval average team episode rewards of agent1: 90.0
idv_policy eval idv catch total num of agent1: 22
idv_policy eval team catch total num: 36
idv_policy eval average step individual rewards of agent2: 1.402616678691973
idv_policy eval average team episode rewards of agent2: 90.0
idv_policy eval idv catch total num of agent2: 57
idv_policy eval team catch total num: 36
idv_policy eval average step individual rewards of agent3: 0.5855367364917174
idv_policy eval average team episode rewards of agent3: 90.0
idv_policy eval idv catch total num of agent3: 25
idv_policy eval team catch total num: 36
idv_policy eval average step individual rewards of agent4: 0.6287847367970457
idv_policy eval average team episode rewards of agent4: 90.0
idv_policy eval idv catch total num of agent4: 27
idv_policy eval team catch total num: 36

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7526/10000 episodes, total num timesteps 1505400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7527/10000 episodes, total num timesteps 1505600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7528/10000 episodes, total num timesteps 1505800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7529/10000 episodes, total num timesteps 1506000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7530/10000 episodes, total num timesteps 1506200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7531/10000 episodes, total num timesteps 1506400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7532/10000 episodes, total num timesteps 1506600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7533/10000 episodes, total num timesteps 1506800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7534/10000 episodes, total num timesteps 1507000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7535/10000 episodes, total num timesteps 1507200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7536/10000 episodes, total num timesteps 1507400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7537/10000 episodes, total num timesteps 1507600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7538/10000 episodes, total num timesteps 1507800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7539/10000 episodes, total num timesteps 1508000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7540/10000 episodes, total num timesteps 1508200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7541/10000 episodes, total num timesteps 1508400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7542/10000 episodes, total num timesteps 1508600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7543/10000 episodes, total num timesteps 1508800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7544/10000 episodes, total num timesteps 1509000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7545/10000 episodes, total num timesteps 1509200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7546/10000 episodes, total num timesteps 1509400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7547/10000 episodes, total num timesteps 1509600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7548/10000 episodes, total num timesteps 1509800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7549/10000 episodes, total num timesteps 1510000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7550/10000 episodes, total num timesteps 1510200/2000000, FPS 238.

team_policy eval average step individual rewards of agent0: 1.095398498730431
team_policy eval average team episode rewards of agent0: 140.0
team_policy eval idv catch total num of agent0: 45
team_policy eval team catch total num: 56
team_policy eval average step individual rewards of agent1: 0.9924966219032332
team_policy eval average team episode rewards of agent1: 140.0
team_policy eval idv catch total num of agent1: 41
team_policy eval team catch total num: 56
team_policy eval average step individual rewards of agent2: 0.8920842958109296
team_policy eval average team episode rewards of agent2: 140.0
team_policy eval idv catch total num of agent2: 37
team_policy eval team catch total num: 56
team_policy eval average step individual rewards of agent3: 1.2197007169979623
team_policy eval average team episode rewards of agent3: 140.0
team_policy eval idv catch total num of agent3: 50
team_policy eval team catch total num: 56
team_policy eval average step individual rewards of agent4: 0.9404086931927981
team_policy eval average team episode rewards of agent4: 140.0
team_policy eval idv catch total num of agent4: 39
team_policy eval team catch total num: 56
idv_policy eval average step individual rewards of agent0: 0.5507638594412476
idv_policy eval average team episode rewards of agent0: 100.0
idv_policy eval idv catch total num of agent0: 24
idv_policy eval team catch total num: 40
idv_policy eval average step individual rewards of agent1: 0.7071443202930169
idv_policy eval average team episode rewards of agent1: 100.0
idv_policy eval idv catch total num of agent1: 30
idv_policy eval team catch total num: 40
idv_policy eval average step individual rewards of agent2: 0.5608012308768331
idv_policy eval average team episode rewards of agent2: 100.0
idv_policy eval idv catch total num of agent2: 24
idv_policy eval team catch total num: 40
idv_policy eval average step individual rewards of agent3: 1.0892217004817422
idv_policy eval average team episode rewards of agent3: 100.0
idv_policy eval idv catch total num of agent3: 45
idv_policy eval team catch total num: 40
idv_policy eval average step individual rewards of agent4: 0.6972595267655606
idv_policy eval average team episode rewards of agent4: 100.0
idv_policy eval idv catch total num of agent4: 29
idv_policy eval team catch total num: 40

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7551/10000 episodes, total num timesteps 1510400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7552/10000 episodes, total num timesteps 1510600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7553/10000 episodes, total num timesteps 1510800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7554/10000 episodes, total num timesteps 1511000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7555/10000 episodes, total num timesteps 1511200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7556/10000 episodes, total num timesteps 1511400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7557/10000 episodes, total num timesteps 1511600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7558/10000 episodes, total num timesteps 1511800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7559/10000 episodes, total num timesteps 1512000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7560/10000 episodes, total num timesteps 1512200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7561/10000 episodes, total num timesteps 1512400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7562/10000 episodes, total num timesteps 1512600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7563/10000 episodes, total num timesteps 1512800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7564/10000 episodes, total num timesteps 1513000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7565/10000 episodes, total num timesteps 1513200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7566/10000 episodes, total num timesteps 1513400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7567/10000 episodes, total num timesteps 1513600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7568/10000 episodes, total num timesteps 1513800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7569/10000 episodes, total num timesteps 1514000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7570/10000 episodes, total num timesteps 1514200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7571/10000 episodes, total num timesteps 1514400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7572/10000 episodes, total num timesteps 1514600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7573/10000 episodes, total num timesteps 1514800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7574/10000 episodes, total num timesteps 1515000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7575/10000 episodes, total num timesteps 1515200/2000000, FPS 238.

team_policy eval average step individual rewards of agent0: 0.961473791811336
team_policy eval average team episode rewards of agent0: 117.5
team_policy eval idv catch total num of agent0: 40
team_policy eval team catch total num: 47
team_policy eval average step individual rewards of agent1: 0.5555770757235596
team_policy eval average team episode rewards of agent1: 117.5
team_policy eval idv catch total num of agent1: 24
team_policy eval team catch total num: 47
team_policy eval average step individual rewards of agent2: 0.8945994362420508
team_policy eval average team episode rewards of agent2: 117.5
team_policy eval idv catch total num of agent2: 37
team_policy eval team catch total num: 47
team_policy eval average step individual rewards of agent3: 1.3495255063545013
team_policy eval average team episode rewards of agent3: 117.5
team_policy eval idv catch total num of agent3: 55
team_policy eval team catch total num: 47
team_policy eval average step individual rewards of agent4: 0.702864250278269
team_policy eval average team episode rewards of agent4: 117.5
team_policy eval idv catch total num of agent4: 30
team_policy eval team catch total num: 47
idv_policy eval average step individual rewards of agent0: 0.8595603399103005
idv_policy eval average team episode rewards of agent0: 110.0
idv_policy eval idv catch total num of agent0: 36
idv_policy eval team catch total num: 44
idv_policy eval average step individual rewards of agent1: 0.8068330922436923
idv_policy eval average team episode rewards of agent1: 110.0
idv_policy eval idv catch total num of agent1: 34
idv_policy eval team catch total num: 44
idv_policy eval average step individual rewards of agent2: 0.603396477313676
idv_policy eval average team episode rewards of agent2: 110.0
idv_policy eval idv catch total num of agent2: 26
idv_policy eval team catch total num: 44
idv_policy eval average step individual rewards of agent3: 0.7300233202756833
idv_policy eval average team episode rewards of agent3: 110.0
idv_policy eval idv catch total num of agent3: 31
idv_policy eval team catch total num: 44
idv_policy eval average step individual rewards of agent4: 0.5851892008079226
idv_policy eval average team episode rewards of agent4: 110.0
idv_policy eval idv catch total num of agent4: 25
idv_policy eval team catch total num: 44

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7576/10000 episodes, total num timesteps 1515400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7577/10000 episodes, total num timesteps 1515600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7578/10000 episodes, total num timesteps 1515800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7579/10000 episodes, total num timesteps 1516000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7580/10000 episodes, total num timesteps 1516200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7581/10000 episodes, total num timesteps 1516400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7582/10000 episodes, total num timesteps 1516600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7583/10000 episodes, total num timesteps 1516800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7584/10000 episodes, total num timesteps 1517000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7585/10000 episodes, total num timesteps 1517200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7586/10000 episodes, total num timesteps 1517400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7587/10000 episodes, total num timesteps 1517600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7588/10000 episodes, total num timesteps 1517800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7589/10000 episodes, total num timesteps 1518000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7590/10000 episodes, total num timesteps 1518200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7591/10000 episodes, total num timesteps 1518400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7592/10000 episodes, total num timesteps 1518600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7593/10000 episodes, total num timesteps 1518800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7594/10000 episodes, total num timesteps 1519000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7595/10000 episodes, total num timesteps 1519200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7596/10000 episodes, total num timesteps 1519400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7597/10000 episodes, total num timesteps 1519600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7598/10000 episodes, total num timesteps 1519800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7599/10000 episodes, total num timesteps 1520000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7600/10000 episodes, total num timesteps 1520200/2000000, FPS 238.

team_policy eval average step individual rewards of agent0: 0.8196740626976061
team_policy eval average team episode rewards of agent0: 115.0
team_policy eval idv catch total num of agent0: 34
team_policy eval team catch total num: 46
team_policy eval average step individual rewards of agent1: 0.6648818206895143
team_policy eval average team episode rewards of agent1: 115.0
team_policy eval idv catch total num of agent1: 28
team_policy eval team catch total num: 46
team_policy eval average step individual rewards of agent2: 1.0385545633916784
team_policy eval average team episode rewards of agent2: 115.0
team_policy eval idv catch total num of agent2: 43
team_policy eval team catch total num: 46
team_policy eval average step individual rewards of agent3: 1.069419428452118
team_policy eval average team episode rewards of agent3: 115.0
team_policy eval idv catch total num of agent3: 44
team_policy eval team catch total num: 46
team_policy eval average step individual rewards of agent4: 0.5648037607335732
team_policy eval average team episode rewards of agent4: 115.0
team_policy eval idv catch total num of agent4: 24
team_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent0: 1.1820464132040647
idv_policy eval average team episode rewards of agent0: 102.5
idv_policy eval idv catch total num of agent0: 49
idv_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent1: 0.7132370425942568
idv_policy eval average team episode rewards of agent1: 102.5
idv_policy eval idv catch total num of agent1: 30
idv_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent2: 0.30372382537509957
idv_policy eval average team episode rewards of agent2: 102.5
idv_policy eval idv catch total num of agent2: 14
idv_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent3: 0.9056124639916575
idv_policy eval average team episode rewards of agent3: 102.5
idv_policy eval idv catch total num of agent3: 38
idv_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent4: 0.6767330469594529
idv_policy eval average team episode rewards of agent4: 102.5
idv_policy eval idv catch total num of agent4: 29
idv_policy eval team catch total num: 41

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7601/10000 episodes, total num timesteps 1520400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7602/10000 episodes, total num timesteps 1520600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7603/10000 episodes, total num timesteps 1520800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7604/10000 episodes, total num timesteps 1521000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7605/10000 episodes, total num timesteps 1521200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7606/10000 episodes, total num timesteps 1521400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7607/10000 episodes, total num timesteps 1521600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7608/10000 episodes, total num timesteps 1521800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7609/10000 episodes, total num timesteps 1522000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7610/10000 episodes, total num timesteps 1522200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7611/10000 episodes, total num timesteps 1522400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7612/10000 episodes, total num timesteps 1522600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7613/10000 episodes, total num timesteps 1522800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7614/10000 episodes, total num timesteps 1523000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7615/10000 episodes, total num timesteps 1523200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7616/10000 episodes, total num timesteps 1523400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7617/10000 episodes, total num timesteps 1523600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7618/10000 episodes, total num timesteps 1523800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7619/10000 episodes, total num timesteps 1524000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7620/10000 episodes, total num timesteps 1524200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7621/10000 episodes, total num timesteps 1524400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7622/10000 episodes, total num timesteps 1524600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7623/10000 episodes, total num timesteps 1524800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7624/10000 episodes, total num timesteps 1525000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7625/10000 episodes, total num timesteps 1525200/2000000, FPS 238.

team_policy eval average step individual rewards of agent0: 1.1036975538597371
team_policy eval average team episode rewards of agent0: 137.5
team_policy eval idv catch total num of agent0: 45
team_policy eval team catch total num: 55
team_policy eval average step individual rewards of agent1: 0.8901442579423176
team_policy eval average team episode rewards of agent1: 137.5
team_policy eval idv catch total num of agent1: 37
team_policy eval team catch total num: 55
team_policy eval average step individual rewards of agent2: 1.0896580048256646
team_policy eval average team episode rewards of agent2: 137.5
team_policy eval idv catch total num of agent2: 45
team_policy eval team catch total num: 55
team_policy eval average step individual rewards of agent3: 1.196230797291704
team_policy eval average team episode rewards of agent3: 137.5
team_policy eval idv catch total num of agent3: 49
team_policy eval team catch total num: 55
team_policy eval average step individual rewards of agent4: 1.1731722477246571
team_policy eval average team episode rewards of agent4: 137.5
team_policy eval idv catch total num of agent4: 48
team_policy eval team catch total num: 55
idv_policy eval average step individual rewards of agent0: 0.8241382686794374
idv_policy eval average team episode rewards of agent0: 102.5
idv_policy eval idv catch total num of agent0: 35
idv_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent1: 0.5834654281245784
idv_policy eval average team episode rewards of agent1: 102.5
idv_policy eval idv catch total num of agent1: 25
idv_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent2: 0.8852337149405384
idv_policy eval average team episode rewards of agent2: 102.5
idv_policy eval idv catch total num of agent2: 37
idv_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent3: 0.4861049182098958
idv_policy eval average team episode rewards of agent3: 102.5
idv_policy eval idv catch total num of agent3: 22
idv_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent4: 0.8131013341934752
idv_policy eval average team episode rewards of agent4: 102.5
idv_policy eval idv catch total num of agent4: 34
idv_policy eval team catch total num: 41

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7626/10000 episodes, total num timesteps 1525400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7627/10000 episodes, total num timesteps 1525600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7628/10000 episodes, total num timesteps 1525800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7629/10000 episodes, total num timesteps 1526000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7630/10000 episodes, total num timesteps 1526200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7631/10000 episodes, total num timesteps 1526400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7632/10000 episodes, total num timesteps 1526600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7633/10000 episodes, total num timesteps 1526800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7634/10000 episodes, total num timesteps 1527000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7635/10000 episodes, total num timesteps 1527200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7636/10000 episodes, total num timesteps 1527400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7637/10000 episodes, total num timesteps 1527600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7638/10000 episodes, total num timesteps 1527800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7639/10000 episodes, total num timesteps 1528000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7640/10000 episodes, total num timesteps 1528200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7641/10000 episodes, total num timesteps 1528400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7642/10000 episodes, total num timesteps 1528600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7643/10000 episodes, total num timesteps 1528800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7644/10000 episodes, total num timesteps 1529000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7645/10000 episodes, total num timesteps 1529200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7646/10000 episodes, total num timesteps 1529400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7647/10000 episodes, total num timesteps 1529600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7648/10000 episodes, total num timesteps 1529800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7649/10000 episodes, total num timesteps 1530000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7650/10000 episodes, total num timesteps 1530200/2000000, FPS 238.

team_policy eval average step individual rewards of agent0: 0.4364185452295558
team_policy eval average team episode rewards of agent0: 57.5
team_policy eval idv catch total num of agent0: 19
team_policy eval team catch total num: 23
team_policy eval average step individual rewards of agent1: 0.5550319144715609
team_policy eval average team episode rewards of agent1: 57.5
team_policy eval idv catch total num of agent1: 24
team_policy eval team catch total num: 23
team_policy eval average step individual rewards of agent2: 0.3044088320695781
team_policy eval average team episode rewards of agent2: 57.5
team_policy eval idv catch total num of agent2: 14
team_policy eval team catch total num: 23
team_policy eval average step individual rewards of agent3: 0.38335967160424533
team_policy eval average team episode rewards of agent3: 57.5
team_policy eval idv catch total num of agent3: 17
team_policy eval team catch total num: 23
team_policy eval average step individual rewards of agent4: 0.38450594854133213
team_policy eval average team episode rewards of agent4: 57.5
team_policy eval idv catch total num of agent4: 17
team_policy eval team catch total num: 23
idv_policy eval average step individual rewards of agent0: 0.45610751594737176
idv_policy eval average team episode rewards of agent0: 132.5
idv_policy eval idv catch total num of agent0: 20
idv_policy eval team catch total num: 53
idv_policy eval average step individual rewards of agent1: 0.762513454189779
idv_policy eval average team episode rewards of agent1: 132.5
idv_policy eval idv catch total num of agent1: 32
idv_policy eval team catch total num: 53
idv_policy eval average step individual rewards of agent2: 1.5201497021846375
idv_policy eval average team episode rewards of agent2: 132.5
idv_policy eval idv catch total num of agent2: 62
idv_policy eval team catch total num: 53
idv_policy eval average step individual rewards of agent3: 0.9346414988060985
idv_policy eval average team episode rewards of agent3: 132.5
idv_policy eval idv catch total num of agent3: 39
idv_policy eval team catch total num: 53
idv_policy eval average step individual rewards of agent4: 0.8537516821075692
idv_policy eval average team episode rewards of agent4: 132.5
idv_policy eval idv catch total num of agent4: 36
idv_policy eval team catch total num: 53

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7651/10000 episodes, total num timesteps 1530400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7652/10000 episodes, total num timesteps 1530600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7653/10000 episodes, total num timesteps 1530800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7654/10000 episodes, total num timesteps 1531000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7655/10000 episodes, total num timesteps 1531200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7656/10000 episodes, total num timesteps 1531400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7657/10000 episodes, total num timesteps 1531600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7658/10000 episodes, total num timesteps 1531800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7659/10000 episodes, total num timesteps 1532000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7660/10000 episodes, total num timesteps 1532200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7661/10000 episodes, total num timesteps 1532400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7662/10000 episodes, total num timesteps 1532600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7663/10000 episodes, total num timesteps 1532800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7664/10000 episodes, total num timesteps 1533000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7665/10000 episodes, total num timesteps 1533200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7666/10000 episodes, total num timesteps 1533400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7667/10000 episodes, total num timesteps 1533600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7668/10000 episodes, total num timesteps 1533800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7669/10000 episodes, total num timesteps 1534000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7670/10000 episodes, total num timesteps 1534200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7671/10000 episodes, total num timesteps 1534400/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7672/10000 episodes, total num timesteps 1534600/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7673/10000 episodes, total num timesteps 1534800/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7674/10000 episodes, total num timesteps 1535000/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7675/10000 episodes, total num timesteps 1535200/2000000, FPS 239.

team_policy eval average step individual rewards of agent0: 0.514756814283771
team_policy eval average team episode rewards of agent0: 110.0
team_policy eval idv catch total num of agent0: 22
team_policy eval team catch total num: 44
team_policy eval average step individual rewards of agent1: 1.147243067087033
team_policy eval average team episode rewards of agent1: 110.0
team_policy eval idv catch total num of agent1: 47
team_policy eval team catch total num: 44
team_policy eval average step individual rewards of agent2: 0.9753999094644848
team_policy eval average team episode rewards of agent2: 110.0
team_policy eval idv catch total num of agent2: 40
team_policy eval team catch total num: 44
team_policy eval average step individual rewards of agent3: 0.9207963796065283
team_policy eval average team episode rewards of agent3: 110.0
team_policy eval idv catch total num of agent3: 38
team_policy eval team catch total num: 44
team_policy eval average step individual rewards of agent4: 0.5393680863463314
team_policy eval average team episode rewards of agent4: 110.0
team_policy eval idv catch total num of agent4: 23
team_policy eval team catch total num: 44
idv_policy eval average step individual rewards of agent0: 1.055801533612427
idv_policy eval average team episode rewards of agent0: 127.5
idv_policy eval idv catch total num of agent0: 44
idv_policy eval team catch total num: 51
idv_policy eval average step individual rewards of agent1: 1.2234073546288644
idv_policy eval average team episode rewards of agent1: 127.5
idv_policy eval idv catch total num of agent1: 50
idv_policy eval team catch total num: 51
idv_policy eval average step individual rewards of agent2: 0.7172761632469422
idv_policy eval average team episode rewards of agent2: 127.5
idv_policy eval idv catch total num of agent2: 30
idv_policy eval team catch total num: 51
idv_policy eval average step individual rewards of agent3: 0.8619917533176765
idv_policy eval average team episode rewards of agent3: 127.5
idv_policy eval idv catch total num of agent3: 36
idv_policy eval team catch total num: 51
idv_policy eval average step individual rewards of agent4: 0.8177269351192846
idv_policy eval average team episode rewards of agent4: 127.5
idv_policy eval idv catch total num of agent4: 34
idv_policy eval team catch total num: 51

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7676/10000 episodes, total num timesteps 1535400/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7677/10000 episodes, total num timesteps 1535600/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7678/10000 episodes, total num timesteps 1535800/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7679/10000 episodes, total num timesteps 1536000/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7680/10000 episodes, total num timesteps 1536200/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7681/10000 episodes, total num timesteps 1536400/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7682/10000 episodes, total num timesteps 1536600/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7683/10000 episodes, total num timesteps 1536800/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7684/10000 episodes, total num timesteps 1537000/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7685/10000 episodes, total num timesteps 1537200/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7686/10000 episodes, total num timesteps 1537400/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7687/10000 episodes, total num timesteps 1537600/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7688/10000 episodes, total num timesteps 1537800/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7689/10000 episodes, total num timesteps 1538000/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7690/10000 episodes, total num timesteps 1538200/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7691/10000 episodes, total num timesteps 1538400/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7692/10000 episodes, total num timesteps 1538600/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7693/10000 episodes, total num timesteps 1538800/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7694/10000 episodes, total num timesteps 1539000/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7695/10000 episodes, total num timesteps 1539200/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7696/10000 episodes, total num timesteps 1539400/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7697/10000 episodes, total num timesteps 1539600/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7698/10000 episodes, total num timesteps 1539800/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7699/10000 episodes, total num timesteps 1540000/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7700/10000 episodes, total num timesteps 1540200/2000000, FPS 239.

team_policy eval average step individual rewards of agent0: 1.1408185318964434
team_policy eval average team episode rewards of agent0: 97.5
team_policy eval idv catch total num of agent0: 47
team_policy eval team catch total num: 39
team_policy eval average step individual rewards of agent1: 0.841468678748655
team_policy eval average team episode rewards of agent1: 97.5
team_policy eval idv catch total num of agent1: 35
team_policy eval team catch total num: 39
team_policy eval average step individual rewards of agent2: 0.43406468098812867
team_policy eval average team episode rewards of agent2: 97.5
team_policy eval idv catch total num of agent2: 19
team_policy eval team catch total num: 39
team_policy eval average step individual rewards of agent3: 0.4323098731113394
team_policy eval average team episode rewards of agent3: 97.5
team_policy eval idv catch total num of agent3: 19
team_policy eval team catch total num: 39
team_policy eval average step individual rewards of agent4: 0.7300897417002861
team_policy eval average team episode rewards of agent4: 97.5
team_policy eval idv catch total num of agent4: 31
team_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent0: 0.39753720555074473
idv_policy eval average team episode rewards of agent0: 112.5
idv_policy eval idv catch total num of agent0: 18
idv_policy eval team catch total num: 45
idv_policy eval average step individual rewards of agent1: 0.6593813565894503
idv_policy eval average team episode rewards of agent1: 112.5
idv_policy eval idv catch total num of agent1: 28
idv_policy eval team catch total num: 45
idv_policy eval average step individual rewards of agent2: 0.9146275390783668
idv_policy eval average team episode rewards of agent2: 112.5
idv_policy eval idv catch total num of agent2: 38
idv_policy eval team catch total num: 45
idv_policy eval average step individual rewards of agent3: 0.8591853440866909
idv_policy eval average team episode rewards of agent3: 112.5
idv_policy eval idv catch total num of agent3: 36
idv_policy eval team catch total num: 45
idv_policy eval average step individual rewards of agent4: 0.7131789616845426
idv_policy eval average team episode rewards of agent4: 112.5
idv_policy eval idv catch total num of agent4: 30
idv_policy eval team catch total num: 45

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7701/10000 episodes, total num timesteps 1540400/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7702/10000 episodes, total num timesteps 1540600/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7703/10000 episodes, total num timesteps 1540800/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7704/10000 episodes, total num timesteps 1541000/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7705/10000 episodes, total num timesteps 1541200/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7706/10000 episodes, total num timesteps 1541400/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7707/10000 episodes, total num timesteps 1541600/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7708/10000 episodes, total num timesteps 1541800/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7709/10000 episodes, total num timesteps 1542000/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7710/10000 episodes, total num timesteps 1542200/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7711/10000 episodes, total num timesteps 1542400/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7712/10000 episodes, total num timesteps 1542600/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7713/10000 episodes, total num timesteps 1542800/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7714/10000 episodes, total num timesteps 1543000/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7715/10000 episodes, total num timesteps 1543200/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7716/10000 episodes, total num timesteps 1543400/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7717/10000 episodes, total num timesteps 1543600/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7718/10000 episodes, total num timesteps 1543800/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7719/10000 episodes, total num timesteps 1544000/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7720/10000 episodes, total num timesteps 1544200/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7721/10000 episodes, total num timesteps 1544400/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7722/10000 episodes, total num timesteps 1544600/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7723/10000 episodes, total num timesteps 1544800/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7724/10000 episodes, total num timesteps 1545000/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7725/10000 episodes, total num timesteps 1545200/2000000, FPS 239.

team_policy eval average step individual rewards of agent0: 0.6764978576177334
team_policy eval average team episode rewards of agent0: 92.5
team_policy eval idv catch total num of agent0: 29
team_policy eval team catch total num: 37
team_policy eval average step individual rewards of agent1: 0.7794947329569304
team_policy eval average team episode rewards of agent1: 92.5
team_policy eval idv catch total num of agent1: 33
team_policy eval team catch total num: 37
team_policy eval average step individual rewards of agent2: 0.6332719848504661
team_policy eval average team episode rewards of agent2: 92.5
team_policy eval idv catch total num of agent2: 27
team_policy eval team catch total num: 37
team_policy eval average step individual rewards of agent3: 0.3961236391845277
team_policy eval average team episode rewards of agent3: 92.5
team_policy eval idv catch total num of agent3: 18
team_policy eval team catch total num: 37
team_policy eval average step individual rewards of agent4: 0.7005739474578512
team_policy eval average team episode rewards of agent4: 92.5
team_policy eval idv catch total num of agent4: 30
team_policy eval team catch total num: 37
idv_policy eval average step individual rewards of agent0: 1.102416186501898
idv_policy eval average team episode rewards of agent0: 150.0
idv_policy eval idv catch total num of agent0: 45
idv_policy eval team catch total num: 60
idv_policy eval average step individual rewards of agent1: 1.1190510436607592
idv_policy eval average team episode rewards of agent1: 150.0
idv_policy eval idv catch total num of agent1: 46
idv_policy eval team catch total num: 60
idv_policy eval average step individual rewards of agent2: 0.7105186360723041
idv_policy eval average team episode rewards of agent2: 150.0
idv_policy eval idv catch total num of agent2: 30
idv_policy eval team catch total num: 60
idv_policy eval average step individual rewards of agent3: 1.0985303982136039
idv_policy eval average team episode rewards of agent3: 150.0
idv_policy eval idv catch total num of agent3: 45
idv_policy eval team catch total num: 60
idv_policy eval average step individual rewards of agent4: 0.75957458718705
idv_policy eval average team episode rewards of agent4: 150.0
idv_policy eval idv catch total num of agent4: 32
idv_policy eval team catch total num: 60

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7726/10000 episodes, total num timesteps 1545400/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7727/10000 episodes, total num timesteps 1545600/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7728/10000 episodes, total num timesteps 1545800/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7729/10000 episodes, total num timesteps 1546000/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7730/10000 episodes, total num timesteps 1546200/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7731/10000 episodes, total num timesteps 1546400/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7732/10000 episodes, total num timesteps 1546600/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7733/10000 episodes, total num timesteps 1546800/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7734/10000 episodes, total num timesteps 1547000/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7735/10000 episodes, total num timesteps 1547200/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7736/10000 episodes, total num timesteps 1547400/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7737/10000 episodes, total num timesteps 1547600/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7738/10000 episodes, total num timesteps 1547800/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7739/10000 episodes, total num timesteps 1548000/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7740/10000 episodes, total num timesteps 1548200/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7741/10000 episodes, total num timesteps 1548400/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7742/10000 episodes, total num timesteps 1548600/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7743/10000 episodes, total num timesteps 1548800/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7744/10000 episodes, total num timesteps 1549000/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7745/10000 episodes, total num timesteps 1549200/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7746/10000 episodes, total num timesteps 1549400/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7747/10000 episodes, total num timesteps 1549600/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7748/10000 episodes, total num timesteps 1549800/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7749/10000 episodes, total num timesteps 1550000/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7750/10000 episodes, total num timesteps 1550200/2000000, FPS 239.

team_policy eval average step individual rewards of agent0: 0.9440527022141123
team_policy eval average team episode rewards of agent0: 142.5
team_policy eval idv catch total num of agent0: 39
team_policy eval team catch total num: 57
team_policy eval average step individual rewards of agent1: 1.3363571855062382
team_policy eval average team episode rewards of agent1: 142.5
team_policy eval idv catch total num of agent1: 55
team_policy eval team catch total num: 57
team_policy eval average step individual rewards of agent2: 0.9917449946723985
team_policy eval average team episode rewards of agent2: 142.5
team_policy eval idv catch total num of agent2: 41
team_policy eval team catch total num: 57
team_policy eval average step individual rewards of agent3: 0.5662965293284291
team_policy eval average team episode rewards of agent3: 142.5
team_policy eval idv catch total num of agent3: 24
team_policy eval team catch total num: 57
team_policy eval average step individual rewards of agent4: 1.1726926657013712
team_policy eval average team episode rewards of agent4: 142.5
team_policy eval idv catch total num of agent4: 48
team_policy eval team catch total num: 57
idv_policy eval average step individual rewards of agent0: 0.27920203367530577
idv_policy eval average team episode rewards of agent0: 85.0
idv_policy eval idv catch total num of agent0: 13
idv_policy eval team catch total num: 34
idv_policy eval average step individual rewards of agent1: 0.6400761555022152
idv_policy eval average team episode rewards of agent1: 85.0
idv_policy eval idv catch total num of agent1: 27
idv_policy eval team catch total num: 34
idv_policy eval average step individual rewards of agent2: 0.8607865334906174
idv_policy eval average team episode rewards of agent2: 85.0
idv_policy eval idv catch total num of agent2: 36
idv_policy eval team catch total num: 34
idv_policy eval average step individual rewards of agent3: 0.17904341837060958
idv_policy eval average team episode rewards of agent3: 85.0
idv_policy eval idv catch total num of agent3: 9
idv_policy eval team catch total num: 34
idv_policy eval average step individual rewards of agent4: 0.915202834392902
idv_policy eval average team episode rewards of agent4: 85.0
idv_policy eval idv catch total num of agent4: 38
idv_policy eval team catch total num: 34

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7751/10000 episodes, total num timesteps 1550400/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7752/10000 episodes, total num timesteps 1550600/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7753/10000 episodes, total num timesteps 1550800/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7754/10000 episodes, total num timesteps 1551000/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7755/10000 episodes, total num timesteps 1551200/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7756/10000 episodes, total num timesteps 1551400/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7757/10000 episodes, total num timesteps 1551600/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7758/10000 episodes, total num timesteps 1551800/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7759/10000 episodes, total num timesteps 1552000/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7760/10000 episodes, total num timesteps 1552200/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7761/10000 episodes, total num timesteps 1552400/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7762/10000 episodes, total num timesteps 1552600/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7763/10000 episodes, total num timesteps 1552800/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7764/10000 episodes, total num timesteps 1553000/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7765/10000 episodes, total num timesteps 1553200/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7766/10000 episodes, total num timesteps 1553400/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7767/10000 episodes, total num timesteps 1553600/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7768/10000 episodes, total num timesteps 1553800/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7769/10000 episodes, total num timesteps 1554000/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7770/10000 episodes, total num timesteps 1554200/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7771/10000 episodes, total num timesteps 1554400/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7772/10000 episodes, total num timesteps 1554600/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7773/10000 episodes, total num timesteps 1554800/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7774/10000 episodes, total num timesteps 1555000/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7775/10000 episodes, total num timesteps 1555200/2000000, FPS 239.

team_policy eval average step individual rewards of agent0: 0.4719990218422217
team_policy eval average team episode rewards of agent0: 110.0
team_policy eval idv catch total num of agent0: 21
team_policy eval team catch total num: 44
team_policy eval average step individual rewards of agent1: 0.5442543887662482
team_policy eval average team episode rewards of agent1: 110.0
team_policy eval idv catch total num of agent1: 24
team_policy eval team catch total num: 44
team_policy eval average step individual rewards of agent2: 0.8296943810426017
team_policy eval average team episode rewards of agent2: 110.0
team_policy eval idv catch total num of agent2: 35
team_policy eval team catch total num: 44
team_policy eval average step individual rewards of agent3: 0.8332896936849272
team_policy eval average team episode rewards of agent3: 110.0
team_policy eval idv catch total num of agent3: 35
team_policy eval team catch total num: 44
team_policy eval average step individual rewards of agent4: 0.9429798007536405
team_policy eval average team episode rewards of agent4: 110.0
team_policy eval idv catch total num of agent4: 39
team_policy eval team catch total num: 44
idv_policy eval average step individual rewards of agent0: 0.9159677379983642
idv_policy eval average team episode rewards of agent0: 120.0
idv_policy eval idv catch total num of agent0: 38
idv_policy eval team catch total num: 48
idv_policy eval average step individual rewards of agent1: 0.7084144803845757
idv_policy eval average team episode rewards of agent1: 120.0
idv_policy eval idv catch total num of agent1: 30
idv_policy eval team catch total num: 48
idv_policy eval average step individual rewards of agent2: 1.0677214995330977
idv_policy eval average team episode rewards of agent2: 120.0
idv_policy eval idv catch total num of agent2: 44
idv_policy eval team catch total num: 48
idv_policy eval average step individual rewards of agent3: 0.503600854419343
idv_policy eval average team episode rewards of agent3: 120.0
idv_policy eval idv catch total num of agent3: 22
idv_policy eval team catch total num: 48
idv_policy eval average step individual rewards of agent4: 0.9148318528605184
idv_policy eval average team episode rewards of agent4: 120.0
idv_policy eval idv catch total num of agent4: 38
idv_policy eval team catch total num: 48

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7776/10000 episodes, total num timesteps 1555400/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7777/10000 episodes, total num timesteps 1555600/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7778/10000 episodes, total num timesteps 1555800/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7779/10000 episodes, total num timesteps 1556000/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7780/10000 episodes, total num timesteps 1556200/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7781/10000 episodes, total num timesteps 1556400/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7782/10000 episodes, total num timesteps 1556600/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7783/10000 episodes, total num timesteps 1556800/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7784/10000 episodes, total num timesteps 1557000/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7785/10000 episodes, total num timesteps 1557200/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7786/10000 episodes, total num timesteps 1557400/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7787/10000 episodes, total num timesteps 1557600/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7788/10000 episodes, total num timesteps 1557800/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7789/10000 episodes, total num timesteps 1558000/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7790/10000 episodes, total num timesteps 1558200/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7791/10000 episodes, total num timesteps 1558400/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7792/10000 episodes, total num timesteps 1558600/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7793/10000 episodes, total num timesteps 1558800/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7794/10000 episodes, total num timesteps 1559000/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7795/10000 episodes, total num timesteps 1559200/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7796/10000 episodes, total num timesteps 1559400/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7797/10000 episodes, total num timesteps 1559600/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7798/10000 episodes, total num timesteps 1559800/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7799/10000 episodes, total num timesteps 1560000/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7800/10000 episodes, total num timesteps 1560200/2000000, FPS 239.

team_policy eval average step individual rewards of agent0: 1.0663208803217603
team_policy eval average team episode rewards of agent0: 127.5
team_policy eval idv catch total num of agent0: 44
team_policy eval team catch total num: 51
team_policy eval average step individual rewards of agent1: 1.0573117428880419
team_policy eval average team episode rewards of agent1: 127.5
team_policy eval idv catch total num of agent1: 44
team_policy eval team catch total num: 51
team_policy eval average step individual rewards of agent2: 0.6849307315312552
team_policy eval average team episode rewards of agent2: 127.5
team_policy eval idv catch total num of agent2: 29
team_policy eval team catch total num: 51
team_policy eval average step individual rewards of agent3: 0.886030166670156
team_policy eval average team episode rewards of agent3: 127.5
team_policy eval idv catch total num of agent3: 37
team_policy eval team catch total num: 51
team_policy eval average step individual rewards of agent4: 0.7336064223196462
team_policy eval average team episode rewards of agent4: 127.5
team_policy eval idv catch total num of agent4: 31
team_policy eval team catch total num: 51
idv_policy eval average step individual rewards of agent0: 0.9419407441272379
idv_policy eval average team episode rewards of agent0: 127.5
idv_policy eval idv catch total num of agent0: 39
idv_policy eval team catch total num: 51
idv_policy eval average step individual rewards of agent1: 0.9392264730039055
idv_policy eval average team episode rewards of agent1: 127.5
idv_policy eval idv catch total num of agent1: 39
idv_policy eval team catch total num: 51
idv_policy eval average step individual rewards of agent2: 1.2706584834926824
idv_policy eval average team episode rewards of agent2: 127.5
idv_policy eval idv catch total num of agent2: 52
idv_policy eval team catch total num: 51
idv_policy eval average step individual rewards of agent3: 0.5011413828577034
idv_policy eval average team episode rewards of agent3: 127.5
idv_policy eval idv catch total num of agent3: 22
idv_policy eval team catch total num: 51
idv_policy eval average step individual rewards of agent4: 0.7401407544064688
idv_policy eval average team episode rewards of agent4: 127.5
idv_policy eval idv catch total num of agent4: 31
idv_policy eval team catch total num: 51

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7801/10000 episodes, total num timesteps 1560400/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7802/10000 episodes, total num timesteps 1560600/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7803/10000 episodes, total num timesteps 1560800/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7804/10000 episodes, total num timesteps 1561000/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7805/10000 episodes, total num timesteps 1561200/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7806/10000 episodes, total num timesteps 1561400/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7807/10000 episodes, total num timesteps 1561600/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7808/10000 episodes, total num timesteps 1561800/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7809/10000 episodes, total num timesteps 1562000/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7810/10000 episodes, total num timesteps 1562200/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7811/10000 episodes, total num timesteps 1562400/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7812/10000 episodes, total num timesteps 1562600/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7813/10000 episodes, total num timesteps 1562800/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7814/10000 episodes, total num timesteps 1563000/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7815/10000 episodes, total num timesteps 1563200/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7816/10000 episodes, total num timesteps 1563400/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7817/10000 episodes, total num timesteps 1563600/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7818/10000 episodes, total num timesteps 1563800/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7819/10000 episodes, total num timesteps 1564000/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7820/10000 episodes, total num timesteps 1564200/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7821/10000 episodes, total num timesteps 1564400/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7822/10000 episodes, total num timesteps 1564600/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7823/10000 episodes, total num timesteps 1564800/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7824/10000 episodes, total num timesteps 1565000/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7825/10000 episodes, total num timesteps 1565200/2000000, FPS 239.

team_policy eval average step individual rewards of agent0: 1.2653450553799157
team_policy eval average team episode rewards of agent0: 180.0
team_policy eval idv catch total num of agent0: 52
team_policy eval team catch total num: 72
team_policy eval average step individual rewards of agent1: 0.8784017680246692
team_policy eval average team episode rewards of agent1: 180.0
team_policy eval idv catch total num of agent1: 37
team_policy eval team catch total num: 72
team_policy eval average step individual rewards of agent2: 0.9326161045583916
team_policy eval average team episode rewards of agent2: 180.0
team_policy eval idv catch total num of agent2: 39
team_policy eval team catch total num: 72
team_policy eval average step individual rewards of agent3: 1.8281420012455172
team_policy eval average team episode rewards of agent3: 180.0
team_policy eval idv catch total num of agent3: 74
team_policy eval team catch total num: 72
team_policy eval average step individual rewards of agent4: 0.8683561047406223
team_policy eval average team episode rewards of agent4: 180.0
team_policy eval idv catch total num of agent4: 36
team_policy eval team catch total num: 72
idv_policy eval average step individual rewards of agent0: 0.7853169493702523
idv_policy eval average team episode rewards of agent0: 120.0
idv_policy eval idv catch total num of agent0: 33
idv_policy eval team catch total num: 48
idv_policy eval average step individual rewards of agent1: 0.9964384430606518
idv_policy eval average team episode rewards of agent1: 120.0
idv_policy eval idv catch total num of agent1: 41
idv_policy eval team catch total num: 48
idv_policy eval average step individual rewards of agent2: 0.913074120442861
idv_policy eval average team episode rewards of agent2: 120.0
idv_policy eval idv catch total num of agent2: 38
idv_policy eval team catch total num: 48
idv_policy eval average step individual rewards of agent3: 0.8452678995596851
idv_policy eval average team episode rewards of agent3: 120.0
idv_policy eval idv catch total num of agent3: 35
idv_policy eval team catch total num: 48
idv_policy eval average step individual rewards of agent4: 0.8422035379730782
idv_policy eval average team episode rewards of agent4: 120.0
idv_policy eval idv catch total num of agent4: 35
idv_policy eval team catch total num: 48

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7826/10000 episodes, total num timesteps 1565400/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7827/10000 episodes, total num timesteps 1565600/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7828/10000 episodes, total num timesteps 1565800/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7829/10000 episodes, total num timesteps 1566000/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7830/10000 episodes, total num timesteps 1566200/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7831/10000 episodes, total num timesteps 1566400/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7832/10000 episodes, total num timesteps 1566600/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7833/10000 episodes, total num timesteps 1566800/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7834/10000 episodes, total num timesteps 1567000/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7835/10000 episodes, total num timesteps 1567200/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7836/10000 episodes, total num timesteps 1567400/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7837/10000 episodes, total num timesteps 1567600/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7838/10000 episodes, total num timesteps 1567800/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7839/10000 episodes, total num timesteps 1568000/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7840/10000 episodes, total num timesteps 1568200/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7841/10000 episodes, total num timesteps 1568400/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7842/10000 episodes, total num timesteps 1568600/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7843/10000 episodes, total num timesteps 1568800/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7844/10000 episodes, total num timesteps 1569000/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7845/10000 episodes, total num timesteps 1569200/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7846/10000 episodes, total num timesteps 1569400/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7847/10000 episodes, total num timesteps 1569600/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7848/10000 episodes, total num timesteps 1569800/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7849/10000 episodes, total num timesteps 1570000/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7850/10000 episodes, total num timesteps 1570200/2000000, FPS 239.

team_policy eval average step individual rewards of agent0: 1.0413966318848393
team_policy eval average team episode rewards of agent0: 117.5
team_policy eval idv catch total num of agent0: 43
team_policy eval team catch total num: 47
team_policy eval average step individual rewards of agent1: 0.691573588200904
team_policy eval average team episode rewards of agent1: 117.5
team_policy eval idv catch total num of agent1: 29
team_policy eval team catch total num: 47
team_policy eval average step individual rewards of agent2: 0.7327765882541422
team_policy eval average team episode rewards of agent2: 117.5
team_policy eval idv catch total num of agent2: 31
team_policy eval team catch total num: 47
team_policy eval average step individual rewards of agent3: 0.8961839345643505
team_policy eval average team episode rewards of agent3: 117.5
team_policy eval idv catch total num of agent3: 37
team_policy eval team catch total num: 47
team_policy eval average step individual rewards of agent4: 0.9423350745535828
team_policy eval average team episode rewards of agent4: 117.5
team_policy eval idv catch total num of agent4: 39
team_policy eval team catch total num: 47
idv_policy eval average step individual rewards of agent0: 0.7669300770818337
idv_policy eval average team episode rewards of agent0: 122.5
idv_policy eval idv catch total num of agent0: 32
idv_policy eval team catch total num: 49
idv_policy eval average step individual rewards of agent1: 1.1445276365301742
idv_policy eval average team episode rewards of agent1: 122.5
idv_policy eval idv catch total num of agent1: 47
idv_policy eval team catch total num: 49
idv_policy eval average step individual rewards of agent2: 0.8373340401786977
idv_policy eval average team episode rewards of agent2: 122.5
idv_policy eval idv catch total num of agent2: 35
idv_policy eval team catch total num: 49
idv_policy eval average step individual rewards of agent3: 0.9670835310183696
idv_policy eval average team episode rewards of agent3: 122.5
idv_policy eval idv catch total num of agent3: 40
idv_policy eval team catch total num: 49
idv_policy eval average step individual rewards of agent4: 1.096850283090211
idv_policy eval average team episode rewards of agent4: 122.5
idv_policy eval idv catch total num of agent4: 45
idv_policy eval team catch total num: 49

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7851/10000 episodes, total num timesteps 1570400/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7852/10000 episodes, total num timesteps 1570600/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7853/10000 episodes, total num timesteps 1570800/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7854/10000 episodes, total num timesteps 1571000/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7855/10000 episodes, total num timesteps 1571200/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7856/10000 episodes, total num timesteps 1571400/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7857/10000 episodes, total num timesteps 1571600/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7858/10000 episodes, total num timesteps 1571800/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7859/10000 episodes, total num timesteps 1572000/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7860/10000 episodes, total num timesteps 1572200/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7861/10000 episodes, total num timesteps 1572400/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7862/10000 episodes, total num timesteps 1572600/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7863/10000 episodes, total num timesteps 1572800/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7864/10000 episodes, total num timesteps 1573000/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7865/10000 episodes, total num timesteps 1573200/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7866/10000 episodes, total num timesteps 1573400/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7867/10000 episodes, total num timesteps 1573600/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7868/10000 episodes, total num timesteps 1573800/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7869/10000 episodes, total num timesteps 1574000/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7870/10000 episodes, total num timesteps 1574200/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7871/10000 episodes, total num timesteps 1574400/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7872/10000 episodes, total num timesteps 1574600/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7873/10000 episodes, total num timesteps 1574800/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7874/10000 episodes, total num timesteps 1575000/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7875/10000 episodes, total num timesteps 1575200/2000000, FPS 239.

