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wandb: \ Waiting for wandb.init()...
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wandb:  $ pip install wandb --upgrade
wandb: Tracking run with wandb version 0.16.6
wandb: Run data is saved locally in /home/user/zhangyang/PycharmProjects/Nips2024-ITPC-v2/Nips2024-ITPC-v2/onpolicy/scripts/results/MPE/simple_tag_tr/rmappotrsyn/exp_train_continue_tag_base_CMT_s2r2_v1/wandb/run-20240802_170926-ub7653v5
wandb: Run `wandb offline` to turn off syncing.
wandb: Syncing run MPE_1
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/ub7653v5
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 202.

team_policy eval average step individual rewards of agent0: 0.029665646428612965
team_policy eval average team episode rewards of agent0: 0.0
team_policy eval idv catch total num of agent0: 4
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent1: 0.03250104722483489
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: 0.024479034637968976
team_policy eval average team episode rewards of agent2: 0.0
team_policy eval idv catch total num of agent2: 5
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent3: -0.06711794093655538
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: -0.03338860154818702
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: -0.12434804269261353
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: 0.013548773167782118
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: -0.034959417242868135
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: -0.08490448783764531
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: -0.0767090558852386
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 1/10000 episodes, total num timesteps 400/2000000, FPS 162.


 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 175.


 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 172.


 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 179.


 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 182.


 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 180.


 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 179.


 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 181.


 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 179.


 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 181.


 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 183.


 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 181.


 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 184.


 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 186.


 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 187.


 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 182.


 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 183.


 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 184.


 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 185.


 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 186.


 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 187.


 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 188.


 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 188.


 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 187.


 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 183.

team_policy eval average step individual rewards of agent0: -0.14763847472111452
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: -0.12091261618327724
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: -0.11055856367951623
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: -0.11671073021528416
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: -0.07029496110837709
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: -0.1354458603160311
idv_policy eval average team episode rewards of agent0: 2.5
idv_policy eval idv catch total num of agent0: 0
idv_policy eval team catch total num: 1
idv_policy eval average step individual rewards of agent1: 0.06107608788830868
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: -0.11994510250734955
idv_policy eval average team episode rewards of agent2: 2.5
idv_policy eval idv catch total num of agent2: 0
idv_policy eval team catch total num: 1
idv_policy eval average step individual rewards of agent3: -0.07504485643293073
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: -0.12800734541658046
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 26/10000 episodes, total num timesteps 5400/2000000, FPS 179.


 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 179.


 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 179.


 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 178.


 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 178.


 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 179.


 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 179.


 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 179.


 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 177.


 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 177.


 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 176.


 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 177.


 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 176.


 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 176.


 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 176.


 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 176.


 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 176.


 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 176.


 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 176.


 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 176.


 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 176.


 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 177.


 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 178.


 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 177.


 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 176.

team_policy eval average step individual rewards of agent0: -0.12671761265243467
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: -0.11349067119951904
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: -0.1345300990224875
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: -0.11735248861037731
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: -0.1657101555735979
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: -0.08052260308850034
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: -0.08000814606479872
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: -0.03867543122054769
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: -0.14294584991353404
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: -0.09425293732779733
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 51/10000 episodes, total num timesteps 10400/2000000, FPS 175.


 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 175.


 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 175.


 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 175.


 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 174.


 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 174.


 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 174.


 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 174.


 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 174.


 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 173.


 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 173.


 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 173.


 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 173.


 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 172.


 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 172.


 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 172.


 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 172.


 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 172.


 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 172.


 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 172.


 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 172.


 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 173.


 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 173.


 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 172.


 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 172.

team_policy eval average step individual rewards of agent0: -0.09086132034358535
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: -0.05583945754594785
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: -0.09191777857325778
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: -0.07183232825734977
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: -0.15637729775476256
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: -0.17296166490857687
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: -0.11550975322746472
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: -0.09881777469106064
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: -0.11332678322700478
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: -0.12458122700873508
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 76/10000 episodes, total num timesteps 15400/2000000, FPS 171.


 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 171.


 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 171.


 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 171.


 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 172.


 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 171.


 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 172.


 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 172.


 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 172.


 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 172.


 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 172.


 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 172.


 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 172.


 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 172.


 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 172.


 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 172.


 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 172.


 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 172.


 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 172.


 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 172.


 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 172.


 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 172.


 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 173.


 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 172.


 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 172.

team_policy eval average step individual rewards of agent0: -0.11869866218823699
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: -0.06621850074228844
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: 0.07473177084412334
team_policy eval average team episode rewards of agent2: 0.0
team_policy eval idv catch total num of agent2: 6
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent3: -0.05577269754339831
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: 0.12966128410822833
team_policy eval average team episode rewards of agent4: 0.0
team_policy eval idv catch total num of agent4: 8
team_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent0: -0.06317100695962413
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: -0.08376998836836087
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: -0.028577355565295157
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: -0.10304093977634171
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: -0.03867706197727371
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 101/10000 episodes, total num timesteps 20400/2000000, FPS 172.


 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 172.


 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 171.


 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 172.


 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 172.


 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 172.


 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 172.


 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 172.


 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 172.


 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 172.


 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 172.


 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 171.


 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 172.


 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 172.


 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 171.

team_policy eval average step individual rewards of agent0: -0.015756991823651968
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: -0.0008090252369498873
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: 0.050895978766980196
team_policy eval average team episode rewards of agent2: 0.0
team_policy eval idv catch total num of agent2: 6
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent3: -0.013679061254791696
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: 0.014361190281037363
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: -0.058995088103634165
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: -0.046681417082954535
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: -0.04684212111196286
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: -0.11527383869840069
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: -0.07876003938186422
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 126/10000 episodes, total num timesteps 25400/2000000, FPS 171.


 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 171.


 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 171.


 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 171.


 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 171.


 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 171.


 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 172.


 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 172.


 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 172.


 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 172.


 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 172.


 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: -0.15688899866200656
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: -0.1258584403759665
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: -0.15103030790606808
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: -0.015124520712436241
team_policy eval average team episode rewards of agent3: 0.0
team_policy eval idv catch total num of agent3: 6
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent4: -0.1011360785711146
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: 0.053547119850054996
idv_policy eval average team episode rewards of agent0: 2.5
idv_policy eval idv catch total num of agent0: 7
idv_policy eval team catch total num: 1
idv_policy eval average step individual rewards of agent1: -0.09822836966795392
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: -0.07348338127131968
idv_policy eval average team episode rewards of agent2: 2.5
idv_policy eval idv catch total num of agent2: 4
idv_policy eval team catch total num: 1
idv_policy eval average step individual rewards of agent3: -0.10458613986180129
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: -0.12157132260483113
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 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 171.


 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 171.


 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 171.


 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 171.


 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 171.


 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 171.


 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 171.


 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 171.


 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 171.


 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 171.


 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 171.


 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 171.


 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 171.


 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 171.


 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 171.


 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 172.


 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 172.


 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: -0.03141868791433553
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: -0.11809980069512871
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: -0.013557508153532334
team_policy eval average team episode rewards of agent2: 0.0
team_policy eval idv catch total num of agent2: 5
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent3: -0.06221584154386247
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: -0.06230489621443873
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: -0.14142259098708315
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: -0.02921605975870538
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: -0.1550276186336237
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: -0.016081328314462722
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: -0.14172591826185374
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 173.


 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 172.


 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 172.


 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 172.


 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 172.


 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 172.


 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 172.


 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 172.


 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 172.


 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 172.


 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 172.


 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 172.


 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 172.


 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 172.


 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 172.


 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 172.


 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 172.


 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 173.


 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 173.


 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 173.


 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 173.

team_policy eval average step individual rewards of agent0: -0.039063022676538016
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: -0.11599720935780802
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: -0.010858365695098335
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: -0.10231135782828624
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: -0.14921691613856633
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: 0.012812562332661792
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: 0.0854416207434586
idv_policy eval average team episode rewards of agent1: 0.0
idv_policy eval idv catch total num of agent1: 8
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent2: -0.09776289332869922
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: -0.07250092448129236
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: -0.0364972424136539
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 201/10000 episodes, total num timesteps 40400/2000000, FPS 173.


 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 173.


 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 172.


 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 172.


 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 172.


 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 172.


 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 172.


 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 172.


 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 172.


 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 172.


 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 172.


 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 172.


 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 172.


 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 172.


 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 171.


 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 171.


 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 171.


 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 171.


 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 171.


 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 171.


 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 171.


 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 171.


 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 171.


 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 171.


 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 171.

team_policy eval average step individual rewards of agent0: 0.08261375676853187
team_policy eval average team episode rewards of agent0: 0.0
team_policy eval idv catch total num of agent0: 7
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent1: -0.04028819943714239
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: -0.051946533622904495
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: -0.06515474830030234
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: -0.022484051443369724
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: -0.01841288135267451
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: -0.07691892376929171
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: -0.03514877305815775
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: -0.07920167109134417
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: 0.006008877656711356
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 226/10000 episodes, total num timesteps 45400/2000000, FPS 170.


 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 170.


 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 170.


 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 170.


 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 170.


 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 170.


 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 170.


 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 170.


 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 170.


 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 170.


 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 170.


 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 170.


 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 170.


 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 170.


 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 170.


 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 169.


 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 169.


 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 169.


 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 169.


 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 169.


 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 169.


 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 169.


 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 169.


 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 169.


 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 169.

team_policy eval average step individual rewards of agent0: 0.04216474117468981
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: -0.0429848332984224
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: -0.10029452157671535
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: -0.12773766264292366
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: -0.13070094336069002
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: -0.1245727774776229
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: -0.06270301245475034
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: -0.10902368626743968
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: -0.04868879720489781
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: -0.13484220861379417
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 251/10000 episodes, total num timesteps 50400/2000000, FPS 168.


 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 168.


 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 168.


 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 168.


 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 168.


 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 168.


 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 168.


 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 168.


 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 167.


 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 167.


 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 167.


 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 167.


 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 167.


 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 167.


 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 167.


 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 167.


 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 167.


 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 166.


 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 166.


 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 166.


 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 166.


 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 166.


 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 166.


 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 166.


 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 166.

team_policy eval average step individual rewards of agent0: -0.10458712830306688
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: -0.11826251454922004
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: -0.08707148017105515
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: -0.1143907834886964
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: -0.06416059340732476
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: -0.1076984512604729
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: -0.09735455481167424
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: 0.037654842741772956
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: -0.007798851847474673
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: -0.02802162575490416
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 276/10000 episodes, total num timesteps 55400/2000000, FPS 166.


 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 166.


 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 166.


 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 166.


 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 166.


 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 165.


 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 165.


 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 165.


 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 165.


 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 165.


 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 165.


 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 165.


 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 165.


 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 165.


 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 165.


 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 165.


 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 165.


 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 165.


 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 165.


 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 165.


 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 165.


 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 165.


 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 165.


 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 164.


 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 164.

team_policy eval average step individual rewards of agent0: 0.04319540226134874
team_policy eval average team episode rewards of agent0: 5.0
team_policy eval idv catch total num of agent0: 6
team_policy eval team catch total num: 2
team_policy eval average step individual rewards of agent1: 0.032646828423552536
team_policy eval average team episode rewards of agent1: 5.0
team_policy eval idv catch total num of agent1: 5
team_policy eval team catch total num: 2
team_policy eval average step individual rewards of agent2: -0.07614032074090306
team_policy eval average team episode rewards of agent2: 5.0
team_policy eval idv catch total num of agent2: 1
team_policy eval team catch total num: 2
team_policy eval average step individual rewards of agent3: -0.02877582264229809
team_policy eval average team episode rewards of agent3: 5.0
team_policy eval idv catch total num of agent3: 3
team_policy eval team catch total num: 2
team_policy eval average step individual rewards of agent4: 0.02247706636528865
team_policy eval average team episode rewards of agent4: 5.0
team_policy eval idv catch total num of agent4: 5
team_policy eval team catch total num: 2
idv_policy eval average step individual rewards of agent0: -0.013468685664038525
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: 0.07138467178353847
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: -0.03120434548981594
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: 0.07038718287939397
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: 0.021133508376632467
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 301/10000 episodes, total num timesteps 60400/2000000, FPS 164.


 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 164.


 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 164.


 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 164.


 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 164.


 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 164.


 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 164.


 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 164.


 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 164.


 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 164.


 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 164.


 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 164.


 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 164.


 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 164.


 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 164.


 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 164.


 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 164.


 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 164.


 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 164.


 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 164.


 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 164.


 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 164.


 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 164.


 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 164.