team_policy eval average step individual rewards of agent0: 1.0741777839450903
team_policy eval average team episode rewards of agent0: 90.0
team_policy eval idv catch total num of agent0: 44
team_policy eval team catch total num: 36
team_policy eval average step individual rewards of agent1: 0.656233016862301
team_policy eval average team episode rewards of agent1: 90.0
team_policy eval idv catch total num of agent1: 28
team_policy eval team catch total num: 36
team_policy eval average step individual rewards of agent2: 0.5332844977661062
team_policy eval average team episode rewards of agent2: 90.0
team_policy eval idv catch total num of agent2: 23
team_policy eval team catch total num: 36
team_policy eval average step individual rewards of agent3: 0.7110164517155613
team_policy eval average team episode rewards of agent3: 90.0
team_policy eval idv catch total num of agent3: 30
team_policy eval team catch total num: 36
team_policy eval average step individual rewards of agent4: 0.6871557938717727
team_policy eval average team episode rewards of agent4: 90.0
team_policy eval idv catch total num of agent4: 29
team_policy eval team catch total num: 36
idv_policy eval average step individual rewards of agent0: 0.8156371725286422
idv_policy eval average team episode rewards of agent0: 147.5
idv_policy eval idv catch total num of agent0: 34
idv_policy eval team catch total num: 59
idv_policy eval average step individual rewards of agent1: 1.7268349210878347
idv_policy eval average team episode rewards of agent1: 147.5
idv_policy eval idv catch total num of agent1: 70
idv_policy eval team catch total num: 59
idv_policy eval average step individual rewards of agent2: 0.6622816474853258
idv_policy eval average team episode rewards of agent2: 147.5
idv_policy eval idv catch total num of agent2: 28
idv_policy eval team catch total num: 59
idv_policy eval average step individual rewards of agent3: 1.1747734257190918
idv_policy eval average team episode rewards of agent3: 147.5
idv_policy eval idv catch total num of agent3: 48
idv_policy eval team catch total num: 59
idv_policy eval average step individual rewards of agent4: 1.1905775152635605
idv_policy eval average team episode rewards of agent4: 147.5
idv_policy eval idv catch total num of agent4: 49
idv_policy eval team catch total num: 59

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7876/10000 episodes, total num timesteps 1575400/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7877/10000 episodes, total num timesteps 1575600/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7878/10000 episodes, total num timesteps 1575800/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7879/10000 episodes, total num timesteps 1576000/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7880/10000 episodes, total num timesteps 1576200/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7881/10000 episodes, total num timesteps 1576400/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7882/10000 episodes, total num timesteps 1576600/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7883/10000 episodes, total num timesteps 1576800/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7884/10000 episodes, total num timesteps 1577000/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7885/10000 episodes, total num timesteps 1577200/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7886/10000 episodes, total num timesteps 1577400/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7887/10000 episodes, total num timesteps 1577600/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7888/10000 episodes, total num timesteps 1577800/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7889/10000 episodes, total num timesteps 1578000/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7890/10000 episodes, total num timesteps 1578200/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7891/10000 episodes, total num timesteps 1578400/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7892/10000 episodes, total num timesteps 1578600/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7893/10000 episodes, total num timesteps 1578800/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7894/10000 episodes, total num timesteps 1579000/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7895/10000 episodes, total num timesteps 1579200/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7896/10000 episodes, total num timesteps 1579400/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7897/10000 episodes, total num timesteps 1579600/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7898/10000 episodes, total num timesteps 1579800/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7899/10000 episodes, total num timesteps 1580000/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7900/10000 episodes, total num timesteps 1580200/2000000, FPS 239.

team_policy eval average step individual rewards of agent0: 0.7023049510025277
team_policy eval average team episode rewards of agent0: 95.0
team_policy eval idv catch total num of agent0: 30
team_policy eval team catch total num: 38
team_policy eval average step individual rewards of agent1: 0.7114307771673648
team_policy eval average team episode rewards of agent1: 95.0
team_policy eval idv catch total num of agent1: 30
team_policy eval team catch total num: 38
team_policy eval average step individual rewards of agent2: 0.7032002929776959
team_policy eval average team episode rewards of agent2: 95.0
team_policy eval idv catch total num of agent2: 30
team_policy eval team catch total num: 38
team_policy eval average step individual rewards of agent3: 0.7357853494305195
team_policy eval average team episode rewards of agent3: 95.0
team_policy eval idv catch total num of agent3: 31
team_policy eval team catch total num: 38
team_policy eval average step individual rewards of agent4: 0.6428965677184796
team_policy eval average team episode rewards of agent4: 95.0
team_policy eval idv catch total num of agent4: 27
team_policy eval team catch total num: 38
idv_policy eval average step individual rewards of agent0: 0.9194572896622749
idv_policy eval average team episode rewards of agent0: 135.0
idv_policy eval idv catch total num of agent0: 38
idv_policy eval team catch total num: 54
idv_policy eval average step individual rewards of agent1: 0.6881459468394908
idv_policy eval average team episode rewards of agent1: 135.0
idv_policy eval idv catch total num of agent1: 29
idv_policy eval team catch total num: 54
idv_policy eval average step individual rewards of agent2: 0.9142861148581006
idv_policy eval average team episode rewards of agent2: 135.0
idv_policy eval idv catch total num of agent2: 38
idv_policy eval team catch total num: 54
idv_policy eval average step individual rewards of agent3: 1.148571909704443
idv_policy eval average team episode rewards of agent3: 135.0
idv_policy eval idv catch total num of agent3: 47
idv_policy eval team catch total num: 54
idv_policy eval average step individual rewards of agent4: 1.3441815665046974
idv_policy eval average team episode rewards of agent4: 135.0
idv_policy eval idv catch total num of agent4: 55
idv_policy eval team catch total num: 54

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7901/10000 episodes, total num timesteps 1580400/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7902/10000 episodes, total num timesteps 1580600/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7903/10000 episodes, total num timesteps 1580800/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7904/10000 episodes, total num timesteps 1581000/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7905/10000 episodes, total num timesteps 1581200/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7906/10000 episodes, total num timesteps 1581400/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7907/10000 episodes, total num timesteps 1581600/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7908/10000 episodes, total num timesteps 1581800/2000000, FPS 240.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7909/10000 episodes, total num timesteps 1582000/2000000, FPS 240.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7910/10000 episodes, total num timesteps 1582200/2000000, FPS 240.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7911/10000 episodes, total num timesteps 1582400/2000000, FPS 240.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7912/10000 episodes, total num timesteps 1582600/2000000, FPS 240.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7913/10000 episodes, total num timesteps 1582800/2000000, FPS 240.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7914/10000 episodes, total num timesteps 1583000/2000000, FPS 240.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7915/10000 episodes, total num timesteps 1583200/2000000, FPS 240.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7916/10000 episodes, total num timesteps 1583400/2000000, FPS 240.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7917/10000 episodes, total num timesteps 1583600/2000000, FPS 240.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7918/10000 episodes, total num timesteps 1583800/2000000, FPS 240.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7919/10000 episodes, total num timesteps 1584000/2000000, FPS 240.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7920/10000 episodes, total num timesteps 1584200/2000000, FPS 240.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7921/10000 episodes, total num timesteps 1584400/2000000, FPS 240.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7922/10000 episodes, total num timesteps 1584600/2000000, FPS 240.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7923/10000 episodes, total num timesteps 1584800/2000000, FPS 240.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7924/10000 episodes, total num timesteps 1585000/2000000, FPS 240.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7925/10000 episodes, total num timesteps 1585200/2000000, FPS 240.

team_policy eval average step individual rewards of agent0: 0.766417338919014
team_policy eval average team episode rewards of agent0: 102.5
team_policy eval idv catch total num of agent0: 32
team_policy eval team catch total num: 41
team_policy eval average step individual rewards of agent1: 0.9329400237866322
team_policy eval average team episode rewards of agent1: 102.5
team_policy eval idv catch total num of agent1: 39
team_policy eval team catch total num: 41
team_policy eval average step individual rewards of agent2: 0.5864037196446834
team_policy eval average team episode rewards of agent2: 102.5
team_policy eval idv catch total num of agent2: 25
team_policy eval team catch total num: 41
team_policy eval average step individual rewards of agent3: 1.0435045587817475
team_policy eval average team episode rewards of agent3: 102.5
team_policy eval idv catch total num of agent3: 43
team_policy eval team catch total num: 41
team_policy eval average step individual rewards of agent4: 0.7444315039430704
team_policy eval average team episode rewards of agent4: 102.5
team_policy eval idv catch total num of agent4: 31
team_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent0: 0.8140586852583
idv_policy eval average team episode rewards of agent0: 115.0
idv_policy eval idv catch total num of agent0: 34
idv_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent1: 1.071565815779288
idv_policy eval average team episode rewards of agent1: 115.0
idv_policy eval idv catch total num of agent1: 44
idv_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent2: 0.8280543730201356
idv_policy eval average team episode rewards of agent2: 115.0
idv_policy eval idv catch total num of agent2: 35
idv_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent3: 0.8444189729675038
idv_policy eval average team episode rewards of agent3: 115.0
idv_policy eval idv catch total num of agent3: 35
idv_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent4: 0.9317809775009058
idv_policy eval average team episode rewards of agent4: 115.0
idv_policy eval idv catch total num of agent4: 39
idv_policy eval team catch total num: 46

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7926/10000 episodes, total num timesteps 1585400/2000000, FPS 240.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7927/10000 episodes, total num timesteps 1585600/2000000, FPS 240.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7928/10000 episodes, total num timesteps 1585800/2000000, FPS 240.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7929/10000 episodes, total num timesteps 1586000/2000000, FPS 240.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7930/10000 episodes, total num timesteps 1586200/2000000, FPS 240.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7931/10000 episodes, total num timesteps 1586400/2000000, FPS 240.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7932/10000 episodes, total num timesteps 1586600/2000000, FPS 240.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7933/10000 episodes, total num timesteps 1586800/2000000, FPS 240.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7934/10000 episodes, total num timesteps 1587000/2000000, FPS 240.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7935/10000 episodes, total num timesteps 1587200/2000000, FPS 240.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7936/10000 episodes, total num timesteps 1587400/2000000, FPS 240.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7937/10000 episodes, total num timesteps 1587600/2000000, FPS 240.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7938/10000 episodes, total num timesteps 1587800/2000000, FPS 240.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7939/10000 episodes, total num timesteps 1588000/2000000, FPS 240.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7940/10000 episodes, total num timesteps 1588200/2000000, FPS 240.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7941/10000 episodes, total num timesteps 1588400/2000000, FPS 240.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7942/10000 episodes, total num timesteps 1588600/2000000, FPS 240.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7943/10000 episodes, total num timesteps 1588800/2000000, FPS 240.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7944/10000 episodes, total num timesteps 1589000/2000000, FPS 240.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7945/10000 episodes, total num timesteps 1589200/2000000, FPS 240.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7946/10000 episodes, total num timesteps 1589400/2000000, FPS 240.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7947/10000 episodes, total num timesteps 1589600/2000000, FPS 240.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7948/10000 episodes, total num timesteps 1589800/2000000, FPS 240.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7949/10000 episodes, total num timesteps 1590000/2000000, FPS 240.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7950/10000 episodes, total num timesteps 1590200/2000000, FPS 240.

team_policy eval average step individual rewards of agent0: 0.6121651970705623
team_policy eval average team episode rewards of agent0: 112.5
team_policy eval idv catch total num of agent0: 26
team_policy eval team catch total num: 45
team_policy eval average step individual rewards of agent1: 0.70908136413166
team_policy eval average team episode rewards of agent1: 112.5
team_policy eval idv catch total num of agent1: 30
team_policy eval team catch total num: 45
team_policy eval average step individual rewards of agent2: 0.995845339150106
team_policy eval average team episode rewards of agent2: 112.5
team_policy eval idv catch total num of agent2: 41
team_policy eval team catch total num: 45
team_policy eval average step individual rewards of agent3: 1.1480668258891569
team_policy eval average team episode rewards of agent3: 112.5
team_policy eval idv catch total num of agent3: 47
team_policy eval team catch total num: 45
team_policy eval average step individual rewards of agent4: 0.6693768233587446
team_policy eval average team episode rewards of agent4: 112.5
team_policy eval idv catch total num of agent4: 28
team_policy eval team catch total num: 45
idv_policy eval average step individual rewards of agent0: 1.123015889432413
idv_policy eval average team episode rewards of agent0: 117.5
idv_policy eval idv catch total num of agent0: 46
idv_policy eval team catch total num: 47
idv_policy eval average step individual rewards of agent1: 1.0459656604802887
idv_policy eval average team episode rewards of agent1: 117.5
idv_policy eval idv catch total num of agent1: 43
idv_policy eval team catch total num: 47
idv_policy eval average step individual rewards of agent2: 0.6125844731465729
idv_policy eval average team episode rewards of agent2: 117.5
idv_policy eval idv catch total num of agent2: 26
idv_policy eval team catch total num: 47
idv_policy eval average step individual rewards of agent3: 0.5606056020881912
idv_policy eval average team episode rewards of agent3: 117.5
idv_policy eval idv catch total num of agent3: 24
idv_policy eval team catch total num: 47
idv_policy eval average step individual rewards of agent4: 1.1225147508926443
idv_policy eval average team episode rewards of agent4: 117.5
idv_policy eval idv catch total num of agent4: 46
idv_policy eval team catch total num: 47

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7951/10000 episodes, total num timesteps 1590400/2000000, FPS 240.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7952/10000 episodes, total num timesteps 1590600/2000000, FPS 240.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7953/10000 episodes, total num timesteps 1590800/2000000, FPS 240.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7954/10000 episodes, total num timesteps 1591000/2000000, FPS 240.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7955/10000 episodes, total num timesteps 1591200/2000000, FPS 240.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7956/10000 episodes, total num timesteps 1591400/2000000, FPS 240.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7957/10000 episodes, total num timesteps 1591600/2000000, FPS 240.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7958/10000 episodes, total num timesteps 1591800/2000000, FPS 240.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7959/10000 episodes, total num timesteps 1592000/2000000, FPS 240.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7960/10000 episodes, total num timesteps 1592200/2000000, FPS 240.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7961/10000 episodes, total num timesteps 1592400/2000000, FPS 240.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7962/10000 episodes, total num timesteps 1592600/2000000, FPS 240.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7963/10000 episodes, total num timesteps 1592800/2000000, FPS 240.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7964/10000 episodes, total num timesteps 1593000/2000000, FPS 240.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7965/10000 episodes, total num timesteps 1593200/2000000, FPS 240.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7966/10000 episodes, total num timesteps 1593400/2000000, FPS 240.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7967/10000 episodes, total num timesteps 1593600/2000000, FPS 240.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7968/10000 episodes, total num timesteps 1593800/2000000, FPS 240.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7969/10000 episodes, total num timesteps 1594000/2000000, FPS 240.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7970/10000 episodes, total num timesteps 1594200/2000000, FPS 240.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7971/10000 episodes, total num timesteps 1594400/2000000, FPS 240.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7972/10000 episodes, total num timesteps 1594600/2000000, FPS 240.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7973/10000 episodes, total num timesteps 1594800/2000000, FPS 240.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7974/10000 episodes, total num timesteps 1595000/2000000, FPS 240.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7975/10000 episodes, total num timesteps 1595200/2000000, FPS 240.

team_policy eval average step individual rewards of agent0: 0.8706706580741289
team_policy eval average team episode rewards of agent0: 127.5
team_policy eval idv catch total num of agent0: 36
team_policy eval team catch total num: 51
team_policy eval average step individual rewards of agent1: 1.1644233349521498
team_policy eval average team episode rewards of agent1: 127.5
team_policy eval idv catch total num of agent1: 48
team_policy eval team catch total num: 51
team_policy eval average step individual rewards of agent2: 0.6318124189537443
team_policy eval average team episode rewards of agent2: 127.5
team_policy eval idv catch total num of agent2: 27
team_policy eval team catch total num: 51
team_policy eval average step individual rewards of agent3: 0.9382201309067747
team_policy eval average team episode rewards of agent3: 127.5
team_policy eval idv catch total num of agent3: 39
team_policy eval team catch total num: 51
team_policy eval average step individual rewards of agent4: 1.1909295682214016
team_policy eval average team episode rewards of agent4: 127.5
team_policy eval idv catch total num of agent4: 49
team_policy eval team catch total num: 51
idv_policy eval average step individual rewards of agent0: 0.5161552989638136
idv_policy eval average team episode rewards of agent0: 132.5
idv_policy eval idv catch total num of agent0: 22
idv_policy eval team catch total num: 53
idv_policy eval average step individual rewards of agent1: 0.7974874763129999
idv_policy eval average team episode rewards of agent1: 132.5
idv_policy eval idv catch total num of agent1: 33
idv_policy eval team catch total num: 53
idv_policy eval average step individual rewards of agent2: 0.6024435179242695
idv_policy eval average team episode rewards of agent2: 132.5
idv_policy eval idv catch total num of agent2: 26
idv_policy eval team catch total num: 53
idv_policy eval average step individual rewards of agent3: 1.3958714929818792
idv_policy eval average team episode rewards of agent3: 132.5
idv_policy eval idv catch total num of agent3: 57
idv_policy eval team catch total num: 53
idv_policy eval average step individual rewards of agent4: 0.7607480766832372
idv_policy eval average team episode rewards of agent4: 132.5
idv_policy eval idv catch total num of agent4: 32
idv_policy eval team catch total num: 53

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7976/10000 episodes, total num timesteps 1595400/2000000, FPS 240.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7977/10000 episodes, total num timesteps 1595600/2000000, FPS 240.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7978/10000 episodes, total num timesteps 1595800/2000000, FPS 240.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7979/10000 episodes, total num timesteps 1596000/2000000, FPS 240.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7980/10000 episodes, total num timesteps 1596200/2000000, FPS 240.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7981/10000 episodes, total num timesteps 1596400/2000000, FPS 240.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7982/10000 episodes, total num timesteps 1596600/2000000, FPS 240.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7983/10000 episodes, total num timesteps 1596800/2000000, FPS 240.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7984/10000 episodes, total num timesteps 1597000/2000000, FPS 240.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7985/10000 episodes, total num timesteps 1597200/2000000, FPS 240.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7986/10000 episodes, total num timesteps 1597400/2000000, FPS 240.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7987/10000 episodes, total num timesteps 1597600/2000000, FPS 240.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7988/10000 episodes, total num timesteps 1597800/2000000, FPS 240.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7989/10000 episodes, total num timesteps 1598000/2000000, FPS 240.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7990/10000 episodes, total num timesteps 1598200/2000000, FPS 240.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7991/10000 episodes, total num timesteps 1598400/2000000, FPS 240.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7992/10000 episodes, total num timesteps 1598600/2000000, FPS 240.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7993/10000 episodes, total num timesteps 1598800/2000000, FPS 240.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7994/10000 episodes, total num timesteps 1599000/2000000, FPS 240.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7995/10000 episodes, total num timesteps 1599200/2000000, FPS 240.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7996/10000 episodes, total num timesteps 1599400/2000000, FPS 240.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7997/10000 episodes, total num timesteps 1599600/2000000, FPS 240.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7998/10000 episodes, total num timesteps 1599800/2000000, FPS 240.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 7999/10000 episodes, total num timesteps 1600000/2000000, FPS 240.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8000/10000 episodes, total num timesteps 1600200/2000000, FPS 240.

team_policy eval average step individual rewards of agent0: 1.0703242604960324
team_policy eval average team episode rewards of agent0: 142.5
team_policy eval idv catch total num of agent0: 44
team_policy eval team catch total num: 57
team_policy eval average step individual rewards of agent1: 1.0662564334311464
team_policy eval average team episode rewards of agent1: 142.5
team_policy eval idv catch total num of agent1: 44
team_policy eval team catch total num: 57
team_policy eval average step individual rewards of agent2: 0.8670567561838547
team_policy eval average team episode rewards of agent2: 142.5
team_policy eval idv catch total num of agent2: 36
team_policy eval team catch total num: 57
team_policy eval average step individual rewards of agent3: 1.039350117771828
team_policy eval average team episode rewards of agent3: 142.5
team_policy eval idv catch total num of agent3: 43
team_policy eval team catch total num: 57
team_policy eval average step individual rewards of agent4: 0.8696454239076777
team_policy eval average team episode rewards of agent4: 142.5
team_policy eval idv catch total num of agent4: 36
team_policy eval team catch total num: 57
idv_policy eval average step individual rewards of agent0: 0.9123073879337837
idv_policy eval average team episode rewards of agent0: 70.0
idv_policy eval idv catch total num of agent0: 38
idv_policy eval team catch total num: 28
idv_policy eval average step individual rewards of agent1: 0.4085791733013673
idv_policy eval average team episode rewards of agent1: 70.0
idv_policy eval idv catch total num of agent1: 18
idv_policy eval team catch total num: 28
idv_policy eval average step individual rewards of agent2: 0.4586874876389239
idv_policy eval average team episode rewards of agent2: 70.0
idv_policy eval idv catch total num of agent2: 20
idv_policy eval team catch total num: 28
idv_policy eval average step individual rewards of agent3: 0.6265428745881253
idv_policy eval average team episode rewards of agent3: 70.0
idv_policy eval idv catch total num of agent3: 27
idv_policy eval team catch total num: 28
idv_policy eval average step individual rewards of agent4: 0.13185562465258407
idv_policy eval average team episode rewards of agent4: 70.0
idv_policy eval idv catch total num of agent4: 7
idv_policy eval team catch total num: 28

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8001/10000 episodes, total num timesteps 1600400/2000000, FPS 240.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8002/10000 episodes, total num timesteps 1600600/2000000, FPS 240.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8003/10000 episodes, total num timesteps 1600800/2000000, FPS 240.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8004/10000 episodes, total num timesteps 1601000/2000000, FPS 240.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8005/10000 episodes, total num timesteps 1601200/2000000, FPS 240.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8006/10000 episodes, total num timesteps 1601400/2000000, FPS 240.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8007/10000 episodes, total num timesteps 1601600/2000000, FPS 240.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8008/10000 episodes, total num timesteps 1601800/2000000, FPS 240.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8009/10000 episodes, total num timesteps 1602000/2000000, FPS 240.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8010/10000 episodes, total num timesteps 1602200/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8011/10000 episodes, total num timesteps 1602400/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8012/10000 episodes, total num timesteps 1602600/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8013/10000 episodes, total num timesteps 1602800/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8014/10000 episodes, total num timesteps 1603000/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8015/10000 episodes, total num timesteps 1603200/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8016/10000 episodes, total num timesteps 1603400/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8017/10000 episodes, total num timesteps 1603600/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8018/10000 episodes, total num timesteps 1603800/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8019/10000 episodes, total num timesteps 1604000/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8020/10000 episodes, total num timesteps 1604200/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8021/10000 episodes, total num timesteps 1604400/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8022/10000 episodes, total num timesteps 1604600/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8023/10000 episodes, total num timesteps 1604800/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8024/10000 episodes, total num timesteps 1605000/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8025/10000 episodes, total num timesteps 1605200/2000000, FPS 239.

team_policy eval average step individual rewards of agent0: 0.8053949251753377
team_policy eval average team episode rewards of agent0: 115.0
team_policy eval idv catch total num of agent0: 34
team_policy eval team catch total num: 46
team_policy eval average step individual rewards of agent1: 0.8145498492720539
team_policy eval average team episode rewards of agent1: 115.0
team_policy eval idv catch total num of agent1: 34
team_policy eval team catch total num: 46
team_policy eval average step individual rewards of agent2: 0.9431339810697718
team_policy eval average team episode rewards of agent2: 115.0
team_policy eval idv catch total num of agent2: 39
team_policy eval team catch total num: 46
team_policy eval average step individual rewards of agent3: 1.0622910408919004
team_policy eval average team episode rewards of agent3: 115.0
team_policy eval idv catch total num of agent3: 44
team_policy eval team catch total num: 46
team_policy eval average step individual rewards of agent4: 0.8670015906047642
team_policy eval average team episode rewards of agent4: 115.0
team_policy eval idv catch total num of agent4: 36
team_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent0: 0.8349546546276876
idv_policy eval average team episode rewards of agent0: 90.0
idv_policy eval idv catch total num of agent0: 35
idv_policy eval team catch total num: 36
idv_policy eval average step individual rewards of agent1: 0.17552361704565253
idv_policy eval average team episode rewards of agent1: 90.0
idv_policy eval idv catch total num of agent1: 9
idv_policy eval team catch total num: 36
idv_policy eval average step individual rewards of agent2: 1.0123130929775592
idv_policy eval average team episode rewards of agent2: 90.0
idv_policy eval idv catch total num of agent2: 42
idv_policy eval team catch total num: 36
idv_policy eval average step individual rewards of agent3: 0.8415018836568777
idv_policy eval average team episode rewards of agent3: 90.0
idv_policy eval idv catch total num of agent3: 35
idv_policy eval team catch total num: 36
idv_policy eval average step individual rewards of agent4: 0.5065954669613234
idv_policy eval average team episode rewards of agent4: 90.0
idv_policy eval idv catch total num of agent4: 22
idv_policy eval team catch total num: 36

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8026/10000 episodes, total num timesteps 1605400/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8027/10000 episodes, total num timesteps 1605600/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8028/10000 episodes, total num timesteps 1605800/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8029/10000 episodes, total num timesteps 1606000/2000000, FPS 239.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8030/10000 episodes, total num timesteps 1606200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8031/10000 episodes, total num timesteps 1606400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8032/10000 episodes, total num timesteps 1606600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8033/10000 episodes, total num timesteps 1606800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8034/10000 episodes, total num timesteps 1607000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8035/10000 episodes, total num timesteps 1607200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8036/10000 episodes, total num timesteps 1607400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8037/10000 episodes, total num timesteps 1607600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8038/10000 episodes, total num timesteps 1607800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8039/10000 episodes, total num timesteps 1608000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8040/10000 episodes, total num timesteps 1608200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8041/10000 episodes, total num timesteps 1608400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8042/10000 episodes, total num timesteps 1608600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8043/10000 episodes, total num timesteps 1608800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8044/10000 episodes, total num timesteps 1609000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8045/10000 episodes, total num timesteps 1609200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8046/10000 episodes, total num timesteps 1609400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8047/10000 episodes, total num timesteps 1609600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8048/10000 episodes, total num timesteps 1609800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8049/10000 episodes, total num timesteps 1610000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8050/10000 episodes, total num timesteps 1610200/2000000, FPS 238.

team_policy eval average step individual rewards of agent0: 0.9798612301911418
team_policy eval average team episode rewards of agent0: 82.5
team_policy eval idv catch total num of agent0: 41
team_policy eval team catch total num: 33
team_policy eval average step individual rewards of agent1: 0.5997743829978475
team_policy eval average team episode rewards of agent1: 82.5
team_policy eval idv catch total num of agent1: 26
team_policy eval team catch total num: 33
team_policy eval average step individual rewards of agent2: 0.4661595703971369
team_policy eval average team episode rewards of agent2: 82.5
team_policy eval idv catch total num of agent2: 21
team_policy eval team catch total num: 33
team_policy eval average step individual rewards of agent3: 0.723347944048426
team_policy eval average team episode rewards of agent3: 82.5
team_policy eval idv catch total num of agent3: 31
team_policy eval team catch total num: 33
team_policy eval average step individual rewards of agent4: 0.6034106600786996
team_policy eval average team episode rewards of agent4: 82.5
team_policy eval idv catch total num of agent4: 26
team_policy eval team catch total num: 33
idv_policy eval average step individual rewards of agent0: 0.8412884621149034
idv_policy eval average team episode rewards of agent0: 165.0
idv_policy eval idv catch total num of agent0: 35
idv_policy eval team catch total num: 66
idv_policy eval average step individual rewards of agent1: 1.3403915176496128
idv_policy eval average team episode rewards of agent1: 165.0
idv_policy eval idv catch total num of agent1: 55
idv_policy eval team catch total num: 66
idv_policy eval average step individual rewards of agent2: 1.4712799478252416
idv_policy eval average team episode rewards of agent2: 165.0
idv_policy eval idv catch total num of agent2: 60
idv_policy eval team catch total num: 66
idv_policy eval average step individual rewards of agent3: 0.8647903784155946
idv_policy eval average team episode rewards of agent3: 165.0
idv_policy eval idv catch total num of agent3: 36
idv_policy eval team catch total num: 66
idv_policy eval average step individual rewards of agent4: 1.6239619165736627
idv_policy eval average team episode rewards of agent4: 165.0
idv_policy eval idv catch total num of agent4: 66
idv_policy eval team catch total num: 66

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8051/10000 episodes, total num timesteps 1610400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8052/10000 episodes, total num timesteps 1610600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8053/10000 episodes, total num timesteps 1610800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8054/10000 episodes, total num timesteps 1611000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8055/10000 episodes, total num timesteps 1611200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8056/10000 episodes, total num timesteps 1611400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8057/10000 episodes, total num timesteps 1611600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8058/10000 episodes, total num timesteps 1611800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8059/10000 episodes, total num timesteps 1612000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8060/10000 episodes, total num timesteps 1612200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8061/10000 episodes, total num timesteps 1612400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8062/10000 episodes, total num timesteps 1612600/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8063/10000 episodes, total num timesteps 1612800/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8064/10000 episodes, total num timesteps 1613000/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8065/10000 episodes, total num timesteps 1613200/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8066/10000 episodes, total num timesteps 1613400/2000000, FPS 238.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8067/10000 episodes, total num timesteps 1613600/2000000, FPS 238.

mappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8068/10000 episodes, total num timesteps 1613800/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8069/10000 episodes, total num timesteps 1614000/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8070/10000 episodes, total num timesteps 1614200/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8071/10000 episodes, total num timesteps 1614400/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8072/10000 episodes, total num timesteps 1614600/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8073/10000 episodes, total num timesteps 1614800/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8074/10000 episodes, total num timesteps 1615000/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8075/10000 episodes, total num timesteps 1615200/2000000, FPS 237.

team_policy eval average step individual rewards of agent0: 0.6653160117618785
team_policy eval average team episode rewards of agent0: 67.5
team_policy eval idv catch total num of agent0: 28
team_policy eval team catch total num: 27
team_policy eval average step individual rewards of agent1: 0.3734713120461909
team_policy eval average team episode rewards of agent1: 67.5
team_policy eval idv catch total num of agent1: 17
team_policy eval team catch total num: 27
team_policy eval average step individual rewards of agent2: 0.5285994691282097
team_policy eval average team episode rewards of agent2: 67.5
team_policy eval idv catch total num of agent2: 23
team_policy eval team catch total num: 27
team_policy eval average step individual rewards of agent3: 1.1187566112322318
team_policy eval average team episode rewards of agent3: 67.5
team_policy eval idv catch total num of agent3: 46
team_policy eval team catch total num: 27
team_policy eval average step individual rewards of agent4: 0.53302821451606
team_policy eval average team episode rewards of agent4: 67.5
team_policy eval idv catch total num of agent4: 23
team_policy eval team catch total num: 27
idv_policy eval average step individual rewards of agent0: 0.9303770980378394
idv_policy eval average team episode rewards of agent0: 102.5
idv_policy eval idv catch total num of agent0: 39
idv_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent1: 0.6961888406417708
idv_policy eval average team episode rewards of agent1: 102.5
idv_policy eval idv catch total num of agent1: 30
idv_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent2: 0.37933127800147304
idv_policy eval average team episode rewards of agent2: 102.5
idv_policy eval idv catch total num of agent2: 17
idv_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent3: 0.8340155936188373
idv_policy eval average team episode rewards of agent3: 102.5
idv_policy eval idv catch total num of agent3: 35
idv_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent4: 1.108823746777941
idv_policy eval average team episode rewards of agent4: 102.5
idv_policy eval idv catch total num of agent4: 46
idv_policy eval team catch total num: 41

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8076/10000 episodes, total num timesteps 1615400/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8077/10000 episodes, total num timesteps 1615600/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8078/10000 episodes, total num timesteps 1615800/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8079/10000 episodes, total num timesteps 1616000/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8080/10000 episodes, total num timesteps 1616200/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8081/10000 episodes, total num timesteps 1616400/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8082/10000 episodes, total num timesteps 1616600/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8083/10000 episodes, total num timesteps 1616800/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8084/10000 episodes, total num timesteps 1617000/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8085/10000 episodes, total num timesteps 1617200/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8086/10000 episodes, total num timesteps 1617400/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8087/10000 episodes, total num timesteps 1617600/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8088/10000 episodes, total num timesteps 1617800/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8089/10000 episodes, total num timesteps 1618000/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8090/10000 episodes, total num timesteps 1618200/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8091/10000 episodes, total num timesteps 1618400/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8092/10000 episodes, total num timesteps 1618600/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8093/10000 episodes, total num timesteps 1618800/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8094/10000 episodes, total num timesteps 1619000/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8095/10000 episodes, total num timesteps 1619200/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8096/10000 episodes, total num timesteps 1619400/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8097/10000 episodes, total num timesteps 1619600/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8098/10000 episodes, total num timesteps 1619800/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8099/10000 episodes, total num timesteps 1620000/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8100/10000 episodes, total num timesteps 1620200/2000000, FPS 237.

team_policy eval average step individual rewards of agent0: 0.45411242120191536
team_policy eval average team episode rewards of agent0: 92.5
team_policy eval idv catch total num of agent0: 20
team_policy eval team catch total num: 37
team_policy eval average step individual rewards of agent1: 0.5534321376087277
team_policy eval average team episode rewards of agent1: 92.5
team_policy eval idv catch total num of agent1: 24
team_policy eval team catch total num: 37
team_policy eval average step individual rewards of agent2: 0.7914883404742149
team_policy eval average team episode rewards of agent2: 92.5
team_policy eval idv catch total num of agent2: 33
team_policy eval team catch total num: 37
team_policy eval average step individual rewards of agent3: 0.9166884558795091
team_policy eval average team episode rewards of agent3: 92.5
team_policy eval idv catch total num of agent3: 38
team_policy eval team catch total num: 37
team_policy eval average step individual rewards of agent4: 0.6140919142751149
team_policy eval average team episode rewards of agent4: 92.5
team_policy eval idv catch total num of agent4: 26
team_policy eval team catch total num: 37
idv_policy eval average step individual rewards of agent0: 0.45702849074306057
idv_policy eval average team episode rewards of agent0: 65.0
idv_policy eval idv catch total num of agent0: 20
idv_policy eval team catch total num: 26
idv_policy eval average step individual rewards of agent1: 0.6041776505549386
idv_policy eval average team episode rewards of agent1: 65.0
idv_policy eval idv catch total num of agent1: 26
idv_policy eval team catch total num: 26
idv_policy eval average step individual rewards of agent2: 0.3667333560352924
idv_policy eval average team episode rewards of agent2: 65.0
idv_policy eval idv catch total num of agent2: 17
idv_policy eval team catch total num: 26
idv_policy eval average step individual rewards of agent3: 0.6631635987302775
idv_policy eval average team episode rewards of agent3: 65.0
idv_policy eval idv catch total num of agent3: 28
idv_policy eval team catch total num: 26
idv_policy eval average step individual rewards of agent4: 0.5125324744064302
idv_policy eval average team episode rewards of agent4: 65.0
idv_policy eval idv catch total num of agent4: 22
idv_policy eval team catch total num: 26

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8101/10000 episodes, total num timesteps 1620400/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8102/10000 episodes, total num timesteps 1620600/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8103/10000 episodes, total num timesteps 1620800/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8104/10000 episodes, total num timesteps 1621000/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8105/10000 episodes, total num timesteps 1621200/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8106/10000 episodes, total num timesteps 1621400/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8107/10000 episodes, total num timesteps 1621600/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8108/10000 episodes, total num timesteps 1621800/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8109/10000 episodes, total num timesteps 1622000/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8110/10000 episodes, total num timesteps 1622200/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8111/10000 episodes, total num timesteps 1622400/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8112/10000 episodes, total num timesteps 1622600/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8113/10000 episodes, total num timesteps 1622800/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8114/10000 episodes, total num timesteps 1623000/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8115/10000 episodes, total num timesteps 1623200/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8116/10000 episodes, total num timesteps 1623400/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8117/10000 episodes, total num timesteps 1623600/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8118/10000 episodes, total num timesteps 1623800/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8119/10000 episodes, total num timesteps 1624000/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8120/10000 episodes, total num timesteps 1624200/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8121/10000 episodes, total num timesteps 1624400/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8122/10000 episodes, total num timesteps 1624600/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8123/10000 episodes, total num timesteps 1624800/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8124/10000 episodes, total num timesteps 1625000/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8125/10000 episodes, total num timesteps 1625200/2000000, FPS 237.

team_policy eval average step individual rewards of agent0: 0.7579629891219644
team_policy eval average team episode rewards of agent0: 102.5
team_policy eval idv catch total num of agent0: 32
team_policy eval team catch total num: 41
team_policy eval average step individual rewards of agent1: 0.5262591217521249
team_policy eval average team episode rewards of agent1: 102.5
team_policy eval idv catch total num of agent1: 23
team_policy eval team catch total num: 41
team_policy eval average step individual rewards of agent2: 0.5908488696564569
team_policy eval average team episode rewards of agent2: 102.5
team_policy eval idv catch total num of agent2: 25
team_policy eval team catch total num: 41
team_policy eval average step individual rewards of agent3: 1.088375333242585
team_policy eval average team episode rewards of agent3: 102.5
team_policy eval idv catch total num of agent3: 45
team_policy eval team catch total num: 41
team_policy eval average step individual rewards of agent4: 1.0208225108617734
team_policy eval average team episode rewards of agent4: 102.5
team_policy eval idv catch total num of agent4: 42
team_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent0: 0.6765016089912007
idv_policy eval average team episode rewards of agent0: 105.0
idv_policy eval idv catch total num of agent0: 29
idv_policy eval team catch total num: 42
idv_policy eval average step individual rewards of agent1: 0.8692698641342967
idv_policy eval average team episode rewards of agent1: 105.0
idv_policy eval idv catch total num of agent1: 36
idv_policy eval team catch total num: 42
idv_policy eval average step individual rewards of agent2: 0.48073369019594275
idv_policy eval average team episode rewards of agent2: 105.0
idv_policy eval idv catch total num of agent2: 21
idv_policy eval team catch total num: 42
idv_policy eval average step individual rewards of agent3: 0.708770244124914
idv_policy eval average team episode rewards of agent3: 105.0
idv_policy eval idv catch total num of agent3: 30
idv_policy eval team catch total num: 42
idv_policy eval average step individual rewards of agent4: 0.754053859140721
idv_policy eval average team episode rewards of agent4: 105.0
idv_policy eval idv catch total num of agent4: 32
idv_policy eval team catch total num: 42

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8126/10000 episodes, total num timesteps 1625400/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8127/10000 episodes, total num timesteps 1625600/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8128/10000 episodes, total num timesteps 1625800/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8129/10000 episodes, total num timesteps 1626000/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8130/10000 episodes, total num timesteps 1626200/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8131/10000 episodes, total num timesteps 1626400/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8132/10000 episodes, total num timesteps 1626600/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8133/10000 episodes, total num timesteps 1626800/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8134/10000 episodes, total num timesteps 1627000/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8135/10000 episodes, total num timesteps 1627200/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8136/10000 episodes, total num timesteps 1627400/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8137/10000 episodes, total num timesteps 1627600/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8138/10000 episodes, total num timesteps 1627800/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8139/10000 episodes, total num timesteps 1628000/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8140/10000 episodes, total num timesteps 1628200/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8141/10000 episodes, total num timesteps 1628400/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8142/10000 episodes, total num timesteps 1628600/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8143/10000 episodes, total num timesteps 1628800/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8144/10000 episodes, total num timesteps 1629000/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8145/10000 episodes, total num timesteps 1629200/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8146/10000 episodes, total num timesteps 1629400/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8147/10000 episodes, total num timesteps 1629600/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8148/10000 episodes, total num timesteps 1629800/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8149/10000 episodes, total num timesteps 1630000/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8150/10000 episodes, total num timesteps 1630200/2000000, FPS 237.

team_policy eval average step individual rewards of agent0: 0.39195025484569
team_policy eval average team episode rewards of agent0: 60.0
team_policy eval idv catch total num of agent0: 18
team_policy eval team catch total num: 24
team_policy eval average step individual rewards of agent1: 0.4789151085726317
team_policy eval average team episode rewards of agent1: 60.0
team_policy eval idv catch total num of agent1: 21
team_policy eval team catch total num: 24
team_policy eval average step individual rewards of agent2: 0.5721196194630859
team_policy eval average team episode rewards of agent2: 60.0
team_policy eval idv catch total num of agent2: 25
team_policy eval team catch total num: 24
team_policy eval average step individual rewards of agent3: 0.30401103094253296
team_policy eval average team episode rewards of agent3: 60.0
team_policy eval idv catch total num of agent3: 14
team_policy eval team catch total num: 24
team_policy eval average step individual rewards of agent4: 0.5087886008806559
team_policy eval average team episode rewards of agent4: 60.0
team_policy eval idv catch total num of agent4: 22
team_policy eval team catch total num: 24
idv_policy eval average step individual rewards of agent0: 0.4098756736256023
idv_policy eval average team episode rewards of agent0: 57.5
idv_policy eval idv catch total num of agent0: 18
idv_policy eval team catch total num: 23
idv_policy eval average step individual rewards of agent1: 0.7668521888263985
idv_policy eval average team episode rewards of agent1: 57.5
idv_policy eval idv catch total num of agent1: 32
idv_policy eval team catch total num: 23
idv_policy eval average step individual rewards of agent2: 0.71870339413596
idv_policy eval average team episode rewards of agent2: 57.5
idv_policy eval idv catch total num of agent2: 30
idv_policy eval team catch total num: 23
idv_policy eval average step individual rewards of agent3: 0.5665966079724416
idv_policy eval average team episode rewards of agent3: 57.5
idv_policy eval idv catch total num of agent3: 24
idv_policy eval team catch total num: 23
idv_policy eval average step individual rewards of agent4: 0.1607772477698331
idv_policy eval average team episode rewards of agent4: 57.5
idv_policy eval idv catch total num of agent4: 8
idv_policy eval team catch total num: 23

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8151/10000 episodes, total num timesteps 1630400/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8152/10000 episodes, total num timesteps 1630600/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8153/10000 episodes, total num timesteps 1630800/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8154/10000 episodes, total num timesteps 1631000/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8155/10000 episodes, total num timesteps 1631200/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8156/10000 episodes, total num timesteps 1631400/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8157/10000 episodes, total num timesteps 1631600/2000000, FPS 237.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8158/10000 episodes, total num timesteps 1631800/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8159/10000 episodes, total num timesteps 1632000/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8160/10000 episodes, total num timesteps 1632200/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8161/10000 episodes, total num timesteps 1632400/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8162/10000 episodes, total num timesteps 1632600/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8163/10000 episodes, total num timesteps 1632800/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8164/10000 episodes, total num timesteps 1633000/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8165/10000 episodes, total num timesteps 1633200/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8166/10000 episodes, total num timesteps 1633400/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8167/10000 episodes, total num timesteps 1633600/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8168/10000 episodes, total num timesteps 1633800/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8169/10000 episodes, total num timesteps 1634000/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8170/10000 episodes, total num timesteps 1634200/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8171/10000 episodes, total num timesteps 1634400/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8172/10000 episodes, total num timesteps 1634600/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8173/10000 episodes, total num timesteps 1634800/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8174/10000 episodes, total num timesteps 1635000/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8175/10000 episodes, total num timesteps 1635200/2000000, FPS 236.

team_policy eval average step individual rewards of agent0: 1.2403833248854255
team_policy eval average team episode rewards of agent0: 75.0
team_policy eval idv catch total num of agent0: 51
team_policy eval team catch total num: 30
team_policy eval average step individual rewards of agent1: 0.39947567634675224
team_policy eval average team episode rewards of agent1: 75.0
team_policy eval idv catch total num of agent1: 18
team_policy eval team catch total num: 30
team_policy eval average step individual rewards of agent2: 0.19600289986309494
team_policy eval average team episode rewards of agent2: 75.0
team_policy eval idv catch total num of agent2: 10
team_policy eval team catch total num: 30
team_policy eval average step individual rewards of agent3: 0.5161342539758521
team_policy eval average team episode rewards of agent3: 75.0
team_policy eval idv catch total num of agent3: 23
team_policy eval team catch total num: 30
team_policy eval average step individual rewards of agent4: 0.6668937130906634
team_policy eval average team episode rewards of agent4: 75.0
team_policy eval idv catch total num of agent4: 28
team_policy eval team catch total num: 30
idv_policy eval average step individual rewards of agent0: 0.8478574965296508
idv_policy eval average team episode rewards of agent0: 117.5
idv_policy eval idv catch total num of agent0: 35
idv_policy eval team catch total num: 47
idv_policy eval average step individual rewards of agent1: 0.8463370843222083
idv_policy eval average team episode rewards of agent1: 117.5
idv_policy eval idv catch total num of agent1: 35
idv_policy eval team catch total num: 47
idv_policy eval average step individual rewards of agent2: 1.1429271532675456
idv_policy eval average team episode rewards of agent2: 117.5
idv_policy eval idv catch total num of agent2: 47
idv_policy eval team catch total num: 47
idv_policy eval average step individual rewards of agent3: 0.6416358113906641
idv_policy eval average team episode rewards of agent3: 117.5
idv_policy eval idv catch total num of agent3: 27
idv_policy eval team catch total num: 47
idv_policy eval average step individual rewards of agent4: 1.0454433247018502
idv_policy eval average team episode rewards of agent4: 117.5
idv_policy eval idv catch total num of agent4: 43
idv_policy eval team catch total num: 47

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8176/10000 episodes, total num timesteps 1635400/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8177/10000 episodes, total num timesteps 1635600/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8178/10000 episodes, total num timesteps 1635800/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8179/10000 episodes, total num timesteps 1636000/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8180/10000 episodes, total num timesteps 1636200/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8181/10000 episodes, total num timesteps 1636400/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8182/10000 episodes, total num timesteps 1636600/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8183/10000 episodes, total num timesteps 1636800/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8184/10000 episodes, total num timesteps 1637000/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8185/10000 episodes, total num timesteps 1637200/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8186/10000 episodes, total num timesteps 1637400/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8187/10000 episodes, total num timesteps 1637600/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8188/10000 episodes, total num timesteps 1637800/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8189/10000 episodes, total num timesteps 1638000/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8190/10000 episodes, total num timesteps 1638200/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8191/10000 episodes, total num timesteps 1638400/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8192/10000 episodes, total num timesteps 1638600/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8193/10000 episodes, total num timesteps 1638800/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8194/10000 episodes, total num timesteps 1639000/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8195/10000 episodes, total num timesteps 1639200/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8196/10000 episodes, total num timesteps 1639400/2000000, FPS 236.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8197/10000 episodes, total num timesteps 1639600/2000000, FPS 236.

00 episodes, total num timesteps 1639800/2000000, FPS 264.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8199/10000 episodes, total num timesteps 1640000/2000000, FPS 264.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8200/10000 episodes, total num timesteps 1640200/2000000, FPS 264.

team_policy eval average step individual rewards of agent0: 0.9672024489972457
team_policy eval average team episode rewards of agent0: 140.0
team_policy eval idv catch total num of agent0: 40
team_policy eval team catch total num: 56
team_policy eval average step individual rewards of agent1: 1.1240502563498693
team_policy eval average team episode rewards of agent1: 140.0
team_policy eval idv catch total num of agent1: 46
team_policy eval team catch total num: 56
team_policy eval average step individual rewards of agent2: 0.74114416461294
team_policy eval average team episode rewards of agent2: 140.0
team_policy eval idv catch total num of agent2: 31
team_policy eval team catch total num: 56
team_policy eval average step individual rewards of agent3: 0.7591906353303554
team_policy eval average team episode rewards of agent3: 140.0
team_policy eval idv catch total num of agent3: 32
team_policy eval team catch total num: 56
team_policy eval average step individual rewards of agent4: 0.9071816413688836
team_policy eval average team episode rewards of agent4: 140.0
team_policy eval idv catch total num of agent4: 38
team_policy eval team catch total num: 56
idv_policy eval average step individual rewards of agent0: 0.4084394127575148
idv_policy eval average team episode rewards of agent0: 55.0
idv_policy eval idv catch total num of agent0: 18
idv_policy eval team catch total num: 22
idv_policy eval average step individual rewards of agent1: 0.3419308380855908
idv_policy eval average team episode rewards of agent1: 55.0
idv_policy eval idv catch total num of agent1: 16
idv_policy eval team catch total num: 22
idv_policy eval average step individual rewards of agent2: 0.13111065169827887
idv_policy eval average team episode rewards of agent2: 55.0
idv_policy eval idv catch total num of agent2: 7
idv_policy eval team catch total num: 22
idv_policy eval average step individual rewards of agent3: 0.6736918873969329
idv_policy eval average team episode rewards of agent3: 55.0
idv_policy eval idv catch total num of agent3: 29
idv_policy eval team catch total num: 22
idv_policy eval average step individual rewards of agent4: 0.4802439504055977
idv_policy eval average team episode rewards of agent4: 55.0
idv_policy eval idv catch total num of agent4: 21
idv_policy eval team catch total num: 22

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8201/10000 episodes, total num timesteps 1640400/2000000, FPS 264.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8202/10000 episodes, total num timesteps 1640600/2000000, FPS 264.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8203/10000 episodes, total num timesteps 1640800/2000000, FPS 264.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8204/10000 episodes, total num timesteps 1641000/2000000, FPS 264.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8205/10000 episodes, total num timesteps 1641200/2000000, FPS 264.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8206/10000 episodes, total num timesteps 1641400/2000000, FPS 264.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8207/10000 episodes, total num timesteps 1641600/2000000, FPS 264.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8208/10000 episodes, total num timesteps 1641800/2000000, FPS 264.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8209/10000 episodes, total num timesteps 1642000/2000000, FPS 264.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8210/10000 episodes, total num timesteps 1642200/2000000, FPS 264.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8211/10000 episodes, total num timesteps 1642400/2000000, FPS 264.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8212/10000 episodes, total num timesteps 1642600/2000000, FPS 264.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8213/10000 episodes, total num timesteps 1642800/2000000, FPS 264.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8214/10000 episodes, total num timesteps 1643000/2000000, FPS 264.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8215/10000 episodes, total num timesteps 1643200/2000000, FPS 264.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8216/10000 episodes, total num timesteps 1643400/2000000, FPS 264.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8217/10000 episodes, total num timesteps 1643600/2000000, FPS 264.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8218/10000 episodes, total num timesteps 1643800/2000000, FPS 264.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8219/10000 episodes, total num timesteps 1644000/2000000, FPS 264.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8220/10000 episodes, total num timesteps 1644200/2000000, FPS 264.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8221/10000 episodes, total num timesteps 1644400/2000000, FPS 264.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8222/10000 episodes, total num timesteps 1644600/2000000, FPS 264.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8223/10000 episodes, total num timesteps 1644800/2000000, FPS 264.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8224/10000 episodes, total num timesteps 1645000/2000000, FPS 264.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8225/10000 episodes, total num timesteps 1645200/2000000, FPS 264.

team_policy eval average step individual rewards of agent0: 0.6282873067655642
team_policy eval average team episode rewards of agent0: 85.0
team_policy eval idv catch total num of agent0: 27
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent1: 0.6582154206432129
team_policy eval average team episode rewards of agent1: 85.0
team_policy eval idv catch total num of agent1: 28
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent2: 0.5597961133431634
team_policy eval average team episode rewards of agent2: 85.0
team_policy eval idv catch total num of agent2: 25
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent3: 0.5659084140982068
team_policy eval average team episode rewards of agent3: 85.0
team_policy eval idv catch total num of agent3: 25
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent4: 0.34856135878632627
team_policy eval average team episode rewards of agent4: 85.0
team_policy eval idv catch total num of agent4: 16
team_policy eval team catch total num: 34
idv_policy eval average step individual rewards of agent0: 0.8185935888826489
idv_policy eval average team episode rewards of agent0: 77.5
idv_policy eval idv catch total num of agent0: 34
idv_policy eval team catch total num: 31
idv_policy eval average step individual rewards of agent1: 0.4051048508314116
idv_policy eval average team episode rewards of agent1: 77.5
idv_policy eval idv catch total num of agent1: 18
idv_policy eval team catch total num: 31
idv_policy eval average step individual rewards of agent2: 0.5378634272317762
idv_policy eval average team episode rewards of agent2: 77.5
idv_policy eval idv catch total num of agent2: 23
idv_policy eval team catch total num: 31
idv_policy eval average step individual rewards of agent3: 0.5787263412637482
idv_policy eval average team episode rewards of agent3: 77.5
idv_policy eval idv catch total num of agent3: 25
idv_policy eval team catch total num: 31
idv_policy eval average step individual rewards of agent4: 0.5250883508569963
idv_policy eval average team episode rewards of agent4: 77.5
idv_policy eval idv catch total num of agent4: 23
idv_policy eval team catch total num: 31

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8226/10000 episodes, total num timesteps 1645400/2000000, FPS 264.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8227/10000 episodes, total num timesteps 1645600/2000000, FPS 264.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8228/10000 episodes, total num timesteps 1645800/2000000, FPS 264.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8229/10000 episodes, total num timesteps 1646000/2000000, FPS 264.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8230/10000 episodes, total num timesteps 1646200/2000000, FPS 264.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8231/10000 episodes, total num timesteps 1646400/2000000, FPS 264.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8232/10000 episodes, total num timesteps 1646600/2000000, FPS 264.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8233/10000 episodes, total num timesteps 1646800/2000000, FPS 264.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8234/10000 episodes, total num timesteps 1647000/2000000, FPS 264.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8235/10000 episodes, total num timesteps 1647200/2000000, FPS 264.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8236/10000 episodes, total num timesteps 1647400/2000000, FPS 264.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8237/10000 episodes, total num timesteps 1647600/2000000, FPS 264.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8238/10000 episodes, total num timesteps 1647800/2000000, FPS 264.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8239/10000 episodes, total num timesteps 1648000/2000000, FPS 264.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8240/10000 episodes, total num timesteps 1648200/2000000, FPS 264.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8241/10000 episodes, total num timesteps 1648400/2000000, FPS 264.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8242/10000 episodes, total num timesteps 1648600/2000000, FPS 264.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8243/10000 episodes, total num timesteps 1648800/2000000, FPS 264.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8244/10000 episodes, total num timesteps 1649000/2000000, FPS 264.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8245/10000 episodes, total num timesteps 1649200/2000000, FPS 264.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8246/10000 episodes, total num timesteps 1649400/2000000, FPS 264.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8247/10000 episodes, total num timesteps 1649600/2000000, FPS 264.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8248/10000 episodes, total num timesteps 1649800/2000000, FPS 264.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8249/10000 episodes, total num timesteps 1650000/2000000, FPS 264.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8250/10000 episodes, total num timesteps 1650200/2000000, FPS 264.

team_policy eval average step individual rewards of agent0: 0.7214005796811307
team_policy eval average team episode rewards of agent0: 55.0
team_policy eval idv catch total num of agent0: 31
team_policy eval team catch total num: 22
team_policy eval average step individual rewards of agent1: 0.39805372440631615
team_policy eval average team episode rewards of agent1: 55.0
team_policy eval idv catch total num of agent1: 18
team_policy eval team catch total num: 22
team_policy eval average step individual rewards of agent2: 0.45386974220846044
team_policy eval average team episode rewards of agent2: 55.0
team_policy eval idv catch total num of agent2: 20
team_policy eval team catch total num: 22
team_policy eval average step individual rewards of agent3: 0.684314755619362
team_policy eval average team episode rewards of agent3: 55.0
team_policy eval idv catch total num of agent3: 29
team_policy eval team catch total num: 22
team_policy eval average step individual rewards of agent4: 0.5981511901199302
team_policy eval average team episode rewards of agent4: 55.0
team_policy eval idv catch total num of agent4: 26
team_policy eval team catch total num: 22
idv_policy eval average step individual rewards of agent0: 1.213479881261722
idv_policy eval average team episode rewards of agent0: 140.0
idv_policy eval idv catch total num of agent0: 50
idv_policy eval team catch total num: 56
idv_policy eval average step individual rewards of agent1: 1.0878566037646598
idv_policy eval average team episode rewards of agent1: 140.0
idv_policy eval idv catch total num of agent1: 45
idv_policy eval team catch total num: 56
idv_policy eval average step individual rewards of agent2: 1.654773183844324
idv_policy eval average team episode rewards of agent2: 140.0
idv_policy eval idv catch total num of agent2: 67
idv_policy eval team catch total num: 56
idv_policy eval average step individual rewards of agent3: 0.44998512781462435
idv_policy eval average team episode rewards of agent3: 140.0
idv_policy eval idv catch total num of agent3: 20
idv_policy eval team catch total num: 56
idv_policy eval average step individual rewards of agent4: 0.8144240780777122
idv_policy eval average team episode rewards of agent4: 140.0
idv_policy eval idv catch total num of agent4: 34
idv_policy eval team catch total num: 56

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8251/10000 episodes, total num timesteps 1650400/2000000, FPS 264.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8252/10000 episodes, total num timesteps 1650600/2000000, FPS 264.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8253/10000 episodes, total num timesteps 1650800/2000000, FPS 264.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8254/10000 episodes, total num timesteps 1651000/2000000, FPS 264.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8255/10000 episodes, total num timesteps 1651200/2000000, FPS 264.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8256/10000 episodes, total num timesteps 1651400/2000000, FPS 264.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8257/10000 episodes, total num timesteps 1651600/2000000, FPS 264.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8258/10000 episodes, total num timesteps 1651800/2000000, FPS 264.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8259/10000 episodes, total num timesteps 1652000/2000000, FPS 264.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8260/10000 episodes, total num timesteps 1652200/2000000, FPS 264.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8261/10000 episodes, total num timesteps 1652400/2000000, FPS 264.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8262/10000 episodes, total num timesteps 1652600/2000000, FPS 264.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8263/10000 episodes, total num timesteps 1652800/2000000, FPS 264.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8264/10000 episodes, total num timesteps 1653000/2000000, FPS 264.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8265/10000 episodes, total num timesteps 1653200/2000000, FPS 264.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8266/10000 episodes, total num timesteps 1653400/2000000, FPS 264.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8267/10000 episodes, total num timesteps 1653600/2000000, FPS 264.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8268/10000 episodes, total num timesteps 1653800/2000000, FPS 264.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8269/10000 episodes, total num timesteps 1654000/2000000, FPS 264.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8270/10000 episodes, total num timesteps 1654200/2000000, FPS 264.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8271/10000 episodes, total num timesteps 1654400/2000000, FPS 264.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8272/10000 episodes, total num timesteps 1654600/2000000, FPS 264.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8273/10000 episodes, total num timesteps 1654800/2000000, FPS 264.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8274/10000 episodes, total num timesteps 1655000/2000000, FPS 264.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8275/10000 episodes, total num timesteps 1655200/2000000, FPS 264.

team_policy eval average step individual rewards of agent0: 0.7138589998899701
team_policy eval average team episode rewards of agent0: 107.5
team_policy eval idv catch total num of agent0: 30
team_policy eval team catch total num: 43
team_policy eval average step individual rewards of agent1: 0.7856491426355791
team_policy eval average team episode rewards of agent1: 107.5
team_policy eval idv catch total num of agent1: 33
team_policy eval team catch total num: 43
team_policy eval average step individual rewards of agent2: 0.6752180820183139
team_policy eval average team episode rewards of agent2: 107.5
team_policy eval idv catch total num of agent2: 29
team_policy eval team catch total num: 43
team_policy eval average step individual rewards of agent3: 0.7084019136617217
team_policy eval average team episode rewards of agent3: 107.5
team_policy eval idv catch total num of agent3: 30
team_policy eval team catch total num: 43
team_policy eval average step individual rewards of agent4: 0.5330744151115152
team_policy eval average team episode rewards of agent4: 107.5
team_policy eval idv catch total num of agent4: 23
team_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent0: 1.0869573983677145
idv_policy eval average team episode rewards of agent0: 127.5
idv_policy eval idv catch total num of agent0: 45
idv_policy eval team catch total num: 51
idv_policy eval average step individual rewards of agent1: 0.5812080165851529
idv_policy eval average team episode rewards of agent1: 127.5
idv_policy eval idv catch total num of agent1: 25
idv_policy eval team catch total num: 51
idv_policy eval average step individual rewards of agent2: 0.5821665057692624
idv_policy eval average team episode rewards of agent2: 127.5
idv_policy eval idv catch total num of agent2: 25
idv_policy eval team catch total num: 51
idv_policy eval average step individual rewards of agent3: 1.0088247954993685
idv_policy eval average team episode rewards of agent3: 127.5
idv_policy eval idv catch total num of agent3: 42
idv_policy eval team catch total num: 51
idv_policy eval average step individual rewards of agent4: 1.1591718509222486
idv_policy eval average team episode rewards of agent4: 127.5
idv_policy eval idv catch total num of agent4: 48
idv_policy eval team catch total num: 51

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8276/10000 episodes, total num timesteps 1655400/2000000, FPS 264.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8277/10000 episodes, total num timesteps 1655600/2000000, FPS 264.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8278/10000 episodes, total num timesteps 1655800/2000000, FPS 264.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8279/10000 episodes, total num timesteps 1656000/2000000, FPS 264.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8280/10000 episodes, total num timesteps 1656200/2000000, FPS 264.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8281/10000 episodes, total num timesteps 1656400/2000000, FPS 264.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8282/10000 episodes, total num timesteps 1656600/2000000, FPS 264.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8283/10000 episodes, total num timesteps 1656800/2000000, FPS 264.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8284/10000 episodes, total num timesteps 1657000/2000000, FPS 264.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8285/10000 episodes, total num timesteps 1657200/2000000, FPS 264.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8286/10000 episodes, total num timesteps 1657400/2000000, FPS 264.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8287/10000 episodes, total num timesteps 1657600/2000000, FPS 264.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8288/10000 episodes, total num timesteps 1657800/2000000, FPS 264.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8289/10000 episodes, total num timesteps 1658000/2000000, FPS 264.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8290/10000 episodes, total num timesteps 1658200/2000000, FPS 264.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8291/10000 episodes, total num timesteps 1658400/2000000, FPS 264.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8292/10000 episodes, total num timesteps 1658600/2000000, FPS 264.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8293/10000 episodes, total num timesteps 1658800/2000000, FPS 264.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8294/10000 episodes, total num timesteps 1659000/2000000, FPS 264.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8295/10000 episodes, total num timesteps 1659200/2000000, FPS 264.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8296/10000 episodes, total num timesteps 1659400/2000000, FPS 264.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8297/10000 episodes, total num timesteps 1659600/2000000, FPS 264.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8298/10000 episodes, total num timesteps 1659800/2000000, FPS 264.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8299/10000 episodes, total num timesteps 1660000/2000000, FPS 264.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8300/10000 episodes, total num timesteps 1660200/2000000, FPS 264.

team_policy eval average step individual rewards of agent0: 0.6290777945502736
team_policy eval average team episode rewards of agent0: 70.0
team_policy eval idv catch total num of agent0: 27
team_policy eval team catch total num: 28
team_policy eval average step individual rewards of agent1: 0.47451395113548434
team_policy eval average team episode rewards of agent1: 70.0
team_policy eval idv catch total num of agent1: 21
team_policy eval team catch total num: 28
team_policy eval average step individual rewards of agent2: 0.6893291466808611
team_policy eval average team episode rewards of agent2: 70.0
team_policy eval idv catch total num of agent2: 29
team_policy eval team catch total num: 28
team_policy eval average step individual rewards of agent3: 0.38476679944283004
team_policy eval average team episode rewards of agent3: 70.0
team_policy eval idv catch total num of agent3: 17
team_policy eval team catch total num: 28
team_policy eval average step individual rewards of agent4: 0.4747319661099707
team_policy eval average team episode rewards of agent4: 70.0
team_policy eval idv catch total num of agent4: 21
team_policy eval team catch total num: 28
idv_policy eval average step individual rewards of agent0: 0.8082612089951783
idv_policy eval average team episode rewards of agent0: 117.5
idv_policy eval idv catch total num of agent0: 34
idv_policy eval team catch total num: 47
idv_policy eval average step individual rewards of agent1: 0.9942785836080327
idv_policy eval average team episode rewards of agent1: 117.5
idv_policy eval idv catch total num of agent1: 41
idv_policy eval team catch total num: 47
idv_policy eval average step individual rewards of agent2: 0.9140570331153626
idv_policy eval average team episode rewards of agent2: 117.5
idv_policy eval idv catch total num of agent2: 38
idv_policy eval team catch total num: 47
idv_policy eval average step individual rewards of agent3: 0.8160283066656759
idv_policy eval average team episode rewards of agent3: 117.5
idv_policy eval idv catch total num of agent3: 34
idv_policy eval team catch total num: 47
idv_policy eval average step individual rewards of agent4: 1.1787483721768302
idv_policy eval average team episode rewards of agent4: 117.5
idv_policy eval idv catch total num of agent4: 49
idv_policy eval team catch total num: 47

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8301/10000 episodes, total num timesteps 1660400/2000000, FPS 264.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8302/10000 episodes, total num timesteps 1660600/2000000, FPS 264.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8303/10000 episodes, total num timesteps 1660800/2000000, FPS 264.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8304/10000 episodes, total num timesteps 1661000/2000000, FPS 264.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8305/10000 episodes, total num timesteps 1661200/2000000, FPS 264.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8306/10000 episodes, total num timesteps 1661400/2000000, FPS 264.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8307/10000 episodes, total num timesteps 1661600/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8308/10000 episodes, total num timesteps 1661800/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8309/10000 episodes, total num timesteps 1662000/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8310/10000 episodes, total num timesteps 1662200/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8311/10000 episodes, total num timesteps 1662400/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8312/10000 episodes, total num timesteps 1662600/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8313/10000 episodes, total num timesteps 1662800/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8314/10000 episodes, total num timesteps 1663000/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8315/10000 episodes, total num timesteps 1663200/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8316/10000 episodes, total num timesteps 1663400/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8317/10000 episodes, total num timesteps 1663600/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8318/10000 episodes, total num timesteps 1663800/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8319/10000 episodes, total num timesteps 1664000/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8320/10000 episodes, total num timesteps 1664200/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8321/10000 episodes, total num timesteps 1664400/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8322/10000 episodes, total num timesteps 1664600/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8323/10000 episodes, total num timesteps 1664800/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8324/10000 episodes, total num timesteps 1665000/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8325/10000 episodes, total num timesteps 1665200/2000000, FPS 263.

team_policy eval average step individual rewards of agent0: 0.7584483133315494
team_policy eval average team episode rewards of agent0: 102.5
team_policy eval idv catch total num of agent0: 32
team_policy eval team catch total num: 41
team_policy eval average step individual rewards of agent1: 0.9425293671624487
team_policy eval average team episode rewards of agent1: 102.5
team_policy eval idv catch total num of agent1: 39
team_policy eval team catch total num: 41
team_policy eval average step individual rewards of agent2: 0.628057821588039
team_policy eval average team episode rewards of agent2: 102.5
team_policy eval idv catch total num of agent2: 27
team_policy eval team catch total num: 41
team_policy eval average step individual rewards of agent3: 0.9633551626597526
team_policy eval average team episode rewards of agent3: 102.5
team_policy eval idv catch total num of agent3: 40
team_policy eval team catch total num: 41
team_policy eval average step individual rewards of agent4: 0.29838790280816213
team_policy eval average team episode rewards of agent4: 102.5
team_policy eval idv catch total num of agent4: 14
team_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent0: 0.8568971233878976
idv_policy eval average team episode rewards of agent0: 90.0
idv_policy eval idv catch total num of agent0: 36
idv_policy eval team catch total num: 36
idv_policy eval average step individual rewards of agent1: 0.5282333356318343
idv_policy eval average team episode rewards of agent1: 90.0
idv_policy eval idv catch total num of agent1: 23
idv_policy eval team catch total num: 36
idv_policy eval average step individual rewards of agent2: 0.4446414487367782
idv_policy eval average team episode rewards of agent2: 90.0
idv_policy eval idv catch total num of agent2: 20
idv_policy eval team catch total num: 36
idv_policy eval average step individual rewards of agent3: 0.39472650317758934
idv_policy eval average team episode rewards of agent3: 90.0
idv_policy eval idv catch total num of agent3: 18
idv_policy eval team catch total num: 36
idv_policy eval average step individual rewards of agent4: 0.677520793125805
idv_policy eval average team episode rewards of agent4: 90.0
idv_policy eval idv catch total num of agent4: 29
idv_policy eval team catch total num: 36

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8326/10000 episodes, total num timesteps 1665400/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8327/10000 episodes, total num timesteps 1665600/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8328/10000 episodes, total num timesteps 1665800/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8329/10000 episodes, total num timesteps 1666000/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8330/10000 episodes, total num timesteps 1666200/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8331/10000 episodes, total num timesteps 1666400/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8332/10000 episodes, total num timesteps 1666600/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8333/10000 episodes, total num timesteps 1666800/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8334/10000 episodes, total num timesteps 1667000/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8335/10000 episodes, total num timesteps 1667200/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8336/10000 episodes, total num timesteps 1667400/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8337/10000 episodes, total num timesteps 1667600/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8338/10000 episodes, total num timesteps 1667800/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8339/10000 episodes, total num timesteps 1668000/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8340/10000 episodes, total num timesteps 1668200/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8341/10000 episodes, total num timesteps 1668400/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8342/10000 episodes, total num timesteps 1668600/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8343/10000 episodes, total num timesteps 1668800/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8344/10000 episodes, total num timesteps 1669000/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8345/10000 episodes, total num timesteps 1669200/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8346/10000 episodes, total num timesteps 1669400/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8347/10000 episodes, total num timesteps 1669600/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8348/10000 episodes, total num timesteps 1669800/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8349/10000 episodes, total num timesteps 1670000/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8350/10000 episodes, total num timesteps 1670200/2000000, FPS 263.

team_policy eval average step individual rewards of agent0: 0.5218072764656049
team_policy eval average team episode rewards of agent0: 80.0
team_policy eval idv catch total num of agent0: 23
team_policy eval team catch total num: 32
team_policy eval average step individual rewards of agent1: 0.8318480220791445
team_policy eval average team episode rewards of agent1: 80.0
team_policy eval idv catch total num of agent1: 35
team_policy eval team catch total num: 32
team_policy eval average step individual rewards of agent2: 0.8597332807577394
team_policy eval average team episode rewards of agent2: 80.0
team_policy eval idv catch total num of agent2: 36
team_policy eval team catch total num: 32
team_policy eval average step individual rewards of agent3: 0.3995652669320647
team_policy eval average team episode rewards of agent3: 80.0
team_policy eval idv catch total num of agent3: 18
team_policy eval team catch total num: 32
team_policy eval average step individual rewards of agent4: 0.1944826445312357
team_policy eval average team episode rewards of agent4: 80.0
team_policy eval idv catch total num of agent4: 10
team_policy eval team catch total num: 32
idv_policy eval average step individual rewards of agent0: 0.9892932486505421
idv_policy eval average team episode rewards of agent0: 102.5
idv_policy eval idv catch total num of agent0: 41
idv_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent1: 0.6534439100017195
idv_policy eval average team episode rewards of agent1: 102.5
idv_policy eval idv catch total num of agent1: 28
idv_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent2: 0.7000805035594523
idv_policy eval average team episode rewards of agent2: 102.5
idv_policy eval idv catch total num of agent2: 30
idv_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent3: 1.0858981658058835
idv_policy eval average team episode rewards of agent3: 102.5
idv_policy eval idv catch total num of agent3: 45
idv_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent4: 0.47257530301958206
idv_policy eval average team episode rewards of agent4: 102.5
idv_policy eval idv catch total num of agent4: 21
idv_policy eval team catch total num: 41

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8351/10000 episodes, total num timesteps 1670400/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8352/10000 episodes, total num timesteps 1670600/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8353/10000 episodes, total num timesteps 1670800/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8354/10000 episodes, total num timesteps 1671000/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8355/10000 episodes, total num timesteps 1671200/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8356/10000 episodes, total num timesteps 1671400/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8357/10000 episodes, total num timesteps 1671600/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8358/10000 episodes, total num timesteps 1671800/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8359/10000 episodes, total num timesteps 1672000/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8360/10000 episodes, total num timesteps 1672200/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8361/10000 episodes, total num timesteps 1672400/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8362/10000 episodes, total num timesteps 1672600/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8363/10000 episodes, total num timesteps 1672800/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8364/10000 episodes, total num timesteps 1673000/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8365/10000 episodes, total num timesteps 1673200/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8366/10000 episodes, total num timesteps 1673400/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8367/10000 episodes, total num timesteps 1673600/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8368/10000 episodes, total num timesteps 1673800/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8369/10000 episodes, total num timesteps 1674000/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8370/10000 episodes, total num timesteps 1674200/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8371/10000 episodes, total num timesteps 1674400/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8372/10000 episodes, total num timesteps 1674600/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8373/10000 episodes, total num timesteps 1674800/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8374/10000 episodes, total num timesteps 1675000/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8375/10000 episodes, total num timesteps 1675200/2000000, FPS 263.

team_policy eval average step individual rewards of agent0: 0.6869560760185788
team_policy eval average team episode rewards of agent0: 72.5
team_policy eval idv catch total num of agent0: 29
team_policy eval team catch total num: 29
team_policy eval average step individual rewards of agent1: 0.6620100404184115
team_policy eval average team episode rewards of agent1: 72.5
team_policy eval idv catch total num of agent1: 28
team_policy eval team catch total num: 29
team_policy eval average step individual rewards of agent2: 0.34381906988178856
team_policy eval average team episode rewards of agent2: 72.5
team_policy eval idv catch total num of agent2: 16
team_policy eval team catch total num: 29
team_policy eval average step individual rewards of agent3: 0.29960874818317806
team_policy eval average team episode rewards of agent3: 72.5
team_policy eval idv catch total num of agent3: 14
team_policy eval team catch total num: 29
team_policy eval average step individual rewards of agent4: 0.9421335565538569
team_policy eval average team episode rewards of agent4: 72.5
team_policy eval idv catch total num of agent4: 39
team_policy eval team catch total num: 29
idv_policy eval average step individual rewards of agent0: 0.4067007278718799
idv_policy eval average team episode rewards of agent0: 67.5
idv_policy eval idv catch total num of agent0: 18
idv_policy eval team catch total num: 27
idv_policy eval average step individual rewards of agent1: 0.7322021350655138
idv_policy eval average team episode rewards of agent1: 67.5
idv_policy eval idv catch total num of agent1: 31
idv_policy eval team catch total num: 27
idv_policy eval average step individual rewards of agent2: 0.21283663575283118
idv_policy eval average team episode rewards of agent2: 67.5
idv_policy eval idv catch total num of agent2: 11
idv_policy eval team catch total num: 27
idv_policy eval average step individual rewards of agent3: 0.6840343583551742
idv_policy eval average team episode rewards of agent3: 67.5
idv_policy eval idv catch total num of agent3: 29
idv_policy eval team catch total num: 27
idv_policy eval average step individual rewards of agent4: 0.987114568717329
idv_policy eval average team episode rewards of agent4: 67.5
idv_policy eval idv catch total num of agent4: 41
idv_policy eval team catch total num: 27

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8376/10000 episodes, total num timesteps 1675400/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8377/10000 episodes, total num timesteps 1675600/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8378/10000 episodes, total num timesteps 1675800/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8379/10000 episodes, total num timesteps 1676000/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8380/10000 episodes, total num timesteps 1676200/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8381/10000 episodes, total num timesteps 1676400/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8382/10000 episodes, total num timesteps 1676600/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8383/10000 episodes, total num timesteps 1676800/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8384/10000 episodes, total num timesteps 1677000/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8385/10000 episodes, total num timesteps 1677200/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8386/10000 episodes, total num timesteps 1677400/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8387/10000 episodes, total num timesteps 1677600/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8388/10000 episodes, total num timesteps 1677800/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8389/10000 episodes, total num timesteps 1678000/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8390/10000 episodes, total num timesteps 1678200/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8391/10000 episodes, total num timesteps 1678400/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8392/10000 episodes, total num timesteps 1678600/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8393/10000 episodes, total num timesteps 1678800/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8394/10000 episodes, total num timesteps 1679000/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8395/10000 episodes, total num timesteps 1679200/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8396/10000 episodes, total num timesteps 1679400/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8397/10000 episodes, total num timesteps 1679600/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8398/10000 episodes, total num timesteps 1679800/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8399/10000 episodes, total num timesteps 1680000/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8400/10000 episodes, total num timesteps 1680200/2000000, FPS 263.

team_policy eval average step individual rewards of agent0: 0.5470762822216375
team_policy eval average team episode rewards of agent0: 95.0
team_policy eval idv catch total num of agent0: 24
team_policy eval team catch total num: 38
team_policy eval average step individual rewards of agent1: 0.7650812611636035
team_policy eval average team episode rewards of agent1: 95.0
team_policy eval idv catch total num of agent1: 32
team_policy eval team catch total num: 38
team_policy eval average step individual rewards of agent2: 0.8637344854511864
team_policy eval average team episode rewards of agent2: 95.0
team_policy eval idv catch total num of agent2: 36
team_policy eval team catch total num: 38
team_policy eval average step individual rewards of agent3: 0.7938627930465267
team_policy eval average team episode rewards of agent3: 95.0
team_policy eval idv catch total num of agent3: 33
team_policy eval team catch total num: 38
team_policy eval average step individual rewards of agent4: 0.6587127611174046
team_policy eval average team episode rewards of agent4: 95.0
team_policy eval idv catch total num of agent4: 28
team_policy eval team catch total num: 38
idv_policy eval average step individual rewards of agent0: 0.702231360003926
idv_policy eval average team episode rewards of agent0: 90.0
idv_policy eval idv catch total num of agent0: 30
idv_policy eval team catch total num: 36
idv_policy eval average step individual rewards of agent1: 0.3225507382294127
idv_policy eval average team episode rewards of agent1: 90.0
idv_policy eval idv catch total num of agent1: 15
idv_policy eval team catch total num: 36
idv_policy eval average step individual rewards of agent2: 0.46023157574502876
idv_policy eval average team episode rewards of agent2: 90.0
idv_policy eval idv catch total num of agent2: 20
idv_policy eval team catch total num: 36
idv_policy eval average step individual rewards of agent3: 0.7858059619945889
idv_policy eval average team episode rewards of agent3: 90.0
idv_policy eval idv catch total num of agent3: 33
idv_policy eval team catch total num: 36
idv_policy eval average step individual rewards of agent4: 0.8142396800766905
idv_policy eval average team episode rewards of agent4: 90.0
idv_policy eval idv catch total num of agent4: 34
idv_policy eval team catch total num: 36

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8401/10000 episodes, total num timesteps 1680400/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8402/10000 episodes, total num timesteps 1680600/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8403/10000 episodes, total num timesteps 1680800/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8404/10000 episodes, total num timesteps 1681000/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8405/10000 episodes, total num timesteps 1681200/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8406/10000 episodes, total num timesteps 1681400/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8407/10000 episodes, total num timesteps 1681600/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8408/10000 episodes, total num timesteps 1681800/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8409/10000 episodes, total num timesteps 1682000/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8410/10000 episodes, total num timesteps 1682200/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8411/10000 episodes, total num timesteps 1682400/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8412/10000 episodes, total num timesteps 1682600/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8413/10000 episodes, total num timesteps 1682800/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8414/10000 episodes, total num timesteps 1683000/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8415/10000 episodes, total num timesteps 1683200/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8416/10000 episodes, total num timesteps 1683400/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8417/10000 episodes, total num timesteps 1683600/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8418/10000 episodes, total num timesteps 1683800/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8419/10000 episodes, total num timesteps 1684000/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8420/10000 episodes, total num timesteps 1684200/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8421/10000 episodes, total num timesteps 1684400/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8422/10000 episodes, total num timesteps 1684600/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8423/10000 episodes, total num timesteps 1684800/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8424/10000 episodes, total num timesteps 1685000/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8425/10000 episodes, total num timesteps 1685200/2000000, FPS 263.

team_policy eval average step individual rewards of agent0: 0.44617448671291676
team_policy eval average team episode rewards of agent0: 62.5
team_policy eval idv catch total num of agent0: 20
team_policy eval team catch total num: 25
team_policy eval average step individual rewards of agent1: 0.35156957804976147
team_policy eval average team episode rewards of agent1: 62.5
team_policy eval idv catch total num of agent1: 16
team_policy eval team catch total num: 25
team_policy eval average step individual rewards of agent2: 0.1312851771143006
team_policy eval average team episode rewards of agent2: 62.5
team_policy eval idv catch total num of agent2: 8
team_policy eval team catch total num: 25
team_policy eval average step individual rewards of agent3: 0.7358445205961792
team_policy eval average team episode rewards of agent3: 62.5
team_policy eval idv catch total num of agent3: 31
team_policy eval team catch total num: 25
team_policy eval average step individual rewards of agent4: 0.5538645452876072
team_policy eval average team episode rewards of agent4: 62.5
team_policy eval idv catch total num of agent4: 24
team_policy eval team catch total num: 25
idv_policy eval average step individual rewards of agent0: 0.6149591889726467
idv_policy eval average team episode rewards of agent0: 77.5
idv_policy eval idv catch total num of agent0: 26
idv_policy eval team catch total num: 31
idv_policy eval average step individual rewards of agent1: 0.8280721564697682
idv_policy eval average team episode rewards of agent1: 77.5
idv_policy eval idv catch total num of agent1: 35
idv_policy eval team catch total num: 31
idv_policy eval average step individual rewards of agent2: 0.8617627359182031
idv_policy eval average team episode rewards of agent2: 77.5
idv_policy eval idv catch total num of agent2: 36
idv_policy eval team catch total num: 31
idv_policy eval average step individual rewards of agent3: 0.2736977497686684
idv_policy eval average team episode rewards of agent3: 77.5
idv_policy eval idv catch total num of agent3: 13
idv_policy eval team catch total num: 31
idv_policy eval average step individual rewards of agent4: 0.2782962806601329
idv_policy eval average team episode rewards of agent4: 77.5
idv_policy eval idv catch total num of agent4: 13
idv_policy eval team catch total num: 31

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8426/10000 episodes, total num timesteps 1685400/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8427/10000 episodes, total num timesteps 1685600/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8428/10000 episodes, total num timesteps 1685800/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8429/10000 episodes, total num timesteps 1686000/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8430/10000 episodes, total num timesteps 1686200/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8431/10000 episodes, total num timesteps 1686400/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8432/10000 episodes, total num timesteps 1686600/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8433/10000 episodes, total num timesteps 1686800/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8434/10000 episodes, total num timesteps 1687000/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8435/10000 episodes, total num timesteps 1687200/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8436/10000 episodes, total num timesteps 1687400/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8437/10000 episodes, total num timesteps 1687600/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8438/10000 episodes, total num timesteps 1687800/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8439/10000 episodes, total num timesteps 1688000/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8440/10000 episodes, total num timesteps 1688200/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8441/10000 episodes, total num timesteps 1688400/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8442/10000 episodes, total num timesteps 1688600/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8443/10000 episodes, total num timesteps 1688800/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8444/10000 episodes, total num timesteps 1689000/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8445/10000 episodes, total num timesteps 1689200/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8446/10000 episodes, total num timesteps 1689400/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8447/10000 episodes, total num timesteps 1689600/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8448/10000 episodes, total num timesteps 1689800/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8449/10000 episodes, total num timesteps 1690000/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8450/10000 episodes, total num timesteps 1690200/2000000, FPS 263.

team_policy eval average step individual rewards of agent0: 0.5876957684897403
team_policy eval average team episode rewards of agent0: 75.0
team_policy eval idv catch total num of agent0: 25
team_policy eval team catch total num: 30
team_policy eval average step individual rewards of agent1: 0.5930674484320992
team_policy eval average team episode rewards of agent1: 75.0
team_policy eval idv catch total num of agent1: 25
team_policy eval team catch total num: 30
team_policy eval average step individual rewards of agent2: 0.7929571820634025
team_policy eval average team episode rewards of agent2: 75.0
team_policy eval idv catch total num of agent2: 33
team_policy eval team catch total num: 30
team_policy eval average step individual rewards of agent3: 0.3857382151786527
team_policy eval average team episode rewards of agent3: 75.0
team_policy eval idv catch total num of agent3: 17
team_policy eval team catch total num: 30
team_policy eval average step individual rewards of agent4: 0.7080523276834978
team_policy eval average team episode rewards of agent4: 75.0
team_policy eval idv catch total num of agent4: 30
team_policy eval team catch total num: 30
idv_policy eval average step individual rewards of agent0: 0.35751828298048005
idv_policy eval average team episode rewards of agent0: 42.5
idv_policy eval idv catch total num of agent0: 17
idv_policy eval team catch total num: 17
idv_policy eval average step individual rewards of agent1: 0.21324537121108325
idv_policy eval average team episode rewards of agent1: 42.5
idv_policy eval idv catch total num of agent1: 11
idv_policy eval team catch total num: 17
idv_policy eval average step individual rewards of agent2: 0.41514626138564
idv_policy eval average team episode rewards of agent2: 42.5
idv_policy eval idv catch total num of agent2: 19
idv_policy eval team catch total num: 17
idv_policy eval average step individual rewards of agent3: 0.5583823902888304
idv_policy eval average team episode rewards of agent3: 42.5
idv_policy eval idv catch total num of agent3: 24
idv_policy eval team catch total num: 17
idv_policy eval average step individual rewards of agent4: 0.18001077341720897
idv_policy eval average team episode rewards of agent4: 42.5
idv_policy eval idv catch total num of agent4: 9
idv_policy eval team catch total num: 17

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8451/10000 episodes, total num timesteps 1690400/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8452/10000 episodes, total num timesteps 1690600/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8453/10000 episodes, total num timesteps 1690800/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8454/10000 episodes, total num timesteps 1691000/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8455/10000 episodes, total num timesteps 1691200/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8456/10000 episodes, total num timesteps 1691400/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8457/10000 episodes, total num timesteps 1691600/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8458/10000 episodes, total num timesteps 1691800/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8459/10000 episodes, total num timesteps 1692000/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8460/10000 episodes, total num timesteps 1692200/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8461/10000 episodes, total num timesteps 1692400/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8462/10000 episodes, total num timesteps 1692600/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8463/10000 episodes, total num timesteps 1692800/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8464/10000 episodes, total num timesteps 1693000/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8465/10000 episodes, total num timesteps 1693200/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8466/10000 episodes, total num timesteps 1693400/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8467/10000 episodes, total num timesteps 1693600/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8468/10000 episodes, total num timesteps 1693800/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8469/10000 episodes, total num timesteps 1694000/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8470/10000 episodes, total num timesteps 1694200/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8471/10000 episodes, total num timesteps 1694400/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8472/10000 episodes, total num timesteps 1694600/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8473/10000 episodes, total num timesteps 1694800/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8474/10000 episodes, total num timesteps 1695000/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8475/10000 episodes, total num timesteps 1695200/2000000, FPS 263.

team_policy eval average step individual rewards of agent0: 0.45388698347921597
team_policy eval average team episode rewards of agent0: 90.0
team_policy eval idv catch total num of agent0: 21
team_policy eval team catch total num: 36
team_policy eval average step individual rewards of agent1: 0.5329257614143406
team_policy eval average team episode rewards of agent1: 90.0
team_policy eval idv catch total num of agent1: 23
team_policy eval team catch total num: 36
team_policy eval average step individual rewards of agent2: 0.7248456162641381
team_policy eval average team episode rewards of agent2: 90.0
team_policy eval idv catch total num of agent2: 31
team_policy eval team catch total num: 36
team_policy eval average step individual rewards of agent3: 0.5960608766132325
team_policy eval average team episode rewards of agent3: 90.0
team_policy eval idv catch total num of agent3: 26
team_policy eval team catch total num: 36
team_policy eval average step individual rewards of agent4: 0.5206287911563529
team_policy eval average team episode rewards of agent4: 90.0
team_policy eval idv catch total num of agent4: 23
team_policy eval team catch total num: 36
idv_policy eval average step individual rewards of agent0: 0.28697752232047596
idv_policy eval average team episode rewards of agent0: 57.5
idv_policy eval idv catch total num of agent0: 14
idv_policy eval team catch total num: 23
idv_policy eval average step individual rewards of agent1: 0.47897812963165265
idv_policy eval average team episode rewards of agent1: 57.5
idv_policy eval idv catch total num of agent1: 21
idv_policy eval team catch total num: 23
idv_policy eval average step individual rewards of agent2: 0.5521530923843786
idv_policy eval average team episode rewards of agent2: 57.5
idv_policy eval idv catch total num of agent2: 24
idv_policy eval team catch total num: 23
idv_policy eval average step individual rewards of agent3: 0.6854674382554813
idv_policy eval average team episode rewards of agent3: 57.5
idv_policy eval idv catch total num of agent3: 29
idv_policy eval team catch total num: 23
idv_policy eval average step individual rewards of agent4: 0.8939530163809902
idv_policy eval average team episode rewards of agent4: 57.5
idv_policy eval idv catch total num of agent4: 37
idv_policy eval team catch total num: 23

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8476/10000 episodes, total num timesteps 1695400/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8477/10000 episodes, total num timesteps 1695600/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8478/10000 episodes, total num timesteps 1695800/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8479/10000 episodes, total num timesteps 1696000/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8480/10000 episodes, total num timesteps 1696200/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8481/10000 episodes, total num timesteps 1696400/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8482/10000 episodes, total num timesteps 1696600/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8483/10000 episodes, total num timesteps 1696800/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8484/10000 episodes, total num timesteps 1697000/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8485/10000 episodes, total num timesteps 1697200/2000000, FPS 263.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8486/10000 episodes, total num timesteps 1697400/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8487/10000 episodes, total num timesteps 1697600/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8488/10000 episodes, total num timesteps 1697800/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8489/10000 episodes, total num timesteps 1698000/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8490/10000 episodes, total num timesteps 1698200/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8491/10000 episodes, total num timesteps 1698400/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8492/10000 episodes, total num timesteps 1698600/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8493/10000 episodes, total num timesteps 1698800/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8494/10000 episodes, total num timesteps 1699000/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8495/10000 episodes, total num timesteps 1699200/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8496/10000 episodes, total num timesteps 1699400/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8497/10000 episodes, total num timesteps 1699600/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8498/10000 episodes, total num timesteps 1699800/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8499/10000 episodes, total num timesteps 1700000/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8500/10000 episodes, total num timesteps 1700200/2000000, FPS 262.

team_policy eval average step individual rewards of agent0: 0.4708120809076919
team_policy eval average team episode rewards of agent0: 92.5
team_policy eval idv catch total num of agent0: 21
team_policy eval team catch total num: 37
team_policy eval average step individual rewards of agent1: 0.6213312960170447
team_policy eval average team episode rewards of agent1: 92.5
team_policy eval idv catch total num of agent1: 27
team_policy eval team catch total num: 37
team_policy eval average step individual rewards of agent2: 0.44591440855696973
team_policy eval average team episode rewards of agent2: 92.5
team_policy eval idv catch total num of agent2: 20
team_policy eval team catch total num: 37
team_policy eval average step individual rewards of agent3: 1.0084143745782788
team_policy eval average team episode rewards of agent3: 92.5
team_policy eval idv catch total num of agent3: 42
team_policy eval team catch total num: 37
team_policy eval average step individual rewards of agent4: 0.8125776977641044
team_policy eval average team episode rewards of agent4: 92.5
team_policy eval idv catch total num of agent4: 34
team_policy eval team catch total num: 37
idv_policy eval average step individual rewards of agent0: 0.8097777293839659
idv_policy eval average team episode rewards of agent0: 97.5
idv_policy eval idv catch total num of agent0: 34
idv_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent1: 0.580706226762723
idv_policy eval average team episode rewards of agent1: 97.5
idv_policy eval idv catch total num of agent1: 25
idv_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent2: 0.8945850997265852
idv_policy eval average team episode rewards of agent2: 97.5
idv_policy eval idv catch total num of agent2: 37
idv_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent3: 0.693178020671829
idv_policy eval average team episode rewards of agent3: 97.5
idv_policy eval idv catch total num of agent3: 29
idv_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent4: 0.7078796626499642
idv_policy eval average team episode rewards of agent4: 97.5
idv_policy eval idv catch total num of agent4: 30
idv_policy eval team catch total num: 39