 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 164.

team_policy eval average step individual rewards of agent0: -0.06889097857199608
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: -0.09894483689749882
team_policy eval average team episode rewards of agent1: 5.0
team_policy eval idv catch total num of agent1: 0
team_policy eval team catch total num: 2
team_policy eval average step individual rewards of agent2: -0.020565599694915813
team_policy eval average team episode rewards of agent2: 5.0
team_policy eval idv catch total num of agent2: 3
team_policy eval team catch total num: 2
team_policy eval average step individual rewards of agent3: -0.051177492632648286
team_policy eval average team episode rewards of agent3: 5.0
team_policy eval idv catch total num of agent3: 2
team_policy eval team catch total num: 2
team_policy eval average step individual rewards of agent4: -0.038323530538741164
team_policy eval average team episode rewards of agent4: 5.0
team_policy eval idv catch total num of agent4: 3
team_policy eval team catch total num: 2
idv_policy eval average step individual rewards of agent0: -0.06665920213557436
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: 0.10989307362449456
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: 0.035832538386711486
idv_policy eval average team episode rewards of agent2: 7.5
idv_policy eval idv catch total num of agent2: 5
idv_policy eval team catch total num: 3
idv_policy eval average step individual rewards of agent3: -0.02303010632942106
idv_policy eval average team episode rewards of agent3: 7.5
idv_policy eval idv catch total num of agent3: 3
idv_policy eval team catch total num: 3
idv_policy eval average step individual rewards of agent4: -0.043060462831461796
idv_policy eval average team episode rewards of agent4: 7.5
idv_policy eval idv catch total num of agent4: 2
idv_policy eval team catch total num: 3

 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 164.


 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 164.


 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 164.


 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 164.


 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 164.


 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 164.


 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 164.


 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 164.


 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 164.


 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 164.


 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 164.


 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 164.


 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 164.


 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 164.


 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 164.


 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 164.


 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 164.


 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 164.


 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 164.


 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 164.


 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 164.


 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 164.


 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 164.


 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 164.


 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 164.

team_policy eval average step individual rewards of agent0: -0.06209728353777367
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: -0.06250246203430877
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: -0.0647990808880088
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: -0.08641667431205581
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: -0.08473984497515528
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: 0.06763349174930072
idv_policy eval average team episode rewards of agent0: 7.5
idv_policy eval idv catch total num of agent0: 6
idv_policy eval team catch total num: 3
idv_policy eval average step individual rewards of agent1: 0.09947576193423842
idv_policy eval average team episode rewards of agent1: 7.5
idv_policy eval idv catch total num of agent1: 7
idv_policy eval team catch total num: 3
idv_policy eval average step individual rewards of agent2: 0.207343634961241
idv_policy eval average team episode rewards of agent2: 7.5
idv_policy eval idv catch total num of agent2: 11
idv_policy eval team catch total num: 3
idv_policy eval average step individual rewards of agent3: 0.03845081932104884
idv_policy eval average team episode rewards of agent3: 7.5
idv_policy eval idv catch total num of agent3: 4
idv_policy eval team catch total num: 3
idv_policy eval average step individual rewards of agent4: 0.02369111105364615
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 351/10000 episodes, total num timesteps 70400/2000000, FPS 164.


 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 164.


 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 164.


 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 164.


 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 164.


 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 164.


 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 164.


 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 164.


 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 164.


 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 164.


 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 164.


 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 164.


 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 164.


 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 164.


 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 164.


 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 164.


 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 164.


 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 164.


 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 164.


 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 164.


 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 164.


 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 164.


 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 164.


 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 164.


 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 164.

team_policy eval average step individual rewards of agent0: -0.05340778541292264
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: 0.05398559245910801
team_policy eval average team episode rewards of agent1: 2.5
team_policy eval idv catch total num of agent1: 6
team_policy eval team catch total num: 1
team_policy eval average step individual rewards of agent2: -0.05387292055315538
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: -0.09894067066533734
team_policy eval average team episode rewards of agent3: 2.5
team_policy eval idv catch total num of agent3: 0
team_policy eval team catch total num: 1
team_policy eval average step individual rewards of agent4: -0.045791669310488795
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: 0.022926831016067233
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: -0.010735564253062817
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: -0.07995130175848777
idv_policy eval average team episode rewards of agent2: 2.5
idv_policy eval idv catch total num of agent2: 0
idv_policy eval team catch total num: 1
idv_policy eval average step individual rewards of agent3: -0.012056553405490859
idv_policy eval average team episode rewards of agent3: 2.5
idv_policy eval idv catch total num of agent3: 3
idv_policy eval team catch total num: 1
idv_policy eval average step individual rewards of agent4: -0.02269558420882917
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 376/10000 episodes, total num timesteps 75400/2000000, FPS 164.


 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 164.


 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 164.


 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 164.


 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 164.


 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 164.


 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 164.


 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 164.


 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 164.


 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 164.


 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 164.


 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 164.


 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 164.


 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 164.


 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 165.


 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: 0.05864504945435103
team_policy eval average team episode rewards of agent0: 7.5
team_policy eval idv catch total num of agent0: 5
team_policy eval team catch total num: 3
team_policy eval average step individual rewards of agent1: 0.08396595128852802
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: 0.18868802770707496
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: -0.04508499135362563
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: -0.019068424589439165
team_policy eval average team episode rewards of agent4: 7.5
team_policy eval idv catch total num of agent4: 2
team_policy eval team catch total num: 3
idv_policy eval average step individual rewards of agent0: 0.03621220909013927
idv_policy eval average team episode rewards of agent0: 15.0
idv_policy eval idv catch total num of agent0: 4
idv_policy eval team catch total num: 6
idv_policy eval average step individual rewards of agent1: 0.033495633100517654
idv_policy eval average team episode rewards of agent1: 15.0
idv_policy eval idv catch total num of agent1: 4
idv_policy eval team catch total num: 6
idv_policy eval average step individual rewards of agent2: 0.08213873389930196
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: 0.09024705717520261
idv_policy eval average team episode rewards of agent3: 15.0
idv_policy eval idv catch total num of agent3: 6
idv_policy eval team catch total num: 6
idv_policy eval average step individual rewards of agent4: 0.16540341679490778
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 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 165.


 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 165.


 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 164.


 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 164.


 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 164.


 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 164.


 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 164.


 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 164.


 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 164.


 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 164.


 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 164.


 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 164.


 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 164.


 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 164.


 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 164.


 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 164.


 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 164.


 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 164.


 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 164.


 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 164.


 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 165.


 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 165.

team_policy eval average step individual rewards of agent0: 0.14061091718728572
team_policy eval average team episode rewards of agent0: 20.0
team_policy eval idv catch total num of agent0: 8
team_policy eval team catch total num: 8
team_policy eval average step individual rewards of agent1: 0.17118424739537627
team_policy eval average team episode rewards of agent1: 20.0
team_policy eval idv catch total num of agent1: 9
team_policy eval team catch total num: 8
team_policy eval average step individual rewards of agent2: 0.14512623226188123
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: 0.11177570349061768
team_policy eval average team episode rewards of agent3: 20.0
team_policy eval idv catch total num of agent3: 7
team_policy eval team catch total num: 8
team_policy eval average step individual rewards of agent4: 0.13858318122574154
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: -0.05393686475194098
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: 0.027162208333780368
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: 0.0015987980527709623
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: -0.020011513025726523
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: 0.0014415872904368454
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 426/10000 episodes, total num timesteps 85400/2000000, FPS 165.


 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 165.


 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 165.


 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 165.


 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 165.


 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 165.


 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 165.


 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 165.


 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 165.


 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 165.


 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 165.


 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 165.


 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 165.


 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 165.


 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 165.


 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 165.


 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 166.


 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 166.


 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 166.


 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 166.


 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 166.


 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 166.


 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 166.


 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 166.


 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 166.

team_policy eval average step individual rewards of agent0: -0.07663337873796858
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: -0.05228392844967025
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: -0.00017122590760805469
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: -0.06873355788172304
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: -0.022053813539182992
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: -0.058030000761835135
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: 0.10068009960569768
idv_policy eval average team episode rewards of agent1: 7.5
idv_policy eval idv catch total num of agent1: 7
idv_policy eval team catch total num: 3
idv_policy eval average step individual rewards of agent2: -0.048601924219071856
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: 0.052809723374832744
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: -0.020912689429843652
idv_policy eval average team episode rewards of agent4: 7.5
idv_policy eval idv catch total num of agent4: 2
idv_policy eval team catch total num: 3

 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 166.


 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 166.


 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 166.


 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 166.


 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 166.


 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 166.


 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 167.


 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 167.


 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 167.


 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 167.


 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 167.


 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 167.


 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 167.


 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 167.


 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 167.


 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 167.


 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 167.


 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: 0.20996350712177908
team_policy eval average team episode rewards of agent0: 20.0
team_policy eval idv catch total num of agent0: 11
team_policy eval team catch total num: 8
team_policy eval average step individual rewards of agent1: -0.06795501275621829
team_policy eval average team episode rewards of agent1: 20.0
team_policy eval idv catch total num of agent1: 0
team_policy eval team catch total num: 8
team_policy eval average step individual rewards of agent2: 0.10895842289383666
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: 0.10785801885994627
team_policy eval average team episode rewards of agent3: 20.0
team_policy eval idv catch total num of agent3: 7
team_policy eval team catch total num: 8
team_policy eval average step individual rewards of agent4: 0.08142533851286533
team_policy eval average team episode rewards of agent4: 20.0
team_policy eval idv catch total num of agent4: 6
team_policy eval team catch total num: 8
idv_policy eval average step individual rewards of agent0: -0.01487016068863939
idv_policy eval average team episode rewards of agent0: 5.0
idv_policy eval idv catch total num of agent0: 2
idv_policy eval team catch total num: 2
idv_policy eval average step individual rewards of agent1: 0.010865870387889703
idv_policy eval average team episode rewards of agent1: 5.0
idv_policy eval idv catch total num of agent1: 3
idv_policy eval team catch total num: 2
idv_policy eval average step individual rewards of agent2: 0.01028720731211325
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: 0.13465943492920338
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: 0.035851635014555405
idv_policy eval average team episode rewards of agent4: 5.0
idv_policy eval idv catch total num of agent4: 4
idv_policy eval team catch total num: 2

 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 168.


 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 168.


 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 168.


 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 168.


 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 168.


 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 168.


 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 168.


 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 168.


 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 168.


 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 168.


 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 168.


 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 168.


 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 168.


 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 168.


 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 168.


 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 168.


 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 168.


 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 168.


 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 168.


 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 168.


 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 168.

team_policy eval average step individual rewards of agent0: 0.09948206533132517
team_policy eval average team episode rewards of agent0: 5.0
team_policy eval idv catch total num of agent0: 7
team_policy eval team catch total num: 2
team_policy eval average step individual rewards of agent1: -0.06705403747638308
team_policy eval average team episode rewards of agent1: 5.0
team_policy eval idv catch total num of agent1: 0
team_policy eval team catch total num: 2
team_policy eval average step individual rewards of agent2: -0.044092073644024427
team_policy eval average team episode rewards of agent2: 5.0
team_policy eval idv catch total num of agent2: 1
team_policy eval team catch total num: 2
team_policy eval average step individual rewards of agent3: 0.03320127649780206
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: -0.06297609789957564
team_policy eval average team episode rewards of agent4: 5.0
team_policy eval idv catch total num of agent4: 0
team_policy eval team catch total num: 2
idv_policy eval average step individual rewards of agent0: 0.2918800554359895
idv_policy eval average team episode rewards of agent0: 30.0
idv_policy eval idv catch total num of agent0: 14
idv_policy eval team catch total num: 12
idv_policy eval average step individual rewards of agent1: 0.1936262485031723
idv_policy eval average team episode rewards of agent1: 30.0
idv_policy eval idv catch total num of agent1: 10
idv_policy eval team catch total num: 12
idv_policy eval average step individual rewards of agent2: 0.11106776014790637
idv_policy eval average team episode rewards of agent2: 30.0
idv_policy eval idv catch total num of agent2: 7
idv_policy eval team catch total num: 12
idv_policy eval average step individual rewards of agent3: 0.08505989603434394
idv_policy eval average team episode rewards of agent3: 30.0
idv_policy eval idv catch total num of agent3: 6
idv_policy eval team catch total num: 12
idv_policy eval average step individual rewards of agent4: 0.13972323219255667
idv_policy eval average team episode rewards of agent4: 30.0
idv_policy eval idv catch total num of agent4: 8
idv_policy eval team catch total num: 12

 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 168.


 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 168.


 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 168.


 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 168.


 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 168.


 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 168.


 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 168.


 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 168.


 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 168.


 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 169.


 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 169.


 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 169.


 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 169.


 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 169.


 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 169.


 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 169.


 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 169.


 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 169.


 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 169.


 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 169.


 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 169.


 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 169.


 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 169.


 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 169.