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8501/10000 episodes, total num timesteps 1700400/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8502/10000 episodes, total num timesteps 1700600/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8503/10000 episodes, total num timesteps 1700800/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8504/10000 episodes, total num timesteps 1701000/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8505/10000 episodes, total num timesteps 1701200/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8506/10000 episodes, total num timesteps 1701400/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8507/10000 episodes, total num timesteps 1701600/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8508/10000 episodes, total num timesteps 1701800/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8509/10000 episodes, total num timesteps 1702000/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8510/10000 episodes, total num timesteps 1702200/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8511/10000 episodes, total num timesteps 1702400/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8512/10000 episodes, total num timesteps 1702600/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8513/10000 episodes, total num timesteps 1702800/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8514/10000 episodes, total num timesteps 1703000/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8515/10000 episodes, total num timesteps 1703200/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8516/10000 episodes, total num timesteps 1703400/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8517/10000 episodes, total num timesteps 1703600/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8518/10000 episodes, total num timesteps 1703800/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8519/10000 episodes, total num timesteps 1704000/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8520/10000 episodes, total num timesteps 1704200/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8521/10000 episodes, total num timesteps 1704400/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8522/10000 episodes, total num timesteps 1704600/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8523/10000 episodes, total num timesteps 1704800/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8524/10000 episodes, total num timesteps 1705000/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8525/10000 episodes, total num timesteps 1705200/2000000, FPS 262.

team_policy eval average step individual rewards of agent0: 0.5587628257474536
team_policy eval average team episode rewards of agent0: 130.0
team_policy eval idv catch total num of agent0: 24
team_policy eval team catch total num: 52
team_policy eval average step individual rewards of agent1: 1.0664472167554249
team_policy eval average team episode rewards of agent1: 130.0
team_policy eval idv catch total num of agent1: 44
team_policy eval team catch total num: 52
team_policy eval average step individual rewards of agent2: 0.9642236969198941
team_policy eval average team episode rewards of agent2: 130.0
team_policy eval idv catch total num of agent2: 40
team_policy eval team catch total num: 52
team_policy eval average step individual rewards of agent3: 0.7429254238791781
team_policy eval average team episode rewards of agent3: 130.0
team_policy eval idv catch total num of agent3: 31
team_policy eval team catch total num: 52
team_policy eval average step individual rewards of agent4: 0.8917169885189731
team_policy eval average team episode rewards of agent4: 130.0
team_policy eval idv catch total num of agent4: 37
team_policy eval team catch total num: 52
idv_policy eval average step individual rewards of agent0: 0.3743852911558026
idv_policy eval average team episode rewards of agent0: 82.5
idv_policy eval idv catch total num of agent0: 17
idv_policy eval team catch total num: 33
idv_policy eval average step individual rewards of agent1: 0.7840982361235573
idv_policy eval average team episode rewards of agent1: 82.5
idv_policy eval idv catch total num of agent1: 33
idv_policy eval team catch total num: 33
idv_policy eval average step individual rewards of agent2: 0.7042388713151412
idv_policy eval average team episode rewards of agent2: 82.5
idv_policy eval idv catch total num of agent2: 30
idv_policy eval team catch total num: 33
idv_policy eval average step individual rewards of agent3: 0.22990738763511304
idv_policy eval average team episode rewards of agent3: 82.5
idv_policy eval idv catch total num of agent3: 11
idv_policy eval team catch total num: 33
idv_policy eval average step individual rewards of agent4: 0.5259145885011722
idv_policy eval average team episode rewards of agent4: 82.5
idv_policy eval idv catch total num of agent4: 23
idv_policy eval team catch total num: 33

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8526/10000 episodes, total num timesteps 1705400/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8527/10000 episodes, total num timesteps 1705600/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8528/10000 episodes, total num timesteps 1705800/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8529/10000 episodes, total num timesteps 1706000/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8530/10000 episodes, total num timesteps 1706200/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8531/10000 episodes, total num timesteps 1706400/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8532/10000 episodes, total num timesteps 1706600/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8533/10000 episodes, total num timesteps 1706800/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8534/10000 episodes, total num timesteps 1707000/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8535/10000 episodes, total num timesteps 1707200/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8536/10000 episodes, total num timesteps 1707400/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8537/10000 episodes, total num timesteps 1707600/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8538/10000 episodes, total num timesteps 1707800/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8539/10000 episodes, total num timesteps 1708000/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8540/10000 episodes, total num timesteps 1708200/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8541/10000 episodes, total num timesteps 1708400/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8542/10000 episodes, total num timesteps 1708600/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8543/10000 episodes, total num timesteps 1708800/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8544/10000 episodes, total num timesteps 1709000/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8545/10000 episodes, total num timesteps 1709200/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8546/10000 episodes, total num timesteps 1709400/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8547/10000 episodes, total num timesteps 1709600/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8548/10000 episodes, total num timesteps 1709800/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8549/10000 episodes, total num timesteps 1710000/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8550/10000 episodes, total num timesteps 1710200/2000000, FPS 262.

team_policy eval average step individual rewards of agent0: 0.2740069284792168
team_policy eval average team episode rewards of agent0: 67.5
team_policy eval idv catch total num of agent0: 13
team_policy eval team catch total num: 27
team_policy eval average step individual rewards of agent1: 0.6947167211863403
team_policy eval average team episode rewards of agent1: 67.5
team_policy eval idv catch total num of agent1: 30
team_policy eval team catch total num: 27
team_policy eval average step individual rewards of agent2: 0.8153905121212894
team_policy eval average team episode rewards of agent2: 67.5
team_policy eval idv catch total num of agent2: 34
team_policy eval team catch total num: 27
team_policy eval average step individual rewards of agent3: 0.48736450316920726
team_policy eval average team episode rewards of agent3: 67.5
team_policy eval idv catch total num of agent3: 21
team_policy eval team catch total num: 27
team_policy eval average step individual rewards of agent4: 0.28461073812675763
team_policy eval average team episode rewards of agent4: 67.5
team_policy eval idv catch total num of agent4: 14
team_policy eval team catch total num: 27
idv_policy eval average step individual rewards of agent0: 1.0634220110504125
idv_policy eval average team episode rewards of agent0: 97.5
idv_policy eval idv catch total num of agent0: 44
idv_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent1: 0.7702281313540575
idv_policy eval average team episode rewards of agent1: 97.5
idv_policy eval idv catch total num of agent1: 33
idv_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent2: 1.1685044614388813
idv_policy eval average team episode rewards of agent2: 97.5
idv_policy eval idv catch total num of agent2: 48
idv_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent3: 0.17426407272101868
idv_policy eval average team episode rewards of agent3: 97.5
idv_policy eval idv catch total num of agent3: 10
idv_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent4: 0.46839872210370104
idv_policy eval average team episode rewards of agent4: 97.5
idv_policy eval idv catch total num of agent4: 21
idv_policy eval team catch total num: 39

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8551/10000 episodes, total num timesteps 1710400/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8552/10000 episodes, total num timesteps 1710600/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8553/10000 episodes, total num timesteps 1710800/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8554/10000 episodes, total num timesteps 1711000/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8555/10000 episodes, total num timesteps 1711200/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8556/10000 episodes, total num timesteps 1711400/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8557/10000 episodes, total num timesteps 1711600/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8558/10000 episodes, total num timesteps 1711800/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8559/10000 episodes, total num timesteps 1712000/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8560/10000 episodes, total num timesteps 1712200/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8561/10000 episodes, total num timesteps 1712400/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8562/10000 episodes, total num timesteps 1712600/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8563/10000 episodes, total num timesteps 1712800/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8564/10000 episodes, total num timesteps 1713000/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8565/10000 episodes, total num timesteps 1713200/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8566/10000 episodes, total num timesteps 1713400/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8567/10000 episodes, total num timesteps 1713600/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8568/10000 episodes, total num timesteps 1713800/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8569/10000 episodes, total num timesteps 1714000/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8570/10000 episodes, total num timesteps 1714200/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8571/10000 episodes, total num timesteps 1714400/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8572/10000 episodes, total num timesteps 1714600/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8573/10000 episodes, total num timesteps 1714800/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8574/10000 episodes, total num timesteps 1715000/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8575/10000 episodes, total num timesteps 1715200/2000000, FPS 262.

team_policy eval average step individual rewards of agent0: 0.9952632552148143
team_policy eval average team episode rewards of agent0: 95.0
team_policy eval idv catch total num of agent0: 41
team_policy eval team catch total num: 38
team_policy eval average step individual rewards of agent1: 1.0090231026427923
team_policy eval average team episode rewards of agent1: 95.0
team_policy eval idv catch total num of agent1: 42
team_policy eval team catch total num: 38
team_policy eval average step individual rewards of agent2: 0.576115728302742
team_policy eval average team episode rewards of agent2: 95.0
team_policy eval idv catch total num of agent2: 25
team_policy eval team catch total num: 38
team_policy eval average step individual rewards of agent3: 0.37478785243696694
team_policy eval average team episode rewards of agent3: 95.0
team_policy eval idv catch total num of agent3: 17
team_policy eval team catch total num: 38
team_policy eval average step individual rewards of agent4: 0.7017939481400185
team_policy eval average team episode rewards of agent4: 95.0
team_policy eval idv catch total num of agent4: 30
team_policy eval team catch total num: 38
idv_policy eval average step individual rewards of agent0: 0.9826575013282363
idv_policy eval average team episode rewards of agent0: 112.5
idv_policy eval idv catch total num of agent0: 41
idv_policy eval team catch total num: 45
idv_policy eval average step individual rewards of agent1: 0.2583373618118818
idv_policy eval average team episode rewards of agent1: 112.5
idv_policy eval idv catch total num of agent1: 12
idv_policy eval team catch total num: 45
idv_policy eval average step individual rewards of agent2: 0.8362741647349636
idv_policy eval average team episode rewards of agent2: 112.5
idv_policy eval idv catch total num of agent2: 35
idv_policy eval team catch total num: 45
idv_policy eval average step individual rewards of agent3: 1.0152916474786986
idv_policy eval average team episode rewards of agent3: 112.5
idv_policy eval idv catch total num of agent3: 42
idv_policy eval team catch total num: 45
idv_policy eval average step individual rewards of agent4: 1.0420989413965223
idv_policy eval average team episode rewards of agent4: 112.5
idv_policy eval idv catch total num of agent4: 43
idv_policy eval team catch total num: 45

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8576/10000 episodes, total num timesteps 1715400/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8577/10000 episodes, total num timesteps 1715600/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8578/10000 episodes, total num timesteps 1715800/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8579/10000 episodes, total num timesteps 1716000/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8580/10000 episodes, total num timesteps 1716200/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8581/10000 episodes, total num timesteps 1716400/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8582/10000 episodes, total num timesteps 1716600/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8583/10000 episodes, total num timesteps 1716800/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8584/10000 episodes, total num timesteps 1717000/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8585/10000 episodes, total num timesteps 1717200/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8586/10000 episodes, total num timesteps 1717400/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8587/10000 episodes, total num timesteps 1717600/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8588/10000 episodes, total num timesteps 1717800/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8589/10000 episodes, total num timesteps 1718000/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8590/10000 episodes, total num timesteps 1718200/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8591/10000 episodes, total num timesteps 1718400/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8592/10000 episodes, total num timesteps 1718600/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8593/10000 episodes, total num timesteps 1718800/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8594/10000 episodes, total num timesteps 1719000/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8595/10000 episodes, total num timesteps 1719200/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8596/10000 episodes, total num timesteps 1719400/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8597/10000 episodes, total num timesteps 1719600/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8598/10000 episodes, total num timesteps 1719800/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8599/10000 episodes, total num timesteps 1720000/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8600/10000 episodes, total num timesteps 1720200/2000000, FPS 262.

team_policy eval average step individual rewards of agent0: 0.558267850451214
team_policy eval average team episode rewards of agent0: 85.0
team_policy eval idv catch total num of agent0: 24
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent1: 0.7432601729621436
team_policy eval average team episode rewards of agent1: 85.0
team_policy eval idv catch total num of agent1: 31
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent2: 0.3244653641374653
team_policy eval average team episode rewards of agent2: 85.0
team_policy eval idv catch total num of agent2: 15
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent3: 0.6321761853634068
team_policy eval average team episode rewards of agent3: 85.0
team_policy eval idv catch total num of agent3: 27
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent4: 0.7326166412532826
team_policy eval average team episode rewards of agent4: 85.0
team_policy eval idv catch total num of agent4: 31
team_policy eval team catch total num: 34
idv_policy eval average step individual rewards of agent0: 0.6565647544355119
idv_policy eval average team episode rewards of agent0: 102.5
idv_policy eval idv catch total num of agent0: 28
idv_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent1: 0.9686156753979114
idv_policy eval average team episode rewards of agent1: 102.5
idv_policy eval idv catch total num of agent1: 40
idv_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent2: 0.3373824198129585
idv_policy eval average team episode rewards of agent2: 102.5
idv_policy eval idv catch total num of agent2: 15
idv_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent3: 0.5431337725448141
idv_policy eval average team episode rewards of agent3: 102.5
idv_policy eval idv catch total num of agent3: 23
idv_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent4: 0.8893828594494434
idv_policy eval average team episode rewards of agent4: 102.5
idv_policy eval idv catch total num of agent4: 37
idv_policy eval team catch total num: 41

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8601/10000 episodes, total num timesteps 1720400/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8602/10000 episodes, total num timesteps 1720600/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8603/10000 episodes, total num timesteps 1720800/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8604/10000 episodes, total num timesteps 1721000/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8605/10000 episodes, total num timesteps 1721200/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8606/10000 episodes, total num timesteps 1721400/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8607/10000 episodes, total num timesteps 1721600/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8608/10000 episodes, total num timesteps 1721800/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8609/10000 episodes, total num timesteps 1722000/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8610/10000 episodes, total num timesteps 1722200/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8611/10000 episodes, total num timesteps 1722400/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8612/10000 episodes, total num timesteps 1722600/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8613/10000 episodes, total num timesteps 1722800/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8614/10000 episodes, total num timesteps 1723000/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8615/10000 episodes, total num timesteps 1723200/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8616/10000 episodes, total num timesteps 1723400/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8617/10000 episodes, total num timesteps 1723600/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8618/10000 episodes, total num timesteps 1723800/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8619/10000 episodes, total num timesteps 1724000/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8620/10000 episodes, total num timesteps 1724200/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8621/10000 episodes, total num timesteps 1724400/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8622/10000 episodes, total num timesteps 1724600/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8623/10000 episodes, total num timesteps 1724800/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8624/10000 episodes, total num timesteps 1725000/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8625/10000 episodes, total num timesteps 1725200/2000000, FPS 262.

team_policy eval average step individual rewards of agent0: 0.41648606266491633
team_policy eval average team episode rewards of agent0: 87.5
team_policy eval idv catch total num of agent0: 18
team_policy eval team catch total num: 35
team_policy eval average step individual rewards of agent1: 0.6628217403134591
team_policy eval average team episode rewards of agent1: 87.5
team_policy eval idv catch total num of agent1: 28
team_policy eval team catch total num: 35
team_policy eval average step individual rewards of agent2: 0.4319021828420184
team_policy eval average team episode rewards of agent2: 87.5
team_policy eval idv catch total num of agent2: 19
team_policy eval team catch total num: 35
team_policy eval average step individual rewards of agent3: 0.7120562786830174
team_policy eval average team episode rewards of agent3: 87.5
team_policy eval idv catch total num of agent3: 30
team_policy eval team catch total num: 35
team_policy eval average step individual rewards of agent4: 0.58082871362532
team_policy eval average team episode rewards of agent4: 87.5
team_policy eval idv catch total num of agent4: 25
team_policy eval team catch total num: 35
idv_policy eval average step individual rewards of agent0: 0.5653516621046067
idv_policy eval average team episode rewards of agent0: 80.0
idv_policy eval idv catch total num of agent0: 24
idv_policy eval team catch total num: 32
idv_policy eval average step individual rewards of agent1: 0.6603982399126516
idv_policy eval average team episode rewards of agent1: 80.0
idv_policy eval idv catch total num of agent1: 28
idv_policy eval team catch total num: 32
idv_policy eval average step individual rewards of agent2: 0.5237101340067547
idv_policy eval average team episode rewards of agent2: 80.0
idv_policy eval idv catch total num of agent2: 23
idv_policy eval team catch total num: 32
idv_policy eval average step individual rewards of agent3: 0.6094158713014977
idv_policy eval average team episode rewards of agent3: 80.0
idv_policy eval idv catch total num of agent3: 26
idv_policy eval team catch total num: 32
idv_policy eval average step individual rewards of agent4: 0.6744041744159608
idv_policy eval average team episode rewards of agent4: 80.0
idv_policy eval idv catch total num of agent4: 29
idv_policy eval team catch total num: 32

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8626/10000 episodes, total num timesteps 1725400/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8627/10000 episodes, total num timesteps 1725600/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8628/10000 episodes, total num timesteps 1725800/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8629/10000 episodes, total num timesteps 1726000/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8630/10000 episodes, total num timesteps 1726200/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8631/10000 episodes, total num timesteps 1726400/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8632/10000 episodes, total num timesteps 1726600/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8633/10000 episodes, total num timesteps 1726800/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8634/10000 episodes, total num timesteps 1727000/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8635/10000 episodes, total num timesteps 1727200/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8636/10000 episodes, total num timesteps 1727400/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8637/10000 episodes, total num timesteps 1727600/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8638/10000 episodes, total num timesteps 1727800/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8639/10000 episodes, total num timesteps 1728000/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8640/10000 episodes, total num timesteps 1728200/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8641/10000 episodes, total num timesteps 1728400/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8642/10000 episodes, total num timesteps 1728600/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8643/10000 episodes, total num timesteps 1728800/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8644/10000 episodes, total num timesteps 1729000/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8645/10000 episodes, total num timesteps 1729200/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8646/10000 episodes, total num timesteps 1729400/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8647/10000 episodes, total num timesteps 1729600/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8648/10000 episodes, total num timesteps 1729800/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8649/10000 episodes, total num timesteps 1730000/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8650/10000 episodes, total num timesteps 1730200/2000000, FPS 262.

team_policy eval average step individual rewards of agent0: 0.4601876960722682
team_policy eval average team episode rewards of agent0: 95.0
team_policy eval idv catch total num of agent0: 20
team_policy eval team catch total num: 38
team_policy eval average step individual rewards of agent1: 0.958728804405506
team_policy eval average team episode rewards of agent1: 95.0
team_policy eval idv catch total num of agent1: 40
team_policy eval team catch total num: 38
team_policy eval average step individual rewards of agent2: 0.8876644109057285
team_policy eval average team episode rewards of agent2: 95.0
team_policy eval idv catch total num of agent2: 37
team_policy eval team catch total num: 38
team_policy eval average step individual rewards of agent3: 1.08984115594337
team_policy eval average team episode rewards of agent3: 95.0
team_policy eval idv catch total num of agent3: 45
team_policy eval team catch total num: 38
team_policy eval average step individual rewards of agent4: 0.4358571286989414
team_policy eval average team episode rewards of agent4: 95.0
team_policy eval idv catch total num of agent4: 19
team_policy eval team catch total num: 38
idv_policy eval average step individual rewards of agent0: 0.4856505686273283
idv_policy eval average team episode rewards of agent0: 82.5
idv_policy eval idv catch total num of agent0: 21
idv_policy eval team catch total num: 33
idv_policy eval average step individual rewards of agent1: 0.7661087432735075
idv_policy eval average team episode rewards of agent1: 82.5
idv_policy eval idv catch total num of agent1: 32
idv_policy eval team catch total num: 33
idv_policy eval average step individual rewards of agent2: 0.8687735894629156
idv_policy eval average team episode rewards of agent2: 82.5
idv_policy eval idv catch total num of agent2: 36
idv_policy eval team catch total num: 33
idv_policy eval average step individual rewards of agent3: 0.6419640816599326
idv_policy eval average team episode rewards of agent3: 82.5
idv_policy eval idv catch total num of agent3: 27
idv_policy eval team catch total num: 33
idv_policy eval average step individual rewards of agent4: 0.5600009644793517
idv_policy eval average team episode rewards of agent4: 82.5
idv_policy eval idv catch total num of agent4: 24
idv_policy eval team catch total num: 33

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8651/10000 episodes, total num timesteps 1730400/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8652/10000 episodes, total num timesteps 1730600/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8653/10000 episodes, total num timesteps 1730800/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8654/10000 episodes, total num timesteps 1731000/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8655/10000 episodes, total num timesteps 1731200/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8656/10000 episodes, total num timesteps 1731400/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8657/10000 episodes, total num timesteps 1731600/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8658/10000 episodes, total num timesteps 1731800/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8659/10000 episodes, total num timesteps 1732000/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8660/10000 episodes, total num timesteps 1732200/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8661/10000 episodes, total num timesteps 1732400/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8662/10000 episodes, total num timesteps 1732600/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8663/10000 episodes, total num timesteps 1732800/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8664/10000 episodes, total num timesteps 1733000/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8665/10000 episodes, total num timesteps 1733200/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8666/10000 episodes, total num timesteps 1733400/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8667/10000 episodes, total num timesteps 1733600/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8668/10000 episodes, total num timesteps 1733800/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8669/10000 episodes, total num timesteps 1734000/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8670/10000 episodes, total num timesteps 1734200/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8671/10000 episodes, total num timesteps 1734400/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8672/10000 episodes, total num timesteps 1734600/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8673/10000 episodes, total num timesteps 1734800/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8674/10000 episodes, total num timesteps 1735000/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8675/10000 episodes, total num timesteps 1735200/2000000, FPS 262.

team_policy eval average step individual rewards of agent0: 0.661819881587884
team_policy eval average team episode rewards of agent0: 95.0
team_policy eval idv catch total num of agent0: 28
team_policy eval team catch total num: 38
team_policy eval average step individual rewards of agent1: 1.006802028531083
team_policy eval average team episode rewards of agent1: 95.0
team_policy eval idv catch total num of agent1: 42
team_policy eval team catch total num: 38
team_policy eval average step individual rewards of agent2: 0.39365645915781
team_policy eval average team episode rewards of agent2: 95.0
team_policy eval idv catch total num of agent2: 18
team_policy eval team catch total num: 38
team_policy eval average step individual rewards of agent3: 0.23800840644124338
team_policy eval average team episode rewards of agent3: 95.0
team_policy eval idv catch total num of agent3: 12
team_policy eval team catch total num: 38
team_policy eval average step individual rewards of agent4: 0.9198873876368161
team_policy eval average team episode rewards of agent4: 95.0
team_policy eval idv catch total num of agent4: 38
team_policy eval team catch total num: 38
idv_policy eval average step individual rewards of agent0: 0.7109456163104102
idv_policy eval average team episode rewards of agent0: 82.5
idv_policy eval idv catch total num of agent0: 30
idv_policy eval team catch total num: 33
idv_policy eval average step individual rewards of agent1: 0.7268823241549556
idv_policy eval average team episode rewards of agent1: 82.5
idv_policy eval idv catch total num of agent1: 31
idv_policy eval team catch total num: 33
idv_policy eval average step individual rewards of agent2: 0.3286164943591997
idv_policy eval average team episode rewards of agent2: 82.5
idv_policy eval idv catch total num of agent2: 15
idv_policy eval team catch total num: 33
idv_policy eval average step individual rewards of agent3: 0.7878390094636836
idv_policy eval average team episode rewards of agent3: 82.5
idv_policy eval idv catch total num of agent3: 33
idv_policy eval team catch total num: 33
idv_policy eval average step individual rewards of agent4: 0.2294727456736055
idv_policy eval average team episode rewards of agent4: 82.5
idv_policy eval idv catch total num of agent4: 11
idv_policy eval team catch total num: 33

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8676/10000 episodes, total num timesteps 1735400/2000000, FPS 262.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8677/10000 episodes, total num timesteps 1735600/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8678/10000 episodes, total num timesteps 1735800/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8679/10000 episodes, total num timesteps 1736000/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8680/10000 episodes, total num timesteps 1736200/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8681/10000 episodes, total num timesteps 1736400/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8682/10000 episodes, total num timesteps 1736600/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8683/10000 episodes, total num timesteps 1736800/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8684/10000 episodes, total num timesteps 1737000/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8685/10000 episodes, total num timesteps 1737200/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8686/10000 episodes, total num timesteps 1737400/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8687/10000 episodes, total num timesteps 1737600/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8688/10000 episodes, total num timesteps 1737800/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8689/10000 episodes, total num timesteps 1738000/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8690/10000 episodes, total num timesteps 1738200/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8691/10000 episodes, total num timesteps 1738400/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8692/10000 episodes, total num timesteps 1738600/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8693/10000 episodes, total num timesteps 1738800/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8694/10000 episodes, total num timesteps 1739000/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8695/10000 episodes, total num timesteps 1739200/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8696/10000 episodes, total num timesteps 1739400/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8697/10000 episodes, total num timesteps 1739600/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8698/10000 episodes, total num timesteps 1739800/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8699/10000 episodes, total num timesteps 1740000/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8700/10000 episodes, total num timesteps 1740200/2000000, FPS 261.

team_policy eval average step individual rewards of agent0: 0.6795848871499104
team_policy eval average team episode rewards of agent0: 67.5
team_policy eval idv catch total num of agent0: 29
team_policy eval team catch total num: 27
team_policy eval average step individual rewards of agent1: 0.586978901048992
team_policy eval average team episode rewards of agent1: 67.5
team_policy eval idv catch total num of agent1: 25
team_policy eval team catch total num: 27
team_policy eval average step individual rewards of agent2: 0.1672431068500874
team_policy eval average team episode rewards of agent2: 67.5
team_policy eval idv catch total num of agent2: 9
team_policy eval team catch total num: 27
team_policy eval average step individual rewards of agent3: 0.5551506994556785
team_policy eval average team episode rewards of agent3: 67.5
team_policy eval idv catch total num of agent3: 24
team_policy eval team catch total num: 27
team_policy eval average step individual rewards of agent4: 0.7590856010379692
team_policy eval average team episode rewards of agent4: 67.5
team_policy eval idv catch total num of agent4: 32
team_policy eval team catch total num: 27
idv_policy eval average step individual rewards of agent0: 1.1071755316741874
idv_policy eval average team episode rewards of agent0: 142.5
idv_policy eval idv catch total num of agent0: 46
idv_policy eval team catch total num: 57
idv_policy eval average step individual rewards of agent1: 0.6421411106078775
idv_policy eval average team episode rewards of agent1: 142.5
idv_policy eval idv catch total num of agent1: 28
idv_policy eval team catch total num: 57
idv_policy eval average step individual rewards of agent2: 0.7291162453151837
idv_policy eval average team episode rewards of agent2: 142.5
idv_policy eval idv catch total num of agent2: 31
idv_policy eval team catch total num: 57
idv_policy eval average step individual rewards of agent3: 0.9412114180360885
idv_policy eval average team episode rewards of agent3: 142.5
idv_policy eval idv catch total num of agent3: 39
idv_policy eval team catch total num: 57
idv_policy eval average step individual rewards of agent4: 0.9862675154383905
idv_policy eval average team episode rewards of agent4: 142.5
idv_policy eval idv catch total num of agent4: 41
idv_policy eval team catch total num: 57

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8701/10000 episodes, total num timesteps 1740400/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8702/10000 episodes, total num timesteps 1740600/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8703/10000 episodes, total num timesteps 1740800/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8704/10000 episodes, total num timesteps 1741000/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8705/10000 episodes, total num timesteps 1741200/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8706/10000 episodes, total num timesteps 1741400/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8707/10000 episodes, total num timesteps 1741600/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8708/10000 episodes, total num timesteps 1741800/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8709/10000 episodes, total num timesteps 1742000/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8710/10000 episodes, total num timesteps 1742200/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8711/10000 episodes, total num timesteps 1742400/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8712/10000 episodes, total num timesteps 1742600/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8713/10000 episodes, total num timesteps 1742800/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8714/10000 episodes, total num timesteps 1743000/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8715/10000 episodes, total num timesteps 1743200/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8716/10000 episodes, total num timesteps 1743400/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8717/10000 episodes, total num timesteps 1743600/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8718/10000 episodes, total num timesteps 1743800/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8719/10000 episodes, total num timesteps 1744000/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8720/10000 episodes, total num timesteps 1744200/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8721/10000 episodes, total num timesteps 1744400/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8722/10000 episodes, total num timesteps 1744600/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8723/10000 episodes, total num timesteps 1744800/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8724/10000 episodes, total num timesteps 1745000/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8725/10000 episodes, total num timesteps 1745200/2000000, FPS 261.

team_policy eval average step individual rewards of agent0: 1.117022411847648
team_policy eval average team episode rewards of agent0: 135.0
team_policy eval idv catch total num of agent0: 46
team_policy eval team catch total num: 54
team_policy eval average step individual rewards of agent1: 0.7064901147816659
team_policy eval average team episode rewards of agent1: 135.0
team_policy eval idv catch total num of agent1: 30
team_policy eval team catch total num: 54
team_policy eval average step individual rewards of agent2: 1.0364794677653004
team_policy eval average team episode rewards of agent2: 135.0
team_policy eval idv catch total num of agent2: 43
team_policy eval team catch total num: 54
team_policy eval average step individual rewards of agent3: 0.9383127595814043
team_policy eval average team episode rewards of agent3: 135.0
team_policy eval idv catch total num of agent3: 39
team_policy eval team catch total num: 54
team_policy eval average step individual rewards of agent4: 0.6552148826600183
team_policy eval average team episode rewards of agent4: 135.0
team_policy eval idv catch total num of agent4: 28
team_policy eval team catch total num: 54
idv_policy eval average step individual rewards of agent0: 0.7626773690618412
idv_policy eval average team episode rewards of agent0: 72.5
idv_policy eval idv catch total num of agent0: 32
idv_policy eval team catch total num: 29
idv_policy eval average step individual rewards of agent1: 0.4549856394135283
idv_policy eval average team episode rewards of agent1: 72.5
idv_policy eval idv catch total num of agent1: 20
idv_policy eval team catch total num: 29
idv_policy eval average step individual rewards of agent2: 0.14394050894823107
idv_policy eval average team episode rewards of agent2: 72.5
idv_policy eval idv catch total num of agent2: 8
idv_policy eval team catch total num: 29
idv_policy eval average step individual rewards of agent3: 0.31757906483725995
idv_policy eval average team episode rewards of agent3: 72.5
idv_policy eval idv catch total num of agent3: 15
idv_policy eval team catch total num: 29
idv_policy eval average step individual rewards of agent4: 0.633253966796751
idv_policy eval average team episode rewards of agent4: 72.5
idv_policy eval idv catch total num of agent4: 27
idv_policy eval team catch total num: 29

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8726/10000 episodes, total num timesteps 1745400/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8727/10000 episodes, total num timesteps 1745600/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8728/10000 episodes, total num timesteps 1745800/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8729/10000 episodes, total num timesteps 1746000/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8730/10000 episodes, total num timesteps 1746200/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8731/10000 episodes, total num timesteps 1746400/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8732/10000 episodes, total num timesteps 1746600/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8733/10000 episodes, total num timesteps 1746800/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8734/10000 episodes, total num timesteps 1747000/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8735/10000 episodes, total num timesteps 1747200/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8736/10000 episodes, total num timesteps 1747400/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8737/10000 episodes, total num timesteps 1747600/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8738/10000 episodes, total num timesteps 1747800/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8739/10000 episodes, total num timesteps 1748000/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8740/10000 episodes, total num timesteps 1748200/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8741/10000 episodes, total num timesteps 1748400/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8742/10000 episodes, total num timesteps 1748600/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8743/10000 episodes, total num timesteps 1748800/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8744/10000 episodes, total num timesteps 1749000/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8745/10000 episodes, total num timesteps 1749200/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8746/10000 episodes, total num timesteps 1749400/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8747/10000 episodes, total num timesteps 1749600/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8748/10000 episodes, total num timesteps 1749800/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8749/10000 episodes, total num timesteps 1750000/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8750/10000 episodes, total num timesteps 1750200/2000000, FPS 261.

team_policy eval average step individual rewards of agent0: 0.6580190616786694
team_policy eval average team episode rewards of agent0: 67.5
team_policy eval idv catch total num of agent0: 28
team_policy eval team catch total num: 27
team_policy eval average step individual rewards of agent1: 0.780538927272505
team_policy eval average team episode rewards of agent1: 67.5
team_policy eval idv catch total num of agent1: 33
team_policy eval team catch total num: 27
team_policy eval average step individual rewards of agent2: 0.5798528276358537
team_policy eval average team episode rewards of agent2: 67.5
team_policy eval idv catch total num of agent2: 25
team_policy eval team catch total num: 27
team_policy eval average step individual rewards of agent3: 0.3534143827979043
team_policy eval average team episode rewards of agent3: 67.5
team_policy eval idv catch total num of agent3: 16
team_policy eval team catch total num: 27
team_policy eval average step individual rewards of agent4: 0.21152013859215196
team_policy eval average team episode rewards of agent4: 67.5
team_policy eval idv catch total num of agent4: 11
team_policy eval team catch total num: 27
idv_policy eval average step individual rewards of agent0: 0.7333568494064706
idv_policy eval average team episode rewards of agent0: 102.5
idv_policy eval idv catch total num of agent0: 31
idv_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent1: 0.4296995174349438
idv_policy eval average team episode rewards of agent1: 102.5
idv_policy eval idv catch total num of agent1: 19
idv_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent2: 0.7123058867369331
idv_policy eval average team episode rewards of agent2: 102.5
idv_policy eval idv catch total num of agent2: 30
idv_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent3: 1.061242901677962
idv_policy eval average team episode rewards of agent3: 102.5
idv_policy eval idv catch total num of agent3: 44
idv_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent4: 0.9146608544466567
idv_policy eval average team episode rewards of agent4: 102.5
idv_policy eval idv catch total num of agent4: 38
idv_policy eval team catch total num: 41

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8751/10000 episodes, total num timesteps 1750400/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8752/10000 episodes, total num timesteps 1750600/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8753/10000 episodes, total num timesteps 1750800/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8754/10000 episodes, total num timesteps 1751000/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8755/10000 episodes, total num timesteps 1751200/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8756/10000 episodes, total num timesteps 1751400/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8757/10000 episodes, total num timesteps 1751600/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8758/10000 episodes, total num timesteps 1751800/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8759/10000 episodes, total num timesteps 1752000/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8760/10000 episodes, total num timesteps 1752200/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8761/10000 episodes, total num timesteps 1752400/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8762/10000 episodes, total num timesteps 1752600/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8763/10000 episodes, total num timesteps 1752800/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8764/10000 episodes, total num timesteps 1753000/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8765/10000 episodes, total num timesteps 1753200/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8766/10000 episodes, total num timesteps 1753400/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8767/10000 episodes, total num timesteps 1753600/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8768/10000 episodes, total num timesteps 1753800/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8769/10000 episodes, total num timesteps 1754000/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8770/10000 episodes, total num timesteps 1754200/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8771/10000 episodes, total num timesteps 1754400/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8772/10000 episodes, total num timesteps 1754600/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8773/10000 episodes, total num timesteps 1754800/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8774/10000 episodes, total num timesteps 1755000/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8775/10000 episodes, total num timesteps 1755200/2000000, FPS 261.

team_policy eval average step individual rewards of agent0: 0.7612437592394568
team_policy eval average team episode rewards of agent0: 92.5
team_policy eval idv catch total num of agent0: 32
team_policy eval team catch total num: 37
team_policy eval average step individual rewards of agent1: 0.9147574241561114
team_policy eval average team episode rewards of agent1: 92.5
team_policy eval idv catch total num of agent1: 38
team_policy eval team catch total num: 37
team_policy eval average step individual rewards of agent2: 0.7145450305920847
team_policy eval average team episode rewards of agent2: 92.5
team_policy eval idv catch total num of agent2: 30
team_policy eval team catch total num: 37
team_policy eval average step individual rewards of agent3: 0.3116495941205403
team_policy eval average team episode rewards of agent3: 92.5
team_policy eval idv catch total num of agent3: 14
team_policy eval team catch total num: 37
team_policy eval average step individual rewards of agent4: 0.35483296036261863
team_policy eval average team episode rewards of agent4: 92.5
team_policy eval idv catch total num of agent4: 16
team_policy eval team catch total num: 37
idv_policy eval average step individual rewards of agent0: 0.6878445637502244
idv_policy eval average team episode rewards of agent0: 72.5
idv_policy eval idv catch total num of agent0: 30
idv_policy eval team catch total num: 29
idv_policy eval average step individual rewards of agent1: 0.531956757689134
idv_policy eval average team episode rewards of agent1: 72.5
idv_policy eval idv catch total num of agent1: 23
idv_policy eval team catch total num: 29
idv_policy eval average step individual rewards of agent2: 0.4154413991946576
idv_policy eval average team episode rewards of agent2: 72.5
idv_policy eval idv catch total num of agent2: 19
idv_policy eval team catch total num: 29
idv_policy eval average step individual rewards of agent3: 0.33727019340039016
idv_policy eval average team episode rewards of agent3: 72.5
idv_policy eval idv catch total num of agent3: 16
idv_policy eval team catch total num: 29
idv_policy eval average step individual rewards of agent4: 0.7396742684725539
idv_policy eval average team episode rewards of agent4: 72.5
idv_policy eval idv catch total num of agent4: 31
idv_policy eval team catch total num: 29

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8776/10000 episodes, total num timesteps 1755400/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8777/10000 episodes, total num timesteps 1755600/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8778/10000 episodes, total num timesteps 1755800/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8779/10000 episodes, total num timesteps 1756000/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8780/10000 episodes, total num timesteps 1756200/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8781/10000 episodes, total num timesteps 1756400/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8782/10000 episodes, total num timesteps 1756600/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8783/10000 episodes, total num timesteps 1756800/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8784/10000 episodes, total num timesteps 1757000/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8785/10000 episodes, total num timesteps 1757200/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8786/10000 episodes, total num timesteps 1757400/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8787/10000 episodes, total num timesteps 1757600/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8788/10000 episodes, total num timesteps 1757800/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8789/10000 episodes, total num timesteps 1758000/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8790/10000 episodes, total num timesteps 1758200/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8791/10000 episodes, total num timesteps 1758400/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8792/10000 episodes, total num timesteps 1758600/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8793/10000 episodes, total num timesteps 1758800/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8794/10000 episodes, total num timesteps 1759000/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8795/10000 episodes, total num timesteps 1759200/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8796/10000 episodes, total num timesteps 1759400/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8797/10000 episodes, total num timesteps 1759600/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8798/10000 episodes, total num timesteps 1759800/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8799/10000 episodes, total num timesteps 1760000/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8800/10000 episodes, total num timesteps 1760200/2000000, FPS 261.

team_policy eval average step individual rewards of agent0: 0.654780182771571
team_policy eval average team episode rewards of agent0: 87.5
team_policy eval idv catch total num of agent0: 28
team_policy eval team catch total num: 35
team_policy eval average step individual rewards of agent1: 0.37330088562662894
team_policy eval average team episode rewards of agent1: 87.5
team_policy eval idv catch total num of agent1: 17
team_policy eval team catch total num: 35
team_policy eval average step individual rewards of agent2: 0.7576840993491207
team_policy eval average team episode rewards of agent2: 87.5
team_policy eval idv catch total num of agent2: 32
team_policy eval team catch total num: 35
team_policy eval average step individual rewards of agent3: 0.3284440289096375
team_policy eval average team episode rewards of agent3: 87.5
team_policy eval idv catch total num of agent3: 15
team_policy eval team catch total num: 35
team_policy eval average step individual rewards of agent4: 0.8623625234056209
team_policy eval average team episode rewards of agent4: 87.5
team_policy eval idv catch total num of agent4: 36
team_policy eval team catch total num: 35
idv_policy eval average step individual rewards of agent0: 0.29604874142196647
idv_policy eval average team episode rewards of agent0: 55.0
idv_policy eval idv catch total num of agent0: 14
idv_policy eval team catch total num: 22
idv_policy eval average step individual rewards of agent1: 0.49701886755533914
idv_policy eval average team episode rewards of agent1: 55.0
idv_policy eval idv catch total num of agent1: 22
idv_policy eval team catch total num: 22
idv_policy eval average step individual rewards of agent2: 0.5731661074739238
idv_policy eval average team episode rewards of agent2: 55.0
idv_policy eval idv catch total num of agent2: 25
idv_policy eval team catch total num: 22
idv_policy eval average step individual rewards of agent3: 0.3257910537291098
idv_policy eval average team episode rewards of agent3: 55.0
idv_policy eval idv catch total num of agent3: 15
idv_policy eval team catch total num: 22
idv_policy eval average step individual rewards of agent4: 0.6686505705098728
idv_policy eval average team episode rewards of agent4: 55.0
idv_policy eval idv catch total num of agent4: 28
idv_policy eval team catch total num: 22

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8801/10000 episodes, total num timesteps 1760400/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8802/10000 episodes, total num timesteps 1760600/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8803/10000 episodes, total num timesteps 1760800/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8804/10000 episodes, total num timesteps 1761000/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8805/10000 episodes, total num timesteps 1761200/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8806/10000 episodes, total num timesteps 1761400/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8807/10000 episodes, total num timesteps 1761600/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8808/10000 episodes, total num timesteps 1761800/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8809/10000 episodes, total num timesteps 1762000/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8810/10000 episodes, total num timesteps 1762200/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8811/10000 episodes, total num timesteps 1762400/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8812/10000 episodes, total num timesteps 1762600/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8813/10000 episodes, total num timesteps 1762800/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8814/10000 episodes, total num timesteps 1763000/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8815/10000 episodes, total num timesteps 1763200/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8816/10000 episodes, total num timesteps 1763400/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8817/10000 episodes, total num timesteps 1763600/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8818/10000 episodes, total num timesteps 1763800/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8819/10000 episodes, total num timesteps 1764000/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8820/10000 episodes, total num timesteps 1764200/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8821/10000 episodes, total num timesteps 1764400/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8822/10000 episodes, total num timesteps 1764600/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8823/10000 episodes, total num timesteps 1764800/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8824/10000 episodes, total num timesteps 1765000/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8825/10000 episodes, total num timesteps 1765200/2000000, FPS 261.

team_policy eval average step individual rewards of agent0: 0.4702920896259238
team_policy eval average team episode rewards of agent0: 72.5
team_policy eval idv catch total num of agent0: 21
team_policy eval team catch total num: 29
team_policy eval average step individual rewards of agent1: 0.22324653021913235
team_policy eval average team episode rewards of agent1: 72.5
team_policy eval idv catch total num of agent1: 11
team_policy eval team catch total num: 29
team_policy eval average step individual rewards of agent2: 0.7371405413063539
team_policy eval average team episode rewards of agent2: 72.5
team_policy eval idv catch total num of agent2: 31
team_policy eval team catch total num: 29
team_policy eval average step individual rewards of agent3: 0.44300846322151577
team_policy eval average team episode rewards of agent3: 72.5
team_policy eval idv catch total num of agent3: 20
team_policy eval team catch total num: 29
team_policy eval average step individual rewards of agent4: 0.6519199006422517
team_policy eval average team episode rewards of agent4: 72.5
team_policy eval idv catch total num of agent4: 28
team_policy eval team catch total num: 29
idv_policy eval average step individual rewards of agent0: 0.5044733487002985
idv_policy eval average team episode rewards of agent0: 90.0
idv_policy eval idv catch total num of agent0: 22
idv_policy eval team catch total num: 36
idv_policy eval average step individual rewards of agent1: 0.884075950073375
idv_policy eval average team episode rewards of agent1: 90.0
idv_policy eval idv catch total num of agent1: 37
idv_policy eval team catch total num: 36
idv_policy eval average step individual rewards of agent2: 0.32476399333573297
idv_policy eval average team episode rewards of agent2: 90.0
idv_policy eval idv catch total num of agent2: 15
idv_policy eval team catch total num: 36
idv_policy eval average step individual rewards of agent3: 0.9368973137664233
idv_policy eval average team episode rewards of agent3: 90.0
idv_policy eval idv catch total num of agent3: 39
idv_policy eval team catch total num: 36
idv_policy eval average step individual rewards of agent4: 0.4809947612336377
idv_policy eval average team episode rewards of agent4: 90.0
idv_policy eval idv catch total num of agent4: 21
idv_policy eval team catch total num: 36

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8826/10000 episodes, total num timesteps 1765400/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8827/10000 episodes, total num timesteps 1765600/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8828/10000 episodes, total num timesteps 1765800/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8829/10000 episodes, total num timesteps 1766000/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8830/10000 episodes, total num timesteps 1766200/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8831/10000 episodes, total num timesteps 1766400/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8832/10000 episodes, total num timesteps 1766600/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8833/10000 episodes, total num timesteps 1766800/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8834/10000 episodes, total num timesteps 1767000/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8835/10000 episodes, total num timesteps 1767200/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8836/10000 episodes, total num timesteps 1767400/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8837/10000 episodes, total num timesteps 1767600/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8838/10000 episodes, total num timesteps 1767800/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8839/10000 episodes, total num timesteps 1768000/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8840/10000 episodes, total num timesteps 1768200/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8841/10000 episodes, total num timesteps 1768400/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8842/10000 episodes, total num timesteps 1768600/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8843/10000 episodes, total num timesteps 1768800/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8844/10000 episodes, total num timesteps 1769000/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8845/10000 episodes, total num timesteps 1769200/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8846/10000 episodes, total num timesteps 1769400/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8847/10000 episodes, total num timesteps 1769600/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8848/10000 episodes, total num timesteps 1769800/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8849/10000 episodes, total num timesteps 1770000/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8850/10000 episodes, total num timesteps 1770200/2000000, FPS 261.