 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 169.

team_policy eval average step individual rewards of agent0: 0.005840873174433305
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: 0.11149915568978194
team_policy eval average team episode rewards of agent1: 5.0
team_policy eval idv catch total num of agent1: 7
team_policy eval team catch total num: 2
team_policy eval average step individual rewards of agent2: 0.0017858693470049092
team_policy eval average team episode rewards of agent2: 5.0
team_policy eval idv catch total num of agent2: 3
team_policy eval team catch total num: 2
team_policy eval average step individual rewards of agent3: 0.05719582704981921
team_policy eval average team episode rewards of agent3: 5.0
team_policy eval idv catch total num of agent3: 5
team_policy eval team catch total num: 2
team_policy eval average step individual rewards of agent4: -0.018528711793475035
team_policy eval average team episode rewards of agent4: 5.0
team_policy eval idv catch total num of agent4: 2
team_policy eval team catch total num: 2
idv_policy eval average step individual rewards of agent0: -0.06674558248290369
idv_policy eval average team episode rewards of agent0: 2.5
idv_policy eval idv catch total num of agent0: 1
idv_policy eval team catch total num: 1
idv_policy eval average step individual rewards of agent1: -0.015005118914714734
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: -0.06352571126916358
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: -0.06424233322930824
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: -0.0883122318220003
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 526/10000 episodes, total num timesteps 105400/2000000, FPS 169.


 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 170.


 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 170.


 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 170.


 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 170.


 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 170.


 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 170.


 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 170.


 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 170.


 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 170.


 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 170.


 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 170.


 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 170.


 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 170.


 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 170.


 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 170.


 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 170.


 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 170.


 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 170.


 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 170.


 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 170.


 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 170.

team_policy eval average step individual rewards of agent0: -0.0757055198423556
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: -0.029667196015919793
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: -0.07687093794730374
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: -0.07365746659175387
team_policy eval average team episode rewards of agent3: 2.5
team_policy eval idv catch total num of agent3: 0
team_policy eval team catch total num: 1
team_policy eval average step individual rewards of agent4: -0.00020176321045658696
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: 0.07499581654001561
idv_policy eval average team episode rewards of agent0: 20.0
idv_policy eval idv catch total num of agent0: 5
idv_policy eval team catch total num: 8
idv_policy eval average step individual rewards of agent1: 0.17315581363288463
idv_policy eval average team episode rewards of agent1: 20.0
idv_policy eval idv catch total num of agent1: 9
idv_policy eval team catch total num: 8
idv_policy eval average step individual rewards of agent2: 0.149742643693125
idv_policy eval average team episode rewards of agent2: 20.0
idv_policy eval idv catch total num of agent2: 8
idv_policy eval team catch total num: 8
idv_policy eval average step individual rewards of agent3: 0.14664950445593003
idv_policy eval average team episode rewards of agent3: 20.0
idv_policy eval idv catch total num of agent3: 8
idv_policy eval team catch total num: 8
idv_policy eval average step individual rewards of agent4: 0.07255932157686847
idv_policy eval average team episode rewards of agent4: 20.0
idv_policy eval idv catch total num of agent4: 5
idv_policy eval team catch total num: 8

 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 170.


 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 170.


 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 170.


 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 170.


 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 171.


 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 171.


 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 171.


 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 171.


 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 171.


 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 171.


 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 171.


 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 171.


 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 171.


 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 171.


 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 171.


 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 171.


 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 171.


 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 171.


 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 171.


 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 171.


 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 171.


 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 171.


 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 171.


 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 171.


 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 171.

team_policy eval average step individual rewards of agent0: 0.0935066607188954
team_policy eval average team episode rewards of agent0: 22.5
team_policy eval idv catch total num of agent0: 7
team_policy eval team catch total num: 9
team_policy eval average step individual rewards of agent1: 0.16940740032529142
team_policy eval average team episode rewards of agent1: 22.5
team_policy eval idv catch total num of agent1: 10
team_policy eval team catch total num: 9
team_policy eval average step individual rewards of agent2: 0.2147862970736899
team_policy eval average team episode rewards of agent2: 22.5
team_policy eval idv catch total num of agent2: 12
team_policy eval team catch total num: 9
team_policy eval average step individual rewards of agent3: 0.06677882408069702
team_policy eval average team episode rewards of agent3: 22.5
team_policy eval idv catch total num of agent3: 6
team_policy eval team catch total num: 9
team_policy eval average step individual rewards of agent4: 0.07082241987093367
team_policy eval average team episode rewards of agent4: 22.5
team_policy eval idv catch total num of agent4: 6
team_policy eval team catch total num: 9
idv_policy eval average step individual rewards of agent0: 0.001335417416898359
idv_policy eval average team episode rewards of agent0: 5.0
idv_policy eval idv catch total num of agent0: 3
idv_policy eval team catch total num: 2
idv_policy eval average step individual rewards of agent1: -0.018602821067517535
idv_policy eval average team episode rewards of agent1: 5.0
idv_policy eval idv catch total num of agent1: 2
idv_policy eval team catch total num: 2
idv_policy eval average step individual rewards of agent2: -0.017662898528697273
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: -0.04150477079136713
idv_policy eval average team episode rewards of agent3: 5.0
idv_policy eval idv catch total num of agent3: 1
idv_policy eval team catch total num: 2
idv_policy eval average step individual rewards of agent4: -0.04527650881944666
idv_policy eval average team episode rewards of agent4: 5.0
idv_policy eval idv catch total num of agent4: 1
idv_policy eval team catch total num: 2

 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 171.


 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 171.


 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 171.


 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 171.


 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 171.


 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 171.


 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 171.


 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 172.


 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 172.


 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 172.


 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 172.


 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 172.


 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 172.


 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 172.


 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 172.


 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 172.


 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 172.


 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 172.


 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 172.


 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 172.


 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 172.


 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 172.


 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 172.


 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 173.


 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 173.

team_policy eval average step individual rewards of agent0: 0.2801401186788347
team_policy eval average team episode rewards of agent0: 35.0
team_policy eval idv catch total num of agent0: 14
team_policy eval team catch total num: 14
team_policy eval average step individual rewards of agent1: 0.11061641082432716
team_policy eval average team episode rewards of agent1: 35.0
team_policy eval idv catch total num of agent1: 7
team_policy eval team catch total num: 14
team_policy eval average step individual rewards of agent2: 0.29099642292051964
team_policy eval average team episode rewards of agent2: 35.0
team_policy eval idv catch total num of agent2: 14
team_policy eval team catch total num: 14
team_policy eval average step individual rewards of agent3: 0.214684501244146
team_policy eval average team episode rewards of agent3: 35.0
team_policy eval idv catch total num of agent3: 11
team_policy eval team catch total num: 14
team_policy eval average step individual rewards of agent4: 0.20986983251842736
team_policy eval average team episode rewards of agent4: 35.0
team_policy eval idv catch total num of agent4: 11
team_policy eval team catch total num: 14
idv_policy eval average step individual rewards of agent0: 0.13480096973275763
idv_policy eval average team episode rewards of agent0: 20.0
idv_policy eval idv catch total num of agent0: 8
idv_policy eval team catch total num: 8
idv_policy eval average step individual rewards of agent1: -0.03347353645445045
idv_policy eval average team episode rewards of agent1: 20.0
idv_policy eval idv catch total num of agent1: 1
idv_policy eval team catch total num: 8
idv_policy eval average step individual rewards of agent2: 0.2891632339085717
idv_policy eval average team episode rewards of agent2: 20.0
idv_policy eval idv catch total num of agent2: 14
idv_policy eval team catch total num: 8
idv_policy eval average step individual rewards of agent3: 0.08477303520136659
idv_policy eval average team episode rewards of agent3: 20.0
idv_policy eval idv catch total num of agent3: 6
idv_policy eval team catch total num: 8
idv_policy eval average step individual rewards of agent4: 0.06533352363415058
idv_policy eval average team episode rewards of agent4: 20.0
idv_policy eval idv catch total num of agent4: 5
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 173.


 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 173.


 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 173.


 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 173.


 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 173.


 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 173.


 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 173.


 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 173.


 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 173.


 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 173.


 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 173.


 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 173.


 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 173.


 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 173.


 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 173.


 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 174.


 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 174.


 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 174.


 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 174.


 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 174.


 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 174.


 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 174.


 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 174.


 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 174.


 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 174.

team_policy eval average step individual rewards of agent0: 0.01749208784009394
team_policy eval average team episode rewards of agent0: 10.0
team_policy eval idv catch total num of agent0: 3
team_policy eval team catch total num: 4
team_policy eval average step individual rewards of agent1: 0.06010515393536114
team_policy eval average team episode rewards of agent1: 10.0
team_policy eval idv catch total num of agent1: 5
team_policy eval team catch total num: 4
team_policy eval average step individual rewards of agent2: 0.06457700592716507
team_policy eval average team episode rewards of agent2: 10.0
team_policy eval idv catch total num of agent2: 5
team_policy eval team catch total num: 4
team_policy eval average step individual rewards of agent3: 0.11650609704320077
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: 0.06468256592537999
team_policy eval average team episode rewards of agent4: 10.0
team_policy eval idv catch total num of agent4: 5
team_policy eval team catch total num: 4
idv_policy eval average step individual rewards of agent0: 0.039159075486756026
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: -0.03944416369825939
idv_policy eval average team episode rewards of agent1: 10.0
idv_policy eval idv catch total num of agent1: 1
idv_policy eval team catch total num: 4
idv_policy eval average step individual rewards of agent2: 0.09225339179743265
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: 0.012119356063676976
idv_policy eval average team episode rewards of agent3: 10.0
idv_policy eval idv catch total num of agent3: 3
idv_policy eval team catch total num: 4
idv_policy eval average step individual rewards of agent4: 0.034986400726084235
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 626/10000 episodes, total num timesteps 125400/2000000, FPS 174.


 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 174.


 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 174.


 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 174.


 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 174.


 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 174.


 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 174.


 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 174.


 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 174.


 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 175.


 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 175.


 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 175.


 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 175.


 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 175.


 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 175.


 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 175.


 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 175.


 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 175.


 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 175.


 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 175.


 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 175.


 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 175.


 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 175.


 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 175.


 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 175.

team_policy eval average step individual rewards of agent0: -0.05231523866081975
team_policy eval average team episode rewards of agent0: 5.0
team_policy eval idv catch total num of agent0: 1
team_policy eval team catch total num: 2
team_policy eval average step individual rewards of agent1: -0.07688938804963319
team_policy eval average team episode rewards of agent1: 5.0
team_policy eval idv catch total num of agent1: 0
team_policy eval team catch total num: 2
team_policy eval average step individual rewards of agent2: 0.028883633823486576
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: 0.0007888363988700942
team_policy eval average team episode rewards of agent3: 5.0
team_policy eval idv catch total num of agent3: 3
team_policy eval team catch total num: 2
team_policy eval average step individual rewards of agent4: 0.058145593073896835
team_policy eval average team episode rewards of agent4: 5.0
team_policy eval idv catch total num of agent4: 5
team_policy eval team catch total num: 2
idv_policy eval average step individual rewards of agent0: -0.041983002907042766
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: 0.16373504349942633
idv_policy eval average team episode rewards of agent1: 5.0
idv_policy eval idv catch total num of agent1: 9
idv_policy eval team catch total num: 2
idv_policy eval average step individual rewards of agent2: 0.0040217814023091525
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: 0.009195384833318894
idv_policy eval average team episode rewards of agent3: 5.0
idv_policy eval idv catch total num of agent3: 3
idv_policy eval team catch total num: 2
idv_policy eval average step individual rewards of agent4: -0.017757271613639117
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 651/10000 episodes, total num timesteps 130400/2000000, FPS 175.


 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 175.


 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 175.


 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 176.


 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 176.


 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 176.


 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 176.


 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 176.


 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 176.


 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 176.


 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 176.


 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 176.


 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 176.


 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 176.


 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 176.


 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 176.


 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 176.


 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 176.


 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 176.


 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 176.


 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 176.


 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 176.


 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 176.


 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 176.


 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 176.

team_policy eval average step individual rewards of agent0: 0.03592408164582662
team_policy eval average team episode rewards of agent0: 10.0
team_policy eval idv catch total num of agent0: 4
team_policy eval team catch total num: 4
team_policy eval average step individual rewards of agent1: 0.041983478280577045
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: -0.04066132895645424
team_policy eval average team episode rewards of agent2: 10.0
team_policy eval idv catch total num of agent2: 1
team_policy eval team catch total num: 4
team_policy eval average step individual rewards of agent3: 0.12965495022797682
team_policy eval average team episode rewards of agent3: 10.0
team_policy eval idv catch total num of agent3: 8
team_policy eval team catch total num: 4
team_policy eval average step individual rewards of agent4: 0.2906123836584961
team_policy eval average team episode rewards of agent4: 10.0
team_policy eval idv catch total num of agent4: 14
team_policy eval team catch total num: 4
idv_policy eval average step individual rewards of agent0: 0.054139495062482466
idv_policy eval average team episode rewards of agent0: 5.0
idv_policy eval idv catch total num of agent0: 5
idv_policy eval team catch total num: 2
idv_policy eval average step individual rewards of agent1: 0.032712360738810495
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: -0.020315482884338914
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: 0.0815845971185897
idv_policy eval average team episode rewards of agent3: 5.0
idv_policy eval idv catch total num of agent3: 6
idv_policy eval team catch total num: 2
idv_policy eval average step individual rewards of agent4: 0.025532000519830077
idv_policy eval average team episode rewards of agent4: 5.0
idv_policy eval idv catch total num of agent4: 4
idv_policy eval team catch total num: 2

 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 176.


 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 176.


 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 177.


 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 177.


 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 177.


 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 177.


 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 177.


 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 177.


 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 177.


 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 177.


 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 177.


 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 177.


 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 177.


 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 177.


 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 177.


 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 177.


 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 177.


 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 177.


 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 177.


 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 177.


 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 177.


 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 177.


 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 178.


 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 178.


 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 178.

team_policy eval average step individual rewards of agent0: 0.05337390407010261
team_policy eval average team episode rewards of agent0: 12.5
team_policy eval idv catch total num of agent0: 4
team_policy eval team catch total num: 5
team_policy eval average step individual rewards of agent1: 0.14638825778628076
team_policy eval average team episode rewards of agent1: 12.5
team_policy eval idv catch total num of agent1: 8
team_policy eval team catch total num: 5
team_policy eval average step individual rewards of agent2: 0.09777039313839632
team_policy eval average team episode rewards of agent2: 12.5
team_policy eval idv catch total num of agent2: 6
team_policy eval team catch total num: 5
team_policy eval average step individual rewards of agent3: 0.1493000993786281
team_policy eval average team episode rewards of agent3: 12.5
team_policy eval idv catch total num of agent3: 8
team_policy eval team catch total num: 5
team_policy eval average step individual rewards of agent4: 0.19840147647297784
team_policy eval average team episode rewards of agent4: 12.5
team_policy eval idv catch total num of agent4: 10
team_policy eval team catch total num: 5
idv_policy eval average step individual rewards of agent0: 0.13716478213876196
idv_policy eval average team episode rewards of agent0: 25.0
idv_policy eval idv catch total num of agent0: 8
idv_policy eval team catch total num: 10
idv_policy eval average step individual rewards of agent1: 0.08945320329517764
idv_policy eval average team episode rewards of agent1: 25.0
idv_policy eval idv catch total num of agent1: 6
idv_policy eval team catch total num: 10
idv_policy eval average step individual rewards of agent2: 0.036252245113213274
idv_policy eval average team episode rewards of agent2: 25.0
idv_policy eval idv catch total num of agent2: 4
idv_policy eval team catch total num: 10
idv_policy eval average step individual rewards of agent3: 0.2700244890926754
idv_policy eval average team episode rewards of agent3: 25.0
idv_policy eval idv catch total num of agent3: 13
idv_policy eval team catch total num: 10
idv_policy eval average step individual rewards of agent4: 0.1433195414193627
idv_policy eval average team episode rewards of agent4: 25.0
idv_policy eval idv catch total num of agent4: 8
idv_policy eval team catch total num: 10

 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 178.


 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 178.


 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 178.


 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 178.


 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 178.


 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 178.


 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 178.


 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 178.


 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 178.


 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 178.


 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 178.


 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 178.


 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 178.


 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 178.


 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 178.


 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 178.


 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 178.


 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 178.


 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 178.


 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 178.


 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 178.


 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 178.


 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 179.


 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 179.


 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 179.

team_policy eval average step individual rewards of agent0: -0.021378489594148373
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: -0.07566245459870036
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: -0.07399257310820576
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: -0.07133172010697042
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: -0.07772761774564517
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: -0.04227025471095725
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: 0.03283723360601916
idv_policy eval average team episode rewards of agent1: 7.5
idv_policy eval idv catch total num of agent1: 4
idv_policy eval team catch total num: 3
idv_policy eval average step individual rewards of agent2: -0.04685496852633563
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: -0.0169169765019496
idv_policy eval average team episode rewards of agent3: 7.5
idv_policy eval idv catch total num of agent3: 2
idv_policy eval team catch total num: 3
idv_policy eval average step individual rewards of agent4: -0.013728214078945849
idv_policy eval average team episode rewards of agent4: 7.5
idv_policy eval idv catch total num of agent4: 2
idv_policy eval team catch total num: 3

 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 179.


 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 179.


 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 179.


 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 179.


 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 179.


 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 179.


 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 179.


 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 179.


 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 179.


 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 179.


 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 179.


 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 179.


 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 179.


 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 179.


 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 179.


 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 179.


 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 179.


 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 179.


 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 179.


 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 179.


 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 179.


 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 179.


 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 180.


 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 180.


 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 180.

team_policy eval average step individual rewards of agent0: 0.1649493123134442
team_policy eval average team episode rewards of agent0: 22.5
team_policy eval idv catch total num of agent0: 9
team_policy eval team catch total num: 9
team_policy eval average step individual rewards of agent1: 0.01016549220223212
team_policy eval average team episode rewards of agent1: 22.5
team_policy eval idv catch total num of agent1: 3
team_policy eval team catch total num: 9
team_policy eval average step individual rewards of agent2: 0.19311587878408695
team_policy eval average team episode rewards of agent2: 22.5
team_policy eval idv catch total num of agent2: 10
team_policy eval team catch total num: 9
team_policy eval average step individual rewards of agent3: 0.060329335894024096
team_policy eval average team episode rewards of agent3: 22.5
team_policy eval idv catch total num of agent3: 5
team_policy eval team catch total num: 9
team_policy eval average step individual rewards of agent4: 0.11402167099944706
team_policy eval average team episode rewards of agent4: 22.5
team_policy eval idv catch total num of agent4: 7
team_policy eval team catch total num: 9
idv_policy eval average step individual rewards of agent0: 0.047452081964471746
idv_policy eval average team episode rewards of agent0: 17.5
idv_policy eval idv catch total num of agent0: 4
idv_policy eval team catch total num: 7
idv_policy eval average step individual rewards of agent1: 0.32356591409502256
idv_policy eval average team episode rewards of agent1: 17.5
idv_policy eval idv catch total num of agent1: 15
idv_policy eval team catch total num: 7
idv_policy eval average step individual rewards of agent2: 0.22552133334719535
idv_policy eval average team episode rewards of agent2: 17.5
idv_policy eval idv catch total num of agent2: 11
idv_policy eval team catch total num: 7
idv_policy eval average step individual rewards of agent3: 0.1415843751333502
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: 0.09693775999201501
idv_policy eval average team episode rewards of agent4: 17.5
idv_policy eval idv catch total num of agent4: 6
idv_policy eval team catch total num: 7

 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 180.


 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 180.


 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 180.


 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 180.


 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 180.


 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 180.


 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 180.


 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 180.


 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 180.


 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 180.


 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 180.


 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 180.


 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 180.


 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 180.


 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 180.


 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 180.


 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 180.


 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 180.


 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 180.


 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 180.


 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 180.


 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 180.


 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 180.


 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 180.


 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 180.

team_policy eval average step individual rewards of agent0: 0.3238036311938053
team_policy eval average team episode rewards of agent0: 40.0
team_policy eval idv catch total num of agent0: 15
team_policy eval team catch total num: 16
team_policy eval average step individual rewards of agent1: 0.2507668645407114
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: 0.19196536938436032
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: 0.04386595900440838
team_policy eval average team episode rewards of agent3: 40.0
team_policy eval idv catch total num of agent3: 4
team_policy eval team catch total num: 16
team_policy eval average step individual rewards of agent4: 0.24599401324264036
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: -0.06060860879611441
idv_policy eval average team episode rewards of agent0: 7.5
idv_policy eval idv catch total num of agent0: 0
idv_policy eval team catch total num: 3
idv_policy eval average step individual rewards of agent1: 0.07270035236499023
idv_policy eval average team episode rewards of agent1: 7.5
idv_policy eval idv catch total num of agent1: 5
idv_policy eval team catch total num: 3
idv_policy eval average step individual rewards of agent2: 0.0150092423314498
idv_policy eval average team episode rewards of agent2: 7.5
idv_policy eval idv catch total num of agent2: 3
idv_policy eval team catch total num: 3
idv_policy eval average step individual rewards of agent3: 0.07072070267072567
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: 0.0180100027606018
idv_policy eval average team episode rewards of agent4: 7.5
idv_policy eval idv catch total num of agent4: 3
idv_policy eval team catch total num: 3

 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 181.


 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 181.


 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 181.


 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 181.


 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 181.


 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 181.


 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 181.


 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 181.


 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 181.


 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 181.


 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 181.


 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 181.


 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 181.


 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 181.


 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 181.


 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 181.


 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 181.


 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 181.


 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 181.


 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 181.


 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 181.


 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 181.


 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 181.


 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 181.


 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 181.

team_policy eval average step individual rewards of agent0: -0.029126442143668777
team_policy eval average team episode rewards of agent0: 5.0
team_policy eval idv catch total num of agent0: 1
team_policy eval team catch total num: 2
team_policy eval average step individual rewards of agent1: -0.013969503990690173
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: 0.09765950796156556
team_policy eval average team episode rewards of agent2: 5.0
team_policy eval idv catch total num of agent2: 6
team_policy eval team catch total num: 2
team_policy eval average step individual rewards of agent3: 0.04441655527444987
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: 0.01730146971331372
team_policy eval average team episode rewards of agent4: 5.0
team_policy eval idv catch total num of agent4: 3
team_policy eval team catch total num: 2
idv_policy eval average step individual rewards of agent0: 0.06126892980101921
idv_policy eval average team episode rewards of agent0: 17.5
idv_policy eval idv catch total num of agent0: 5
idv_policy eval team catch total num: 7
idv_policy eval average step individual rewards of agent1: 0.16346215988591503
idv_policy eval average team episode rewards of agent1: 17.5
idv_policy eval idv catch total num of agent1: 9
idv_policy eval team catch total num: 7
idv_policy eval average step individual rewards of agent2: 0.13464176322582375
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: 0.03057670306958249
idv_policy eval average team episode rewards of agent3: 17.5
idv_policy eval idv catch total num of agent3: 4
idv_policy eval team catch total num: 7
idv_policy eval average step individual rewards of agent4: 0.05596298982801084
idv_policy eval average team episode rewards of agent4: 17.5
idv_policy eval idv catch total num of agent4: 5
idv_policy eval team catch total num: 7

 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 181.


 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 181.


 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 181.


 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 181.


 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 181.


 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 181.


 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 181.


 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 182.


 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 182.


 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 182.


 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 182.


 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 182.


 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 182.


 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 182.


 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 182.


 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 182.


 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 182.


 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 182.


 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 182.


 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 182.


 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 182.


 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 182.


 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 182.


 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 182.


 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 182.

team_policy eval average step individual rewards of agent0: -0.015913300025488554
team_policy eval average team episode rewards of agent0: 7.5
team_policy eval idv catch total num of agent0: 2
team_policy eval team catch total num: 3
team_policy eval average step individual rewards of agent1: 0.03730882833128781
team_policy eval average team episode rewards of agent1: 7.5
team_policy eval idv catch total num of agent1: 4
team_policy eval team catch total num: 3
team_policy eval average step individual rewards of agent2: 0.03326101909036482
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: 0.06252250352827576
team_policy eval average team episode rewards of agent3: 7.5
team_policy eval idv catch total num of agent3: 5
team_policy eval team catch total num: 3
team_policy eval average step individual rewards of agent4: 0.05505654503113786
team_policy eval average team episode rewards of agent4: 7.5
team_policy eval idv catch total num of agent4: 5
team_policy eval team catch total num: 3
idv_policy eval average step individual rewards of agent0: -0.03642444103234024
idv_policy eval average team episode rewards of agent0: 2.5
idv_policy eval idv catch total num of agent0: 1
idv_policy eval team catch total num: 1
idv_policy eval average step individual rewards of agent1: 0.06624331204188742
idv_policy eval average team episode rewards of agent1: 2.5
idv_policy eval idv catch total num of agent1: 5
idv_policy eval team catch total num: 1
idv_policy eval average step individual rewards of agent2: 0.06387772985559201
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: 0.1207639793227991
idv_policy eval average team episode rewards of agent3: 2.5
idv_policy eval idv catch total num of agent3: 7
idv_policy eval team catch total num: 1
idv_policy eval average step individual rewards of agent4: -0.008152665393692572
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 826/10000 episodes, total num timesteps 165400/2000000, FPS 182.


 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 182.


 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 182.


 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 182.


 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 182.


 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 182.


 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 182.


 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 182.


 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 182.


 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 182.


 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 182.


 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 183.


 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 183.


 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 183.


 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 183.


 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 183.


 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 183.


 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 183.


 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 183.


 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 183.


 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 183.


 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 183.


 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 183.


 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 183.