team_policy eval average step individual rewards of agent0: 0.6346188784386646
team_policy eval average team episode rewards of agent0: 47.5
team_policy eval idv catch total num of agent0: 27
team_policy eval team catch total num: 19
team_policy eval average step individual rewards of agent1: 0.4905240169509508
team_policy eval average team episode rewards of agent1: 47.5
team_policy eval idv catch total num of agent1: 21
team_policy eval team catch total num: 19
team_policy eval average step individual rewards of agent2: 0.8132348229548874
team_policy eval average team episode rewards of agent2: 47.5
team_policy eval idv catch total num of agent2: 34
team_policy eval team catch total num: 19
team_policy eval average step individual rewards of agent3: 0.8600683593745376
team_policy eval average team episode rewards of agent3: 47.5
team_policy eval idv catch total num of agent3: 36
team_policy eval team catch total num: 19
team_policy eval average step individual rewards of agent4: 0.1375943401079287
team_policy eval average team episode rewards of agent4: 47.5
team_policy eval idv catch total num of agent4: 8
team_policy eval team catch total num: 19
idv_policy eval average step individual rewards of agent0: 0.37431483926347153
idv_policy eval average team episode rewards of agent0: 60.0
idv_policy eval idv catch total num of agent0: 17
idv_policy eval team catch total num: 24
idv_policy eval average step individual rewards of agent1: 0.8714445395559501
idv_policy eval average team episode rewards of agent1: 60.0
idv_policy eval idv catch total num of agent1: 36
idv_policy eval team catch total num: 24
idv_policy eval average step individual rewards of agent2: 0.3949954098564936
idv_policy eval average team episode rewards of agent2: 60.0
idv_policy eval idv catch total num of agent2: 18
idv_policy eval team catch total num: 24
idv_policy eval average step individual rewards of agent3: 0.8481547985808854
idv_policy eval average team episode rewards of agent3: 60.0
idv_policy eval idv catch total num of agent3: 35
idv_policy eval team catch total num: 24
idv_policy eval average step individual rewards of agent4: 0.2747712599742038
idv_policy eval average team episode rewards of agent4: 60.0
idv_policy eval idv catch total num of agent4: 13
idv_policy eval team catch total num: 24

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8851/10000 episodes, total num timesteps 1770400/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8852/10000 episodes, total num timesteps 1770600/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8853/10000 episodes, total num timesteps 1770800/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8854/10000 episodes, total num timesteps 1771000/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8855/10000 episodes, total num timesteps 1771200/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8856/10000 episodes, total num timesteps 1771400/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8857/10000 episodes, total num timesteps 1771600/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8858/10000 episodes, total num timesteps 1771800/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8859/10000 episodes, total num timesteps 1772000/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8860/10000 episodes, total num timesteps 1772200/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8861/10000 episodes, total num timesteps 1772400/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8862/10000 episodes, total num timesteps 1772600/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8863/10000 episodes, total num timesteps 1772800/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8864/10000 episodes, total num timesteps 1773000/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8865/10000 episodes, total num timesteps 1773200/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8866/10000 episodes, total num timesteps 1773400/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8867/10000 episodes, total num timesteps 1773600/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8868/10000 episodes, total num timesteps 1773800/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8869/10000 episodes, total num timesteps 1774000/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8870/10000 episodes, total num timesteps 1774200/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8871/10000 episodes, total num timesteps 1774400/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8872/10000 episodes, total num timesteps 1774600/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8873/10000 episodes, total num timesteps 1774800/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8874/10000 episodes, total num timesteps 1775000/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8875/10000 episodes, total num timesteps 1775200/2000000, FPS 261.

team_policy eval average step individual rewards of agent0: 0.3575251445790614
team_policy eval average team episode rewards of agent0: 60.0
team_policy eval idv catch total num of agent0: 16
team_policy eval team catch total num: 24
team_policy eval average step individual rewards of agent1: 0.5366669121784462
team_policy eval average team episode rewards of agent1: 60.0
team_policy eval idv catch total num of agent1: 23
team_policy eval team catch total num: 24
team_policy eval average step individual rewards of agent2: 0.656341703432072
team_policy eval average team episode rewards of agent2: 60.0
team_policy eval idv catch total num of agent2: 28
team_policy eval team catch total num: 24
team_policy eval average step individual rewards of agent3: 0.4635494615991443
team_policy eval average team episode rewards of agent3: 60.0
team_policy eval idv catch total num of agent3: 21
team_policy eval team catch total num: 24
team_policy eval average step individual rewards of agent4: 0.5027308466532034
team_policy eval average team episode rewards of agent4: 60.0
team_policy eval idv catch total num of agent4: 22
team_policy eval team catch total num: 24
idv_policy eval average step individual rewards of agent0: 0.9191404816101675
idv_policy eval average team episode rewards of agent0: 110.0
idv_policy eval idv catch total num of agent0: 38
idv_policy eval team catch total num: 44
idv_policy eval average step individual rewards of agent1: 0.545735061920774
idv_policy eval average team episode rewards of agent1: 110.0
idv_policy eval idv catch total num of agent1: 23
idv_policy eval team catch total num: 44
idv_policy eval average step individual rewards of agent2: 0.6601156046707493
idv_policy eval average team episode rewards of agent2: 110.0
idv_policy eval idv catch total num of agent2: 28
idv_policy eval team catch total num: 44
idv_policy eval average step individual rewards of agent3: 1.0426331010243208
idv_policy eval average team episode rewards of agent3: 110.0
idv_policy eval idv catch total num of agent3: 43
idv_policy eval team catch total num: 44
idv_policy eval average step individual rewards of agent4: 0.8688966757378664
idv_policy eval average team episode rewards of agent4: 110.0
idv_policy eval idv catch total num of agent4: 36
idv_policy eval team catch total num: 44

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8876/10000 episodes, total num timesteps 1775400/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8877/10000 episodes, total num timesteps 1775600/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8878/10000 episodes, total num timesteps 1775800/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8879/10000 episodes, total num timesteps 1776000/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8880/10000 episodes, total num timesteps 1776200/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8881/10000 episodes, total num timesteps 1776400/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8882/10000 episodes, total num timesteps 1776600/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8883/10000 episodes, total num timesteps 1776800/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8884/10000 episodes, total num timesteps 1777000/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8885/10000 episodes, total num timesteps 1777200/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8886/10000 episodes, total num timesteps 1777400/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8887/10000 episodes, total num timesteps 1777600/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8888/10000 episodes, total num timesteps 1777800/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8889/10000 episodes, total num timesteps 1778000/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8890/10000 episodes, total num timesteps 1778200/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8891/10000 episodes, total num timesteps 1778400/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8892/10000 episodes, total num timesteps 1778600/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8893/10000 episodes, total num timesteps 1778800/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8894/10000 episodes, total num timesteps 1779000/2000000, FPS 261.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8895/10000 episodes, total num timesteps 1779200/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8896/10000 episodes, total num timesteps 1779400/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8897/10000 episodes, total num timesteps 1779600/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8898/10000 episodes, total num timesteps 1779800/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8899/10000 episodes, total num timesteps 1780000/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8900/10000 episodes, total num timesteps 1780200/2000000, FPS 260.

team_policy eval average step individual rewards of agent0: 0.717503794584998
team_policy eval average team episode rewards of agent0: 67.5
team_policy eval idv catch total num of agent0: 30
team_policy eval team catch total num: 27
team_policy eval average step individual rewards of agent1: 1.0096598624128454
team_policy eval average team episode rewards of agent1: 67.5
team_policy eval idv catch total num of agent1: 42
team_policy eval team catch total num: 27
team_policy eval average step individual rewards of agent2: 0.31016688932397407
team_policy eval average team episode rewards of agent2: 67.5
team_policy eval idv catch total num of agent2: 15
team_policy eval team catch total num: 27
team_policy eval average step individual rewards of agent3: 0.20059829601007992
team_policy eval average team episode rewards of agent3: 67.5
team_policy eval idv catch total num of agent3: 10
team_policy eval team catch total num: 27
team_policy eval average step individual rewards of agent4: 0.26996225378592575
team_policy eval average team episode rewards of agent4: 67.5
team_policy eval idv catch total num of agent4: 13
team_policy eval team catch total num: 27
idv_policy eval average step individual rewards of agent0: 0.6779400904826671
idv_policy eval average team episode rewards of agent0: 50.0
idv_policy eval idv catch total num of agent0: 29
idv_policy eval team catch total num: 20
idv_policy eval average step individual rewards of agent1: 0.37603416375090887
idv_policy eval average team episode rewards of agent1: 50.0
idv_policy eval idv catch total num of agent1: 17
idv_policy eval team catch total num: 20
idv_policy eval average step individual rewards of agent2: 0.42227331312944016
idv_policy eval average team episode rewards of agent2: 50.0
idv_policy eval idv catch total num of agent2: 19
idv_policy eval team catch total num: 20
idv_policy eval average step individual rewards of agent3: 0.35756432931199433
idv_policy eval average team episode rewards of agent3: 50.0
idv_policy eval idv catch total num of agent3: 16
idv_policy eval team catch total num: 20
idv_policy eval average step individual rewards of agent4: 0.610397207433346
idv_policy eval average team episode rewards of agent4: 50.0
idv_policy eval idv catch total num of agent4: 26
idv_policy eval team catch total num: 20

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8901/10000 episodes, total num timesteps 1780400/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8902/10000 episodes, total num timesteps 1780600/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8903/10000 episodes, total num timesteps 1780800/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8904/10000 episodes, total num timesteps 1781000/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8905/10000 episodes, total num timesteps 1781200/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8906/10000 episodes, total num timesteps 1781400/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8907/10000 episodes, total num timesteps 1781600/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8908/10000 episodes, total num timesteps 1781800/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8909/10000 episodes, total num timesteps 1782000/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8910/10000 episodes, total num timesteps 1782200/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8911/10000 episodes, total num timesteps 1782400/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8912/10000 episodes, total num timesteps 1782600/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8913/10000 episodes, total num timesteps 1782800/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8914/10000 episodes, total num timesteps 1783000/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8915/10000 episodes, total num timesteps 1783200/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8916/10000 episodes, total num timesteps 1783400/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8917/10000 episodes, total num timesteps 1783600/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8918/10000 episodes, total num timesteps 1783800/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8919/10000 episodes, total num timesteps 1784000/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8920/10000 episodes, total num timesteps 1784200/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8921/10000 episodes, total num timesteps 1784400/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8922/10000 episodes, total num timesteps 1784600/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8923/10000 episodes, total num timesteps 1784800/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8924/10000 episodes, total num timesteps 1785000/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8925/10000 episodes, total num timesteps 1785200/2000000, FPS 260.

team_policy eval average step individual rewards of agent0: 0.8154810042333446
team_policy eval average team episode rewards of agent0: 72.5
team_policy eval idv catch total num of agent0: 34
team_policy eval team catch total num: 29
team_policy eval average step individual rewards of agent1: 0.5216179668750525
team_policy eval average team episode rewards of agent1: 72.5
team_policy eval idv catch total num of agent1: 23
team_policy eval team catch total num: 29
team_policy eval average step individual rewards of agent2: 0.830699074694348
team_policy eval average team episode rewards of agent2: 72.5
team_policy eval idv catch total num of agent2: 35
team_policy eval team catch total num: 29
team_policy eval average step individual rewards of agent3: 0.3389605956791193
team_policy eval average team episode rewards of agent3: 72.5
team_policy eval idv catch total num of agent3: 16
team_policy eval team catch total num: 29
team_policy eval average step individual rewards of agent4: 0.5355871748923673
team_policy eval average team episode rewards of agent4: 72.5
team_policy eval idv catch total num of agent4: 24
team_policy eval team catch total num: 29
idv_policy eval average step individual rewards of agent0: 0.36680497972114456
idv_policy eval average team episode rewards of agent0: 37.5
idv_policy eval idv catch total num of agent0: 17
idv_policy eval team catch total num: 15
idv_policy eval average step individual rewards of agent1: 0.36815260427238855
idv_policy eval average team episode rewards of agent1: 37.5
idv_policy eval idv catch total num of agent1: 17
idv_policy eval team catch total num: 15
idv_policy eval average step individual rewards of agent2: 0.23541346480554431
idv_policy eval average team episode rewards of agent2: 37.5
idv_policy eval idv catch total num of agent2: 12
idv_policy eval team catch total num: 15
idv_policy eval average step individual rewards of agent3: 0.4941462919507103
idv_policy eval average team episode rewards of agent3: 37.5
idv_policy eval idv catch total num of agent3: 22
idv_policy eval team catch total num: 15
idv_policy eval average step individual rewards of agent4: 0.45859908826868817
idv_policy eval average team episode rewards of agent4: 37.5
idv_policy eval idv catch total num of agent4: 20
idv_policy eval team catch total num: 15

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8926/10000 episodes, total num timesteps 1785400/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8927/10000 episodes, total num timesteps 1785600/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8928/10000 episodes, total num timesteps 1785800/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8929/10000 episodes, total num timesteps 1786000/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8930/10000 episodes, total num timesteps 1786200/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8931/10000 episodes, total num timesteps 1786400/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8932/10000 episodes, total num timesteps 1786600/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8933/10000 episodes, total num timesteps 1786800/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8934/10000 episodes, total num timesteps 1787000/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8935/10000 episodes, total num timesteps 1787200/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8936/10000 episodes, total num timesteps 1787400/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8937/10000 episodes, total num timesteps 1787600/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8938/10000 episodes, total num timesteps 1787800/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8939/10000 episodes, total num timesteps 1788000/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8940/10000 episodes, total num timesteps 1788200/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8941/10000 episodes, total num timesteps 1788400/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8942/10000 episodes, total num timesteps 1788600/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8943/10000 episodes, total num timesteps 1788800/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8944/10000 episodes, total num timesteps 1789000/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8945/10000 episodes, total num timesteps 1789200/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8946/10000 episodes, total num timesteps 1789400/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8947/10000 episodes, total num timesteps 1789600/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8948/10000 episodes, total num timesteps 1789800/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8949/10000 episodes, total num timesteps 1790000/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8950/10000 episodes, total num timesteps 1790200/2000000, FPS 260.

team_policy eval average step individual rewards of agent0: 0.41753931416506296
team_policy eval average team episode rewards of agent0: 30.0
team_policy eval idv catch total num of agent0: 19
team_policy eval team catch total num: 12
team_policy eval average step individual rewards of agent1: 0.1652249522876528
team_policy eval average team episode rewards of agent1: 30.0
team_policy eval idv catch total num of agent1: 9
team_policy eval team catch total num: 12
team_policy eval average step individual rewards of agent2: 0.24065121368721779
team_policy eval average team episode rewards of agent2: 30.0
team_policy eval idv catch total num of agent2: 12
team_policy eval team catch total num: 12
team_policy eval average step individual rewards of agent3: 0.5743841576668713
team_policy eval average team episode rewards of agent3: 30.0
team_policy eval idv catch total num of agent3: 25
team_policy eval team catch total num: 12
team_policy eval average step individual rewards of agent4: 0.22675799592432
team_policy eval average team episode rewards of agent4: 30.0
team_policy eval idv catch total num of agent4: 11
team_policy eval team catch total num: 12
idv_policy eval average step individual rewards of agent0: 0.5588136739142984
idv_policy eval average team episode rewards of agent0: 35.0
idv_policy eval idv catch total num of agent0: 24
idv_policy eval team catch total num: 14
idv_policy eval average step individual rewards of agent1: 0.33989758433307926
idv_policy eval average team episode rewards of agent1: 35.0
idv_policy eval idv catch total num of agent1: 16
idv_policy eval team catch total num: 14
idv_policy eval average step individual rewards of agent2: 0.09510385297064236
idv_policy eval average team episode rewards of agent2: 35.0
idv_policy eval idv catch total num of agent2: 6
idv_policy eval team catch total num: 14
idv_policy eval average step individual rewards of agent3: 0.3052832996429077
idv_policy eval average team episode rewards of agent3: 35.0
idv_policy eval idv catch total num of agent3: 15
idv_policy eval team catch total num: 14
idv_policy eval average step individual rewards of agent4: 0.19852024434268437
idv_policy eval average team episode rewards of agent4: 35.0
idv_policy eval idv catch total num of agent4: 10
idv_policy eval team catch total num: 14

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8951/10000 episodes, total num timesteps 1790400/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8952/10000 episodes, total num timesteps 1790600/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8953/10000 episodes, total num timesteps 1790800/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8954/10000 episodes, total num timesteps 1791000/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8955/10000 episodes, total num timesteps 1791200/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8956/10000 episodes, total num timesteps 1791400/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8957/10000 episodes, total num timesteps 1791600/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8958/10000 episodes, total num timesteps 1791800/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8959/10000 episodes, total num timesteps 1792000/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8960/10000 episodes, total num timesteps 1792200/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8961/10000 episodes, total num timesteps 1792400/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8962/10000 episodes, total num timesteps 1792600/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8963/10000 episodes, total num timesteps 1792800/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8964/10000 episodes, total num timesteps 1793000/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8965/10000 episodes, total num timesteps 1793200/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8966/10000 episodes, total num timesteps 1793400/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8967/10000 episodes, total num timesteps 1793600/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8968/10000 episodes, total num timesteps 1793800/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8969/10000 episodes, total num timesteps 1794000/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8970/10000 episodes, total num timesteps 1794200/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8971/10000 episodes, total num timesteps 1794400/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8972/10000 episodes, total num timesteps 1794600/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8973/10000 episodes, total num timesteps 1794800/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8974/10000 episodes, total num timesteps 1795000/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8975/10000 episodes, total num timesteps 1795200/2000000, FPS 260.

team_policy eval average step individual rewards of agent0: 0.40505345801813647
team_policy eval average team episode rewards of agent0: 20.0
team_policy eval idv catch total num of agent0: 18
team_policy eval team catch total num: 8
team_policy eval average step individual rewards of agent1: 0.6306118574446739
team_policy eval average team episode rewards of agent1: 20.0
team_policy eval idv catch total num of agent1: 27
team_policy eval team catch total num: 8
team_policy eval average step individual rewards of agent2: 0.23405419719929185
team_policy eval average team episode rewards of agent2: 20.0
team_policy eval idv catch total num of agent2: 12
team_policy eval team catch total num: 8
team_policy eval average step individual rewards of agent3: -0.05101766977875451
team_policy eval average team episode rewards of agent3: 20.0
team_policy eval idv catch total num of agent3: 0
team_policy eval team catch total num: 8
team_policy eval average step individual rewards of agent4: 0.4530933364866264
team_policy eval average team episode rewards of agent4: 20.0
team_policy eval idv catch total num of agent4: 20
team_policy eval team catch total num: 8
idv_policy eval average step individual rewards of agent0: 0.22699165557375953
idv_policy eval average team episode rewards of agent0: 40.0
idv_policy eval idv catch total num of agent0: 12
idv_policy eval team catch total num: 16
idv_policy eval average step individual rewards of agent1: 0.37412915533505264
idv_policy eval average team episode rewards of agent1: 40.0
idv_policy eval idv catch total num of agent1: 17
idv_policy eval team catch total num: 16
idv_policy eval average step individual rewards of agent2: 0.732622670614154
idv_policy eval average team episode rewards of agent2: 40.0
idv_policy eval idv catch total num of agent2: 31
idv_policy eval team catch total num: 16
idv_policy eval average step individual rewards of agent3: 0.33686465577863706
idv_policy eval average team episode rewards of agent3: 40.0
idv_policy eval idv catch total num of agent3: 16
idv_policy eval team catch total num: 16
idv_policy eval average step individual rewards of agent4: 0.05133032236137084
idv_policy eval average team episode rewards of agent4: 40.0
idv_policy eval idv catch total num of agent4: 5
idv_policy eval team catch total num: 16

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8976/10000 episodes, total num timesteps 1795400/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8977/10000 episodes, total num timesteps 1795600/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8978/10000 episodes, total num timesteps 1795800/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8979/10000 episodes, total num timesteps 1796000/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8980/10000 episodes, total num timesteps 1796200/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8981/10000 episodes, total num timesteps 1796400/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8982/10000 episodes, total num timesteps 1796600/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8983/10000 episodes, total num timesteps 1796800/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8984/10000 episodes, total num timesteps 1797000/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8985/10000 episodes, total num timesteps 1797200/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8986/10000 episodes, total num timesteps 1797400/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8987/10000 episodes, total num timesteps 1797600/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8988/10000 episodes, total num timesteps 1797800/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8989/10000 episodes, total num timesteps 1798000/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8990/10000 episodes, total num timesteps 1798200/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8991/10000 episodes, total num timesteps 1798400/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8992/10000 episodes, total num timesteps 1798600/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8993/10000 episodes, total num timesteps 1798800/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8994/10000 episodes, total num timesteps 1799000/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8995/10000 episodes, total num timesteps 1799200/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8996/10000 episodes, total num timesteps 1799400/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8997/10000 episodes, total num timesteps 1799600/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8998/10000 episodes, total num timesteps 1799800/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8999/10000 episodes, total num timesteps 1800000/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9000/10000 episodes, total num timesteps 1800200/2000000, FPS 260.

team_policy eval average step individual rewards of agent0: 0.32461203774030317
team_policy eval average team episode rewards of agent0: 50.0
team_policy eval idv catch total num of agent0: 15
team_policy eval team catch total num: 20
team_policy eval average step individual rewards of agent1: 0.33148794097297235
team_policy eval average team episode rewards of agent1: 50.0
team_policy eval idv catch total num of agent1: 15
team_policy eval team catch total num: 20
team_policy eval average step individual rewards of agent2: 0.2855850220998717
team_policy eval average team episode rewards of agent2: 50.0
team_policy eval idv catch total num of agent2: 14
team_policy eval team catch total num: 20
team_policy eval average step individual rewards of agent3: 0.6316478803287812
team_policy eval average team episode rewards of agent3: 50.0
team_policy eval idv catch total num of agent3: 27
team_policy eval team catch total num: 20
team_policy eval average step individual rewards of agent4: 0.36153385208592764
team_policy eval average team episode rewards of agent4: 50.0
team_policy eval idv catch total num of agent4: 17
team_policy eval team catch total num: 20
idv_policy eval average step individual rewards of agent0: 0.7003965405718761
idv_policy eval average team episode rewards of agent0: 72.5
idv_policy eval idv catch total num of agent0: 30
idv_policy eval team catch total num: 29
idv_policy eval average step individual rewards of agent1: 0.5513176476955972
idv_policy eval average team episode rewards of agent1: 72.5
idv_policy eval idv catch total num of agent1: 24
idv_policy eval team catch total num: 29
idv_policy eval average step individual rewards of agent2: 0.5882299565877527
idv_policy eval average team episode rewards of agent2: 72.5
idv_policy eval idv catch total num of agent2: 25
idv_policy eval team catch total num: 29
idv_policy eval average step individual rewards of agent3: 0.552603677536844
idv_policy eval average team episode rewards of agent3: 72.5
idv_policy eval idv catch total num of agent3: 24
idv_policy eval team catch total num: 29
idv_policy eval average step individual rewards of agent4: 0.2973648238145664
idv_policy eval average team episode rewards of agent4: 72.5
idv_policy eval idv catch total num of agent4: 14
idv_policy eval team catch total num: 29

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9001/10000 episodes, total num timesteps 1800400/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9002/10000 episodes, total num timesteps 1800600/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9003/10000 episodes, total num timesteps 1800800/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9004/10000 episodes, total num timesteps 1801000/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9005/10000 episodes, total num timesteps 1801200/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9006/10000 episodes, total num timesteps 1801400/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9007/10000 episodes, total num timesteps 1801600/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9008/10000 episodes, total num timesteps 1801800/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9009/10000 episodes, total num timesteps 1802000/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9010/10000 episodes, total num timesteps 1802200/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9011/10000 episodes, total num timesteps 1802400/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9012/10000 episodes, total num timesteps 1802600/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9013/10000 episodes, total num timesteps 1802800/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9014/10000 episodes, total num timesteps 1803000/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9015/10000 episodes, total num timesteps 1803200/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9016/10000 episodes, total num timesteps 1803400/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9017/10000 episodes, total num timesteps 1803600/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9018/10000 episodes, total num timesteps 1803800/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9019/10000 episodes, total num timesteps 1804000/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9020/10000 episodes, total num timesteps 1804200/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9021/10000 episodes, total num timesteps 1804400/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9022/10000 episodes, total num timesteps 1804600/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9023/10000 episodes, total num timesteps 1804800/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9024/10000 episodes, total num timesteps 1805000/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9025/10000 episodes, total num timesteps 1805200/2000000, FPS 260.

team_policy eval average step individual rewards of agent0: 0.5179861581840509
team_policy eval average team episode rewards of agent0: 40.0
team_policy eval idv catch total num of agent0: 23
team_policy eval team catch total num: 16
team_policy eval average step individual rewards of agent1: 0.3958844885131112
team_policy eval average team episode rewards of agent1: 40.0
team_policy eval idv catch total num of agent1: 18
team_policy eval team catch total num: 16
team_policy eval average step individual rewards of agent2: 0.4010159260381525
team_policy eval average team episode rewards of agent2: 40.0
team_policy eval idv catch total num of agent2: 18
team_policy eval team catch total num: 16
team_policy eval average step individual rewards of agent3: 0.3369538666729063
team_policy eval average team episode rewards of agent3: 40.0
team_policy eval idv catch total num of agent3: 16
team_policy eval team catch total num: 16
team_policy eval average step individual rewards of agent4: 0.3207481309553213
team_policy eval average team episode rewards of agent4: 40.0
team_policy eval idv catch total num of agent4: 15
team_policy eval team catch total num: 16
idv_policy eval average step individual rewards of agent0: 0.45689292096989803
idv_policy eval average team episode rewards of agent0: 82.5
idv_policy eval idv catch total num of agent0: 20
idv_policy eval team catch total num: 33
idv_policy eval average step individual rewards of agent1: 1.0498797747991322
idv_policy eval average team episode rewards of agent1: 82.5
idv_policy eval idv catch total num of agent1: 43
idv_policy eval team catch total num: 33
idv_policy eval average step individual rewards of agent2: 0.7134217100572251
idv_policy eval average team episode rewards of agent2: 82.5
idv_policy eval idv catch total num of agent2: 30
idv_policy eval team catch total num: 33
idv_policy eval average step individual rewards of agent3: 0.8052411973331735
idv_policy eval average team episode rewards of agent3: 82.5
idv_policy eval idv catch total num of agent3: 34
idv_policy eval team catch total num: 33
idv_policy eval average step individual rewards of agent4: 0.1891804385999391
idv_policy eval average team episode rewards of agent4: 82.5
idv_policy eval idv catch total num of agent4: 10
idv_policy eval team catch total num: 33

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9026/10000 episodes, total num timesteps 1805400/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9027/10000 episodes, total num timesteps 1805600/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9028/10000 episodes, total num timesteps 1805800/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9029/10000 episodes, total num timesteps 1806000/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9030/10000 episodes, total num timesteps 1806200/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9031/10000 episodes, total num timesteps 1806400/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9032/10000 episodes, total num timesteps 1806600/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9033/10000 episodes, total num timesteps 1806800/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9034/10000 episodes, total num timesteps 1807000/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9035/10000 episodes, total num timesteps 1807200/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9036/10000 episodes, total num timesteps 1807400/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9037/10000 episodes, total num timesteps 1807600/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9038/10000 episodes, total num timesteps 1807800/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9039/10000 episodes, total num timesteps 1808000/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9040/10000 episodes, total num timesteps 1808200/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9041/10000 episodes, total num timesteps 1808400/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9042/10000 episodes, total num timesteps 1808600/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9043/10000 episodes, total num timesteps 1808800/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9044/10000 episodes, total num timesteps 1809000/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9045/10000 episodes, total num timesteps 1809200/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9046/10000 episodes, total num timesteps 1809400/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9047/10000 episodes, total num timesteps 1809600/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9048/10000 episodes, total num timesteps 1809800/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9049/10000 episodes, total num timesteps 1810000/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9050/10000 episodes, total num timesteps 1810200/2000000, FPS 260.

team_policy eval average step individual rewards of agent0: 0.5071242361403059
team_policy eval average team episode rewards of agent0: 42.5
team_policy eval idv catch total num of agent0: 22
team_policy eval team catch total num: 17
team_policy eval average step individual rewards of agent1: 0.3261826795707117
team_policy eval average team episode rewards of agent1: 42.5
team_policy eval idv catch total num of agent1: 15
team_policy eval team catch total num: 17
team_policy eval average step individual rewards of agent2: 0.7264732850126202
team_policy eval average team episode rewards of agent2: 42.5
team_policy eval idv catch total num of agent2: 31
team_policy eval team catch total num: 17
team_policy eval average step individual rewards of agent3: 0.8120539150555659
team_policy eval average team episode rewards of agent3: 42.5
team_policy eval idv catch total num of agent3: 34
team_policy eval team catch total num: 17
team_policy eval average step individual rewards of agent4: 0.2949851852148071
team_policy eval average team episode rewards of agent4: 42.5
team_policy eval idv catch total num of agent4: 14
team_policy eval team catch total num: 17
idv_policy eval average step individual rewards of agent0: 0.5494920177162349
idv_policy eval average team episode rewards of agent0: 67.5
idv_policy eval idv catch total num of agent0: 24
idv_policy eval team catch total num: 27
idv_policy eval average step individual rewards of agent1: 0.4232271762800132
idv_policy eval average team episode rewards of agent1: 67.5
idv_policy eval idv catch total num of agent1: 19
idv_policy eval team catch total num: 27
idv_policy eval average step individual rewards of agent2: 0.43237120305587395
idv_policy eval average team episode rewards of agent2: 67.5
idv_policy eval idv catch total num of agent2: 19
idv_policy eval team catch total num: 27
idv_policy eval average step individual rewards of agent3: 0.35398768107872436
idv_policy eval average team episode rewards of agent3: 67.5
idv_policy eval idv catch total num of agent3: 16
idv_policy eval team catch total num: 27
idv_policy eval average step individual rewards of agent4: 0.5773394866517854
idv_policy eval average team episode rewards of agent4: 67.5
idv_policy eval idv catch total num of agent4: 25
idv_policy eval team catch total num: 27

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9051/10000 episodes, total num timesteps 1810400/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9052/10000 episodes, total num timesteps 1810600/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9053/10000 episodes, total num timesteps 1810800/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9054/10000 episodes, total num timesteps 1811000/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9055/10000 episodes, total num timesteps 1811200/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9056/10000 episodes, total num timesteps 1811400/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9057/10000 episodes, total num timesteps 1811600/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9058/10000 episodes, total num timesteps 1811800/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9059/10000 episodes, total num timesteps 1812000/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9060/10000 episodes, total num timesteps 1812200/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9061/10000 episodes, total num timesteps 1812400/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9062/10000 episodes, total num timesteps 1812600/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9063/10000 episodes, total num timesteps 1812800/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9064/10000 episodes, total num timesteps 1813000/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9065/10000 episodes, total num timesteps 1813200/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9066/10000 episodes, total num timesteps 1813400/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9067/10000 episodes, total num timesteps 1813600/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9068/10000 episodes, total num timesteps 1813800/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9069/10000 episodes, total num timesteps 1814000/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9070/10000 episodes, total num timesteps 1814200/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9071/10000 episodes, total num timesteps 1814400/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9072/10000 episodes, total num timesteps 1814600/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9073/10000 episodes, total num timesteps 1814800/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9074/10000 episodes, total num timesteps 1815000/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9075/10000 episodes, total num timesteps 1815200/2000000, FPS 260.

team_policy eval average step individual rewards of agent0: 0.4657250907179652
team_policy eval average team episode rewards of agent0: 30.0
team_policy eval idv catch total num of agent0: 21
team_policy eval team catch total num: 12
team_policy eval average step individual rewards of agent1: 0.3564818854250483
team_policy eval average team episode rewards of agent1: 30.0
team_policy eval idv catch total num of agent1: 16
team_policy eval team catch total num: 12
team_policy eval average step individual rewards of agent2: 0.34114529357079676
team_policy eval average team episode rewards of agent2: 30.0
team_policy eval idv catch total num of agent2: 16
team_policy eval team catch total num: 12
team_policy eval average step individual rewards of agent3: 0.18992922127059028
team_policy eval average team episode rewards of agent3: 30.0
team_policy eval idv catch total num of agent3: 10
team_policy eval team catch total num: 12
team_policy eval average step individual rewards of agent4: 0.3734947797672035
team_policy eval average team episode rewards of agent4: 30.0
team_policy eval idv catch total num of agent4: 17
team_policy eval team catch total num: 12
idv_policy eval average step individual rewards of agent0: 0.37878890759743733
idv_policy eval average team episode rewards of agent0: 2.5
idv_policy eval idv catch total num of agent0: 17
idv_policy eval team catch total num: 1
idv_policy eval average step individual rewards of agent1: 0.2518830671145722
idv_policy eval average team episode rewards of agent1: 2.5
idv_policy eval idv catch total num of agent1: 12
idv_policy eval team catch total num: 1
idv_policy eval average step individual rewards of agent2: 0.0658709642884297
idv_policy eval average team episode rewards of agent2: 2.5
idv_policy eval idv catch total num of agent2: 5
idv_policy eval team catch total num: 1
idv_policy eval average step individual rewards of agent3: 0.1264081460221217
idv_policy eval average team episode rewards of agent3: 2.5
idv_policy eval idv catch total num of agent3: 8
idv_policy eval team catch total num: 1
idv_policy eval average step individual rewards of agent4: 0.15565663433553606
idv_policy eval average team episode rewards of agent4: 2.5
idv_policy eval idv catch total num of agent4: 9
idv_policy eval team catch total num: 1

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9076/10000 episodes, total num timesteps 1815400/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9077/10000 episodes, total num timesteps 1815600/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9078/10000 episodes, total num timesteps 1815800/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9079/10000 episodes, total num timesteps 1816000/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9080/10000 episodes, total num timesteps 1816200/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9081/10000 episodes, total num timesteps 1816400/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9082/10000 episodes, total num timesteps 1816600/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9083/10000 episodes, total num timesteps 1816800/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9084/10000 episodes, total num timesteps 1817000/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9085/10000 episodes, total num timesteps 1817200/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9086/10000 episodes, total num timesteps 1817400/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9087/10000 episodes, total num timesteps 1817600/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9088/10000 episodes, total num timesteps 1817800/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9089/10000 episodes, total num timesteps 1818000/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9090/10000 episodes, total num timesteps 1818200/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9091/10000 episodes, total num timesteps 1818400/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9092/10000 episodes, total num timesteps 1818600/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9093/10000 episodes, total num timesteps 1818800/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9094/10000 episodes, total num timesteps 1819000/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9095/10000 episodes, total num timesteps 1819200/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9096/10000 episodes, total num timesteps 1819400/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9097/10000 episodes, total num timesteps 1819600/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9098/10000 episodes, total num timesteps 1819800/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9099/10000 episodes, total num timesteps 1820000/2000000, FPS 260.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9100/10000 episodes, total num timesteps 1820200/2000000, FPS 260.

team_policy eval average step individual rewards of agent0: 0.44055996357583505
team_policy eval average team episode rewards of agent0: 30.0
team_policy eval idv catch total num of agent0: 20
team_policy eval team catch total num: 12
team_policy eval average step individual rewards of agent1: 0.30247718888579217
team_policy eval average team episode rewards of agent1: 30.0
team_policy eval idv catch total num of agent1: 14
team_policy eval team catch total num: 12
team_policy eval average step individual rewards of agent2: 0.4631100992826856
team_policy eval average team episode rewards of agent2: 30.0
team_policy eval idv catch total num of agent2: 20
team_policy eval team catch total num: 12
team_policy eval average step individual rewards of agent3: 0.42846819021403065
team_policy eval average team episode rewards of agent3: 30.0
team_policy eval idv catch total num of agent3: 19
team_policy eval team catch total num: 12
team_policy eval average step individual rewards of agent4: 0.4509516433637477
team_policy eval average team episode rewards of agent4: 30.0
team_policy eval idv catch total num of agent4: 20
team_policy eval team catch total num: 12
idv_policy eval average step individual rewards of agent0: 0.5765176886267797
idv_policy eval average team episode rewards of agent0: 20.0
idv_policy eval idv catch total num of agent0: 25
idv_policy eval team catch total num: 8
idv_policy eval average step individual rewards of agent1: 0.2776896690598555
idv_policy eval average team episode rewards of agent1: 20.0
idv_policy eval idv catch total num of agent1: 13
idv_policy eval team catch total num: 8
idv_policy eval average step individual rewards of agent2: 0.271469603013393
idv_policy eval average team episode rewards of agent2: 20.0
idv_policy eval idv catch total num of agent2: 13
idv_policy eval team catch total num: 8
idv_policy eval average step individual rewards of agent3: 0.5199589494168751
idv_policy eval average team episode rewards of agent3: 20.0
idv_policy eval idv catch total num of agent3: 23
idv_policy eval team catch total num: 8
idv_policy eval average step individual rewards of agent4: 0.3033261745564412
idv_policy eval average team episode rewards of agent4: 20.0
idv_policy eval idv catch total num of agent4: 14
idv_policy eval team catch total num: 8

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9101/10000 episodes, total num timesteps 1820400/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9102/10000 episodes, total num timesteps 1820600/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9103/10000 episodes, total num timesteps 1820800/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9104/10000 episodes, total num timesteps 1821000/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9105/10000 episodes, total num timesteps 1821200/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9106/10000 episodes, total num timesteps 1821400/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9107/10000 episodes, total num timesteps 1821600/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9108/10000 episodes, total num timesteps 1821800/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9109/10000 episodes, total num timesteps 1822000/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9110/10000 episodes, total num timesteps 1822200/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9111/10000 episodes, total num timesteps 1822400/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9112/10000 episodes, total num timesteps 1822600/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9113/10000 episodes, total num timesteps 1822800/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9114/10000 episodes, total num timesteps 1823000/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9115/10000 episodes, total num timesteps 1823200/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9116/10000 episodes, total num timesteps 1823400/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9117/10000 episodes, total num timesteps 1823600/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9118/10000 episodes, total num timesteps 1823800/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9119/10000 episodes, total num timesteps 1824000/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9120/10000 episodes, total num timesteps 1824200/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9121/10000 episodes, total num timesteps 1824400/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9122/10000 episodes, total num timesteps 1824600/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9123/10000 episodes, total num timesteps 1824800/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9124/10000 episodes, total num timesteps 1825000/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9125/10000 episodes, total num timesteps 1825200/2000000, FPS 259.

team_policy eval average step individual rewards of agent0: 0.3972894603356765
team_policy eval average team episode rewards of agent0: 30.0
team_policy eval idv catch total num of agent0: 18
team_policy eval team catch total num: 12
team_policy eval average step individual rewards of agent1: 0.14874266335642988
team_policy eval average team episode rewards of agent1: 30.0
team_policy eval idv catch total num of agent1: 9
team_policy eval team catch total num: 12
team_policy eval average step individual rewards of agent2: 0.41388100899145314
team_policy eval average team episode rewards of agent2: 30.0
team_policy eval idv catch total num of agent2: 19
team_policy eval team catch total num: 12
team_policy eval average step individual rewards of agent3: 0.23002894270433205
team_policy eval average team episode rewards of agent3: 30.0
team_policy eval idv catch total num of agent3: 12
team_policy eval team catch total num: 12
team_policy eval average step individual rewards of agent4: 0.7038429736807243
team_policy eval average team episode rewards of agent4: 30.0
team_policy eval idv catch total num of agent4: 30
team_policy eval team catch total num: 12
idv_policy eval average step individual rewards of agent0: 0.15456863888924458
idv_policy eval average team episode rewards of agent0: 15.0
idv_policy eval idv catch total num of agent0: 9
idv_policy eval team catch total num: 6
idv_policy eval average step individual rewards of agent1: 0.16385561731577866
idv_policy eval average team episode rewards of agent1: 15.0
idv_policy eval idv catch total num of agent1: 9
idv_policy eval team catch total num: 6
idv_policy eval average step individual rewards of agent2: 0.0913418453904326
idv_policy eval average team episode rewards of agent2: 15.0
idv_policy eval idv catch total num of agent2: 6
idv_policy eval team catch total num: 6
idv_policy eval average step individual rewards of agent3: 0.210135741781018
idv_policy eval average team episode rewards of agent3: 15.0
idv_policy eval idv catch total num of agent3: 11
idv_policy eval team catch total num: 6
idv_policy eval average step individual rewards of agent4: 0.1948170828664178
idv_policy eval average team episode rewards of agent4: 15.0
idv_policy eval idv catch total num of agent4: 10
idv_policy eval team catch total num: 6

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9126/10000 episodes, total num timesteps 1825400/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9127/10000 episodes, total num timesteps 1825600/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9128/10000 episodes, total num timesteps 1825800/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9129/10000 episodes, total num timesteps 1826000/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9130/10000 episodes, total num timesteps 1826200/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9131/10000 episodes, total num timesteps 1826400/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9132/10000 episodes, total num timesteps 1826600/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9133/10000 episodes, total num timesteps 1826800/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9134/10000 episodes, total num timesteps 1827000/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9135/10000 episodes, total num timesteps 1827200/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9136/10000 episodes, total num timesteps 1827400/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9137/10000 episodes, total num timesteps 1827600/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9138/10000 episodes, total num timesteps 1827800/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9139/10000 episodes, total num timesteps 1828000/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9140/10000 episodes, total num timesteps 1828200/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9141/10000 episodes, total num timesteps 1828400/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9142/10000 episodes, total num timesteps 1828600/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9143/10000 episodes, total num timesteps 1828800/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9144/10000 episodes, total num timesteps 1829000/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9145/10000 episodes, total num timesteps 1829200/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9146/10000 episodes, total num timesteps 1829400/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9147/10000 episodes, total num timesteps 1829600/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9148/10000 episodes, total num timesteps 1829800/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9149/10000 episodes, total num timesteps 1830000/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9150/10000 episodes, total num timesteps 1830200/2000000, FPS 259.

team_policy eval average step individual rewards of agent0: 0.5340001830753198
team_policy eval average team episode rewards of agent0: 25.0
team_policy eval idv catch total num of agent0: 23
team_policy eval team catch total num: 10
team_policy eval average step individual rewards of agent1: 0.10425241289189653
team_policy eval average team episode rewards of agent1: 25.0
team_policy eval idv catch total num of agent1: 7
team_policy eval team catch total num: 10
team_policy eval average step individual rewards of agent2: 0.17209263533062938
team_policy eval average team episode rewards of agent2: 25.0
team_policy eval idv catch total num of agent2: 10
team_policy eval team catch total num: 10
team_policy eval average step individual rewards of agent3: 0.4070027937310827
team_policy eval average team episode rewards of agent3: 25.0
team_policy eval idv catch total num of agent3: 19
team_policy eval team catch total num: 10
team_policy eval average step individual rewards of agent4: 0.7505954886508677
team_policy eval average team episode rewards of agent4: 25.0
team_policy eval idv catch total num of agent4: 32
team_policy eval team catch total num: 10
idv_policy eval average step individual rewards of agent0: 0.012807901331145283
idv_policy eval average team episode rewards of agent0: 15.0
idv_policy eval idv catch total num of agent0: 3
idv_policy eval team catch total num: 6
idv_policy eval average step individual rewards of agent1: 0.5304600413492107
idv_policy eval average team episode rewards of agent1: 15.0
idv_policy eval idv catch total num of agent1: 23
idv_policy eval team catch total num: 6
idv_policy eval average step individual rewards of agent2: 0.37780062743345527
idv_policy eval average team episode rewards of agent2: 15.0
idv_policy eval idv catch total num of agent2: 17
idv_policy eval team catch total num: 6
idv_policy eval average step individual rewards of agent3: 0.15453652054313805
idv_policy eval average team episode rewards of agent3: 15.0
idv_policy eval idv catch total num of agent3: 9
idv_policy eval team catch total num: 6
idv_policy eval average step individual rewards of agent4: 0.37690465394214456
idv_policy eval average team episode rewards of agent4: 15.0
idv_policy eval idv catch total num of agent4: 17
idv_policy eval team catch total num: 6

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9151/10000 episodes, total num timesteps 1830400/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9152/10000 episodes, total num timesteps 1830600/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9153/10000 episodes, total num timesteps 1830800/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9154/10000 episodes, total num timesteps 1831000/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9155/10000 episodes, total num timesteps 1831200/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9156/10000 episodes, total num timesteps 1831400/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9157/10000 episodes, total num timesteps 1831600/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9158/10000 episodes, total num timesteps 1831800/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9159/10000 episodes, total num timesteps 1832000/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9160/10000 episodes, total num timesteps 1832200/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9161/10000 episodes, total num timesteps 1832400/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9162/10000 episodes, total num timesteps 1832600/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9163/10000 episodes, total num timesteps 1832800/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9164/10000 episodes, total num timesteps 1833000/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9165/10000 episodes, total num timesteps 1833200/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9166/10000 episodes, total num timesteps 1833400/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9167/10000 episodes, total num timesteps 1833600/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9168/10000 episodes, total num timesteps 1833800/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9169/10000 episodes, total num timesteps 1834000/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9170/10000 episodes, total num timesteps 1834200/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9171/10000 episodes, total num timesteps 1834400/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9172/10000 episodes, total num timesteps 1834600/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9173/10000 episodes, total num timesteps 1834800/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9174/10000 episodes, total num timesteps 1835000/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9175/10000 episodes, total num timesteps 1835200/2000000, FPS 259.

team_policy eval average step individual rewards of agent0: 0.1439835752542643
team_policy eval average team episode rewards of agent0: 10.0
team_policy eval idv catch total num of agent0: 8
team_policy eval team catch total num: 4
team_policy eval average step individual rewards of agent1: 0.09430271032201164
team_policy eval average team episode rewards of agent1: 10.0
team_policy eval idv catch total num of agent1: 6
team_policy eval team catch total num: 4
team_policy eval average step individual rewards of agent2: 0.18473692697773408
team_policy eval average team episode rewards of agent2: 10.0
team_policy eval idv catch total num of agent2: 10
team_policy eval team catch total num: 4
team_policy eval average step individual rewards of agent3: 0.2704463887594132
team_policy eval average team episode rewards of agent3: 10.0
team_policy eval idv catch total num of agent3: 13
team_policy eval team catch total num: 4
team_policy eval average step individual rewards of agent4: 0.4614156781926749
team_policy eval average team episode rewards of agent4: 10.0
team_policy eval idv catch total num of agent4: 20
team_policy eval team catch total num: 4
idv_policy eval average step individual rewards of agent0: 0.5566825803218678
idv_policy eval average team episode rewards of agent0: 25.0
idv_policy eval idv catch total num of agent0: 24
idv_policy eval team catch total num: 10
idv_policy eval average step individual rewards of agent1: 0.42393563997748485
idv_policy eval average team episode rewards of agent1: 25.0
idv_policy eval idv catch total num of agent1: 19
idv_policy eval team catch total num: 10
idv_policy eval average step individual rewards of agent2: 0.27234374660945354
idv_policy eval average team episode rewards of agent2: 25.0
idv_policy eval idv catch total num of agent2: 13
idv_policy eval team catch total num: 10
idv_policy eval average step individual rewards of agent3: 0.11661083575313844
idv_policy eval average team episode rewards of agent3: 25.0
idv_policy eval idv catch total num of agent3: 7
idv_policy eval team catch total num: 10
idv_policy eval average step individual rewards of agent4: 0.29460566142907607
idv_policy eval average team episode rewards of agent4: 25.0
idv_policy eval idv catch total num of agent4: 14
idv_policy eval team catch total num: 10

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9176/10000 episodes, total num timesteps 1835400/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9177/10000 episodes, total num timesteps 1835600/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9178/10000 episodes, total num timesteps 1835800/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9179/10000 episodes, total num timesteps 1836000/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9180/10000 episodes, total num timesteps 1836200/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9181/10000 episodes, total num timesteps 1836400/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9182/10000 episodes, total num timesteps 1836600/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9183/10000 episodes, total num timesteps 1836800/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9184/10000 episodes, total num timesteps 1837000/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9185/10000 episodes, total num timesteps 1837200/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9186/10000 episodes, total num timesteps 1837400/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9187/10000 episodes, total num timesteps 1837600/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9188/10000 episodes, total num timesteps 1837800/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9189/10000 episodes, total num timesteps 1838000/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9190/10000 episodes, total num timesteps 1838200/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9191/10000 episodes, total num timesteps 1838400/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9192/10000 episodes, total num timesteps 1838600/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9193/10000 episodes, total num timesteps 1838800/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9194/10000 episodes, total num timesteps 1839000/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9195/10000 episodes, total num timesteps 1839200/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9196/10000 episodes, total num timesteps 1839400/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9197/10000 episodes, total num timesteps 1839600/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9198/10000 episodes, total num timesteps 1839800/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9199/10000 episodes, total num timesteps 1840000/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9200/10000 episodes, total num timesteps 1840200/2000000, FPS 259.

team_policy eval average step individual rewards of agent0: 0.36608368216118364
team_policy eval average team episode rewards of agent0: 32.5
team_policy eval idv catch total num of agent0: 17
team_policy eval team catch total num: 13
team_policy eval average step individual rewards of agent1: 0.26696631984280594
team_policy eval average team episode rewards of agent1: 32.5
team_policy eval idv catch total num of agent1: 13
team_policy eval team catch total num: 13
team_policy eval average step individual rewards of agent2: 0.5435474430589768
team_policy eval average team episode rewards of agent2: 32.5
team_policy eval idv catch total num of agent2: 24
team_policy eval team catch total num: 13
team_policy eval average step individual rewards of agent3: 0.32779158647762735
team_policy eval average team episode rewards of agent3: 32.5
team_policy eval idv catch total num of agent3: 15
team_policy eval team catch total num: 13
team_policy eval average step individual rewards of agent4: 0.3609292769712001
team_policy eval average team episode rewards of agent4: 32.5
team_policy eval idv catch total num of agent4: 17
team_policy eval team catch total num: 13
idv_policy eval average step individual rewards of agent0: 0.3124848639120227
idv_policy eval average team episode rewards of agent0: 12.5
idv_policy eval idv catch total num of agent0: 15
idv_policy eval team catch total num: 5
idv_policy eval average step individual rewards of agent1: 0.39941021623607736
idv_policy eval average team episode rewards of agent1: 12.5
idv_policy eval idv catch total num of agent1: 18
idv_policy eval team catch total num: 5
idv_policy eval average step individual rewards of agent2: 0.11468803173441888
idv_policy eval average team episode rewards of agent2: 12.5
idv_policy eval idv catch total num of agent2: 7
idv_policy eval team catch total num: 5
idv_policy eval average step individual rewards of agent3: 0.06237444037223269
idv_policy eval average team episode rewards of agent3: 12.5
idv_policy eval idv catch total num of agent3: 5
idv_policy eval team catch total num: 5
idv_policy eval average step individual rewards of agent4: 0.03640097216105184
idv_policy eval average team episode rewards of agent4: 12.5
idv_policy eval idv catch total num of agent4: 4
idv_policy eval team catch total num: 5

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9201/10000 episodes, total num timesteps 1840400/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9202/10000 episodes, total num timesteps 1840600/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9203/10000 episodes, total num timesteps 1840800/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9204/10000 episodes, total num timesteps 1841000/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9205/10000 episodes, total num timesteps 1841200/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9206/10000 episodes, total num timesteps 1841400/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9207/10000 episodes, total num timesteps 1841600/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9208/10000 episodes, total num timesteps 1841800/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9209/10000 episodes, total num timesteps 1842000/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9210/10000 episodes, total num timesteps 1842200/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9211/10000 episodes, total num timesteps 1842400/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9212/10000 episodes, total num timesteps 1842600/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9213/10000 episodes, total num timesteps 1842800/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9214/10000 episodes, total num timesteps 1843000/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9215/10000 episodes, total num timesteps 1843200/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9216/10000 episodes, total num timesteps 1843400/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9217/10000 episodes, total num timesteps 1843600/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9218/10000 episodes, total num timesteps 1843800/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9219/10000 episodes, total num timesteps 1844000/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9220/10000 episodes, total num timesteps 1844200/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9221/10000 episodes, total num timesteps 1844400/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9222/10000 episodes, total num timesteps 1844600/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9223/10000 episodes, total num timesteps 1844800/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9224/10000 episodes, total num timesteps 1845000/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9225/10000 episodes, total num timesteps 1845200/2000000, FPS 259.

team_policy eval average step individual rewards of agent0: 0.24494348127735674
team_policy eval average team episode rewards of agent0: 25.0
team_policy eval idv catch total num of agent0: 12
team_policy eval team catch total num: 10
team_policy eval average step individual rewards of agent1: 0.1930254931660002
team_policy eval average team episode rewards of agent1: 25.0
team_policy eval idv catch total num of agent1: 10
team_policy eval team catch total num: 10
team_policy eval average step individual rewards of agent2: 0.21881671460542187
team_policy eval average team episode rewards of agent2: 25.0
team_policy eval idv catch total num of agent2: 11
team_policy eval team catch total num: 10
team_policy eval average step individual rewards of agent3: 0.32437233959648465
team_policy eval average team episode rewards of agent3: 25.0
team_policy eval idv catch total num of agent3: 15
team_policy eval team catch total num: 10
team_policy eval average step individual rewards of agent4: 0.13675617319913158
team_policy eval average team episode rewards of agent4: 25.0
team_policy eval idv catch total num of agent4: 8
team_policy eval team catch total num: 10
idv_policy eval average step individual rewards of agent0: 0.33998370263752054
idv_policy eval average team episode rewards of agent0: 50.0
idv_policy eval idv catch total num of agent0: 16
idv_policy eval team catch total num: 20
idv_policy eval average step individual rewards of agent1: 0.18699933558000967
idv_policy eval average team episode rewards of agent1: 50.0
idv_policy eval idv catch total num of agent1: 10
idv_policy eval team catch total num: 20
idv_policy eval average step individual rewards of agent2: 0.7284411607914575
idv_policy eval average team episode rewards of agent2: 50.0
idv_policy eval idv catch total num of agent2: 31
idv_policy eval team catch total num: 20
idv_policy eval average step individual rewards of agent3: 0.34515225979611736
idv_policy eval average team episode rewards of agent3: 50.0
idv_policy eval idv catch total num of agent3: 16
idv_policy eval team catch total num: 20
idv_policy eval average step individual rewards of agent4: 0.5272967326373714
idv_policy eval average team episode rewards of agent4: 50.0
idv_policy eval idv catch total num of agent4: 23
idv_policy eval team catch total num: 20

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9226/10000 episodes, total num timesteps 1845400/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9227/10000 episodes, total num timesteps 1845600/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9228/10000 episodes, total num timesteps 1845800/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9229/10000 episodes, total num timesteps 1846000/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9230/10000 episodes, total num timesteps 1846200/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9231/10000 episodes, total num timesteps 1846400/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9232/10000 episodes, total num timesteps 1846600/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9233/10000 episodes, total num timesteps 1846800/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9234/10000 episodes, total num timesteps 1847000/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9235/10000 episodes, total num timesteps 1847200/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9236/10000 episodes, total num timesteps 1847400/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9237/10000 episodes, total num timesteps 1847600/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9238/10000 episodes, total num timesteps 1847800/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9239/10000 episodes, total num timesteps 1848000/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9240/10000 episodes, total num timesteps 1848200/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9241/10000 episodes, total num timesteps 1848400/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9242/10000 episodes, total num timesteps 1848600/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9243/10000 episodes, total num timesteps 1848800/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9244/10000 episodes, total num timesteps 1849000/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9245/10000 episodes, total num timesteps 1849200/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9246/10000 episodes, total num timesteps 1849400/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9247/10000 episodes, total num timesteps 1849600/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9248/10000 episodes, total num timesteps 1849800/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9249/10000 episodes, total num timesteps 1850000/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9250/10000 episodes, total num timesteps 1850200/2000000, FPS 259.

team_policy eval average step individual rewards of agent0: 0.09390337147059069
team_policy eval average team episode rewards of agent0: 7.5
team_policy eval idv catch total num of agent0: 6
team_policy eval team catch total num: 3
team_policy eval average step individual rewards of agent1: 0.3546481719317839
team_policy eval average team episode rewards of agent1: 7.5
team_policy eval idv catch total num of agent1: 16
team_policy eval team catch total num: 3
team_policy eval average step individual rewards of agent2: 0.1848744478202427
team_policy eval average team episode rewards of agent2: 7.5
team_policy eval idv catch total num of agent2: 10
team_policy eval team catch total num: 3
team_policy eval average step individual rewards of agent3: 0.1783835558716496
team_policy eval average team episode rewards of agent3: 7.5
team_policy eval idv catch total num of agent3: 9
team_policy eval team catch total num: 3
team_policy eval average step individual rewards of agent4: 0.13429728344886233
team_policy eval average team episode rewards of agent4: 7.5
team_policy eval idv catch total num of agent4: 8
team_policy eval team catch total num: 3
idv_policy eval average step individual rewards of agent0: 0.2067600962121513
idv_policy eval average team episode rewards of agent0: 32.5
idv_policy eval idv catch total num of agent0: 11
idv_policy eval team catch total num: 13
idv_policy eval average step individual rewards of agent1: 0.696672749480698
idv_policy eval average team episode rewards of agent1: 32.5
idv_policy eval idv catch total num of agent1: 30
idv_policy eval team catch total num: 13
idv_policy eval average step individual rewards of agent2: 0.5890351686392284
idv_policy eval average team episode rewards of agent2: 32.5
idv_policy eval idv catch total num of agent2: 25
idv_policy eval team catch total num: 13
idv_policy eval average step individual rewards of agent3: 0.18417209897170456
idv_policy eval average team episode rewards of agent3: 32.5
idv_policy eval idv catch total num of agent3: 10
idv_policy eval team catch total num: 13
idv_policy eval average step individual rewards of agent4: 0.5061503948085183
idv_policy eval average team episode rewards of agent4: 32.5
idv_policy eval idv catch total num of agent4: 22
idv_policy eval team catch total num: 13

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9251/10000 episodes, total num timesteps 1850400/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9252/10000 episodes, total num timesteps 1850600/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9253/10000 episodes, total num timesteps 1850800/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9254/10000 episodes, total num timesteps 1851000/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9255/10000 episodes, total num timesteps 1851200/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9256/10000 episodes, total num timesteps 1851400/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9257/10000 episodes, total num timesteps 1851600/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9258/10000 episodes, total num timesteps 1851800/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9259/10000 episodes, total num timesteps 1852000/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9260/10000 episodes, total num timesteps 1852200/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9261/10000 episodes, total num timesteps 1852400/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9262/10000 episodes, total num timesteps 1852600/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9263/10000 episodes, total num timesteps 1852800/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9264/10000 episodes, total num timesteps 1853000/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9265/10000 episodes, total num timesteps 1853200/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9266/10000 episodes, total num timesteps 1853400/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9267/10000 episodes, total num timesteps 1853600/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9268/10000 episodes, total num timesteps 1853800/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9269/10000 episodes, total num timesteps 1854000/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9270/10000 episodes, total num timesteps 1854200/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9271/10000 episodes, total num timesteps 1854400/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9272/10000 episodes, total num timesteps 1854600/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9273/10000 episodes, total num timesteps 1854800/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9274/10000 episodes, total num timesteps 1855000/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9275/10000 episodes, total num timesteps 1855200/2000000, FPS 259.

team_policy eval average step individual rewards of agent0: 0.15315701506803597
team_policy eval average team episode rewards of agent0: 17.5
team_policy eval idv catch total num of agent0: 9
team_policy eval team catch total num: 7
team_policy eval average step individual rewards of agent1: 0.25533803301650715
team_policy eval average team episode rewards of agent1: 17.5
team_policy eval idv catch total num of agent1: 13
team_policy eval team catch total num: 7
team_policy eval average step individual rewards of agent2: 0.28101862140403655
team_policy eval average team episode rewards of agent2: 17.5
team_policy eval idv catch total num of agent2: 14
team_policy eval team catch total num: 7
team_policy eval average step individual rewards of agent3: 0.1485948718994124
team_policy eval average team episode rewards of agent3: 17.5
team_policy eval idv catch total num of agent3: 9
team_policy eval team catch total num: 7
team_policy eval average step individual rewards of agent4: 0.20543442453689365
team_policy eval average team episode rewards of agent4: 17.5
team_policy eval idv catch total num of agent4: 11
team_policy eval team catch total num: 7
idv_policy eval average step individual rewards of agent0: 0.642682291588687
idv_policy eval average team episode rewards of agent0: 15.0
idv_policy eval idv catch total num of agent0: 28
idv_policy eval team catch total num: 6
idv_policy eval average step individual rewards of agent1: 0.09247368715388188
idv_policy eval average team episode rewards of agent1: 15.0
idv_policy eval idv catch total num of agent1: 6
idv_policy eval team catch total num: 6
idv_policy eval average step individual rewards of agent2: 0.13340953864146904
idv_policy eval average team episode rewards of agent2: 15.0
idv_policy eval idv catch total num of agent2: 8
idv_policy eval team catch total num: 6
idv_policy eval average step individual rewards of agent3: 0.4030525408169458
idv_policy eval average team episode rewards of agent3: 15.0
idv_policy eval idv catch total num of agent3: 18
idv_policy eval team catch total num: 6
idv_policy eval average step individual rewards of agent4: 0.11688387116927848
idv_policy eval average team episode rewards of agent4: 15.0
idv_policy eval idv catch total num of agent4: 7
idv_policy eval team catch total num: 6

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9276/10000 episodes, total num timesteps 1855400/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9277/10000 episodes, total num timesteps 1855600/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9278/10000 episodes, total num timesteps 1855800/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9279/10000 episodes, total num timesteps 1856000/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9280/10000 episodes, total num timesteps 1856200/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9281/10000 episodes, total num timesteps 1856400/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9282/10000 episodes, total num timesteps 1856600/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9283/10000 episodes, total num timesteps 1856800/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9284/10000 episodes, total num timesteps 1857000/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9285/10000 episodes, total num timesteps 1857200/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9286/10000 episodes, total num timesteps 1857400/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9287/10000 episodes, total num timesteps 1857600/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9288/10000 episodes, total num timesteps 1857800/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9289/10000 episodes, total num timesteps 1858000/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9290/10000 episodes, total num timesteps 1858200/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9291/10000 episodes, total num timesteps 1858400/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9292/10000 episodes, total num timesteps 1858600/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9293/10000 episodes, total num timesteps 1858800/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9294/10000 episodes, total num timesteps 1859000/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9295/10000 episodes, total num timesteps 1859200/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9296/10000 episodes, total num timesteps 1859400/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9297/10000 episodes, total num timesteps 1859600/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9298/10000 episodes, total num timesteps 1859800/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9299/10000 episodes, total num timesteps 1860000/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9300/10000 episodes, total num timesteps 1860200/2000000, FPS 259.

team_policy eval average step individual rewards of agent0: 0.05296428887138089
team_policy eval average team episode rewards of agent0: 2.5
team_policy eval idv catch total num of agent0: 5
team_policy eval team catch total num: 1
team_policy eval average step individual rewards of agent1: 0.10194870064926961
team_policy eval average team episode rewards of agent1: 2.5
team_policy eval idv catch total num of agent1: 7
team_policy eval team catch total num: 1
team_policy eval average step individual rewards of agent2: 0.2748056659820099
team_policy eval average team episode rewards of agent2: 2.5
team_policy eval idv catch total num of agent2: 14
team_policy eval team catch total num: 1
team_policy eval average step individual rewards of agent3: 0.2109898903952694
team_policy eval average team episode rewards of agent3: 2.5
team_policy eval idv catch total num of agent3: 11
team_policy eval team catch total num: 1
team_policy eval average step individual rewards of agent4: 0.08205057330295686
team_policy eval average team episode rewards of agent4: 2.5
team_policy eval idv catch total num of agent4: 6
team_policy eval team catch total num: 1
idv_policy eval average step individual rewards of agent0: 0.6974437411931461
idv_policy eval average team episode rewards of agent0: 35.0
idv_policy eval idv catch total num of agent0: 30
idv_policy eval team catch total num: 14
idv_policy eval average step individual rewards of agent1: 0.10549807605870437
idv_policy eval average team episode rewards of agent1: 35.0
idv_policy eval idv catch total num of agent1: 7
idv_policy eval team catch total num: 14
idv_policy eval average step individual rewards of agent2: 0.19506824160024575
idv_policy eval average team episode rewards of agent2: 35.0
idv_policy eval idv catch total num of agent2: 11
idv_policy eval team catch total num: 14
idv_policy eval average step individual rewards of agent3: 0.24560496142175758
idv_policy eval average team episode rewards of agent3: 35.0
idv_policy eval idv catch total num of agent3: 12
idv_policy eval team catch total num: 14
idv_policy eval average step individual rewards of agent4: 0.21278441725930514
idv_policy eval average team episode rewards of agent4: 35.0
idv_policy eval idv catch total num of agent4: 11
idv_policy eval team catch total num: 14

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9301/10000 episodes, total num timesteps 1860400/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9302/10000 episodes, total num timesteps 1860600/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9303/10000 episodes, total num timesteps 1860800/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9304/10000 episodes, total num timesteps 1861000/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9305/10000 episodes, total num timesteps 1861200/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9306/10000 episodes, total num timesteps 1861400/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9307/10000 episodes, total num timesteps 1861600/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9308/10000 episodes, total num timesteps 1861800/2000000, FPS 259.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9309/10000 episodes, total num timesteps 1862000/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9310/10000 episodes, total num timesteps 1862200/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9311/10000 episodes, total num timesteps 1862400/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9312/10000 episodes, total num timesteps 1862600/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9313/10000 episodes, total num timesteps 1862800/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9314/10000 episodes, total num timesteps 1863000/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9315/10000 episodes, total num timesteps 1863200/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9316/10000 episodes, total num timesteps 1863400/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9317/10000 episodes, total num timesteps 1863600/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9318/10000 episodes, total num timesteps 1863800/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9319/10000 episodes, total num timesteps 1864000/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9320/10000 episodes, total num timesteps 1864200/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9321/10000 episodes, total num timesteps 1864400/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9322/10000 episodes, total num timesteps 1864600/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9323/10000 episodes, total num timesteps 1864800/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9324/10000 episodes, total num timesteps 1865000/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9325/10000 episodes, total num timesteps 1865200/2000000, FPS 258.

team_policy eval average step individual rewards of agent0: 0.10582800820906609
team_policy eval average team episode rewards of agent0: 0.0
team_policy eval idv catch total num of agent0: 8
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent1: 0.04240195466008918
team_policy eval average team episode rewards of agent1: 0.0
team_policy eval idv catch total num of agent1: 5
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent2: -0.04935547669726198
team_policy eval average team episode rewards of agent2: 0.0
team_policy eval idv catch total num of agent2: 3
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent3: -0.03193933585684677
team_policy eval average team episode rewards of agent3: 0.0
team_policy eval idv catch total num of agent3: 3
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent4: -0.02143642525641776
team_policy eval average team episode rewards of agent4: 0.0
team_policy eval idv catch total num of agent4: 3
team_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent0: 0.21688166679155646
idv_policy eval average team episode rewards of agent0: 10.0
idv_policy eval idv catch total num of agent0: 11
idv_policy eval team catch total num: 4
idv_policy eval average step individual rewards of agent1: 0.3366622087531359
idv_policy eval average team episode rewards of agent1: 10.0
idv_policy eval idv catch total num of agent1: 16
idv_policy eval team catch total num: 4
idv_policy eval average step individual rewards of agent2: 0.27373901751825813
idv_policy eval average team episode rewards of agent2: 10.0
idv_policy eval idv catch total num of agent2: 14
idv_policy eval team catch total num: 4
idv_policy eval average step individual rewards of agent3: 0.2892339437753396
idv_policy eval average team episode rewards of agent3: 10.0
idv_policy eval idv catch total num of agent3: 14
idv_policy eval team catch total num: 4
idv_policy eval average step individual rewards of agent4: 0.02587970449326999
idv_policy eval average team episode rewards of agent4: 10.0
idv_policy eval idv catch total num of agent4: 4
idv_policy eval team catch total num: 4

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9326/10000 episodes, total num timesteps 1865400/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9327/10000 episodes, total num timesteps 1865600/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9328/10000 episodes, total num timesteps 1865800/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9329/10000 episodes, total num timesteps 1866000/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9330/10000 episodes, total num timesteps 1866200/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9331/10000 episodes, total num timesteps 1866400/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9332/10000 episodes, total num timesteps 1866600/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9333/10000 episodes, total num timesteps 1866800/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9334/10000 episodes, total num timesteps 1867000/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9335/10000 episodes, total num timesteps 1867200/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9336/10000 episodes, total num timesteps 1867400/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9337/10000 episodes, total num timesteps 1867600/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9338/10000 episodes, total num timesteps 1867800/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9339/10000 episodes, total num timesteps 1868000/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9340/10000 episodes, total num timesteps 1868200/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9341/10000 episodes, total num timesteps 1868400/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9342/10000 episodes, total num timesteps 1868600/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9343/10000 episodes, total num timesteps 1868800/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9344/10000 episodes, total num timesteps 1869000/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9345/10000 episodes, total num timesteps 1869200/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9346/10000 episodes, total num timesteps 1869400/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9347/10000 episodes, total num timesteps 1869600/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9348/10000 episodes, total num timesteps 1869800/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9349/10000 episodes, total num timesteps 1870000/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9350/10000 episodes, total num timesteps 1870200/2000000, FPS 258.

team_policy eval average step individual rewards of agent0: 0.2364104133599225
team_policy eval average team episode rewards of agent0: 12.5
team_policy eval idv catch total num of agent0: 12
team_policy eval team catch total num: 5
team_policy eval average step individual rewards of agent1: 0.28743800309775625
team_policy eval average team episode rewards of agent1: 12.5
team_policy eval idv catch total num of agent1: 14
team_policy eval team catch total num: 5
team_policy eval average step individual rewards of agent2: 0.0433638734082721
team_policy eval average team episode rewards of agent2: 12.5
team_policy eval idv catch total num of agent2: 5
team_policy eval team catch total num: 5
team_policy eval average step individual rewards of agent3: 0.10755965805877614
team_policy eval average team episode rewards of agent3: 12.5
team_policy eval idv catch total num of agent3: 7
team_policy eval team catch total num: 5
team_policy eval average step individual rewards of agent4: 0.09004667077107492
team_policy eval average team episode rewards of agent4: 12.5
team_policy eval idv catch total num of agent4: 7
team_policy eval team catch total num: 5
idv_policy eval average step individual rewards of agent0: -0.04084399673193718
idv_policy eval average team episode rewards of agent0: 7.5
idv_policy eval idv catch total num of agent0: 2
idv_policy eval team catch total num: 3
idv_policy eval average step individual rewards of agent1: 0.2746508999418648
idv_policy eval average team episode rewards of agent1: 7.5
idv_policy eval idv catch total num of agent1: 14
idv_policy eval team catch total num: 3
idv_policy eval average step individual rewards of agent2: 0.14842027755597073
idv_policy eval average team episode rewards of agent2: 7.5
idv_policy eval idv catch total num of agent2: 9
idv_policy eval team catch total num: 3
idv_policy eval average step individual rewards of agent3: 0.11233047770462233
idv_policy eval average team episode rewards of agent3: 7.5
idv_policy eval idv catch total num of agent3: 7
idv_policy eval team catch total num: 3
idv_policy eval average step individual rewards of agent4: 0.019126341113532012
idv_policy eval average team episode rewards of agent4: 7.5
idv_policy eval idv catch total num of agent4: 4
idv_policy eval team catch total num: 3

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9351/10000 episodes, total num timesteps 1870400/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9352/10000 episodes, total num timesteps 1870600/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9353/10000 episodes, total num timesteps 1870800/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9354/10000 episodes, total num timesteps 1871000/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9355/10000 episodes, total num timesteps 1871200/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9356/10000 episodes, total num timesteps 1871400/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9357/10000 episodes, total num timesteps 1871600/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9358/10000 episodes, total num timesteps 1871800/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9359/10000 episodes, total num timesteps 1872000/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9360/10000 episodes, total num timesteps 1872200/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9361/10000 episodes, total num timesteps 1872400/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9362/10000 episodes, total num timesteps 1872600/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9363/10000 episodes, total num timesteps 1872800/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9364/10000 episodes, total num timesteps 1873000/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9365/10000 episodes, total num timesteps 1873200/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9366/10000 episodes, total num timesteps 1873400/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9367/10000 episodes, total num timesteps 1873600/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9368/10000 episodes, total num timesteps 1873800/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9369/10000 episodes, total num timesteps 1874000/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9370/10000 episodes, total num timesteps 1874200/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9371/10000 episodes, total num timesteps 1874400/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9372/10000 episodes, total num timesteps 1874600/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9373/10000 episodes, total num timesteps 1874800/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9374/10000 episodes, total num timesteps 1875000/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9375/10000 episodes, total num timesteps 1875200/2000000, FPS 258.

team_policy eval average step individual rewards of agent0: 0.2512199722089504
team_policy eval average team episode rewards of agent0: 20.0
team_policy eval idv catch total num of agent0: 13
team_policy eval team catch total num: 8
team_policy eval average step individual rewards of agent1: 0.08013504121851907
team_policy eval average team episode rewards of agent1: 20.0
team_policy eval idv catch total num of agent1: 6
team_policy eval team catch total num: 8
team_policy eval average step individual rewards of agent2: 0.08318663858479108
team_policy eval average team episode rewards of agent2: 20.0
team_policy eval idv catch total num of agent2: 6
team_policy eval team catch total num: 8
team_policy eval average step individual rewards of agent3: 0.445022176475551
team_policy eval average team episode rewards of agent3: 20.0
team_policy eval idv catch total num of agent3: 20
team_policy eval team catch total num: 8
team_policy eval average step individual rewards of agent4: 0.32342172906670164
team_policy eval average team episode rewards of agent4: 20.0
team_policy eval idv catch total num of agent4: 16
team_policy eval team catch total num: 8
idv_policy eval average step individual rewards of agent0: 0.14256100044820483
idv_policy eval average team episode rewards of agent0: 7.5
idv_policy eval idv catch total num of agent0: 8
idv_policy eval team catch total num: 3
idv_policy eval average step individual rewards of agent1: 0.13435058586033966
idv_policy eval average team episode rewards of agent1: 7.5
idv_policy eval idv catch total num of agent1: 9
idv_policy eval team catch total num: 3
idv_policy eval average step individual rewards of agent2: 0.39217272480663284
idv_policy eval average team episode rewards of agent2: 7.5
idv_policy eval idv catch total num of agent2: 19
idv_policy eval team catch total num: 3
idv_policy eval average step individual rewards of agent3: 0.1211906326205001
idv_policy eval average team episode rewards of agent3: 7.5
idv_policy eval idv catch total num of agent3: 8
idv_policy eval team catch total num: 3
idv_policy eval average step individual rewards of agent4: -0.07119212193619108
idv_policy eval average team episode rewards of agent4: 7.5
idv_policy eval idv catch total num of agent4: 1
idv_policy eval team catch total num: 3

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9376/10000 episodes, total num timesteps 1875400/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9377/10000 episodes, total num timesteps 1875600/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9378/10000 episodes, total num timesteps 1875800/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9379/10000 episodes, total num timesteps 1876000/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9380/10000 episodes, total num timesteps 1876200/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9381/10000 episodes, total num timesteps 1876400/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9382/10000 episodes, total num timesteps 1876600/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9383/10000 episodes, total num timesteps 1876800/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9384/10000 episodes, total num timesteps 1877000/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9385/10000 episodes, total num timesteps 1877200/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9386/10000 episodes, total num timesteps 1877400/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9387/10000 episodes, total num timesteps 1877600/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9388/10000 episodes, total num timesteps 1877800/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9389/10000 episodes, total num timesteps 1878000/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9390/10000 episodes, total num timesteps 1878200/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9391/10000 episodes, total num timesteps 1878400/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9392/10000 episodes, total num timesteps 1878600/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9393/10000 episodes, total num timesteps 1878800/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9394/10000 episodes, total num timesteps 1879000/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9395/10000 episodes, total num timesteps 1879200/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9396/10000 episodes, total num timesteps 1879400/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9397/10000 episodes, total num timesteps 1879600/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9398/10000 episodes, total num timesteps 1879800/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9399/10000 episodes, total num timesteps 1880000/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9400/10000 episodes, total num timesteps 1880200/2000000, FPS 258.

team_policy eval average step individual rewards of agent0: 0.24678363594545644
team_policy eval average team episode rewards of agent0: 25.0
team_policy eval idv catch total num of agent0: 13
team_policy eval team catch total num: 10
team_policy eval average step individual rewards of agent1: 0.15957915551082508
team_policy eval average team episode rewards of agent1: 25.0
team_policy eval idv catch total num of agent1: 9
team_policy eval team catch total num: 10
team_policy eval average step individual rewards of agent2: 0.33777327659956474
team_policy eval average team episode rewards of agent2: 25.0
team_policy eval idv catch total num of agent2: 16
team_policy eval team catch total num: 10
team_policy eval average step individual rewards of agent3: 0.4074194769848669
team_policy eval average team episode rewards of agent3: 25.0
team_policy eval idv catch total num of agent3: 18
team_policy eval team catch total num: 10
team_policy eval average step individual rewards of agent4: 0.29995414901808637
team_policy eval average team episode rewards of agent4: 25.0
team_policy eval idv catch total num of agent4: 14
team_policy eval team catch total num: 10
idv_policy eval average step individual rewards of agent0: -0.008473573011146237
idv_policy eval average team episode rewards of agent0: 7.5
idv_policy eval idv catch total num of agent0: 3
idv_policy eval team catch total num: 3
idv_policy eval average step individual rewards of agent1: 0.11593077927252118
idv_policy eval average team episode rewards of agent1: 7.5
idv_policy eval idv catch total num of agent1: 8
idv_policy eval team catch total num: 3
idv_policy eval average step individual rewards of agent2: -0.06004080841909113
idv_policy eval average team episode rewards of agent2: 7.5
idv_policy eval idv catch total num of agent2: 1
idv_policy eval team catch total num: 3
idv_policy eval average step individual rewards of agent3: 0.015047475585353012
idv_policy eval average team episode rewards of agent3: 7.5
idv_policy eval idv catch total num of agent3: 5
idv_policy eval team catch total num: 3
idv_policy eval average step individual rewards of agent4: 0.17920032755431212
idv_policy eval average team episode rewards of agent4: 7.5
idv_policy eval idv catch total num of agent4: 11
idv_policy eval team catch total num: 3

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9401/10000 episodes, total num timesteps 1880400/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9402/10000 episodes, total num timesteps 1880600/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9403/10000 episodes, total num timesteps 1880800/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9404/10000 episodes, total num timesteps 1881000/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9405/10000 episodes, total num timesteps 1881200/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9406/10000 episodes, total num timesteps 1881400/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9407/10000 episodes, total num timesteps 1881600/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9408/10000 episodes, total num timesteps 1881800/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9409/10000 episodes, total num timesteps 1882000/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9410/10000 episodes, total num timesteps 1882200/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9411/10000 episodes, total num timesteps 1882400/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9412/10000 episodes, total num timesteps 1882600/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9413/10000 episodes, total num timesteps 1882800/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9414/10000 episodes, total num timesteps 1883000/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9415/10000 episodes, total num timesteps 1883200/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9416/10000 episodes, total num timesteps 1883400/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9417/10000 episodes, total num timesteps 1883600/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9418/10000 episodes, total num timesteps 1883800/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9419/10000 episodes, total num timesteps 1884000/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9420/10000 episodes, total num timesteps 1884200/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9421/10000 episodes, total num timesteps 1884400/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9422/10000 episodes, total num timesteps 1884600/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9423/10000 episodes, total num timesteps 1884800/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9424/10000 episodes, total num timesteps 1885000/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9425/10000 episodes, total num timesteps 1885200/2000000, FPS 258.

team_policy eval average step individual rewards of agent0: 0.5452794346589216
team_policy eval average team episode rewards of agent0: 17.5
team_policy eval idv catch total num of agent0: 24
team_policy eval team catch total num: 7
team_policy eval average step individual rewards of agent1: 0.13285061968170475
team_policy eval average team episode rewards of agent1: 17.5
team_policy eval idv catch total num of agent1: 8
team_policy eval team catch total num: 7
team_policy eval average step individual rewards of agent2: 0.20605210695735393
team_policy eval average team episode rewards of agent2: 17.5
team_policy eval idv catch total num of agent2: 11
team_policy eval team catch total num: 7
team_policy eval average step individual rewards of agent3: 0.2631219787728368
team_policy eval average team episode rewards of agent3: 17.5
team_policy eval idv catch total num of agent3: 13
team_policy eval team catch total num: 7
team_policy eval average step individual rewards of agent4: 0.24828114889409406
team_policy eval average team episode rewards of agent4: 17.5
team_policy eval idv catch total num of agent4: 12
team_policy eval team catch total num: 7
idv_policy eval average step individual rewards of agent0: 0.26566877615817897
idv_policy eval average team episode rewards of agent0: 10.0
idv_policy eval idv catch total num of agent0: 14
idv_policy eval team catch total num: 4
idv_policy eval average step individual rewards of agent1: 0.08419006462088811
idv_policy eval average team episode rewards of agent1: 10.0
idv_policy eval idv catch total num of agent1: 6
idv_policy eval team catch total num: 4
idv_policy eval average step individual rewards of agent2: 0.013379704805260384
idv_policy eval average team episode rewards of agent2: 10.0
idv_policy eval idv catch total num of agent2: 4
idv_policy eval team catch total num: 4
idv_policy eval average step individual rewards of agent3: 0.20163541890189068
idv_policy eval average team episode rewards of agent3: 10.0
idv_policy eval idv catch total num of agent3: 11
idv_policy eval team catch total num: 4
idv_policy eval average step individual rewards of agent4: 0.24142445168799015
idv_policy eval average team episode rewards of agent4: 10.0
idv_policy eval idv catch total num of agent4: 12
idv_policy eval team catch total num: 4

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9426/10000 episodes, total num timesteps 1885400/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9427/10000 episodes, total num timesteps 1885600/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9428/10000 episodes, total num timesteps 1885800/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9429/10000 episodes, total num timesteps 1886000/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9430/10000 episodes, total num timesteps 1886200/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9431/10000 episodes, total num timesteps 1886400/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9432/10000 episodes, total num timesteps 1886600/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9433/10000 episodes, total num timesteps 1886800/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9434/10000 episodes, total num timesteps 1887000/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9435/10000 episodes, total num timesteps 1887200/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9436/10000 episodes, total num timesteps 1887400/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9437/10000 episodes, total num timesteps 1887600/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9438/10000 episodes, total num timesteps 1887800/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9439/10000 episodes, total num timesteps 1888000/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9440/10000 episodes, total num timesteps 1888200/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9441/10000 episodes, total num timesteps 1888400/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9442/10000 episodes, total num timesteps 1888600/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9443/10000 episodes, total num timesteps 1888800/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9444/10000 episodes, total num timesteps 1889000/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9445/10000 episodes, total num timesteps 1889200/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9446/10000 episodes, total num timesteps 1889400/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9447/10000 episodes, total num timesteps 1889600/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9448/10000 episodes, total num timesteps 1889800/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9449/10000 episodes, total num timesteps 1890000/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9450/10000 episodes, total num timesteps 1890200/2000000, FPS 258.

team_policy eval average step individual rewards of agent0: 0.15546387881718593
team_policy eval average team episode rewards of agent0: 10.0
team_policy eval idv catch total num of agent0: 9
team_policy eval team catch total num: 4
team_policy eval average step individual rewards of agent1: 0.0051157080276365095
team_policy eval average team episode rewards of agent1: 10.0
team_policy eval idv catch total num of agent1: 3
team_policy eval team catch total num: 4
team_policy eval average step individual rewards of agent2: 0.3897529462273445
team_policy eval average team episode rewards of agent2: 10.0
team_policy eval idv catch total num of agent2: 18
team_policy eval team catch total num: 4
team_policy eval average step individual rewards of agent3: 0.3529330501459724
team_policy eval average team episode rewards of agent3: 10.0
team_policy eval idv catch total num of agent3: 17
team_policy eval team catch total num: 4
team_policy eval average step individual rewards of agent4: 0.26421618324310325
team_policy eval average team episode rewards of agent4: 10.0
team_policy eval idv catch total num of agent4: 13
team_policy eval team catch total num: 4
idv_policy eval average step individual rewards of agent0: 0.02943497839644344
idv_policy eval average team episode rewards of agent0: 2.5
idv_policy eval idv catch total num of agent0: 6
idv_policy eval team catch total num: 1
idv_policy eval average step individual rewards of agent1: 0.20542346120035573
idv_policy eval average team episode rewards of agent1: 2.5
idv_policy eval idv catch total num of agent1: 11
idv_policy eval team catch total num: 1
idv_policy eval average step individual rewards of agent2: -0.04173901931272479
idv_policy eval average team episode rewards of agent2: 2.5
idv_policy eval idv catch total num of agent2: 3
idv_policy eval team catch total num: 1
idv_policy eval average step individual rewards of agent3: 0.1251769466556324
idv_policy eval average team episode rewards of agent3: 2.5
idv_policy eval idv catch total num of agent3: 10
idv_policy eval team catch total num: 1
idv_policy eval average step individual rewards of agent4: -0.07767510683683254
idv_policy eval average team episode rewards of agent4: 2.5
idv_policy eval idv catch total num of agent4: 1
idv_policy eval team catch total num: 1

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9451/10000 episodes, total num timesteps 1890400/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9452/10000 episodes, total num timesteps 1890600/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9453/10000 episodes, total num timesteps 1890800/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9454/10000 episodes, total num timesteps 1891000/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9455/10000 episodes, total num timesteps 1891200/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9456/10000 episodes, total num timesteps 1891400/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9457/10000 episodes, total num timesteps 1891600/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9458/10000 episodes, total num timesteps 1891800/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9459/10000 episodes, total num timesteps 1892000/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9460/10000 episodes, total num timesteps 1892200/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9461/10000 episodes, total num timesteps 1892400/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9462/10000 episodes, total num timesteps 1892600/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9463/10000 episodes, total num timesteps 1892800/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9464/10000 episodes, total num timesteps 1893000/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9465/10000 episodes, total num timesteps 1893200/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9466/10000 episodes, total num timesteps 1893400/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9467/10000 episodes, total num timesteps 1893600/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9468/10000 episodes, total num timesteps 1893800/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9469/10000 episodes, total num timesteps 1894000/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9470/10000 episodes, total num timesteps 1894200/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9471/10000 episodes, total num timesteps 1894400/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9472/10000 episodes, total num timesteps 1894600/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9473/10000 episodes, total num timesteps 1894800/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9474/10000 episodes, total num timesteps 1895000/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9475/10000 episodes, total num timesteps 1895200/2000000, FPS 258.

team_policy eval average step individual rewards of agent0: 0.1548664612463515
team_policy eval average team episode rewards of agent0: 7.5
team_policy eval idv catch total num of agent0: 9
team_policy eval team catch total num: 3
team_policy eval average step individual rewards of agent1: 0.061466630932882814
team_policy eval average team episode rewards of agent1: 7.5
team_policy eval idv catch total num of agent1: 6
team_policy eval team catch total num: 3
team_policy eval average step individual rewards of agent2: 0.034026637672196464
team_policy eval average team episode rewards of agent2: 7.5
team_policy eval idv catch total num of agent2: 5
team_policy eval team catch total num: 3
team_policy eval average step individual rewards of agent3: 0.24785806188210963
team_policy eval average team episode rewards of agent3: 7.5
team_policy eval idv catch total num of agent3: 13
team_policy eval team catch total num: 3
team_policy eval average step individual rewards of agent4: -0.09096525389528537
team_policy eval average team episode rewards of agent4: 7.5
team_policy eval idv catch total num of agent4: 1
team_policy eval team catch total num: 3
idv_policy eval average step individual rewards of agent0: -0.0743341803839543
idv_policy eval average team episode rewards of agent0: 5.0
idv_policy eval idv catch total num of agent0: 1
idv_policy eval team catch total num: 2
idv_policy eval average step individual rewards of agent1: 0.5823694523585643
idv_policy eval average team episode rewards of agent1: 5.0
idv_policy eval idv catch total num of agent1: 26
idv_policy eval team catch total num: 2
idv_policy eval average step individual rewards of agent2: 0.0006388285706112073
idv_policy eval average team episode rewards of agent2: 5.0
idv_policy eval idv catch total num of agent2: 3
idv_policy eval team catch total num: 2
idv_policy eval average step individual rewards of agent3: 0.14099864787790464
idv_policy eval average team episode rewards of agent3: 5.0
idv_policy eval idv catch total num of agent3: 8
idv_policy eval team catch total num: 2
idv_policy eval average step individual rewards of agent4: -0.037214073444974405
idv_policy eval average team episode rewards of agent4: 5.0
idv_policy eval idv catch total num of agent4: 2
idv_policy eval team catch total num: 2

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9476/10000 episodes, total num timesteps 1895400/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9477/10000 episodes, total num timesteps 1895600/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9478/10000 episodes, total num timesteps 1895800/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9479/10000 episodes, total num timesteps 1896000/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9480/10000 episodes, total num timesteps 1896200/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9481/10000 episodes, total num timesteps 1896400/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9482/10000 episodes, total num timesteps 1896600/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9483/10000 episodes, total num timesteps 1896800/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9484/10000 episodes, total num timesteps 1897000/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9485/10000 episodes, total num timesteps 1897200/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9486/10000 episodes, total num timesteps 1897400/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9487/10000 episodes, total num timesteps 1897600/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9488/10000 episodes, total num timesteps 1897800/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9489/10000 episodes, total num timesteps 1898000/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9490/10000 episodes, total num timesteps 1898200/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9491/10000 episodes, total num timesteps 1898400/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9492/10000 episodes, total num timesteps 1898600/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9493/10000 episodes, total num timesteps 1898800/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9494/10000 episodes, total num timesteps 1899000/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9495/10000 episodes, total num timesteps 1899200/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9496/10000 episodes, total num timesteps 1899400/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9497/10000 episodes, total num timesteps 1899600/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9498/10000 episodes, total num timesteps 1899800/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9499/10000 episodes, total num timesteps 1900000/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9500/10000 episodes, total num timesteps 1900200/2000000, FPS 258.

team_policy eval average step individual rewards of agent0: 0.05537417164172426
team_policy eval average team episode rewards of agent0: 15.0
team_policy eval idv catch total num of agent0: 5
team_policy eval team catch total num: 6
team_policy eval average step individual rewards of agent1: 0.0859896526787307
team_policy eval average team episode rewards of agent1: 15.0
team_policy eval idv catch total num of agent1: 6
team_policy eval team catch total num: 6
team_policy eval average step individual rewards of agent2: 0.3181804523402723
team_policy eval average team episode rewards of agent2: 15.0
team_policy eval idv catch total num of agent2: 15
team_policy eval team catch total num: 6
team_policy eval average step individual rewards of agent3: 0.7022861493360808
team_policy eval average team episode rewards of agent3: 15.0
team_policy eval idv catch total num of agent3: 30
team_policy eval team catch total num: 6
team_policy eval average step individual rewards of agent4: 0.1312427403998954
team_policy eval average team episode rewards of agent4: 15.0
team_policy eval idv catch total num of agent4: 8
team_policy eval team catch total num: 6
idv_policy eval average step individual rewards of agent0: 0.07560605369354124
idv_policy eval average team episode rewards of agent0: 2.5
idv_policy eval idv catch total num of agent0: 6
idv_policy eval team catch total num: 1
idv_policy eval average step individual rewards of agent1: 0.15003905806557902
idv_policy eval average team episode rewards of agent1: 2.5
idv_policy eval idv catch total num of agent1: 9
idv_policy eval team catch total num: 1
idv_policy eval average step individual rewards of agent2: 0.2312876457045592
idv_policy eval average team episode rewards of agent2: 2.5
idv_policy eval idv catch total num of agent2: 12
idv_policy eval team catch total num: 1
idv_policy eval average step individual rewards of agent3: -0.050563408147723095
idv_policy eval average team episode rewards of agent3: 2.5
idv_policy eval idv catch total num of agent3: 1
idv_policy eval team catch total num: 1
idv_policy eval average step individual rewards of agent4: 0.05563019212732038
idv_policy eval average team episode rewards of agent4: 2.5
idv_policy eval idv catch total num of agent4: 5
idv_policy eval team catch total num: 1

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9501/10000 episodes, total num timesteps 1900400/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9502/10000 episodes, total num timesteps 1900600/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9503/10000 episodes, total num timesteps 1900800/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9504/10000 episodes, total num timesteps 1901000/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9505/10000 episodes, total num timesteps 1901200/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9506/10000 episodes, total num timesteps 1901400/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9507/10000 episodes, total num timesteps 1901600/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9508/10000 episodes, total num timesteps 1901800/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9509/10000 episodes, total num timesteps 1902000/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9510/10000 episodes, total num timesteps 1902200/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9511/10000 episodes, total num timesteps 1902400/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9512/10000 episodes, total num timesteps 1902600/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9513/10000 episodes, total num timesteps 1902800/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9514/10000 episodes, total num timesteps 1903000/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9515/10000 episodes, total num timesteps 1903200/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9516/10000 episodes, total num timesteps 1903400/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9517/10000 episodes, total num timesteps 1903600/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9518/10000 episodes, total num timesteps 1903800/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9519/10000 episodes, total num timesteps 1904000/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9520/10000 episodes, total num timesteps 1904200/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9521/10000 episodes, total num timesteps 1904400/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9522/10000 episodes, total num timesteps 1904600/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9523/10000 episodes, total num timesteps 1904800/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9524/10000 episodes, total num timesteps 1905000/2000000, FPS 258.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9525/10000 episodes, total num timesteps 1905200/2000000, FPS 258.