 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 183.

team_policy eval average step individual rewards of agent0: -0.03911022766902449
team_policy eval average team episode rewards of agent0: 5.0
team_policy eval idv catch total num of agent0: 1
team_policy eval team catch total num: 2
team_policy eval average step individual rewards of agent1: 0.05905122418463251
team_policy eval average team episode rewards of agent1: 5.0
team_policy eval idv catch total num of agent1: 5
team_policy eval team catch total num: 2
team_policy eval average step individual rewards of agent2: 0.04222947033788559
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: 0.0625499158239848
team_policy eval average team episode rewards of agent3: 5.0
team_policy eval idv catch total num of agent3: 5
team_policy eval team catch total num: 2
team_policy eval average step individual rewards of agent4: 0.04006813830396276
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: 0.03681205144660015
idv_policy eval average team episode rewards of agent0: 7.5
idv_policy eval idv catch total num of agent0: 4
idv_policy eval team catch total num: 3
idv_policy eval average step individual rewards of agent1: 0.036650159186144816
idv_policy eval average team episode rewards of agent1: 7.5
idv_policy eval idv catch total num of agent1: 4
idv_policy eval team catch total num: 3
idv_policy eval average step individual rewards of agent2: 0.02836407488955384
idv_policy eval average team episode rewards of agent2: 7.5
idv_policy eval idv catch total num of agent2: 4
idv_policy eval team catch total num: 3
idv_policy eval average step individual rewards of agent3: 0.05817015499598941
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: -0.06462345647417575
idv_policy eval average team episode rewards of agent4: 7.5
idv_policy eval idv catch total num of agent4: 0
idv_policy eval team catch total num: 3

 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 183.


 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 183.


 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 183.


 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 183.


 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 183.


 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 183.


 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 183.


 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 183.


 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 183.


 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 183.


 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 183.


 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 183.


 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 183.


 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 183.


 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 183.


 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 183.


 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 183.


 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 183.


 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 183.


 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 184.


 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 184.


 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 184.


 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 184.


 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 184.


 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 184.

team_policy eval average step individual rewards of agent0: 0.1380956214454667
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: 0.08936044907008238
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: -0.01643110942723894
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: -0.03868567414829063
team_policy eval average team episode rewards of agent3: 10.0
team_policy eval idv catch total num of agent3: 1
team_policy eval team catch total num: 4
team_policy eval average step individual rewards of agent4: 0.06355299021716503
team_policy eval average team episode rewards of agent4: 10.0
team_policy eval idv catch total num of agent4: 5
team_policy eval team catch total num: 4
idv_policy eval average step individual rewards of agent0: -0.010045213309377465
idv_policy eval average team episode rewards of agent0: 5.0
idv_policy eval idv catch total num of agent0: 2
idv_policy eval team catch total num: 2
idv_policy eval average step individual rewards of agent1: 0.01778398791894622
idv_policy eval average team episode rewards of agent1: 5.0
idv_policy eval idv catch total num of agent1: 3
idv_policy eval team catch total num: 2
idv_policy eval average step individual rewards of agent2: -0.008800689252848473
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: 0.07151504576328067
idv_policy eval average team episode rewards of agent3: 5.0
idv_policy eval idv catch total num of agent3: 5
idv_policy eval team catch total num: 2
idv_policy eval average step individual rewards of agent4: -0.015610970508498325
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 876/10000 episodes, total num timesteps 175400/2000000, FPS 184.


 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 184.


 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 184.


 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 184.


 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 184.


 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 184.


 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 184.


 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 184.


 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 184.


 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 184.


 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 184.


 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 184.


 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 184.


 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 184.


 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 184.


 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 184.


 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 184.


 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 184.


 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 184.


 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 184.


 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 184.


 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 184.


 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 184.


 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 184.


 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 184.

team_policy eval average step individual rewards of agent0: 0.09321582486870106
team_policy eval average team episode rewards of agent0: 20.0
team_policy eval idv catch total num of agent0: 6
team_policy eval team catch total num: 8
team_policy eval average step individual rewards of agent1: 0.11327980396828612
team_policy eval average team episode rewards of agent1: 20.0
team_policy eval idv catch total num of agent1: 7
team_policy eval team catch total num: 8
team_policy eval average step individual rewards of agent2: 0.11764216564308456
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: -0.024654165128778275
team_policy eval average team episode rewards of agent3: 20.0
team_policy eval idv catch total num of agent3: 2
team_policy eval team catch total num: 8
team_policy eval average step individual rewards of agent4: 0.1686159114412664
team_policy eval average team episode rewards of agent4: 20.0
team_policy eval idv catch total num of agent4: 9
team_policy eval team catch total num: 8
idv_policy eval average step individual rewards of agent0: 0.1227856193030239
idv_policy eval average team episode rewards of agent0: 25.0
idv_policy eval idv catch total num of agent0: 7
idv_policy eval team catch total num: 10
idv_policy eval average step individual rewards of agent1: 0.0967601022327332
idv_policy eval average team episode rewards of agent1: 25.0
idv_policy eval idv catch total num of agent1: 6
idv_policy eval team catch total num: 10
idv_policy eval average step individual rewards of agent2: 0.19688248325062196
idv_policy eval average team episode rewards of agent2: 25.0
idv_policy eval idv catch total num of agent2: 10
idv_policy eval team catch total num: 10
idv_policy eval average step individual rewards of agent3: 0.19822345620794876
idv_policy eval average team episode rewards of agent3: 25.0
idv_policy eval idv catch total num of agent3: 10
idv_policy eval team catch total num: 10
idv_policy eval average step individual rewards of agent4: 0.1219672044509983
idv_policy eval average team episode rewards of agent4: 25.0
idv_policy eval idv catch total num of agent4: 7
idv_policy eval team catch total num: 10

 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 184.


 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 184.


 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 184.


 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 184.


 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 185.


 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 185.


 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 185.


 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 185.


 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 185.


 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 185.


 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 185.


 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 185.


 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 185.


 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 185.


 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 185.


 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 185.


 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 185.


 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 185.


 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 185.


 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 185.


 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 185.


 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 185.


 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 185.


 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 185.


 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 185.

team_policy eval average step individual rewards of agent0: -0.011516053451453638
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: -0.01734221497838756
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: -0.03800856251970714
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: 0.0022191747312274844
team_policy eval average team episode rewards of agent3: 2.5
team_policy eval idv catch total num of agent3: 3
team_policy eval team catch total num: 1
team_policy eval average step individual rewards of agent4: 0.0927653513708727
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: 0.06816290550273103
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: -0.03455314655959803
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: -0.01387000346146635
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: -0.06652647908713942
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: -0.009970337220412588
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 926/10000 episodes, total num timesteps 185400/2000000, FPS 185.


 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 185.


 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 185.


 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 185.


 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 185.


 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 185.


 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 185.


 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 185.


 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 185.


 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 185.


 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 185.


 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 185.


 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 185.


 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 185.


 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 185.


 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 186.


 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 186.


 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 186.


 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 186.


 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 186.


 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 186.


 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 186.


 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 186.


 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 186.


 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 186.

team_policy eval average step individual rewards of agent0: 0.06653842058587801
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: 0.1362189225868589
team_policy eval average team episode rewards of agent1: 10.0
team_policy eval idv catch total num of agent1: 8
team_policy eval team catch total num: 4
team_policy eval average step individual rewards of agent2: 0.03561518944535549
team_policy eval average team episode rewards of agent2: 10.0
team_policy eval idv catch total num of agent2: 4
team_policy eval team catch total num: 4
team_policy eval average step individual rewards of agent3: 0.011537225154857098
team_policy eval average team episode rewards of agent3: 10.0
team_policy eval idv catch total num of agent3: 3
team_policy eval team catch total num: 4
team_policy eval average step individual rewards of agent4: -0.012465932656184675
team_policy eval average team episode rewards of agent4: 10.0
team_policy eval idv catch total num of agent4: 2
team_policy eval team catch total num: 4
idv_policy eval average step individual rewards of agent0: 0.06876075825050276
idv_policy eval average team episode rewards of agent0: 27.5
idv_policy eval idv catch total num of agent0: 5
idv_policy eval team catch total num: 11
idv_policy eval average step individual rewards of agent1: 0.06527838660394276
idv_policy eval average team episode rewards of agent1: 27.5
idv_policy eval idv catch total num of agent1: 5
idv_policy eval team catch total num: 11
idv_policy eval average step individual rewards of agent2: 0.2181077550493925
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: 0.19063335824225944
idv_policy eval average team episode rewards of agent3: 27.5
idv_policy eval idv catch total num of agent3: 10
idv_policy eval team catch total num: 11
idv_policy eval average step individual rewards of agent4: 0.2438017074556053
idv_policy eval average team episode rewards of agent4: 27.5
idv_policy eval idv catch total num of agent4: 12
idv_policy eval team catch total num: 11

 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 186.


 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 186.


 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 186.


 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 186.


 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 186.


 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 186.


 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 186.


 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 186.


 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 186.


 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 186.


 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 186.


 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 186.


 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 186.


 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 186.


 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 186.


 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 186.


 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 186.


 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 186.


 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 186.


 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 186.


 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 186.


 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 186.


 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 186.


 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 186.


 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 186.

team_policy eval average step individual rewards of agent0: 0.043573833311741285
team_policy eval average team episode rewards of agent0: 12.5
team_policy eval idv catch total num of agent0: 4
team_policy eval team catch total num: 5
team_policy eval average step individual rewards of agent1: 0.06771256398749646
team_policy eval average team episode rewards of agent1: 12.5
team_policy eval idv catch total num of agent1: 5
team_policy eval team catch total num: 5
team_policy eval average step individual rewards of agent2: 0.014934544833582745
team_policy eval average team episode rewards of agent2: 12.5
team_policy eval idv catch total num of agent2: 3
team_policy eval team catch total num: 5
team_policy eval average step individual rewards of agent3: -0.014547614156505078
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: 0.06750199075470141
team_policy eval average team episode rewards of agent4: 12.5
team_policy eval idv catch total num of agent4: 5
team_policy eval team catch total num: 5
idv_policy eval average step individual rewards of agent0: 0.10485441156711203
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: 0.10278883845818237
idv_policy eval average team episode rewards of agent1: 20.0
idv_policy eval idv catch total num of agent1: 7
idv_policy eval team catch total num: 8
idv_policy eval average step individual rewards of agent2: 0.055724084951012734
idv_policy eval average team episode rewards of agent2: 20.0
idv_policy eval idv catch total num of agent2: 5
idv_policy eval team catch total num: 8
idv_policy eval average step individual rewards of agent3: -0.0434084261579683
idv_policy eval average team episode rewards of agent3: 20.0
idv_policy eval idv catch total num of agent3: 1
idv_policy eval team catch total num: 8
idv_policy eval average step individual rewards of agent4: 0.031211764882152923
idv_policy eval average team episode rewards of agent4: 20.0
idv_policy eval idv catch total num of agent4: 4
idv_policy eval team catch total num: 8

 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 186.


 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 186.


 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 186.


 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 186.


 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 186.


 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 186.


 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 186.


 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 187.


 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 187.


 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 187.


 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 187.


 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 187.


 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 187.


 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 187.


 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 187.


 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 187.


 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 187.


 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 187.


 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 187.


 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 187.


 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 187.


 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 187.


 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 187.


 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 187.


 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 187.

team_policy eval average step individual rewards of agent0: 0.008975113318633415
team_policy eval average team episode rewards of agent0: 15.0
team_policy eval idv catch total num of agent0: 3
team_policy eval team catch total num: 6
team_policy eval average step individual rewards of agent1: 0.015822408415995883
team_policy eval average team episode rewards of agent1: 15.0
team_policy eval idv catch total num of agent1: 3
team_policy eval team catch total num: 6
team_policy eval average step individual rewards of agent2: 0.21556747333582726
team_policy eval average team episode rewards of agent2: 15.0
team_policy eval idv catch total num of agent2: 11
team_policy eval team catch total num: 6
team_policy eval average step individual rewards of agent3: 0.08625074928379224
team_policy eval average team episode rewards of agent3: 15.0
team_policy eval idv catch total num of agent3: 6
team_policy eval team catch total num: 6
team_policy eval average step individual rewards of agent4: -0.036956716333514675
team_policy eval average team episode rewards of agent4: 15.0
team_policy eval idv catch total num of agent4: 1
team_policy eval team catch total num: 6
idv_policy eval average step individual rewards of agent0: 0.0892056190025861
idv_policy eval average team episode rewards of agent0: 30.0
idv_policy eval idv catch total num of agent0: 6
idv_policy eval team catch total num: 12
idv_policy eval average step individual rewards of agent1: 0.18773374612539903
idv_policy eval average team episode rewards of agent1: 30.0
idv_policy eval idv catch total num of agent1: 10
idv_policy eval team catch total num: 12
idv_policy eval average step individual rewards of agent2: 0.08945045914068338
idv_policy eval average team episode rewards of agent2: 30.0
idv_policy eval idv catch total num of agent2: 6
idv_policy eval team catch total num: 12
idv_policy eval average step individual rewards of agent3: 0.06614474450111704
idv_policy eval average team episode rewards of agent3: 30.0
idv_policy eval idv catch total num of agent3: 5
idv_policy eval team catch total num: 12
idv_policy eval average step individual rewards of agent4: 0.1331983579512036
idv_policy eval average team episode rewards of agent4: 30.0
idv_policy eval idv catch total num of agent4: 8
idv_policy eval team catch total num: 12

 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 187.


 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 187.


 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 187.


 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 187.


 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 187.


 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 187.


 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 187.


 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 187.


 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 187.


 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 187.


 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 187.


 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 187.


 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 187.


 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 187.


 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 188.


 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 188.


 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 188.


 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 188.


 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 188.


 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 188.


 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 188.


 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 188.


 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 188.


 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 188.


 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 188.

team_policy eval average step individual rewards of agent0: 0.22080368662742658
team_policy eval average team episode rewards of agent0: 22.5
team_policy eval idv catch total num of agent0: 11
team_policy eval team catch total num: 9
team_policy eval average step individual rewards of agent1: 0.27507292716611187
team_policy eval average team episode rewards of agent1: 22.5
team_policy eval idv catch total num of agent1: 13
team_policy eval team catch total num: 9
team_policy eval average step individual rewards of agent2: 0.07041704231176156
team_policy eval average team episode rewards of agent2: 22.5
team_policy eval idv catch total num of agent2: 5
team_policy eval team catch total num: 9
team_policy eval average step individual rewards of agent3: 0.023369516088174214
team_policy eval average team episode rewards of agent3: 22.5
team_policy eval idv catch total num of agent3: 3
team_policy eval team catch total num: 9
team_policy eval average step individual rewards of agent4: 0.22658336668307258
team_policy eval average team episode rewards of agent4: 22.5
team_policy eval idv catch total num of agent4: 11
team_policy eval team catch total num: 9
idv_policy eval average step individual rewards of agent0: 0.14033641719611886
idv_policy eval average team episode rewards of agent0: 12.5
idv_policy eval idv catch total num of agent0: 8
idv_policy eval team catch total num: 5
idv_policy eval average step individual rewards of agent1: 0.0670967519874668
idv_policy eval average team episode rewards of agent1: 12.5
idv_policy eval idv catch total num of agent1: 5
idv_policy eval team catch total num: 5
idv_policy eval average step individual rewards of agent2: 0.07083794982818556
idv_policy eval average team episode rewards of agent2: 12.5
idv_policy eval idv catch total num of agent2: 5
idv_policy eval team catch total num: 5
idv_policy eval average step individual rewards of agent3: 0.08611083140064339
idv_policy eval average team episode rewards of agent3: 12.5
idv_policy eval idv catch total num of agent3: 6
idv_policy eval team catch total num: 5
idv_policy eval average step individual rewards of agent4: 0.004113740352306121
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 1026/10000 episodes, total num timesteps 205400/2000000, FPS 188.


 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 188.


 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 188.


 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 188.


 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 188.


 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 188.


 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 188.


 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 188.


 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 188.


 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 188.


 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 188.


 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 188.


 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 188.


 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 188.


 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 188.


 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 188.


 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 188.


 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 188.


 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 188.


 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 188.


 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 188.


 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 189.


 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 189.


 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 189.


 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 189.

team_policy eval average step individual rewards of agent0: 0.16207345681505042
team_policy eval average team episode rewards of agent0: 15.0
team_policy eval idv catch total num of agent0: 9
team_policy eval team catch total num: 6
team_policy eval average step individual rewards of agent1: 0.005796182494147639
team_policy eval average team episode rewards of agent1: 15.0
team_policy eval idv catch total num of agent1: 3
team_policy eval team catch total num: 6
team_policy eval average step individual rewards of agent2: 0.08229487596684608
team_policy eval average team episode rewards of agent2: 15.0
team_policy eval idv catch total num of agent2: 6
team_policy eval team catch total num: 6
team_policy eval average step individual rewards of agent3: 0.10539036579401834
team_policy eval average team episode rewards of agent3: 15.0
team_policy eval idv catch total num of agent3: 7
team_policy eval team catch total num: 6
team_policy eval average step individual rewards of agent4: 0.029277950867708662
team_policy eval average team episode rewards of agent4: 15.0
team_policy eval idv catch total num of agent4: 4
team_policy eval team catch total num: 6
idv_policy eval average step individual rewards of agent0: 0.006917284376154052
idv_policy eval average team episode rewards of agent0: 10.0
idv_policy eval idv catch total num of agent0: 3
idv_policy eval team catch total num: 4
idv_policy eval average step individual rewards of agent1: 0.029240574659464374
idv_policy eval average team episode rewards of agent1: 10.0
idv_policy eval idv catch total num of agent1: 4
idv_policy eval team catch total num: 4
idv_policy eval average step individual rewards of agent2: 0.034547784312399424
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: 0.11602037304265407
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: 0.0039971388859681945
idv_policy eval average team episode rewards of agent4: 10.0
idv_policy eval idv catch total num of agent4: 3
idv_policy eval team catch total num: 4

 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 189.


 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 189.


 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 189.


 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 189.


 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 189.


 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 189.


 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 189.


 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 189.


 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 189.


 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 189.


 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 189.


 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 189.


 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 189.


 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 189.


 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 189.


 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 189.


 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 189.


 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 189.


 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 189.


 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 189.


 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 189.


 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 189.


 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 189.


 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 189.


 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 189.

team_policy eval average step individual rewards of agent0: 0.14068468675071977
team_policy eval average team episode rewards of agent0: 25.0
team_policy eval idv catch total num of agent0: 8
team_policy eval team catch total num: 10
team_policy eval average step individual rewards of agent1: 0.09531105252522798
team_policy eval average team episode rewards of agent1: 25.0
team_policy eval idv catch total num of agent1: 6
team_policy eval team catch total num: 10
team_policy eval average step individual rewards of agent2: -0.01375498957836196
team_policy eval average team episode rewards of agent2: 25.0
team_policy eval idv catch total num of agent2: 2
team_policy eval team catch total num: 10
team_policy eval average step individual rewards of agent3: 0.1645359866972227
team_policy eval average team episode rewards of agent3: 25.0
team_policy eval idv catch total num of agent3: 9
team_policy eval team catch total num: 10
team_policy eval average step individual rewards of agent4: 0.1608799977072572
team_policy eval average team episode rewards of agent4: 25.0
team_policy eval idv catch total num of agent4: 9
team_policy eval team catch total num: 10
idv_policy eval average step individual rewards of agent0: 0.0418531738383326
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: 0.012796105437385191
idv_policy eval average team episode rewards of agent1: 10.0
idv_policy eval idv catch total num of agent1: 3
idv_policy eval team catch total num: 4
idv_policy eval average step individual rewards of agent2: 0.11359577719642036
idv_policy eval average team episode rewards of agent2: 10.0
idv_policy eval idv catch total num of agent2: 7
idv_policy eval team catch total num: 4
idv_policy eval average step individual rewards of agent3: 0.03858430883563746
idv_policy eval average team episode rewards of agent3: 10.0
idv_policy eval idv catch total num of agent3: 4
idv_policy eval team catch total num: 4
idv_policy eval average step individual rewards of agent4: 0.00928048464279854
idv_policy eval average team episode rewards of agent4: 10.0
idv_policy eval idv catch total num of agent4: 3
idv_policy eval team catch total num: 4

 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 189.


 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 189.


 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 189.


 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 189.


 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 189.


 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 189.


 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 189.


 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 189.


 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 190.


 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 190.


 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 190.


 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 190.


 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 190.


 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 190.


 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 190.


 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 190.


 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 190.


 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 190.


 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 190.


 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 190.


 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 190.


 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 190.


 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 190.


 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 190.


 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 190.

team_policy eval average step individual rewards of agent0: 0.0876595778306196
team_policy eval average team episode rewards of agent0: 15.0
team_policy eval idv catch total num of agent0: 6
team_policy eval team catch total num: 6
team_policy eval average step individual rewards of agent1: -0.00978903023297239
team_policy eval average team episode rewards of agent1: 15.0
team_policy eval idv catch total num of agent1: 2
team_policy eval team catch total num: 6
team_policy eval average step individual rewards of agent2: 0.03242114146985476
team_policy eval average team episode rewards of agent2: 15.0
team_policy eval idv catch total num of agent2: 4
team_policy eval team catch total num: 6
team_policy eval average step individual rewards of agent3: 0.03924149814743206
team_policy eval average team episode rewards of agent3: 15.0
team_policy eval idv catch total num of agent3: 4
team_policy eval team catch total num: 6
team_policy eval average step individual rewards of agent4: 0.012830560915566171
team_policy eval average team episode rewards of agent4: 15.0
team_policy eval idv catch total num of agent4: 3
team_policy eval team catch total num: 6
idv_policy eval average step individual rewards of agent0: 0.17538590016753822
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: 0.12007641015240476
idv_policy eval average team episode rewards of agent1: 25.0
idv_policy eval idv catch total num of agent1: 7
idv_policy eval team catch total num: 10
idv_policy eval average step individual rewards of agent2: 0.11850442647312191
idv_policy eval average team episode rewards of agent2: 25.0
idv_policy eval idv catch total num of agent2: 7
idv_policy eval team catch total num: 10
idv_policy eval average step individual rewards of agent3: 0.19796324496514392
idv_policy eval average team episode rewards of agent3: 25.0
idv_policy eval idv catch total num of agent3: 10
idv_policy eval team catch total num: 10
idv_policy eval average step individual rewards of agent4: 0.01197114677599828
idv_policy eval average team episode rewards of agent4: 25.0
idv_policy eval idv catch total num of agent4: 3
idv_policy eval team catch total num: 10

 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 190.


 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 190.


 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 190.


 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 190.


 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 190.


 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 190.


 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 190.


 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 190.


 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 190.


 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 190.


 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 190.


 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 190.


 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 190.


 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 190.


 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 190.


 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 190.


 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 190.


 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 190.


 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 191.


 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 191.


 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 191.


 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 191.


 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 191.


 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 191.


 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 191.

team_policy eval average step individual rewards of agent0: 0.19426132413979033
team_policy eval average team episode rewards of agent0: 22.5
team_policy eval idv catch total num of agent0: 10
team_policy eval team catch total num: 9
team_policy eval average step individual rewards of agent1: 0.2623489073054741
team_policy eval average team episode rewards of agent1: 22.5
team_policy eval idv catch total num of agent1: 13
team_policy eval team catch total num: 9
team_policy eval average step individual rewards of agent2: 0.06348383451470016
team_policy eval average team episode rewards of agent2: 22.5
team_policy eval idv catch total num of agent2: 5
team_policy eval team catch total num: 9
team_policy eval average step individual rewards of agent3: 0.061016684002789055
team_policy eval average team episode rewards of agent3: 22.5
team_policy eval idv catch total num of agent3: 5
team_policy eval team catch total num: 9
team_policy eval average step individual rewards of agent4: 0.21877569413361567
team_policy eval average team episode rewards of agent4: 22.5
team_policy eval idv catch total num of agent4: 11
team_policy eval team catch total num: 9
idv_policy eval average step individual rewards of agent0: 0.05626843805358452
idv_policy eval average team episode rewards of agent0: 10.0
idv_policy eval idv catch total num of agent0: 5
idv_policy eval team catch total num: 4
idv_policy eval average step individual rewards of agent1: -0.04798183811607679
idv_policy eval average team episode rewards of agent1: 10.0
idv_policy eval idv catch total num of agent1: 1
idv_policy eval team catch total num: 4
idv_policy eval average step individual rewards of agent2: 0.03112211253654836
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: 0.15802353440087175
idv_policy eval average team episode rewards of agent3: 10.0
idv_policy eval idv catch total num of agent3: 9
idv_policy eval team catch total num: 4
idv_policy eval average step individual rewards of agent4: 0.0035607694790347733
idv_policy eval average team episode rewards of agent4: 10.0
idv_policy eval idv catch total num of agent4: 3
idv_policy eval team catch total num: 4

 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 191.


 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 191.


 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 191.


 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 191.


 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 191.


 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 191.


 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 191.


 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 191.


 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 191.


 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 191.


 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 191.


 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 191.


 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 191.


 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 191.


 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 191.


 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 191.


 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 191.


 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 191.


 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 191.


 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 191.


 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 191.


 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 191.


 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 191.


 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 191.