team_policy eval average step individual rewards of agent0: 0.4862121564244133
team_policy eval average team episode rewards of agent0: 12.5
team_policy eval idv catch total num of agent0: 23
team_policy eval team catch total num: 5
team_policy eval average step individual rewards of agent1: 0.3228202806377238
team_policy eval average team episode rewards of agent1: 12.5
team_policy eval idv catch total num of agent1: 16
team_policy eval team catch total num: 5
team_policy eval average step individual rewards of agent2: 0.12314615380691314
team_policy eval average team episode rewards of agent2: 12.5
team_policy eval idv catch total num of agent2: 8
team_policy eval team catch total num: 5
team_policy eval average step individual rewards of agent3: -0.06329563040292062
team_policy eval average team episode rewards of agent3: 12.5
team_policy eval idv catch total num of agent3: 2
team_policy eval team catch total num: 5
team_policy eval average step individual rewards of agent4: -0.08506448959191686
team_policy eval average team episode rewards of agent4: 12.5
team_policy eval idv catch total num of agent4: 1
team_policy eval team catch total num: 5
idv_policy eval average step individual rewards of agent0: -0.03017778849908681
idv_policy eval average team episode rewards of agent0: 7.5
idv_policy eval idv catch total num of agent0: 2
idv_policy eval team catch total num: 3
idv_policy eval average step individual rewards of agent1: 0.043588003413050434
idv_policy eval average team episode rewards of agent1: 7.5
idv_policy eval idv catch total num of agent1: 6
idv_policy eval team catch total num: 3
idv_policy eval average step individual rewards of agent2: 0.10240785341089831
idv_policy eval average team episode rewards of agent2: 7.5
idv_policy eval idv catch total num of agent2: 7
idv_policy eval team catch total num: 3
idv_policy eval average step individual rewards of agent3: 0.12489575931364999
idv_policy eval average team episode rewards of agent3: 7.5
idv_policy eval idv catch total num of agent3: 9
idv_policy eval team catch total num: 3
idv_policy eval average step individual rewards of agent4: 0.1424149218656914
idv_policy eval average team episode rewards of agent4: 7.5
idv_policy eval idv catch total num of agent4: 9
idv_policy eval team catch total num: 3

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9526/10000 episodes, total num timesteps 1905400/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9527/10000 episodes, total num timesteps 1905600/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9528/10000 episodes, total num timesteps 1905800/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9529/10000 episodes, total num timesteps 1906000/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9530/10000 episodes, total num timesteps 1906200/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9531/10000 episodes, total num timesteps 1906400/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9532/10000 episodes, total num timesteps 1906600/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9533/10000 episodes, total num timesteps 1906800/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9534/10000 episodes, total num timesteps 1907000/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9535/10000 episodes, total num timesteps 1907200/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9536/10000 episodes, total num timesteps 1907400/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9537/10000 episodes, total num timesteps 1907600/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9538/10000 episodes, total num timesteps 1907800/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9539/10000 episodes, total num timesteps 1908000/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9540/10000 episodes, total num timesteps 1908200/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9541/10000 episodes, total num timesteps 1908400/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9542/10000 episodes, total num timesteps 1908600/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9543/10000 episodes, total num timesteps 1908800/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9544/10000 episodes, total num timesteps 1909000/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9545/10000 episodes, total num timesteps 1909200/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9546/10000 episodes, total num timesteps 1909400/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9547/10000 episodes, total num timesteps 1909600/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9548/10000 episodes, total num timesteps 1909800/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9549/10000 episodes, total num timesteps 1910000/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9550/10000 episodes, total num timesteps 1910200/2000000, FPS 257.

team_policy eval average step individual rewards of agent0: -0.0642582988798805
team_policy eval average team episode rewards of agent0: 7.5
team_policy eval idv catch total num of agent0: 1
team_policy eval team catch total num: 3
team_policy eval average step individual rewards of agent1: 0.06943744599532976
team_policy eval average team episode rewards of agent1: 7.5
team_policy eval idv catch total num of agent1: 6
team_policy eval team catch total num: 3
team_policy eval average step individual rewards of agent2: 0.37203306653976426
team_policy eval average team episode rewards of agent2: 7.5
team_policy eval idv catch total num of agent2: 17
team_policy eval team catch total num: 3
team_policy eval average step individual rewards of agent3: 0.40519707723012643
team_policy eval average team episode rewards of agent3: 7.5
team_policy eval idv catch total num of agent3: 19
team_policy eval team catch total num: 3
team_policy eval average step individual rewards of agent4: 0.12652220226238117
team_policy eval average team episode rewards of agent4: 7.5
team_policy eval idv catch total num of agent4: 8
team_policy eval team catch total num: 3
idv_policy eval average step individual rewards of agent0: 0.14558215868769914
idv_policy eval average team episode rewards of agent0: 25.0
idv_policy eval idv catch total num of agent0: 9
idv_policy eval team catch total num: 10
idv_policy eval average step individual rewards of agent1: 0.15947317194949429
idv_policy eval average team episode rewards of agent1: 25.0
idv_policy eval idv catch total num of agent1: 9
idv_policy eval team catch total num: 10
idv_policy eval average step individual rewards of agent2: 0.19581915528338217
idv_policy eval average team episode rewards of agent2: 25.0
idv_policy eval idv catch total num of agent2: 11
idv_policy eval team catch total num: 10
idv_policy eval average step individual rewards of agent3: 0.621389673396452
idv_policy eval average team episode rewards of agent3: 25.0
idv_policy eval idv catch total num of agent3: 28
idv_policy eval team catch total num: 10
idv_policy eval average step individual rewards of agent4: 0.18858073782219634
idv_policy eval average team episode rewards of agent4: 25.0
idv_policy eval idv catch total num of agent4: 10
idv_policy eval team catch total num: 10

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9551/10000 episodes, total num timesteps 1910400/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9552/10000 episodes, total num timesteps 1910600/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9553/10000 episodes, total num timesteps 1910800/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9554/10000 episodes, total num timesteps 1911000/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9555/10000 episodes, total num timesteps 1911200/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9556/10000 episodes, total num timesteps 1911400/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9557/10000 episodes, total num timesteps 1911600/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9558/10000 episodes, total num timesteps 1911800/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9559/10000 episodes, total num timesteps 1912000/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9560/10000 episodes, total num timesteps 1912200/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9561/10000 episodes, total num timesteps 1912400/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9562/10000 episodes, total num timesteps 1912600/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9563/10000 episodes, total num timesteps 1912800/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9564/10000 episodes, total num timesteps 1913000/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9565/10000 episodes, total num timesteps 1913200/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9566/10000 episodes, total num timesteps 1913400/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9567/10000 episodes, total num timesteps 1913600/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9568/10000 episodes, total num timesteps 1913800/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9569/10000 episodes, total num timesteps 1914000/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9570/10000 episodes, total num timesteps 1914200/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9571/10000 episodes, total num timesteps 1914400/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9572/10000 episodes, total num timesteps 1914600/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9573/10000 episodes, total num timesteps 1914800/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9574/10000 episodes, total num timesteps 1915000/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9575/10000 episodes, total num timesteps 1915200/2000000, FPS 257.

team_policy eval average step individual rewards of agent0: -0.025525429373928626
team_policy eval average team episode rewards of agent0: 0.0
team_policy eval idv catch total num of agent0: 2
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent1: 0.1236602549544022
team_policy eval average team episode rewards of agent1: 0.0
team_policy eval idv catch total num of agent1: 8
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent2: 0.1071516121699775
team_policy eval average team episode rewards of agent2: 0.0
team_policy eval idv catch total num of agent2: 7
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent3: -0.014051907854270186
team_policy eval average team episode rewards of agent3: 0.0
team_policy eval idv catch total num of agent3: 3
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent4: 0.028193483084007882
team_policy eval average team episode rewards of agent4: 0.0
team_policy eval idv catch total num of agent4: 5
team_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent0: 0.35563716499661835
idv_policy eval average team episode rewards of agent0: 2.5
idv_policy eval idv catch total num of agent0: 18
idv_policy eval team catch total num: 1
idv_policy eval average step individual rewards of agent1: -0.03892066930655958
idv_policy eval average team episode rewards of agent1: 2.5
idv_policy eval idv catch total num of agent1: 2
idv_policy eval team catch total num: 1
idv_policy eval average step individual rewards of agent2: -0.013662654589505926
idv_policy eval average team episode rewards of agent2: 2.5
idv_policy eval idv catch total num of agent2: 3
idv_policy eval team catch total num: 1
idv_policy eval average step individual rewards of agent3: 0.004213729371215851
idv_policy eval average team episode rewards of agent3: 2.5
idv_policy eval idv catch total num of agent3: 4
idv_policy eval team catch total num: 1
idv_policy eval average step individual rewards of agent4: 0.3555647767570733
idv_policy eval average team episode rewards of agent4: 2.5
idv_policy eval idv catch total num of agent4: 17
idv_policy eval team catch total num: 1

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9576/10000 episodes, total num timesteps 1915400/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9577/10000 episodes, total num timesteps 1915600/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9578/10000 episodes, total num timesteps 1915800/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9579/10000 episodes, total num timesteps 1916000/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9580/10000 episodes, total num timesteps 1916200/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9581/10000 episodes, total num timesteps 1916400/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9582/10000 episodes, total num timesteps 1916600/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9583/10000 episodes, total num timesteps 1916800/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9584/10000 episodes, total num timesteps 1917000/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9585/10000 episodes, total num timesteps 1917200/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9586/10000 episodes, total num timesteps 1917400/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9587/10000 episodes, total num timesteps 1917600/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9588/10000 episodes, total num timesteps 1917800/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9589/10000 episodes, total num timesteps 1918000/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9590/10000 episodes, total num timesteps 1918200/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9591/10000 episodes, total num timesteps 1918400/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9592/10000 episodes, total num timesteps 1918600/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9593/10000 episodes, total num timesteps 1918800/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9594/10000 episodes, total num timesteps 1919000/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9595/10000 episodes, total num timesteps 1919200/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9596/10000 episodes, total num timesteps 1919400/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9597/10000 episodes, total num timesteps 1919600/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9598/10000 episodes, total num timesteps 1919800/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9599/10000 episodes, total num timesteps 1920000/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9600/10000 episodes, total num timesteps 1920200/2000000, FPS 257.

team_policy eval average step individual rewards of agent0: -0.034077589567252584
team_policy eval average team episode rewards of agent0: 0.0
team_policy eval idv catch total num of agent0: 3
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent1: -0.08843849364306461
team_policy eval average team episode rewards of agent1: 0.0
team_policy eval idv catch total num of agent1: 1
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent2: -0.034786716498597464
team_policy eval average team episode rewards of agent2: 0.0
team_policy eval idv catch total num of agent2: 3
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent3: -0.02955612453171796
team_policy eval average team episode rewards of agent3: 0.0
team_policy eval idv catch total num of agent3: 3
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent4: -0.10505656545372861
team_policy eval average team episode rewards of agent4: 0.0
team_policy eval idv catch total num of agent4: 2
team_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent0: 0.43275672640608404
idv_policy eval average team episode rewards of agent0: 0.0
idv_policy eval idv catch total num of agent0: 21
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent1: 0.006210714455821958
idv_policy eval average team episode rewards of agent1: 0.0
idv_policy eval idv catch total num of agent1: 5
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent2: 0.18709990423404513
idv_policy eval average team episode rewards of agent2: 0.0
idv_policy eval idv catch total num of agent2: 11
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent3: -0.12498814991470088
idv_policy eval average team episode rewards of agent3: 0.0
idv_policy eval idv catch total num of agent3: 0
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent4: -0.14089116822207018
idv_policy eval average team episode rewards of agent4: 0.0
idv_policy eval idv catch total num of agent4: 0
idv_policy eval team catch total num: 0

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9601/10000 episodes, total num timesteps 1920400/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9602/10000 episodes, total num timesteps 1920600/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9603/10000 episodes, total num timesteps 1920800/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9604/10000 episodes, total num timesteps 1921000/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9605/10000 episodes, total num timesteps 1921200/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9606/10000 episodes, total num timesteps 1921400/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9607/10000 episodes, total num timesteps 1921600/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9608/10000 episodes, total num timesteps 1921800/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9609/10000 episodes, total num timesteps 1922000/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9610/10000 episodes, total num timesteps 1922200/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9611/10000 episodes, total num timesteps 1922400/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9612/10000 episodes, total num timesteps 1922600/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9613/10000 episodes, total num timesteps 1922800/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9614/10000 episodes, total num timesteps 1923000/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9615/10000 episodes, total num timesteps 1923200/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9616/10000 episodes, total num timesteps 1923400/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9617/10000 episodes, total num timesteps 1923600/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9618/10000 episodes, total num timesteps 1923800/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9619/10000 episodes, total num timesteps 1924000/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9620/10000 episodes, total num timesteps 1924200/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9621/10000 episodes, total num timesteps 1924400/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9622/10000 episodes, total num timesteps 1924600/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9623/10000 episodes, total num timesteps 1924800/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9624/10000 episodes, total num timesteps 1925000/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9625/10000 episodes, total num timesteps 1925200/2000000, FPS 257.

team_policy eval average step individual rewards of agent0: -0.0989425263026615
team_policy eval average team episode rewards of agent0: 0.0
team_policy eval idv catch total num of agent0: 1
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent1: 0.12865513482743077
team_policy eval average team episode rewards of agent1: 0.0
team_policy eval idv catch total num of agent1: 9
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent2: 0.4841728836843572
team_policy eval average team episode rewards of agent2: 0.0
team_policy eval idv catch total num of agent2: 23
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent3: -0.0644220685920973
team_policy eval average team episode rewards of agent3: 0.0
team_policy eval idv catch total num of agent3: 2
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent4: -0.0035633807911628203
team_policy eval average team episode rewards of agent4: 0.0
team_policy eval idv catch total num of agent4: 4
team_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent0: 0.03345137889190948
idv_policy eval average team episode rewards of agent0: 10.0
idv_policy eval idv catch total num of agent0: 6
idv_policy eval team catch total num: 4
idv_policy eval average step individual rewards of agent1: 0.03224837029131878
idv_policy eval average team episode rewards of agent1: 10.0
idv_policy eval idv catch total num of agent1: 6
idv_policy eval team catch total num: 4
idv_policy eval average step individual rewards of agent2: 0.0235302780545228
idv_policy eval average team episode rewards of agent2: 10.0
idv_policy eval idv catch total num of agent2: 6
idv_policy eval team catch total num: 4
idv_policy eval average step individual rewards of agent3: 0.045689770617538296
idv_policy eval average team episode rewards of agent3: 10.0
idv_policy eval idv catch total num of agent3: 6
idv_policy eval team catch total num: 4
idv_policy eval average step individual rewards of agent4: 0.28677582692039777
idv_policy eval average team episode rewards of agent4: 10.0
idv_policy eval idv catch total num of agent4: 15
idv_policy eval team catch total num: 4

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9626/10000 episodes, total num timesteps 1925400/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9627/10000 episodes, total num timesteps 1925600/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9628/10000 episodes, total num timesteps 1925800/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9629/10000 episodes, total num timesteps 1926000/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9630/10000 episodes, total num timesteps 1926200/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9631/10000 episodes, total num timesteps 1926400/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9632/10000 episodes, total num timesteps 1926600/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9633/10000 episodes, total num timesteps 1926800/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9634/10000 episodes, total num timesteps 1927000/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9635/10000 episodes, total num timesteps 1927200/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9636/10000 episodes, total num timesteps 1927400/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9637/10000 episodes, total num timesteps 1927600/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9638/10000 episodes, total num timesteps 1927800/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9639/10000 episodes, total num timesteps 1928000/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9640/10000 episodes, total num timesteps 1928200/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9641/10000 episodes, total num timesteps 1928400/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9642/10000 episodes, total num timesteps 1928600/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9643/10000 episodes, total num timesteps 1928800/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9644/10000 episodes, total num timesteps 1929000/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9645/10000 episodes, total num timesteps 1929200/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9646/10000 episodes, total num timesteps 1929400/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9647/10000 episodes, total num timesteps 1929600/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9648/10000 episodes, total num timesteps 1929800/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9649/10000 episodes, total num timesteps 1930000/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9650/10000 episodes, total num timesteps 1930200/2000000, FPS 257.

team_policy eval average step individual rewards of agent0: -0.015835787051071658
team_policy eval average team episode rewards of agent0: 2.5
team_policy eval idv catch total num of agent0: 4
team_policy eval team catch total num: 1
team_policy eval average step individual rewards of agent1: 0.1198975468778005
team_policy eval average team episode rewards of agent1: 2.5
team_policy eval idv catch total num of agent1: 9
team_policy eval team catch total num: 1
team_policy eval average step individual rewards of agent2: -0.06875207065270512
team_policy eval average team episode rewards of agent2: 2.5
team_policy eval idv catch total num of agent2: 2
team_policy eval team catch total num: 1
team_policy eval average step individual rewards of agent3: 0.28012852648702574
team_policy eval average team episode rewards of agent3: 2.5
team_policy eval idv catch total num of agent3: 15
team_policy eval team catch total num: 1
team_policy eval average step individual rewards of agent4: -0.04233991437340867
team_policy eval average team episode rewards of agent4: 2.5
team_policy eval idv catch total num of agent4: 2
team_policy eval team catch total num: 1
idv_policy eval average step individual rewards of agent0: 0.15954603449871388
idv_policy eval average team episode rewards of agent0: 5.0
idv_policy eval idv catch total num of agent0: 10
idv_policy eval team catch total num: 2
idv_policy eval average step individual rewards of agent1: 0.3804054999979034
idv_policy eval average team episode rewards of agent1: 5.0
idv_policy eval idv catch total num of agent1: 18
idv_policy eval team catch total num: 2
idv_policy eval average step individual rewards of agent2: -0.11319008755188477
idv_policy eval average team episode rewards of agent2: 5.0
idv_policy eval idv catch total num of agent2: 0
idv_policy eval team catch total num: 2
idv_policy eval average step individual rewards of agent3: 0.09276049218006749
idv_policy eval average team episode rewards of agent3: 5.0
idv_policy eval idv catch total num of agent3: 7
idv_policy eval team catch total num: 2
idv_policy eval average step individual rewards of agent4: 0.06283014123939315
idv_policy eval average team episode rewards of agent4: 5.0
idv_policy eval idv catch total num of agent4: 6
idv_policy eval team catch total num: 2

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9651/10000 episodes, total num timesteps 1930400/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9652/10000 episodes, total num timesteps 1930600/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9653/10000 episodes, total num timesteps 1930800/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9654/10000 episodes, total num timesteps 1931000/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9655/10000 episodes, total num timesteps 1931200/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9656/10000 episodes, total num timesteps 1931400/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9657/10000 episodes, total num timesteps 1931600/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9658/10000 episodes, total num timesteps 1931800/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9659/10000 episodes, total num timesteps 1932000/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9660/10000 episodes, total num timesteps 1932200/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9661/10000 episodes, total num timesteps 1932400/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9662/10000 episodes, total num timesteps 1932600/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9663/10000 episodes, total num timesteps 1932800/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9664/10000 episodes, total num timesteps 1933000/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9665/10000 episodes, total num timesteps 1933200/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9666/10000 episodes, total num timesteps 1933400/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9667/10000 episodes, total num timesteps 1933600/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9668/10000 episodes, total num timesteps 1933800/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9669/10000 episodes, total num timesteps 1934000/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9670/10000 episodes, total num timesteps 1934200/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9671/10000 episodes, total num timesteps 1934400/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9672/10000 episodes, total num timesteps 1934600/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9673/10000 episodes, total num timesteps 1934800/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9674/10000 episodes, total num timesteps 1935000/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9675/10000 episodes, total num timesteps 1935200/2000000, FPS 257.

team_policy eval average step individual rewards of agent0: 0.014706458916617567
team_policy eval average team episode rewards of agent0: 2.5
team_policy eval idv catch total num of agent0: 4
team_policy eval team catch total num: 1
team_policy eval average step individual rewards of agent1: -0.03031098662090992
team_policy eval average team episode rewards of agent1: 2.5
team_policy eval idv catch total num of agent1: 3
team_policy eval team catch total num: 1
team_policy eval average step individual rewards of agent2: 0.09533196483655332
team_policy eval average team episode rewards of agent2: 2.5
team_policy eval idv catch total num of agent2: 7
team_policy eval team catch total num: 1
team_policy eval average step individual rewards of agent3: 0.03381288772235863
team_policy eval average team episode rewards of agent3: 2.5
team_policy eval idv catch total num of agent3: 5
team_policy eval team catch total num: 1
team_policy eval average step individual rewards of agent4: -0.06581089845425861
team_policy eval average team episode rewards of agent4: 2.5
team_policy eval idv catch total num of agent4: 0
team_policy eval team catch total num: 1
idv_policy eval average step individual rewards of agent0: 0.20529620524802156
idv_policy eval average team episode rewards of agent0: 5.0
idv_policy eval idv catch total num of agent0: 12
idv_policy eval team catch total num: 2
idv_policy eval average step individual rewards of agent1: 0.09171896685334426
idv_policy eval average team episode rewards of agent1: 5.0
idv_policy eval idv catch total num of agent1: 7
idv_policy eval team catch total num: 2
idv_policy eval average step individual rewards of agent2: -0.11489030516353341
idv_policy eval average team episode rewards of agent2: 5.0
idv_policy eval idv catch total num of agent2: 0
idv_policy eval team catch total num: 2
idv_policy eval average step individual rewards of agent3: 0.003474314660584623
idv_policy eval average team episode rewards of agent3: 5.0
idv_policy eval idv catch total num of agent3: 4
idv_policy eval team catch total num: 2
idv_policy eval average step individual rewards of agent4: -0.04490492018075529
idv_policy eval average team episode rewards of agent4: 5.0
idv_policy eval idv catch total num of agent4: 2
idv_policy eval team catch total num: 2

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9676/10000 episodes, total num timesteps 1935400/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9677/10000 episodes, total num timesteps 1935600/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9678/10000 episodes, total num timesteps 1935800/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9679/10000 episodes, total num timesteps 1936000/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9680/10000 episodes, total num timesteps 1936200/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9681/10000 episodes, total num timesteps 1936400/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9682/10000 episodes, total num timesteps 1936600/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9683/10000 episodes, total num timesteps 1936800/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9684/10000 episodes, total num timesteps 1937000/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9685/10000 episodes, total num timesteps 1937200/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9686/10000 episodes, total num timesteps 1937400/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9687/10000 episodes, total num timesteps 1937600/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9688/10000 episodes, total num timesteps 1937800/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9689/10000 episodes, total num timesteps 1938000/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9690/10000 episodes, total num timesteps 1938200/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9691/10000 episodes, total num timesteps 1938400/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9692/10000 episodes, total num timesteps 1938600/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9693/10000 episodes, total num timesteps 1938800/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9694/10000 episodes, total num timesteps 1939000/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9695/10000 episodes, total num timesteps 1939200/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9696/10000 episodes, total num timesteps 1939400/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9697/10000 episodes, total num timesteps 1939600/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9698/10000 episodes, total num timesteps 1939800/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9699/10000 episodes, total num timesteps 1940000/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9700/10000 episodes, total num timesteps 1940200/2000000, FPS 257.

team_policy eval average step individual rewards of agent0: -0.1344776927025702
team_policy eval average team episode rewards of agent0: 0.0
team_policy eval idv catch total num of agent0: 0
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent1: -0.1250223309581387
team_policy eval average team episode rewards of agent1: 0.0
team_policy eval idv catch total num of agent1: 0
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent2: -0.050371191032061766
team_policy eval average team episode rewards of agent2: 0.0
team_policy eval idv catch total num of agent2: 3
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent3: -0.14739377032395884
team_policy eval average team episode rewards of agent3: 0.0
team_policy eval idv catch total num of agent3: 0
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent4: -0.04028073855088668
team_policy eval average team episode rewards of agent4: 0.0
team_policy eval idv catch total num of agent4: 3
team_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent0: -0.0313981176655022
idv_policy eval average team episode rewards of agent0: 0.0
idv_policy eval idv catch total num of agent0: 3
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent1: -0.11400701886943279
idv_policy eval average team episode rewards of agent1: 0.0
idv_policy eval idv catch total num of agent1: 1
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent2: 0.01135376631300776
idv_policy eval average team episode rewards of agent2: 0.0
idv_policy eval idv catch total num of agent2: 5
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent3: -0.06599348761169033
idv_policy eval average team episode rewards of agent3: 0.0
idv_policy eval idv catch total num of agent3: 2
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent4: -0.08218367360284418
idv_policy eval average team episode rewards of agent4: 0.0
idv_policy eval idv catch total num of agent4: 2
idv_policy eval team catch total num: 0

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9701/10000 episodes, total num timesteps 1940400/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9702/10000 episodes, total num timesteps 1940600/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9703/10000 episodes, total num timesteps 1940800/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9704/10000 episodes, total num timesteps 1941000/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9705/10000 episodes, total num timesteps 1941200/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9706/10000 episodes, total num timesteps 1941400/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9707/10000 episodes, total num timesteps 1941600/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9708/10000 episodes, total num timesteps 1941800/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9709/10000 episodes, total num timesteps 1942000/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9710/10000 episodes, total num timesteps 1942200/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9711/10000 episodes, total num timesteps 1942400/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9712/10000 episodes, total num timesteps 1942600/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9713/10000 episodes, total num timesteps 1942800/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9714/10000 episodes, total num timesteps 1943000/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9715/10000 episodes, total num timesteps 1943200/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9716/10000 episodes, total num timesteps 1943400/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9717/10000 episodes, total num timesteps 1943600/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9718/10000 episodes, total num timesteps 1943800/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9719/10000 episodes, total num timesteps 1944000/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9720/10000 episodes, total num timesteps 1944200/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9721/10000 episodes, total num timesteps 1944400/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9722/10000 episodes, total num timesteps 1944600/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9723/10000 episodes, total num timesteps 1944800/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9724/10000 episodes, total num timesteps 1945000/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9725/10000 episodes, total num timesteps 1945200/2000000, FPS 257.

team_policy eval average step individual rewards of agent0: -0.019476450214351702
team_policy eval average team episode rewards of agent0: 7.5
team_policy eval idv catch total num of agent0: 3
team_policy eval team catch total num: 3
team_policy eval average step individual rewards of agent1: 0.20582091167278804
team_policy eval average team episode rewards of agent1: 7.5
team_policy eval idv catch total num of agent1: 12
team_policy eval team catch total num: 3
team_policy eval average step individual rewards of agent2: -0.007505534624428885
team_policy eval average team episode rewards of agent2: 7.5
team_policy eval idv catch total num of agent2: 3
team_policy eval team catch total num: 3
team_policy eval average step individual rewards of agent3: -0.08154623605600278
team_policy eval average team episode rewards of agent3: 7.5
team_policy eval idv catch total num of agent3: 1
team_policy eval team catch total num: 3
team_policy eval average step individual rewards of agent4: 0.019297596111629813
team_policy eval average team episode rewards of agent4: 7.5
team_policy eval idv catch total num of agent4: 4
team_policy eval team catch total num: 3
idv_policy eval average step individual rewards of agent0: 0.12959218353088436
idv_policy eval average team episode rewards of agent0: 7.5
idv_policy eval idv catch total num of agent0: 8
idv_policy eval team catch total num: 3
idv_policy eval average step individual rewards of agent1: -0.02161088225675434
idv_policy eval average team episode rewards of agent1: 7.5
idv_policy eval idv catch total num of agent1: 3
idv_policy eval team catch total num: 3
idv_policy eval average step individual rewards of agent2: 0.16077389201955797
idv_policy eval average team episode rewards of agent2: 7.5
idv_policy eval idv catch total num of agent2: 10
idv_policy eval team catch total num: 3
idv_policy eval average step individual rewards of agent3: 0.029484041882866
idv_policy eval average team episode rewards of agent3: 7.5
idv_policy eval idv catch total num of agent3: 5
idv_policy eval team catch total num: 3
idv_policy eval average step individual rewards of agent4: 0.13095740993241395
idv_policy eval average team episode rewards of agent4: 7.5
idv_policy eval idv catch total num of agent4: 9
idv_policy eval team catch total num: 3

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9726/10000 episodes, total num timesteps 1945400/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9727/10000 episodes, total num timesteps 1945600/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9728/10000 episodes, total num timesteps 1945800/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9729/10000 episodes, total num timesteps 1946000/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9730/10000 episodes, total num timesteps 1946200/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9731/10000 episodes, total num timesteps 1946400/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9732/10000 episodes, total num timesteps 1946600/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9733/10000 episodes, total num timesteps 1946800/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9734/10000 episodes, total num timesteps 1947000/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9735/10000 episodes, total num timesteps 1947200/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9736/10000 episodes, total num timesteps 1947400/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9737/10000 episodes, total num timesteps 1947600/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9738/10000 episodes, total num timesteps 1947800/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9739/10000 episodes, total num timesteps 1948000/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9740/10000 episodes, total num timesteps 1948200/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9741/10000 episodes, total num timesteps 1948400/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9742/10000 episodes, total num timesteps 1948600/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9743/10000 episodes, total num timesteps 1948800/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9744/10000 episodes, total num timesteps 1949000/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9745/10000 episodes, total num timesteps 1949200/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9746/10000 episodes, total num timesteps 1949400/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9747/10000 episodes, total num timesteps 1949600/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9748/10000 episodes, total num timesteps 1949800/2000000, FPS 257.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9749/10000 episodes, total num timesteps 1950000/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9750/10000 episodes, total num timesteps 1950200/2000000, FPS 257.

team_policy eval average step individual rewards of agent0: -0.10238978607348273
team_policy eval average team episode rewards of agent0: 0.0
team_policy eval idv catch total num of agent0: 0
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent1: -0.11838226984128934
team_policy eval average team episode rewards of agent1: 0.0
team_policy eval idv catch total num of agent1: 0
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent2: -0.016115279951874298
team_policy eval average team episode rewards of agent2: 0.0
team_policy eval idv catch total num of agent2: 3
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent3: -0.035040086163354876
team_policy eval average team episode rewards of agent3: 0.0
team_policy eval idv catch total num of agent3: 3
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent4: 0.013070836293285844
team_policy eval average team episode rewards of agent4: 0.0
team_policy eval idv catch total num of agent4: 5
team_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent0: -0.09213970054064628
idv_policy eval average team episode rewards of agent0: 0.0
idv_policy eval idv catch total num of agent0: 0
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent1: 0.10800296858278138
idv_policy eval average team episode rewards of agent1: 0.0
idv_policy eval idv catch total num of agent1: 7
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent2: -0.06503214944405586
idv_policy eval average team episode rewards of agent2: 0.0
idv_policy eval idv catch total num of agent2: 2
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent3: 0.18050233370503108
idv_policy eval average team episode rewards of agent3: 0.0
idv_policy eval idv catch total num of agent3: 10
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent4: -0.056548688662769325
idv_policy eval average team episode rewards of agent4: 0.0
idv_policy eval idv catch total num of agent4: 1
idv_policy eval team catch total num: 0

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9751/10000 episodes, total num timesteps 1950400/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9752/10000 episodes, total num timesteps 1950600/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9753/10000 episodes, total num timesteps 1950800/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9754/10000 episodes, total num timesteps 1951000/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9755/10000 episodes, total num timesteps 1951200/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9756/10000 episodes, total num timesteps 1951400/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9757/10000 episodes, total num timesteps 1951600/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9758/10000 episodes, total num timesteps 1951800/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9759/10000 episodes, total num timesteps 1952000/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9760/10000 episodes, total num timesteps 1952200/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9761/10000 episodes, total num timesteps 1952400/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9762/10000 episodes, total num timesteps 1952600/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9763/10000 episodes, total num timesteps 1952800/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9764/10000 episodes, total num timesteps 1953000/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9765/10000 episodes, total num timesteps 1953200/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9766/10000 episodes, total num timesteps 1953400/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9767/10000 episodes, total num timesteps 1953600/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9768/10000 episodes, total num timesteps 1953800/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9769/10000 episodes, total num timesteps 1954000/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9770/10000 episodes, total num timesteps 1954200/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9771/10000 episodes, total num timesteps 1954400/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9772/10000 episodes, total num timesteps 1954600/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9773/10000 episodes, total num timesteps 1954800/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9774/10000 episodes, total num timesteps 1955000/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9775/10000 episodes, total num timesteps 1955200/2000000, FPS 256.

team_policy eval average step individual rewards of agent0: -0.003695748184777945
team_policy eval average team episode rewards of agent0: 10.0
team_policy eval idv catch total num of agent0: 5
team_policy eval team catch total num: 4
team_policy eval average step individual rewards of agent1: -0.006177454625905756
team_policy eval average team episode rewards of agent1: 10.0
team_policy eval idv catch total num of agent1: 4
team_policy eval team catch total num: 4
team_policy eval average step individual rewards of agent2: 0.14247201638486878
team_policy eval average team episode rewards of agent2: 10.0
team_policy eval idv catch total num of agent2: 9
team_policy eval team catch total num: 4
team_policy eval average step individual rewards of agent3: 0.06124386393967834
team_policy eval average team episode rewards of agent3: 10.0
team_policy eval idv catch total num of agent3: 6
team_policy eval team catch total num: 4
team_policy eval average step individual rewards of agent4: 0.11324436825622637
team_policy eval average team episode rewards of agent4: 10.0
team_policy eval idv catch total num of agent4: 9
team_policy eval team catch total num: 4
idv_policy eval average step individual rewards of agent0: -0.04330946557652273
idv_policy eval average team episode rewards of agent0: 2.5
idv_policy eval idv catch total num of agent0: 4
idv_policy eval team catch total num: 1
idv_policy eval average step individual rewards of agent1: 0.08288128796895154
idv_policy eval average team episode rewards of agent1: 2.5
idv_policy eval idv catch total num of agent1: 7
idv_policy eval team catch total num: 1
idv_policy eval average step individual rewards of agent2: -0.07096086070347102
idv_policy eval average team episode rewards of agent2: 2.5
idv_policy eval idv catch total num of agent2: 2
idv_policy eval team catch total num: 1
idv_policy eval average step individual rewards of agent3: 0.15428036786253516
idv_policy eval average team episode rewards of agent3: 2.5
idv_policy eval idv catch total num of agent3: 11
idv_policy eval team catch total num: 1
idv_policy eval average step individual rewards of agent4: 0.07511646806700285
idv_policy eval average team episode rewards of agent4: 2.5
idv_policy eval idv catch total num of agent4: 7
idv_policy eval team catch total num: 1

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9776/10000 episodes, total num timesteps 1955400/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9777/10000 episodes, total num timesteps 1955600/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9778/10000 episodes, total num timesteps 1955800/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9779/10000 episodes, total num timesteps 1956000/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9780/10000 episodes, total num timesteps 1956200/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9781/10000 episodes, total num timesteps 1956400/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9782/10000 episodes, total num timesteps 1956600/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9783/10000 episodes, total num timesteps 1956800/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9784/10000 episodes, total num timesteps 1957000/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9785/10000 episodes, total num timesteps 1957200/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9786/10000 episodes, total num timesteps 1957400/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9787/10000 episodes, total num timesteps 1957600/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9788/10000 episodes, total num timesteps 1957800/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9789/10000 episodes, total num timesteps 1958000/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9790/10000 episodes, total num timesteps 1958200/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9791/10000 episodes, total num timesteps 1958400/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9792/10000 episodes, total num timesteps 1958600/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9793/10000 episodes, total num timesteps 1958800/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9794/10000 episodes, total num timesteps 1959000/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9795/10000 episodes, total num timesteps 1959200/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9796/10000 episodes, total num timesteps 1959400/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9797/10000 episodes, total num timesteps 1959600/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9798/10000 episodes, total num timesteps 1959800/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9799/10000 episodes, total num timesteps 1960000/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9800/10000 episodes, total num timesteps 1960200/2000000, FPS 256.

team_policy eval average step individual rewards of agent0: 0.07299145657952438
team_policy eval average team episode rewards of agent0: 0.0
team_policy eval idv catch total num of agent0: 6
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent1: -0.06548847570513897
team_policy eval average team episode rewards of agent1: 0.0
team_policy eval idv catch total num of agent1: 1
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent2: 0.18738684782589174
team_policy eval average team episode rewards of agent2: 0.0
team_policy eval idv catch total num of agent2: 11
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent3: 0.15577206051336195
team_policy eval average team episode rewards of agent3: 0.0
team_policy eval idv catch total num of agent3: 9
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent4: 0.17584354774396435
team_policy eval average team episode rewards of agent4: 0.0
team_policy eval idv catch total num of agent4: 10
team_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent0: 0.0438431458654537
idv_policy eval average team episode rewards of agent0: 12.5
idv_policy eval idv catch total num of agent0: 5
idv_policy eval team catch total num: 5
idv_policy eval average step individual rewards of agent1: 0.2113355866985161
idv_policy eval average team episode rewards of agent1: 12.5
idv_policy eval idv catch total num of agent1: 11
idv_policy eval team catch total num: 5
idv_policy eval average step individual rewards of agent2: 0.12420158768683093
idv_policy eval average team episode rewards of agent2: 12.5
idv_policy eval idv catch total num of agent2: 8
idv_policy eval team catch total num: 5
idv_policy eval average step individual rewards of agent3: 0.2561545406280563
idv_policy eval average team episode rewards of agent3: 12.5
idv_policy eval idv catch total num of agent3: 13
idv_policy eval team catch total num: 5
idv_policy eval average step individual rewards of agent4: -0.03038794103648698
idv_policy eval average team episode rewards of agent4: 12.5
idv_policy eval idv catch total num of agent4: 3
idv_policy eval team catch total num: 5

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9801/10000 episodes, total num timesteps 1960400/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9802/10000 episodes, total num timesteps 1960600/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9803/10000 episodes, total num timesteps 1960800/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9804/10000 episodes, total num timesteps 1961000/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9805/10000 episodes, total num timesteps 1961200/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9806/10000 episodes, total num timesteps 1961400/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9807/10000 episodes, total num timesteps 1961600/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9808/10000 episodes, total num timesteps 1961800/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9809/10000 episodes, total num timesteps 1962000/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9810/10000 episodes, total num timesteps 1962200/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9811/10000 episodes, total num timesteps 1962400/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9812/10000 episodes, total num timesteps 1962600/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9813/10000 episodes, total num timesteps 1962800/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9814/10000 episodes, total num timesteps 1963000/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9815/10000 episodes, total num timesteps 1963200/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9816/10000 episodes, total num timesteps 1963400/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9817/10000 episodes, total num timesteps 1963600/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9818/10000 episodes, total num timesteps 1963800/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9819/10000 episodes, total num timesteps 1964000/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9820/10000 episodes, total num timesteps 1964200/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9821/10000 episodes, total num timesteps 1964400/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9822/10000 episodes, total num timesteps 1964600/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9823/10000 episodes, total num timesteps 1964800/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9824/10000 episodes, total num timesteps 1965000/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9825/10000 episodes, total num timesteps 1965200/2000000, FPS 256.

team_policy eval average step individual rewards of agent0: 0.12384730819793094
team_policy eval average team episode rewards of agent0: 0.0
team_policy eval idv catch total num of agent0: 9
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent1: 0.1328646666364552
team_policy eval average team episode rewards of agent1: 0.0
team_policy eval idv catch total num of agent1: 9
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent2: -0.10762626602282775
team_policy eval average team episode rewards of agent2: 0.0
team_policy eval idv catch total num of agent2: 0
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent3: 0.20999445774367886
team_policy eval average team episode rewards of agent3: 0.0
team_policy eval idv catch total num of agent3: 11
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent4: -0.10299572216044074
team_policy eval average team episode rewards of agent4: 0.0
team_policy eval idv catch total num of agent4: 0
team_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent0: -0.06579926433802673
idv_policy eval average team episode rewards of agent0: 7.5
idv_policy eval idv catch total num of agent0: 1
idv_policy eval team catch total num: 3
idv_policy eval average step individual rewards of agent1: 0.3158611084138723
idv_policy eval average team episode rewards of agent1: 7.5
idv_policy eval idv catch total num of agent1: 15
idv_policy eval team catch total num: 3
idv_policy eval average step individual rewards of agent2: -0.036425365651426296
idv_policy eval average team episode rewards of agent2: 7.5
idv_policy eval idv catch total num of agent2: 2
idv_policy eval team catch total num: 3
idv_policy eval average step individual rewards of agent3: 0.17382992542651898
idv_policy eval average team episode rewards of agent3: 7.5
idv_policy eval idv catch total num of agent3: 10
idv_policy eval team catch total num: 3
idv_policy eval average step individual rewards of agent4: 0.0959793157739459
idv_policy eval average team episode rewards of agent4: 7.5
idv_policy eval idv catch total num of agent4: 7
idv_policy eval team catch total num: 3

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9826/10000 episodes, total num timesteps 1965400/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9827/10000 episodes, total num timesteps 1965600/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9828/10000 episodes, total num timesteps 1965800/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9829/10000 episodes, total num timesteps 1966000/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9830/10000 episodes, total num timesteps 1966200/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9831/10000 episodes, total num timesteps 1966400/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9832/10000 episodes, total num timesteps 1966600/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9833/10000 episodes, total num timesteps 1966800/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9834/10000 episodes, total num timesteps 1967000/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9835/10000 episodes, total num timesteps 1967200/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9836/10000 episodes, total num timesteps 1967400/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9837/10000 episodes, total num timesteps 1967600/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9838/10000 episodes, total num timesteps 1967800/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9839/10000 episodes, total num timesteps 1968000/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9840/10000 episodes, total num timesteps 1968200/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9841/10000 episodes, total num timesteps 1968400/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9842/10000 episodes, total num timesteps 1968600/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9843/10000 episodes, total num timesteps 1968800/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9844/10000 episodes, total num timesteps 1969000/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9845/10000 episodes, total num timesteps 1969200/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9846/10000 episodes, total num timesteps 1969400/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9847/10000 episodes, total num timesteps 1969600/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9848/10000 episodes, total num timesteps 1969800/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9849/10000 episodes, total num timesteps 1970000/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9850/10000 episodes, total num timesteps 1970200/2000000, FPS 256.

team_policy eval average step individual rewards of agent0: 0.04692278463978658
team_policy eval average team episode rewards of agent0: 2.5
team_policy eval idv catch total num of agent0: 5
team_policy eval team catch total num: 1
team_policy eval average step individual rewards of agent1: -0.04815893709741314
team_policy eval average team episode rewards of agent1: 2.5
team_policy eval idv catch total num of agent1: 2
team_policy eval team catch total num: 1
team_policy eval average step individual rewards of agent2: 0.10885060364123862
team_policy eval average team episode rewards of agent2: 2.5
team_policy eval idv catch total num of agent2: 7
team_policy eval team catch total num: 1
team_policy eval average step individual rewards of agent3: 0.150432564824289
team_policy eval average team episode rewards of agent3: 2.5
team_policy eval idv catch total num of agent3: 9
team_policy eval team catch total num: 1
team_policy eval average step individual rewards of agent4: 0.06644216557386268
team_policy eval average team episode rewards of agent4: 2.5
team_policy eval idv catch total num of agent4: 6
team_policy eval team catch total num: 1
idv_policy eval average step individual rewards of agent0: 0.025068387503552635
idv_policy eval average team episode rewards of agent0: 0.0
idv_policy eval idv catch total num of agent0: 4
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent1: 0.08947228701748447
idv_policy eval average team episode rewards of agent1: 0.0
idv_policy eval idv catch total num of agent1: 7
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent2: 0.0037787116416806766
idv_policy eval average team episode rewards of agent2: 0.0
idv_policy eval idv catch total num of agent2: 4
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent3: -0.10270832175299638
idv_policy eval average team episode rewards of agent3: 0.0
idv_policy eval idv catch total num of agent3: 0
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent4: 0.003332691599657922
idv_policy eval average team episode rewards of agent4: 0.0
idv_policy eval idv catch total num of agent4: 4
idv_policy eval team catch total num: 0

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9851/10000 episodes, total num timesteps 1970400/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9852/10000 episodes, total num timesteps 1970600/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9853/10000 episodes, total num timesteps 1970800/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9854/10000 episodes, total num timesteps 1971000/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9855/10000 episodes, total num timesteps 1971200/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9856/10000 episodes, total num timesteps 1971400/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9857/10000 episodes, total num timesteps 1971600/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9858/10000 episodes, total num timesteps 1971800/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9859/10000 episodes, total num timesteps 1972000/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9860/10000 episodes, total num timesteps 1972200/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9861/10000 episodes, total num timesteps 1972400/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9862/10000 episodes, total num timesteps 1972600/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9863/10000 episodes, total num timesteps 1972800/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9864/10000 episodes, total num timesteps 1973000/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9865/10000 episodes, total num timesteps 1973200/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9866/10000 episodes, total num timesteps 1973400/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9867/10000 episodes, total num timesteps 1973600/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9868/10000 episodes, total num timesteps 1973800/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9869/10000 episodes, total num timesteps 1974000/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9870/10000 episodes, total num timesteps 1974200/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9871/10000 episodes, total num timesteps 1974400/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9872/10000 episodes, total num timesteps 1974600/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9873/10000 episodes, total num timesteps 1974800/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9874/10000 episodes, total num timesteps 1975000/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9875/10000 episodes, total num timesteps 1975200/2000000, FPS 256.