 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 191.

team_policy eval average step individual rewards of agent0: 0.033180843723409355
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: 0.005966246024987032
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: 0.02955368050964616
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: -0.021832789458820438
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: 0.005391630901074231
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: 0.0110788025203064
idv_policy eval average team episode rewards of agent0: 5.0
idv_policy eval idv catch total num of agent0: 3
idv_policy eval team catch total num: 2
idv_policy eval average step individual rewards of agent1: 0.028904169317851894
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: 0.08189368151855538
idv_policy eval average team episode rewards of agent2: 5.0
idv_policy eval idv catch total num of agent2: 6
idv_policy eval team catch total num: 2
idv_policy eval average step individual rewards of agent3: 0.002949446549208026
idv_policy eval average team episode rewards of agent3: 5.0
idv_policy eval idv catch total num of agent3: 3
idv_policy eval team catch total num: 2
idv_policy eval average step individual rewards of agent4: 0.0822079895335695
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 1151/10000 episodes, total num timesteps 230400/2000000, FPS 191.


 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 191.


 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 191.


 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 191.


 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 191.


 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 191.


 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 191.


 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 192.


 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 192.


 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 192.


 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 192.


 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 192.


 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 192.


 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 192.


 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 192.


 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 192.


 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 192.


 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 192.


 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 192.


 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 192.


 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 192.


 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 192.


 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 192.


 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 192.


 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 192.

team_policy eval average step individual rewards of agent0: -0.006343842983048069
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: -0.030511562031647052
team_policy eval average team episode rewards of agent1: 2.5
team_policy eval idv catch total num of agent1: 1
team_policy eval team catch total num: 1
team_policy eval average step individual rewards of agent2: -0.02943374874987032
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: 0.02315306703297844
team_policy eval average team episode rewards of agent3: 2.5
team_policy eval idv catch total num of agent3: 3
team_policy eval team catch total num: 1
team_policy eval average step individual rewards of agent4: -0.008394409396290423
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: 0.03578342133351895
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: 0.0630701945732617
idv_policy eval average team episode rewards of agent1: 5.0
idv_policy eval idv catch total num of agent1: 5
idv_policy eval team catch total num: 2
idv_policy eval average step individual rewards of agent2: -0.013600426774565681
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: -0.013879194958278471
idv_policy eval average team episode rewards of agent3: 5.0
idv_policy eval idv catch total num of agent3: 2
idv_policy eval team catch total num: 2
idv_policy eval average step individual rewards of agent4: 0.006322859302500556
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 1176/10000 episodes, total num timesteps 235400/2000000, FPS 192.


 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 192.


 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 192.


 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 192.


 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 192.


 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 192.


 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 192.


 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 192.


 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 192.


 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 192.


 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 192.


 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 192.


 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 192.


 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 192.


 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 192.


 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 192.


 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 192.


 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 192.


 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 192.


 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 192.


 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 192.


 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 192.


 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 192.


 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 192.


 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 192.

team_policy eval average step individual rewards of agent0: 0.032722797439353205
team_policy eval average team episode rewards of agent0: 12.5
team_policy eval idv catch total num of agent0: 4
team_policy eval team catch total num: 5
team_policy eval average step individual rewards of agent1: 0.08908480605631983
team_policy eval average team episode rewards of agent1: 12.5
team_policy eval idv catch total num of agent1: 6
team_policy eval team catch total num: 5
team_policy eval average step individual rewards of agent2: 0.14608893706583878
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: 0.06435499769121543
team_policy eval average team episode rewards of agent3: 12.5
team_policy eval idv catch total num of agent3: 5
team_policy eval team catch total num: 5
team_policy eval average step individual rewards of agent4: 0.041370619491835986
team_policy eval average team episode rewards of agent4: 12.5
team_policy eval idv catch total num of agent4: 4
team_policy eval team catch total num: 5
idv_policy eval average step individual rewards of agent0: 0.06903626632446395
idv_policy eval average team episode rewards of agent0: 20.0
idv_policy eval idv catch total num of agent0: 5
idv_policy eval team catch total num: 8
idv_policy eval average step individual rewards of agent1: 0.21506224061651164
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: 0.11139489772725619
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: 0.09296496382365334
idv_policy eval average team episode rewards of agent3: 20.0
idv_policy eval idv catch total num of agent3: 6
idv_policy eval team catch total num: 8
idv_policy eval average step individual rewards of agent4: 0.09516105492227911
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 1201/10000 episodes, total num timesteps 240400/2000000, FPS 192.


 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 192.


 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 193.


 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 193.


 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 193.


 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 193.


 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 193.


 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 193.


 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 193.


 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 193.


 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 193.


 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 193.


 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 193.


 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 193.


 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 193.


 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 193.


 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 193.


 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 193.


 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 193.


 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 193.


 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 193.


 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 193.


 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 193.


 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 193.


 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 193.

team_policy eval average step individual rewards of agent0: 0.04231487568773831
team_policy eval average team episode rewards of agent0: 17.5
team_policy eval idv catch total num of agent0: 5
team_policy eval team catch total num: 7
team_policy eval average step individual rewards of agent1: 0.12319586110905024
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: 0.09733107657596092
team_policy eval average team episode rewards of agent2: 17.5
team_policy eval idv catch total num of agent2: 7
team_policy eval team catch total num: 7
team_policy eval average step individual rewards of agent3: -0.003772714987217638
team_policy eval average team episode rewards of agent3: 17.5
team_policy eval idv catch total num of agent3: 3
team_policy eval team catch total num: 7
team_policy eval average step individual rewards of agent4: 0.07094385808720167
team_policy eval average team episode rewards of agent4: 17.5
team_policy eval idv catch total num of agent4: 6
team_policy eval team catch total num: 7
idv_policy eval average step individual rewards of agent0: 0.059586174968451006
idv_policy eval average team episode rewards of agent0: 5.0
idv_policy eval idv catch total num of agent0: 5
idv_policy eval team catch total num: 2
idv_policy eval average step individual rewards of agent1: -0.025920785074833724
idv_policy eval average team episode rewards of agent1: 5.0
idv_policy eval idv catch total num of agent1: 2
idv_policy eval team catch total num: 2
idv_policy eval average step individual rewards of agent2: 0.05922317916038425
idv_policy eval average team episode rewards of agent2: 5.0
idv_policy eval idv catch total num of agent2: 5
idv_policy eval team catch total num: 2
idv_policy eval average step individual rewards of agent3: -0.017116106750180587
idv_policy eval average team episode rewards of agent3: 5.0
idv_policy eval idv catch total num of agent3: 2
idv_policy eval team catch total num: 2
idv_policy eval average step individual rewards of agent4: 0.006004964358042684
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 1226/10000 episodes, total num timesteps 245400/2000000, FPS 193.


 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 193.


 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 193.


 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 193.


 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 193.


 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 193.


 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 193.


 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 193.


 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 193.


 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 193.


 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 193.


 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 193.


 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 193.


 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 193.


 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 193.


 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 193.


 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 193.


 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 193.


 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 193.


 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 193.


 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 193.


 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 193.


 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 193.


 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 193.


 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 193.

team_policy eval average step individual rewards of agent0: 0.15200501294443072
team_policy eval average team episode rewards of agent0: 12.5
team_policy eval idv catch total num of agent0: 8
team_policy eval team catch total num: 5
team_policy eval average step individual rewards of agent1: 0.0662963077747176
team_policy eval average team episode rewards of agent1: 12.5
team_policy eval idv catch total num of agent1: 5
team_policy eval team catch total num: 5
team_policy eval average step individual rewards of agent2: 0.16864042621443598
team_policy eval average team episode rewards of agent2: 12.5
team_policy eval idv catch total num of agent2: 9
team_policy eval team catch total num: 5
team_policy eval average step individual rewards of agent3: 0.017757032177492638
team_policy eval average team episode rewards of agent3: 12.5
team_policy eval idv catch total num of agent3: 3
team_policy eval team catch total num: 5
team_policy eval average step individual rewards of agent4: 0.04640010027801466
team_policy eval average team episode rewards of agent4: 12.5
team_policy eval idv catch total num of agent4: 4
team_policy eval team catch total num: 5
idv_policy eval average step individual rewards of agent0: -0.02752370713713944
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: 0.04560867109913731
idv_policy eval average team episode rewards of agent1: 7.5
idv_policy eval idv catch total num of agent1: 4
idv_policy eval team catch total num: 3
idv_policy eval average step individual rewards of agent2: 0.023705783828986027
idv_policy eval average team episode rewards of agent2: 7.5
idv_policy eval idv catch total num of agent2: 3
idv_policy eval team catch total num: 3
idv_policy eval average step individual rewards of agent3: 0.04411748205876445
idv_policy eval average team episode rewards of agent3: 7.5
idv_policy eval idv catch total num of agent3: 4
idv_policy eval team catch total num: 3
idv_policy eval average step individual rewards of agent4: -0.006996388510132499
idv_policy eval average team episode rewards of agent4: 7.5
idv_policy eval idv catch total num of agent4: 2
idv_policy eval team catch total num: 3

 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 193.


 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 193.


 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 193.


 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 193.


 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 194.


 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 194.


 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 194.


 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 194.


 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 194.


 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 194.


 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 194.


 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 194.


 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 194.


 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 194.


 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 194.


 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 194.


 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 194.


 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 194.


 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 194.


 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 194.


 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 194.


 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 194.


 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 194.


 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 194.


 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 194.

team_policy eval average step individual rewards of agent0: 0.24082521360702958
team_policy eval average team episode rewards of agent0: 15.0
team_policy eval idv catch total num of agent0: 12
team_policy eval team catch total num: 6
team_policy eval average step individual rewards of agent1: 0.11959082261868147
team_policy eval average team episode rewards of agent1: 15.0
team_policy eval idv catch total num of agent1: 7
team_policy eval team catch total num: 6
team_policy eval average step individual rewards of agent2: 0.03949210524979306
team_policy eval average team episode rewards of agent2: 15.0
team_policy eval idv catch total num of agent2: 4
team_policy eval team catch total num: 6
team_policy eval average step individual rewards of agent3: 0.01102960247671203
team_policy eval average team episode rewards of agent3: 15.0
team_policy eval idv catch total num of agent3: 3
team_policy eval team catch total num: 6
team_policy eval average step individual rewards of agent4: 0.014209078945874922
team_policy eval average team episode rewards of agent4: 15.0
team_policy eval idv catch total num of agent4: 3
team_policy eval team catch total num: 6
idv_policy eval average step individual rewards of agent0: 0.07944442393856946
idv_policy eval average team episode rewards of agent0: 15.0
idv_policy eval idv catch total num of agent0: 6
idv_policy eval team catch total num: 6
idv_policy eval average step individual rewards of agent1: 0.02644470239097384
idv_policy eval average team episode rewards of agent1: 15.0
idv_policy eval idv catch total num of agent1: 4
idv_policy eval team catch total num: 6
idv_policy eval average step individual rewards of agent2: 0.050749605776224734
idv_policy eval average team episode rewards of agent2: 15.0
idv_policy eval idv catch total num of agent2: 5
idv_policy eval team catch total num: 6
idv_policy eval average step individual rewards of agent3: 0.08403516780649489
idv_policy eval average team episode rewards of agent3: 15.0
idv_policy eval idv catch total num of agent3: 6
idv_policy eval team catch total num: 6
idv_policy eval average step individual rewards of agent4: 0.035280153777069875
idv_policy eval average team episode rewards of agent4: 15.0
idv_policy eval idv catch total num of agent4: 4
idv_policy eval team catch total num: 6

 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 194.


 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 194.


 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 194.


 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 194.


 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 194.


 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 194.


 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 194.


 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 194.


 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 194.


 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 194.


 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 194.


 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 194.


 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 194.


 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 194.


 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 194.


 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 194.


 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 194.


 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 194.


 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 194.


 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 194.


 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 194.


 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 194.


 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 194.


 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 194.


 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 194.

team_policy eval average step individual rewards of agent0: 0.19173244583719082
team_policy eval average team episode rewards of agent0: 12.5
team_policy eval idv catch total num of agent0: 10
team_policy eval team catch total num: 5
team_policy eval average step individual rewards of agent1: 0.056879669246822084
team_policy eval average team episode rewards of agent1: 12.5
team_policy eval idv catch total num of agent1: 5
team_policy eval team catch total num: 5
team_policy eval average step individual rewards of agent2: 0.03243882853785809
team_policy eval average team episode rewards of agent2: 12.5
team_policy eval idv catch total num of agent2: 4
team_policy eval team catch total num: 5
team_policy eval average step individual rewards of agent3: 0.0808520768286252
team_policy eval average team episode rewards of agent3: 12.5
team_policy eval idv catch total num of agent3: 6
team_policy eval team catch total num: 5
team_policy eval average step individual rewards of agent4: 0.05635970716852081
team_policy eval average team episode rewards of agent4: 12.5
team_policy eval idv catch total num of agent4: 5
team_policy eval team catch total num: 5
idv_policy eval average step individual rewards of agent0: 0.0046565934632446385
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: 0.009343104862808321
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: -0.04586352466612924
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: 0.04062754618348199
idv_policy eval average team episode rewards of agent3: 7.5
idv_policy eval idv catch total num of agent3: 4
idv_policy eval team catch total num: 3
idv_policy eval average step individual rewards of agent4: 0.11378492788776555
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 1301/10000 episodes, total num timesteps 260400/2000000, FPS 194.