team_policy eval average step individual rewards of agent0: 0.1322794388895123
team_policy eval average team episode rewards of agent0: 0.0
team_policy eval idv catch total num of agent0: 9
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent1: -0.01606824199515945
team_policy eval average team episode rewards of agent1: 0.0
team_policy eval idv catch total num of agent1: 4
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent2: 0.2414356516576082
team_policy eval average team episode rewards of agent2: 0.0
team_policy eval idv catch total num of agent2: 12
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent3: -0.10455183829270293
team_policy eval average team episode rewards of agent3: 0.0
team_policy eval idv catch total num of agent3: 0
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent4: 0.06982274273977512
team_policy eval average team episode rewards of agent4: 0.0
team_policy eval idv catch total num of agent4: 6
team_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent0: 0.090315612911334
idv_policy eval average team episode rewards of agent0: 0.0
idv_policy eval idv catch total num of agent0: 7
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent1: 0.2021222406247211
idv_policy eval average team episode rewards of agent1: 0.0
idv_policy eval idv catch total num of agent1: 11
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent2: 0.06805546150435285
idv_policy eval average team episode rewards of agent2: 0.0
idv_policy eval idv catch total num of agent2: 6
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent3: 0.2023486808988186
idv_policy eval average team episode rewards of agent3: 0.0
idv_policy eval idv catch total num of agent3: 11
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent4: -0.0693825279892164
idv_policy eval average team episode rewards of agent4: 0.0
idv_policy eval idv catch total num of agent4: 1
idv_policy eval team catch total num: 0

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9876/10000 episodes, total num timesteps 1975400/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9877/10000 episodes, total num timesteps 1975600/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9878/10000 episodes, total num timesteps 1975800/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9879/10000 episodes, total num timesteps 1976000/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9880/10000 episodes, total num timesteps 1976200/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9881/10000 episodes, total num timesteps 1976400/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9882/10000 episodes, total num timesteps 1976600/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9883/10000 episodes, total num timesteps 1976800/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9884/10000 episodes, total num timesteps 1977000/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9885/10000 episodes, total num timesteps 1977200/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9886/10000 episodes, total num timesteps 1977400/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9887/10000 episodes, total num timesteps 1977600/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9888/10000 episodes, total num timesteps 1977800/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9889/10000 episodes, total num timesteps 1978000/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9890/10000 episodes, total num timesteps 1978200/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9891/10000 episodes, total num timesteps 1978400/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9892/10000 episodes, total num timesteps 1978600/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9893/10000 episodes, total num timesteps 1978800/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9894/10000 episodes, total num timesteps 1979000/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9895/10000 episodes, total num timesteps 1979200/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9896/10000 episodes, total num timesteps 1979400/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9897/10000 episodes, total num timesteps 1979600/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9898/10000 episodes, total num timesteps 1979800/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9899/10000 episodes, total num timesteps 1980000/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9900/10000 episodes, total num timesteps 1980200/2000000, FPS 256.

team_policy eval average step individual rewards of agent0: 0.3964330599646814
team_policy eval average team episode rewards of agent0: 17.5
team_policy eval idv catch total num of agent0: 19
team_policy eval team catch total num: 7
team_policy eval average step individual rewards of agent1: 0.2343748236025302
team_policy eval average team episode rewards of agent1: 17.5
team_policy eval idv catch total num of agent1: 12
team_policy eval team catch total num: 7
team_policy eval average step individual rewards of agent2: 0.26846916074103494
team_policy eval average team episode rewards of agent2: 17.5
team_policy eval idv catch total num of agent2: 13
team_policy eval team catch total num: 7
team_policy eval average step individual rewards of agent3: 0.3004031225194957
team_policy eval average team episode rewards of agent3: 17.5
team_policy eval idv catch total num of agent3: 15
team_policy eval team catch total num: 7
team_policy eval average step individual rewards of agent4: 0.021914698677385654
team_policy eval average team episode rewards of agent4: 17.5
team_policy eval idv catch total num of agent4: 5
team_policy eval team catch total num: 7
idv_policy eval average step individual rewards of agent0: 0.1984727357945463
idv_policy eval average team episode rewards of agent0: 12.5
idv_policy eval idv catch total num of agent0: 11
idv_policy eval team catch total num: 5
idv_policy eval average step individual rewards of agent1: -0.0055902983963781695
idv_policy eval average team episode rewards of agent1: 12.5
idv_policy eval idv catch total num of agent1: 3
idv_policy eval team catch total num: 5
idv_policy eval average step individual rewards of agent2: 0.2748674129205018
idv_policy eval average team episode rewards of agent2: 12.5
idv_policy eval idv catch total num of agent2: 14
idv_policy eval team catch total num: 5
idv_policy eval average step individual rewards of agent3: 0.10942677507047162
idv_policy eval average team episode rewards of agent3: 12.5
idv_policy eval idv catch total num of agent3: 8
idv_policy eval team catch total num: 5
idv_policy eval average step individual rewards of agent4: 0.27799589848270617
idv_policy eval average team episode rewards of agent4: 12.5
idv_policy eval idv catch total num of agent4: 14
idv_policy eval team catch total num: 5

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9901/10000 episodes, total num timesteps 1980400/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9902/10000 episodes, total num timesteps 1980600/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9903/10000 episodes, total num timesteps 1980800/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9904/10000 episodes, total num timesteps 1981000/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9905/10000 episodes, total num timesteps 1981200/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9906/10000 episodes, total num timesteps 1981400/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9907/10000 episodes, total num timesteps 1981600/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9908/10000 episodes, total num timesteps 1981800/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9909/10000 episodes, total num timesteps 1982000/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9910/10000 episodes, total num timesteps 1982200/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9911/10000 episodes, total num timesteps 1982400/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9912/10000 episodes, total num timesteps 1982600/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9913/10000 episodes, total num timesteps 1982800/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9914/10000 episodes, total num timesteps 1983000/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9915/10000 episodes, total num timesteps 1983200/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9916/10000 episodes, total num timesteps 1983400/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9917/10000 episodes, total num timesteps 1983600/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9918/10000 episodes, total num timesteps 1983800/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9919/10000 episodes, total num timesteps 1984000/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9920/10000 episodes, total num timesteps 1984200/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9921/10000 episodes, total num timesteps 1984400/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9922/10000 episodes, total num timesteps 1984600/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9923/10000 episodes, total num timesteps 1984800/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9924/10000 episodes, total num timesteps 1985000/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9925/10000 episodes, total num timesteps 1985200/2000000, FPS 256.

team_policy eval average step individual rewards of agent0: 0.15121340580642412
team_policy eval average team episode rewards of agent0: 2.5
team_policy eval idv catch total num of agent0: 9
team_policy eval team catch total num: 1
team_policy eval average step individual rewards of agent1: 0.33661088572138015
team_policy eval average team episode rewards of agent1: 2.5
team_policy eval idv catch total num of agent1: 16
team_policy eval team catch total num: 1
team_policy eval average step individual rewards of agent2: -0.048221934846247344
team_policy eval average team episode rewards of agent2: 2.5
team_policy eval idv catch total num of agent2: 1
team_policy eval team catch total num: 1
team_policy eval average step individual rewards of agent3: 0.24396937732872442
team_policy eval average team episode rewards of agent3: 2.5
team_policy eval idv catch total num of agent3: 12
team_policy eval team catch total num: 1
team_policy eval average step individual rewards of agent4: -0.06245065801696237
team_policy eval average team episode rewards of agent4: 2.5
team_policy eval idv catch total num of agent4: 1
team_policy eval team catch total num: 1
idv_policy eval average step individual rewards of agent0: 0.260870588484455
idv_policy eval average team episode rewards of agent0: 0.0
idv_policy eval idv catch total num of agent0: 14
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent1: 0.17739271715463278
idv_policy eval average team episode rewards of agent1: 0.0
idv_policy eval idv catch total num of agent1: 11
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent2: 0.1871709987682769
idv_policy eval average team episode rewards of agent2: 0.0
idv_policy eval idv catch total num of agent2: 11
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent3: 0.041612015413322795
idv_policy eval average team episode rewards of agent3: 0.0
idv_policy eval idv catch total num of agent3: 6
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent4: 0.039144645739391616
idv_policy eval average team episode rewards of agent4: 0.0
idv_policy eval idv catch total num of agent4: 5
idv_policy eval team catch total num: 0

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9926/10000 episodes, total num timesteps 1985400/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9927/10000 episodes, total num timesteps 1985600/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9928/10000 episodes, total num timesteps 1985800/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9929/10000 episodes, total num timesteps 1986000/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9930/10000 episodes, total num timesteps 1986200/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9931/10000 episodes, total num timesteps 1986400/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9932/10000 episodes, total num timesteps 1986600/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9933/10000 episodes, total num timesteps 1986800/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9934/10000 episodes, total num timesteps 1987000/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9935/10000 episodes, total num timesteps 1987200/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9936/10000 episodes, total num timesteps 1987400/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9937/10000 episodes, total num timesteps 1987600/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9938/10000 episodes, total num timesteps 1987800/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9939/10000 episodes, total num timesteps 1988000/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9940/10000 episodes, total num timesteps 1988200/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9941/10000 episodes, total num timesteps 1988400/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9942/10000 episodes, total num timesteps 1988600/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9943/10000 episodes, total num timesteps 1988800/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9944/10000 episodes, total num timesteps 1989000/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9945/10000 episodes, total num timesteps 1989200/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9946/10000 episodes, total num timesteps 1989400/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9947/10000 episodes, total num timesteps 1989600/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9948/10000 episodes, total num timesteps 1989800/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9949/10000 episodes, total num timesteps 1990000/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9950/10000 episodes, total num timesteps 1990200/2000000, FPS 256.

team_policy eval average step individual rewards of agent0: -0.012165105165358086
team_policy eval average team episode rewards of agent0: 0.0
team_policy eval idv catch total num of agent0: 3
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent1: 0.009526625643309852
team_policy eval average team episode rewards of agent1: 0.0
team_policy eval idv catch total num of agent1: 4
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent2: -0.11104371683928918
team_policy eval average team episode rewards of agent2: 0.0
team_policy eval idv catch total num of agent2: 0
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent3: 0.14006006977761973
team_policy eval average team episode rewards of agent3: 0.0
team_policy eval idv catch total num of agent3: 9
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent4: -0.0644892392373155
team_policy eval average team episode rewards of agent4: 0.0
team_policy eval idv catch total num of agent4: 2
team_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent0: -0.04202412636756206
idv_policy eval average team episode rewards of agent0: 2.5
idv_policy eval idv catch total num of agent0: 3
idv_policy eval team catch total num: 1
idv_policy eval average step individual rewards of agent1: 0.30281406405525535
idv_policy eval average team episode rewards of agent1: 2.5
idv_policy eval idv catch total num of agent1: 15
idv_policy eval team catch total num: 1
idv_policy eval average step individual rewards of agent2: 0.06609931182427672
idv_policy eval average team episode rewards of agent2: 2.5
idv_policy eval idv catch total num of agent2: 6
idv_policy eval team catch total num: 1
idv_policy eval average step individual rewards of agent3: -0.05249195998325616
idv_policy eval average team episode rewards of agent3: 2.5
idv_policy eval idv catch total num of agent3: 2
idv_policy eval team catch total num: 1
idv_policy eval average step individual rewards of agent4: 0.04132586972979284
idv_policy eval average team episode rewards of agent4: 2.5
idv_policy eval idv catch total num of agent4: 5
idv_policy eval team catch total num: 1

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9951/10000 episodes, total num timesteps 1990400/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9952/10000 episodes, total num timesteps 1990600/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9953/10000 episodes, total num timesteps 1990800/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9954/10000 episodes, total num timesteps 1991000/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9955/10000 episodes, total num timesteps 1991200/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9956/10000 episodes, total num timesteps 1991400/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9957/10000 episodes, total num timesteps 1991600/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9958/10000 episodes, total num timesteps 1991800/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9959/10000 episodes, total num timesteps 1992000/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9960/10000 episodes, total num timesteps 1992200/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9961/10000 episodes, total num timesteps 1992400/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9962/10000 episodes, total num timesteps 1992600/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9963/10000 episodes, total num timesteps 1992800/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9964/10000 episodes, total num timesteps 1993000/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9965/10000 episodes, total num timesteps 1993200/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9966/10000 episodes, total num timesteps 1993400/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9967/10000 episodes, total num timesteps 1993600/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9968/10000 episodes, total num timesteps 1993800/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9969/10000 episodes, total num timesteps 1994000/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9970/10000 episodes, total num timesteps 1994200/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9971/10000 episodes, total num timesteps 1994400/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9972/10000 episodes, total num timesteps 1994600/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9973/10000 episodes, total num timesteps 1994800/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9974/10000 episodes, total num timesteps 1995000/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9975/10000 episodes, total num timesteps 1995200/2000000, FPS 256.

team_policy eval average step individual rewards of agent0: -0.006488291037444954
team_policy eval average team episode rewards of agent0: 5.0
team_policy eval idv catch total num of agent0: 3
team_policy eval team catch total num: 2
team_policy eval average step individual rewards of agent1: -0.033624466813480164
team_policy eval average team episode rewards of agent1: 5.0
team_policy eval idv catch total num of agent1: 2
team_policy eval team catch total num: 2
team_policy eval average step individual rewards of agent2: 0.018388681322761277
team_policy eval average team episode rewards of agent2: 5.0
team_policy eval idv catch total num of agent2: 4
team_policy eval team catch total num: 2
team_policy eval average step individual rewards of agent3: 0.1570698537839193
team_policy eval average team episode rewards of agent3: 5.0
team_policy eval idv catch total num of agent3: 9
team_policy eval team catch total num: 2
team_policy eval average step individual rewards of agent4: 0.2561953888527878
team_policy eval average team episode rewards of agent4: 5.0
team_policy eval idv catch total num of agent4: 13
team_policy eval team catch total num: 2
idv_policy eval average step individual rewards of agent0: -0.02355314789628085
idv_policy eval average team episode rewards of agent0: 0.0
idv_policy eval idv catch total num of agent0: 3
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent1: 0.0851952058077989
idv_policy eval average team episode rewards of agent1: 0.0
idv_policy eval idv catch total num of agent1: 7
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent2: -0.1164266921204685
idv_policy eval average team episode rewards of agent2: 0.0
idv_policy eval idv catch total num of agent2: 0
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent3: 0.045922845050268266
idv_policy eval average team episode rewards of agent3: 0.0
idv_policy eval idv catch total num of agent3: 5
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent4: -0.010119816857209975
idv_policy eval average team episode rewards of agent4: 0.0
idv_policy eval idv catch total num of agent4: 3
idv_policy eval team catch total num: 0

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9976/10000 episodes, total num timesteps 1995400/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9977/10000 episodes, total num timesteps 1995600/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9978/10000 episodes, total num timesteps 1995800/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9979/10000 episodes, total num timesteps 1996000/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9980/10000 episodes, total num timesteps 1996200/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9981/10000 episodes, total num timesteps 1996400/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9982/10000 episodes, total num timesteps 1996600/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9983/10000 episodes, total num timesteps 1996800/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9984/10000 episodes, total num timesteps 1997000/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9985/10000 episodes, total num timesteps 1997200/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9986/10000 episodes, total num timesteps 1997400/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9987/10000 episodes, total num timesteps 1997600/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9988/10000 episodes, total num timesteps 1997800/2000000, FPS 256.

wandb: - 0.006 MB of 0.006 MB uploaded
wandb: \ 0.006 MB of 3.048 MB uploaded
wandb: | 0.011 MB of 3.048 MB uploaded
wandb: / 0.971 MB of 3.048 MB uploaded
wandb: - 2.706 MB of 3.048 MB uploaded
wandb: \ 2.706 MB of 3.048 MB uploaded
wandb: | 3.048 MB of 3.048 MB uploaded
wandb:                                                                                
wandb: 
wandb: Run history:
wandb:                                       Aa_idv_actor_loss ▁▁▁▂▂▂▂▃▃▃▃▃▄▄▄▅▅▅▅▅▆▆▆▆▇▇▇▇▇▇██████████
wandb:                                          Ab_policy_loss ▄▅▅▄▅▄█▆▅▅▂▆▇▂▅▆▇▂▃▅▅▁▅▂▆▃▇▂▁▂▃▂▄▃▄▃▂▂▄▂
wandb:                                     Ac_idv_ppo_loss_abs ▄▄▃▃▂▅▇▇▆▅▅▇▇█▇▇▇█▆▇▇▇▇▇█▇█▇█▇▇▆▇▇▇▆▆▅▁▃
wandb:                                         Ad_idv_ppo_prop ▂▂▂▂▁▂▃▃▃▃▃▄▄▄▄▄▅▅▅▅▅▆▆▆▆▆▇▇▇▇████████▇▇
wandb:                                                  Ae_eta ▅▅▆▅▅▆▅▅▄▅▃▅▅▂▁▆▅▄▅█▄▄▅▃▁▂▆▄▅▅▅▅▃▃▆▅▄▄▅▄
wandb:                                    Af_noclip_proportion █████▇▄▆▇▆▇▇▇▆▄▂▅██▁█▇▄▆▆▅▃█▅▇▇▇▇▇▇███▇█
wandb:                                    Ag_update_proportion ▇▇▁▄▅▄▄▅▅▇▆▇▅▇▅▆▅▅▇▇▇▆▆▇█▇█▅▇█▇███▆▆▆▆▄▃
wandb:                                          Ah_update_loss ▁▂█▄▄▆▇▆▆▄▄▂▄▃▄▃▆▄▂▂▂▅▄▄▄▂▃▄▃▁▂▃▃▃▂▂▂▄▃▅
wandb:                                         Ai_idv_epsilon' ▁▁▁▁▂▂▂▂▂▃▃▃▃▃▃▄▄▄▄▄▅▅▅▅▅▅▆▆▆▆▆▇▇▇▇▇▇███
wandb:                                            Aj_idv_sigma ▁▁▁▂▂▂▃▃▃▃▃▄▄▃▄█▇▄▄▅▃▄▃▄█▅▆▂▄▃▃▂▃▇▃▂▂▂▂▃
wandb:              Ak_idv_clip(sigma, 1-epislon', 1+epislon') ▁▁▁▁▁▂▂▂▂▃▂▄▃▃▃▄▆▃▃▅▃▄▄▄█▅▅▁▄▃▃▃▄▆▄▂▃▂▄▄
wandb:                                Al_idv_noclip_proportion ▁▂▃▅▄▅▅▆▆▆▇▇▆▇▇▆▆▇▇▇▇███▇███████████████
wandb:                       Am_idv_(sigma*A)update_proportion ▁▂▃▄▄▄▅▅▅▅▅▅▅▆▆▅▆▆▆▅▆▆▆▆▅▆▅▇▆▆▆▆▆▆▆▆▇▇▇█
wandb:                             An_idv_(sigma*A)update_loss █▇▆▆▆▄▁▃▄▃▃█▅▅▅▄▁▂▆▆▆▂▄▂▂▆▄▃▅█▄▃▄▄▅▇▆▄▆▄
wandb:                                     Ao_idv_entropy_prop ▇▇█▇█▇▆▆▆▆▆▅▅▅▅▅▄▄▄▄▄▃▃▃▃▂▂▂▂▂▁▁▁▁▁▁▁▁▂▁
wandb:                                         Ap_dist_entropy ▁▅▆▇████████████████████████████████████
wandb:                                          Aq_idv_kl_prop ▁▁▁▁▂▁▂▂▂▂▂▃▃▂▃▅▅▄▄▅▄▄▅▅▆▅▇▄▇▄▄▄▄▆▆▄▄▅▇█
wandb:                                          Ar_idv_kl_coef ▁▁▁▁▂▂▂▂▂▃▃▃▃▃▃▄▄▄▄▄▅▅▅▅▅▅▆▆▆▆▆▇▇▇▇▇▇███
wandb:                                          As_idv_kl_loss ▁▁▂▁▄▃▄▃▄▄▄▅▅▃▅██▅▅▆▅▄▅▅▆▅▆▃▆▃▃▃▃▄▄▃▃▃▃▄
wandb:                                    At_idv_cross_entropy ▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁
wandb:                                           Au_value_loss ▁▁█▃▄▃▅▆█▃▅▂▃▅▂▂▃▂▂▂▃▂▂▂▃▂▂▂▂▂▁▁▁▂▁▂▁▁▁▁
wandb:                                           Av_advantages ▄▃▅▃▆▄▇▄▄▆▅▂▃▆▂▅▄▃▁▅▅▅▂▄▆▅▅▅▃▇▆▅▅█▄▄▆▄▅▄
wandb:                                       Aw_idv_actor_norm ▄▃▁▁▂▂▃▃▂▃▃▄▆▄▅▆▇▃▅▆▆▄▇▅▆▇█▃▇▄▃▄▄▄▄▂▂▃▃▄
wandb:                                      Ax_idv_critic_norm ▅▃█▄▄▃▃▃▄▂▂▂▂▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▁▂▁▂▂▁▁▁▁▁
wandb:                                     Ba_idv_org_min_prop ▄▃▁▄▄▅▃▄▃▆▅▅▄▄▄▅▃▅▅▅▅▅▅▆▆▅▆▅▆▆▇█▇▇▆▆▇▆▅▄
wandb:                                     Bb_idv_org_max_prop ██▂▃▄▂▅▄▅▄▅▅▅▇▄▅▅▃▅▅▅▃▄▄▅▅▅▄▅▅▃▃▄▃▃▂▂▂▁▁
wandb:                                     Bc_idv_org_org_prop ▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁
wandb:                                     Bd_idv_new_min_prop ▁▂▃▃▃▃▅▅▅▄▄▄▅▆▅▅▆▆▅▅▅▆▅▄▅▅▄▆▄▄▃▃▃▄▅▃▃▄▅█
wandb:                                     Be_idv_new_max_prop ▁▁▃▅▄▅▄▅▅▅▆▅▅▅▆▅▅▆▆▅▆▆▆▇▅▆▆▇▆▆▇▇▇▆▇█████
wandb:                                      Ta_team_actor_loss ▁▁▁▂▂▂▂▃▃▃▃▃▄▄▄▄▅▅▅▅▅▆▆▆▆▇▇▇▇▇██████████
wandb:                                     Tb_team_policy_loss ▃▃▂▁▄▅▅▄▄▃▆▂▃▅█▆█▅▆▇▅▁▅▄█▄▇▄▃▁▃▃██▅▄▂▄▄▅
wandb:                                    Tc_team_ppo_loss_abs ▆▄▃▂▁▄▇▇▇▅▇▇▇█▆▆▆▅█▇▆▄▅▅▆▇▇██▇█▇▇▆▇▆▇▆▁▃
wandb:                                        Td_team_ppo_prop ▂▂▂▁▁▂▃▄▄▃▄▄▄▅▄▄▅▄▅▅▅▅▆▆▆▇▇▇▇▇████████▇█
wandb:                                        Te_team_epsilon^ ▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁
wandb:                                          Tf_team_sigma^ ▆▆▇▆▆▆▇▅▇▅▄▄▅▅▆▆▃▅▇▅▆▅▇▇▁▃▆█▆▆▅▆▅▄▆▇▆▆▆▆
wandb:          Tg_team_clip(sigma^, 1-epislon^', 1+epislon^') ▇▇▇▇▆▇▆▅▇▅▅▄▅▅▆▅▃▅▆▄▆▄▆▆▁▄▆█▆▆▆▆▆▄▅▇▆▇▆▅
wandb:                               Th_team_noclip_proportion ██▇▇▅▅▄▅▅▅▆▄▃▅▄▂▁▅▃▃▄▃▃▃▂▅▂▅▂▅▆▆▅▄▄▆▆▆▅▃
wandb:                     Ti_team_(sigma^*A)update_proportion ██▇▇▅▅▄▅▅▅▆▄▃▅▄▂▁▅▃▃▄▃▃▃▂▄▂▄▂▅▆▆▅▄▅▆▆▆▆▃
wandb:                           Tj_team_(sigma^*A)update_loss ████▇▇▄▇▆▆▆▆█▆▆▃▄▃▁▂▆▇▃▆▆▇▅▄▆▅▆▇▅▆▄▅▇▇▇▅
wandb:                                    Tk_team_entropy_prop ▇▇▇██▇▆▅▅▆▅▅▅▄▅▄▄▅▄▄▄▄▃▃▃▂▂▂▂▂▁▁▁▁▁▁▁▁▂▁
wandb:                                    Tl_team_dist_entropy ▁▆▇█████████████████████████████████████
wandb:                                         Tm_team_kl_prop ▁▂▃▂▅▄▅▄▄▅▄▅▆▄▆██▅▅▅▄▅▅▄▅▃▄▂▄▂▂▂▂▂▂▁▁▁▁▁
wandb:                                         Tn_team_kl_coef ████▇▇▇▇▇▆▆▆▆▆▆▅▅▅▅▅▄▄▄▄▄▄▃▃▃▃▃▂▂▂▂▂▂▁▁▁
wandb:                                         To_team_kl_loss ▁▂▂▁▃▃▅▄▄▄▄▅▆▄▅██▅▅▆▄▅▅▅▆▅▆▃▇▃▃▃▃▄▄▃▃▃▃▄
wandb:                                   Tp_team_cross_entropy ▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁
wandb:                                      Tq_team_value_loss ▁▁▆▂▄▃▅▅█▃▄▂▃▅▂▂▃▂▂▂▃▂▂▂▃▂▂▂▂▂▁▁▂▂▂▂▁▁▁▁
wandb:                                      Tr_team_advantages ▅▅▆▇▃▆▄▅▅▆▅▇▆▃█▇▆▁▄▅▄▆▆█▃▇▆█▄▆▄▆▂▅▅▅▅▃▅▃
wandb:                                      Ts_team_actor_norm ▁▁▁▁▃▃▄▄▄▃▃▄▆▄▆▆█▅▆▅▄▃▆▄▆▆▅▂▄▄▃▃▃▄▃▂▂▂▃▂
wandb:                                     Tt_team_critic_norm ▂▁█▅▅▄▄▃▅▃▂▃▃▅▃▂▂▂▃▂▂▂▂▂▂▂▂▂▂▂▁▂▂▂▂▁▁▁▁▁
wandb:                     agent0/average_episode_team_rewards ▁▁▁▁▁▁▂▃▄▄▅▆▅█▅▅▅▄▇▇▇▄▅▄▆█▆▅▆▇▄▃▃▄▃▃▂▂▁▁
wandb:                  agent0/average_step_individual_rewards ▂▁▂▁▂▁▂▃▂▄▅▆▄▇▇▇▅▃▇█▅▄▆▄▄▅▅▆▃▇▅▄▅▅▄▂▄▂▂▁
wandb:     agent0/idv_policy_eval_average_episode_team_rewards ▁▁▂▂▁▂▃▄▆▅▆█▄▃▅▇▅▇▆▆▅▇▇▅▅▆▆▃▄█▆▇▄▄▅▆▂▁▁▁
wandb:  agent0/idv_policy_eval_average_step_individual_rewards ▁▁▂▁▁▂▄▄▆▇▅▅▆▄▄▅▅▇▄▄▅▇▆▃▅█▇▄▄▆▅▆▄▄▅▆▄▂▃▁
wandb:              agent0/idv_policy_eval_idv_catch_total_num ▁▁▂▁▁▂▄▄▆▇▅▅▆▃▄▅▅▇▄▄▅▇▆▃▅█▇▄▄▆▅▆▄▃▅▆▄▂▃▁
wandb:             agent0/idv_policy_eval_team_catch_total_num ▁▁▂▂▁▂▃▄▆▅▆█▄▃▅▇▅▇▆▆▅▇▇▅▅▆▆▃▄█▆▇▄▄▅▆▂▁▁▁
wandb:    agent0/team_policy_eval_average_episode_team_rewards ▁▁▁▁▃▃▄▅▆▅▅▅▆▅▄▅▅▆▄▆▅▇▅▅▇▇▆▆▆█▅▄▅▄▅▄▁▂▁▁
wandb: agent0/team_policy_eval_average_step_individual_rewards ▁▁▂▂▄▃▄▆▆▄▅▅▆▅▄▄▅▆▅▇▆▆▅▆▇█▆█▆▆▅▇▆▅▅▄▃▃▂▂
wandb:             agent0/team_policy_eval_idv_catch_total_num ▁▁▁▁▄▂▄▆▆▄▅▄▆▅▄▄▄▆▅▆▆▆▅▆▆█▆█▆▅▅▆▆▅▅▃▂▃▂▁
wandb:            agent0/team_policy_eval_team_catch_total_num ▁▁▁▁▃▃▄▅▆▅▅▅▆▅▄▅▅▆▄▆▅▇▅▅▇▇▆▆▆█▅▄▅▄▅▄▁▂▁▁
wandb:                     agent1/average_episode_team_rewards ▁▁▁▁▁▁▂▃▄▄▅▆▅█▅▅▅▄▇▇▇▄▅▄▆█▆▅▆▇▄▃▃▄▃▃▂▂▁▁
wandb:                  agent1/average_step_individual_rewards ▁▁▁▂▁▂▂▄▃▅▅▅▄█▃▃▃▄▇▅▅▄▅▄▆▆▆▅▅▄▄▃▂▄▂▄▁▃▁▂
wandb:     agent1/idv_policy_eval_average_episode_team_rewards ▁▁▂▂▁▂▃▄▆▅▆█▄▃▅▇▅▇▆▆▅▇▇▅▅▆▆▃▄█▆▇▄▄▅▆▂▁▁▁
wandb:  agent1/idv_policy_eval_average_step_individual_rewards ▁▁▃▂▁▁▃▄▄▄▆▇▃▃▄▆▄▅▅▃▅▅▇▅▄▅▂▃▄█▆▇▃▄▄▄▃▂▂▂
wandb:              agent1/idv_policy_eval_idv_catch_total_num ▁▁▂▂▁▁▃▄▄▄▆▇▃▂▄▆▃▅▄▃▅▅▆▅▄▅▂▃▃█▆▇▂▄▄▃▃▂▂▂
wandb:             agent1/idv_policy_eval_team_catch_total_num ▁▁▂▂▁▂▃▄▆▅▆█▄▃▅▇▅▇▆▆▅▇▇▅▅▆▆▃▄█▆▇▄▄▅▆▂▁▁▁
wandb:    agent1/team_policy_eval_average_episode_team_rewards ▁▁▁▁▃▃▄▅▆▅▅▅▆▅▄▅▅▆▄▆▅▇▅▅▇▇▆▆▆█▅▄▅▄▅▄▁▂▁▁
wandb: agent1/team_policy_eval_average_step_individual_rewards ▁▁▁▁▃▃▃▆▄▆▅▆▆▃▇▅▆▅▄▄▄▄▆▅██▆▇█▆▆▄▃▅▇▄▂▂▁▁
wandb:             agent1/team_policy_eval_idv_catch_total_num ▁▁▁▁▃▃▃▆▄▆▅▆▆▃▇▅▆▅▄▄▄▄▆▅██▅▇█▆▆▄▃▅▇▄▂▂▁▁
wandb:            agent1/team_policy_eval_team_catch_total_num ▁▁▁▁▃▃▄▅▆▅▅▅▆▅▄▅▅▆▄▆▅▇▅▅▇▇▆▆▆█▅▄▅▄▅▄▁▂▁▁
wandb:                     agent2/average_episode_team_rewards ▁▁▁▁▁▁▂▃▄▄▅▆▅█▅▅▅▄▇▇▇▄▅▄▆█▆▅▆▇▄▃▃▄▃▃▂▂▁▁
wandb:                  agent2/average_step_individual_rewards ▁▁▂▂▂▂▂▄▆▄▄▆▆▅▅▄▅▄▅▆▇▄▄▃▅█▅▅▅▄▅▃▅▄▆▄▃▃▂▂
wandb:     agent2/idv_policy_eval_average_episode_team_rewards ▁▁▂▂▁▂▃▄▆▅▆█▄▃▅▇▅▇▆▆▅▇▇▅▅▆▆▃▄█▆▇▄▄▅▆▂▁▁▁
wandb:  agent2/idv_policy_eval_average_step_individual_rewards ▁▁▂▃▂▂▅▇▇▅▆▇▄▆▆▆▆█▆▆▄▆▆█▆▅▆▅▃█▅▅▃▃▄▆▃▄▁▁
wandb:              agent2/idv_policy_eval_idv_catch_total_num ▁▁▂▃▁▂▄▇▇▅▆▇▃▆▆▆▆█▆▆▄▆▆█▆▅▅▅▃█▅▅▃▃▃▅▃▄▁▁
wandb:             agent2/idv_policy_eval_team_catch_total_num ▁▁▂▂▁▂▃▄▆▅▆█▄▃▅▇▅▇▆▆▅▇▇▅▅▆▆▃▄█▆▇▄▄▅▆▂▁▁▁
wandb:    agent2/team_policy_eval_average_episode_team_rewards ▁▁▁▁▃▃▄▅▆▅▅▅▆▅▄▅▅▆▄▆▅▇▅▅▇▇▆▆▆█▅▄▅▄▅▄▁▂▁▁
wandb: agent2/team_policy_eval_average_step_individual_rewards ▂▁▂▂▃▂▄▆▅▅▄▄▄▆▃▅▅▆▄▅▅█▅▅▆▆▆▅▅█▆▃▆▃▃▅▂▂▂▂
wandb:             agent2/team_policy_eval_idv_catch_total_num ▂▁▁▁▃▂▄▆▄▅▄▃▄▅▂▄▅▆▃▅▅█▅▅▆▅▆▅▄█▆▃▆▃▃▄▂▂▂▁
wandb:            agent2/team_policy_eval_team_catch_total_num ▁▁▁▁▃▃▄▅▆▅▅▅▆▅▄▅▅▆▄▆▅▇▅▅▇▇▆▆▆█▅▄▅▄▅▄▁▂▁▁
wandb:                     agent3/average_episode_team_rewards ▁▁▁▁▁▁▂▃▄▄▅▆▅█▅▅▅▄▇▇▇▄▅▄▆█▆▅▆▇▄▃▃▄▃▃▂▂▁▁
wandb:                  agent3/average_step_individual_rewards ▂▁▁▂▂▂▃▄▄▃▅▆▆▇▄▆▄▅▆▆█▄▅▆▆█▇▄▄▆▄▂▄▆▂▅▄▃▂▂
wandb:     agent3/idv_policy_eval_average_episode_team_rewards ▁▁▂▂▁▂▃▄▆▅▆█▄▃▅▇▅▇▆▆▅▇▇▅▅▆▆▃▄█▆▇▄▄▅▆▂▁▁▁
wandb:  agent3/idv_policy_eval_average_step_individual_rewards ▁▁▂▃▁▂▄▄▅▅█▇▄▂▅▆▄▅▇▆▅▆▅▅▄▅▇▃▄▇▅▆▅▅▅▆▂▂▂▂
wandb:              agent3/idv_policy_eval_idv_catch_total_num ▁▁▂▂▁▂▃▃▄▅█▇▄▂▅▆▃▅▇▆▅▆▅▄▄▅▇▃▄▇▅▆▅▅▅▆▂▂▁▂
wandb:             agent3/idv_policy_eval_team_catch_total_num ▁▁▂▂▁▂▃▄▆▅▆█▄▃▅▇▅▇▆▆▅▇▇▅▅▆▆▃▄█▆▇▄▄▅▆▂▁▁▁
wandb:    agent3/team_policy_eval_average_episode_team_rewards ▁▁▁▁▃▃▄▅▆▅▅▅▆▅▄▅▅▆▄▆▅▇▅▅▇▇▆▆▆█▅▄▅▄▅▄▁▂▁▁
wandb: agent3/team_policy_eval_average_step_individual_rewards ▁▂▂▁▃▄▃▄▃▅▄▅▅▅▅▅▄▄▅▆▄▆▄▆▄▆▅▄▆█▅▄▅▃▃▄▃▄▂▂
wandb:             agent3/team_policy_eval_idv_catch_total_num ▁▁▂▁▃▃▃▃▃▅▃▅▅▄▅▅▃▃▅▆▄▅▄▆▄▅▅▄▅█▄▃▅▃▂▄▃▃▂▂
wandb:            agent3/team_policy_eval_team_catch_total_num ▁▁▁▁▃▃▄▅▆▅▅▅▆▅▄▅▅▆▄▆▅▇▅▅▇▇▆▆▆█▅▄▅▄▅▄▁▂▁▁
wandb:                     agent4/average_episode_team_rewards ▁▁▁▁▁▁▂▃▄▄▅▆▅█▅▅▅▄▇▇▇▄▅▄▆█▆▅▆▇▄▃▃▄▃▃▂▂▁▁
wandb:                  agent4/average_step_individual_rewards ▁▁▁▂▂▂▃▄▆▄▅▅▆▇▄▆▄▄▆▅▆▅▅▅▆█▄▅▇█▃▅▄▄▂▃▃▂▂▂
wandb:     agent4/idv_policy_eval_average_episode_team_rewards ▁▁▂▂▁▂▃▄▆▅▆█▄▃▅▇▅▇▆▆▅▇▇▅▅▆▆▃▄█▆▇▄▄▅▆▂▁▁▁
wandb:  agent4/idv_policy_eval_average_step_individual_rewards ▁▁▃▂▂▂▄▅▆▅█▇▆▄▃▆▇▇▅▇▅▇▆▅▇▅▆▄▇▇▇█▇▇▃▇▃▁▂▂
wandb:              agent4/idv_policy_eval_idv_catch_total_num ▁▁▃▁▂▂▄▅▅▅█▇▆▄▂▅▇▇▅▇▄▇▆▅▇▅▆▃▇▆▇█▇▇▃▆▃▁▁▁
wandb:             agent4/idv_policy_eval_team_catch_total_num ▁▁▂▂▁▂▃▄▆▅▆█▄▃▅▇▅▇▆▆▅▇▇▅▅▆▆▃▄█▆▇▄▄▅▆▂▁▁▁
wandb:    agent4/team_policy_eval_average_episode_team_rewards ▁▁▁▁▃▃▄▅▆▅▅▅▆▅▄▅▅▆▄▆▅▇▅▅▇▇▆▆▆█▅▄▅▄▅▄▁▂▁▁
wandb: agent4/team_policy_eval_average_step_individual_rewards ▁▁▁▂▃▃▄▅▇▄▇▆█▅▃▄▆▅▆▆▅▇▅▄▄▅▅▆▅▇▆▄▅▆▆▄▄▃▁▃
wandb:             agent4/team_policy_eval_idv_catch_total_num ▁▁▁▁▂▃▄▅▇▄▇▆█▅▃▄▆▅▆▆▅▇▅▄▄▅▅▆▅▇▆▄▅▆▆▄▄▃▁▃
wandb:            agent4/team_policy_eval_team_catch_total_num ▁▁▁▁▃▃▄▅▆▅▅▅▆▅▄▅▅▆▄▆▅▇▅▅▇▇▆▆▆█▅▄▅▄▅▄▁▂▁▁
wandb: 
wandb: Run summary:
wandb:                                       Aa_idv_actor_loss -0.0271
wandb:                                          Ab_policy_loss -0.0014
wandb:                                     Ac_idv_ppo_loss_abs 0.56747
wandb:                                         Ad_idv_ppo_prop 0.88873
wandb:                                                  Ae_eta 0.99887
wandb:                                    Af_noclip_proportion 0.9806
wandb:                                    Ag_update_proportion 0.4463
wandb:                                          Ah_update_loss 0.15039
wandb:                                         Ai_idv_epsilon' 0.49995
wandb:                                            Aj_idv_sigma 1.01118
wandb:              Ak_idv_clip(sigma, 1-epislon', 1+epislon') 1.00782
wandb:                                Al_idv_noclip_proportion 0.9898
wandb:                       Am_idv_(sigma*A)update_proportion 0.5472
wandb:                             An_idv_(sigma*A)update_loss -0.10571
wandb:                                     Ao_idv_entropy_prop 0.07577
wandb:                                         Ap_dist_entropy 4.8378
wandb:                                          Aq_idv_kl_prop 0.0355
wandb:                                          Ar_idv_kl_coef 6.9993
wandb:                                          As_idv_kl_loss 0.00324
wandb:                                    At_idv_cross_entropy 0.0
wandb:                                           Au_value_loss 0.03201
wandb:                                           Av_advantages 0.0
wandb:                                       Aw_idv_actor_norm 0.93091
wandb:                                      Ax_idv_critic_norm 0.02403
wandb:                                     Ba_idv_org_min_prop 0.4052
wandb:                                     Bb_idv_org_max_prop 0.0411
wandb:                                     Bc_idv_org_org_prop 0.0
wandb:                                     Bd_idv_new_min_prop 0.1192
wandb:                                     Be_idv_new_max_prop 0.428
wandb:                                      Ta_team_actor_loss -0.04885
wandb:                                     Tb_team_policy_loss -0.00047
wandb:                                    Tc_team_ppo_loss_abs 0.52382
wandb:                                        Td_team_ppo_prop 0.91545
wandb:                                        Te_team_epsilon^ 0.2
wandb:                                          Tf_team_sigma^ 0.99932
wandb:          Tg_team_clip(sigma^, 1-epislon^', 1+epislon^') 0.99522
wandb:                               Th_team_noclip_proportion 0.9412
wandb:                     Ti_team_(sigma^*A)update_proportion 0.9687
wandb:                           Tj_team_(sigma^*A)update_loss -0.01502
wandb:                                    Tk_team_entropy_prop 0.08455
wandb:                                    Tl_team_dist_entropy 4.83763
wandb:                                         Tm_team_kl_prop 0.0
wandb:                                         Tn_team_kl_coef 0.00012
wandb:                                         To_team_kl_loss 0.00288
wandb:                                   Tp_team_cross_entropy 0.0
wandb:                                      Tq_team_value_loss 0.02729
wandb:                                      Tr_team_advantages 0.0
wandb:                                      Ts_team_actor_norm 0.2002
wandb:                                     Tt_team_critic_norm 0.0137
wandb:                     agent0/average_episode_team_rewards 7.5
wandb:                  agent0/average_step_individual_rewards 0.22602
wandb:     agent0/idv_policy_eval_average_episode_team_rewards 0.0
wandb:  agent0/idv_policy_eval_average_step_individual_rewards -0.02355
wandb:              agent0/idv_policy_eval_idv_catch_total_num 3
wandb:             agent0/idv_policy_eval_team_catch_total_num 0
wandb:    agent0/team_policy_eval_average_episode_team_rewards 5.0
wandb: agent0/team_policy_eval_average_step_individual_rewards -0.00649
wandb:             agent0/team_policy_eval_idv_catch_total_num 3
wandb:            agent0/team_policy_eval_team_catch_total_num 2
wandb:                     agent1/average_episode_team_rewards 7.5
wandb:                  agent1/average_step_individual_rewards 0.15923
wandb:     agent1/idv_policy_eval_average_episode_team_rewards 0.0
wandb:  agent1/idv_policy_eval_average_step_individual_rewards 0.0852
wandb:              agent1/idv_policy_eval_idv_catch_total_num 7
wandb:             agent1/idv_policy_eval_team_catch_total_num 0
wandb:    agent1/team_policy_eval_average_episode_team_rewards 5.0
wandb: agent1/team_policy_eval_average_step_individual_rewards -0.03362
wandb:             agent1/team_policy_eval_idv_catch_total_num 2
wandb:            agent1/team_policy_eval_team_catch_total_num 2
wandb:                     agent2/average_episode_team_rewards 7.5
wandb:                  agent2/average_step_individual_rewards 0.01017
wandb:     agent2/idv_policy_eval_average_episode_team_rewards 0.0
wandb:  agent2/idv_policy_eval_average_step_individual_rewards -0.11643
wandb:              agent2/idv_policy_eval_idv_catch_total_num 0
wandb:             agent2/idv_policy_eval_team_catch_total_num 0
wandb:    agent2/team_policy_eval_average_episode_team_rewards 5.0
wandb: agent2/team_policy_eval_average_step_individual_rewards 0.01839
wandb:             agent2/team_policy_eval_idv_catch_total_num 4
wandb:            agent2/team_policy_eval_team_catch_total_num 2
wandb:                     agent3/average_episode_team_rewards 7.5
wandb:                  agent3/average_step_individual_rewards 0.30779
wandb:     agent3/idv_policy_eval_average_episode_team_rewards 0.0
wandb:  agent3/idv_policy_eval_average_step_individual_rewards 0.04592
wandb:              agent3/idv_policy_eval_idv_catch_total_num 5
wandb:             agent3/idv_policy_eval_team_catch_total_num 0
wandb:    agent3/team_policy_eval_average_episode_team_rewards 5.0
wandb: agent3/team_policy_eval_average_step_individual_rewards 0.15707
wandb:             agent3/team_policy_eval_idv_catch_total_num 9
wandb:            agent3/team_policy_eval_team_catch_total_num 2
wandb:                     agent4/average_episode_team_rewards 7.5
wandb:                  agent4/average_step_individual_rewards 0.04706
wandb:     agent4/idv_policy_eval_average_episode_team_rewards 0.0
wandb:  agent4/idv_policy_eval_average_step_individual_rewards -0.01012
wandb:              agent4/idv_policy_eval_idv_catch_total_num 3
wandb:             agent4/idv_policy_eval_team_catch_total_num 0
wandb:    agent4/team_policy_eval_average_episode_team_rewards 5.0
wandb: agent4/team_policy_eval_average_step_individual_rewards 0.2562
wandb:             agent4/team_policy_eval_idv_catch_total_num 13
wandb:            agent4/team_policy_eval_team_catch_total_num 2
wandb: 
wandb: 🚀 View run MPE_5 at: https://wandb.ai/804703098/Continue_Tag_Base_v1/runs/xzyihgj2
wandb: ⭐️ View project at: https://wandb.ai/804703098/Continue_Tag_Base_v1
wandb: Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 4 other file(s)
wandb: Find logs at: ./results/MPE/simple_tag_tr/rmappotrsyn/exp_train_continue_tag_base_CMT_s2r2_v1/wandb/run-20240802_155522-xzyihgj2/logs

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9989/10000 episodes, total num timesteps 1998000/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9990/10000 episodes, total num timesteps 1998200/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9991/10000 episodes, total num timesteps 1998400/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9992/10000 episodes, total num timesteps 1998600/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9993/10000 episodes, total num timesteps 1998800/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9994/10000 episodes, total num timesteps 1999000/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9995/10000 episodes, total num timesteps 1999200/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9996/10000 episodes, total num timesteps 1999400/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9997/10000 episodes, total num timesteps 1999600/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9998/10000 episodes, total num timesteps 1999800/2000000, FPS 256.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9999/10000 episodes, total num timesteps 2000000/2000000, FPS 256.

_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9997/10000 episodes, total num timesteps 1999600/2000000, FPS 303.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9998/10000 episodes, total num timesteps 1999800/2000000, FPS 303.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 9999/10000 episodes, total num timesteps 2000000/2000000, FPS 303.