 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 194.


 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 194.


 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 194.


 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 194.


 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 195.


 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 195.


 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 195.


 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 195.


 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 195.


 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 195.


 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 195.


 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 195.


 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 195.


 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 195.


 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 195.


 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 195.


 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 195.


 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 195.


 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 195.


 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 195.


 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 195.


 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 195.


 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 195.


 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 195.

team_policy eval average step individual rewards of agent0: 0.016122026931751802
team_policy eval average team episode rewards of agent0: 10.0
team_policy eval idv catch total num of agent0: 3
team_policy eval team catch total num: 4
team_policy eval average step individual rewards of agent1: -0.03503723224014015
team_policy eval average team episode rewards of agent1: 10.0
team_policy eval idv catch total num of agent1: 1
team_policy eval team catch total num: 4
team_policy eval average step individual rewards of agent2: 0.04199562983910201
team_policy eval average team episode rewards of agent2: 10.0
team_policy eval idv catch total num of agent2: 4
team_policy eval team catch total num: 4
team_policy eval average step individual rewards of agent3: 0.09049982855721185
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: -0.06852934866611493
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: 0.03936308896442097
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: -0.011359612500474405
idv_policy eval average team episode rewards of agent1: 10.0
idv_policy eval idv catch total num of agent1: 2
idv_policy eval team catch total num: 4
idv_policy eval average step individual rewards of agent2: 0.045888427161216876
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: 0.01716624445620131
idv_policy eval average team episode rewards of agent3: 10.0
idv_policy eval idv catch total num of agent3: 3
idv_policy eval team catch total num: 4
idv_policy eval average step individual rewards of agent4: 0.01780307101243646
idv_policy eval average team episode rewards of agent4: 10.0
idv_policy eval idv catch total num of agent4: 3
idv_policy eval team catch total num: 4

 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 195.


 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 195.


 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 195.


 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 195.


 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 195.


 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 195.


 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 195.


 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 195.


 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 195.


 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 195.


 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 195.


 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 195.


 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 195.


 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 195.


 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 195.


 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 195.


 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 195.


 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 195.


 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 195.


 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 195.


 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 195.


 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 195.


 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 195.


 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 195.


 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 195.

team_policy eval average step individual rewards of agent0: 0.16149833186815946
team_policy eval average team episode rewards of agent0: 27.5
team_policy eval idv catch total num of agent0: 9
team_policy eval team catch total num: 11
team_policy eval average step individual rewards of agent1: 0.054517216283282786
team_policy eval average team episode rewards of agent1: 27.5
team_policy eval idv catch total num of agent1: 5
team_policy eval team catch total num: 11
team_policy eval average step individual rewards of agent2: 0.16625901769920012
team_policy eval average team episode rewards of agent2: 27.5
team_policy eval idv catch total num of agent2: 9
team_policy eval team catch total num: 11
team_policy eval average step individual rewards of agent3: 0.08803666071691169
team_policy eval average team episode rewards of agent3: 27.5
team_policy eval idv catch total num of agent3: 6
team_policy eval team catch total num: 11
team_policy eval average step individual rewards of agent4: 0.11151814264544971
team_policy eval average team episode rewards of agent4: 27.5
team_policy eval idv catch total num of agent4: 7
team_policy eval team catch total num: 11
idv_policy eval average step individual rewards of agent0: 0.04622773175820925
idv_policy eval average team episode rewards of agent0: 15.0
idv_policy eval idv catch total num of agent0: 4
idv_policy eval team catch total num: 6
idv_policy eval average step individual rewards of agent1: 0.09669866785106067
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: 0.0194525170990841
idv_policy eval average team episode rewards of agent2: 15.0
idv_policy eval idv catch total num of agent2: 3
idv_policy eval team catch total num: 6
idv_policy eval average step individual rewards of agent3: 0.0756222440491287
idv_policy eval average team episode rewards of agent3: 15.0
idv_policy eval idv catch total num of agent3: 5
idv_policy eval team catch total num: 6
idv_policy eval average step individual rewards of agent4: 0.196377135410137
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 1351/10000 episodes, total num timesteps 270400/2000000, FPS 195.


 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 195.


 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 195.


 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 195.


 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 196.


 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 196.


 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 196.


 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 196.


 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 196.


 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 196.


 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 196.


 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 196.


 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 196.


 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 196.


 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 196.


 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 196.


 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 196.


 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 196.


 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 196.


 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 196.


 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 196.


 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 196.


 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 196.


 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 196.


 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 196.

team_policy eval average step individual rewards of agent0: 0.03383619379002618
team_policy eval average team episode rewards of agent0: 7.5
team_policy eval idv catch total num of agent0: 4
team_policy eval team catch total num: 3
team_policy eval average step individual rewards of agent1: 0.06061413487039188
team_policy eval average team episode rewards of agent1: 7.5
team_policy eval idv catch total num of agent1: 5
team_policy eval team catch total num: 3
team_policy eval average step individual rewards of agent2: 0.006993318056310267
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: -0.009429817580406454
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: -0.03328554369393897
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: 0.0363154930104739
idv_policy eval average team episode rewards of agent0: 15.0
idv_policy eval idv catch total num of agent0: 4
idv_policy eval team catch total num: 6
idv_policy eval average step individual rewards of agent1: 0.02825972681420554
idv_policy eval average team episode rewards of agent1: 15.0
idv_policy eval idv catch total num of agent1: 4
idv_policy eval team catch total num: 6
idv_policy eval average step individual rewards of agent2: 0.13805855613869894
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: 0.06299991077090099
idv_policy eval average team episode rewards of agent3: 15.0
idv_policy eval idv catch total num of agent3: 5
idv_policy eval team catch total num: 6
idv_policy eval average step individual rewards of agent4: 0.13456922357701034
idv_policy eval average team episode rewards of agent4: 15.0
idv_policy eval idv catch total num of agent4: 8
idv_policy eval team catch total num: 6

 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 196.


 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 196.


 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 196.


 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 196.


 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 196.


 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 196.


 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 196.


 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 196.


 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 196.


 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 196.


 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 196.


 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 196.


 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 196.


 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 196.


 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 196.


 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 196.


 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 196.


 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 196.


 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 196.


 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 196.


 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 196.


 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 196.


 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 196.


 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 196.


 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 196.

team_policy eval average step individual rewards of agent0: -0.0027130945575051823
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: 0.032774667138537175
team_policy eval average team episode rewards of agent1: 7.5
team_policy eval idv catch total num of agent1: 4
team_policy eval team catch total num: 3
team_policy eval average step individual rewards of agent2: -0.05158076939381163
team_policy eval average team episode rewards of agent2: 7.5
team_policy eval idv catch total num of agent2: 1
team_policy eval team catch total num: 3
team_policy eval average step individual rewards of agent3: 0.08178246971120508
team_policy eval average team episode rewards of agent3: 7.5
team_policy eval idv catch total num of agent3: 6
team_policy eval team catch total num: 3
team_policy eval average step individual rewards of agent4: 0.028176176476487486
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: 0.4271979140966622
idv_policy eval average team episode rewards of agent0: 47.5
idv_policy eval idv catch total num of agent0: 19
idv_policy eval team catch total num: 19
idv_policy eval average step individual rewards of agent1: 0.1272662951240621
idv_policy eval average team episode rewards of agent1: 47.5
idv_policy eval idv catch total num of agent1: 7
idv_policy eval team catch total num: 19
idv_policy eval average step individual rewards of agent2: 0.3515035505622005
idv_policy eval average team episode rewards of agent2: 47.5
idv_policy eval idv catch total num of agent2: 16
idv_policy eval team catch total num: 19
idv_policy eval average step individual rewards of agent3: 0.22548049223017866
idv_policy eval average team episode rewards of agent3: 47.5
idv_policy eval idv catch total num of agent3: 11
idv_policy eval team catch total num: 19
idv_policy eval average step individual rewards of agent4: 0.345083085441934
idv_policy eval average team episode rewards of agent4: 47.5
idv_policy eval idv catch total num of agent4: 16
idv_policy eval team catch total num: 19

 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 196.


 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 196.


 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 196.


 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 196.


 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 196.


 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 196.


 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 197.


 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 197.


 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 197.


 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 197.


 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 197.


 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 197.


 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 197.


 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 197.


 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 197.


 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 197.


 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 197.


 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 197.


 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 197.


 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 197.


 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 197.


 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 197.


 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 197.


 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 197.


 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 197.

team_policy eval average step individual rewards of agent0: 0.22259044000602857
team_policy eval average team episode rewards of agent0: 22.5
team_policy eval idv catch total num of agent0: 11
team_policy eval team catch total num: 9
team_policy eval average step individual rewards of agent1: 0.2027014208243724
team_policy eval average team episode rewards of agent1: 22.5
team_policy eval idv catch total num of agent1: 10
team_policy eval team catch total num: 9
team_policy eval average step individual rewards of agent2: 0.1965345853142316
team_policy eval average team episode rewards of agent2: 22.5
team_policy eval idv catch total num of agent2: 10
team_policy eval team catch total num: 9
team_policy eval average step individual rewards of agent3: 0.30032368001413784
team_policy eval average team episode rewards of agent3: 22.5
team_policy eval idv catch total num of agent3: 14
team_policy eval team catch total num: 9
team_policy eval average step individual rewards of agent4: 0.07392431579517297
team_policy eval average team episode rewards of agent4: 22.5
team_policy eval idv catch total num of agent4: 5
team_policy eval team catch total num: 9
idv_policy eval average step individual rewards of agent0: 0.09103857456797058
idv_policy eval average team episode rewards of agent0: 30.0
idv_policy eval idv catch total num of agent0: 6
idv_policy eval team catch total num: 12
idv_policy eval average step individual rewards of agent1: 0.2202688198235299
idv_policy eval average team episode rewards of agent1: 30.0
idv_policy eval idv catch total num of agent1: 11
idv_policy eval team catch total num: 12
idv_policy eval average step individual rewards of agent2: 0.2482241211156881
idv_policy eval average team episode rewards of agent2: 30.0
idv_policy eval idv catch total num of agent2: 12
idv_policy eval team catch total num: 12
idv_policy eval average step individual rewards of agent3: 0.2179918927556696
idv_policy eval average team episode rewards of agent3: 30.0
idv_policy eval idv catch total num of agent3: 11
idv_policy eval team catch total num: 12
idv_policy eval average step individual rewards of agent4: 0.07373595308129172
idv_policy eval average team episode rewards of agent4: 30.0
idv_policy eval idv catch total num of agent4: 5
idv_policy eval team catch total num: 12

 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 197.


 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 197.


 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 197.


 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 197.


 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 197.


 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 197.


 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 197.


 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 197.


 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 197.


 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 197.


 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 197.


 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 197.


 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 197.


 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 197.


 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 197.


 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 197.


 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 197.


 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 197.


 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 197.


 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 197.


 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 197.


 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 197.


 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 197.


 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 197.


 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 197.

team_policy eval average step individual rewards of agent0: 0.25090946276874837
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: 0.1262374558514425
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: 0.1963634872759323
team_policy eval average team episode rewards of agent2: 37.5
team_policy eval idv catch total num of agent2: 10
team_policy eval team catch total num: 15
team_policy eval average step individual rewards of agent3: 0.35133273076755084
team_policy eval average team episode rewards of agent3: 37.5
team_policy eval idv catch total num of agent3: 16
team_policy eval team catch total num: 15
team_policy eval average step individual rewards of agent4: 0.3324283749397617
team_policy eval average team episode rewards of agent4: 37.5
team_policy eval idv catch total num of agent4: 15
team_policy eval team catch total num: 15
idv_policy eval average step individual rewards of agent0: 0.2219729711770343
idv_policy eval average team episode rewards of agent0: 22.5
idv_policy eval idv catch total num of agent0: 11
idv_policy eval team catch total num: 9
idv_policy eval average step individual rewards of agent1: 0.09188779257922279
idv_policy eval average team episode rewards of agent1: 22.5
idv_policy eval idv catch total num of agent1: 6
idv_policy eval team catch total num: 9
idv_policy eval average step individual rewards of agent2: 0.0910661518466312
idv_policy eval average team episode rewards of agent2: 22.5
idv_policy eval idv catch total num of agent2: 6
idv_policy eval team catch total num: 9
idv_policy eval average step individual rewards of agent3: 0.21416010759535606
idv_policy eval average team episode rewards of agent3: 22.5
idv_policy eval idv catch total num of agent3: 11
idv_policy eval team catch total num: 9
idv_policy eval average step individual rewards of agent4: 0.1679390800240981
idv_policy eval average team episode rewards of agent4: 22.5
idv_policy eval idv catch total num of agent4: 9
idv_policy eval team catch total num: 9

 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 197.


 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 197.


 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 197.


 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 197.


 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 197.


 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 197.


 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 197.


 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 197.


 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 197.


 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 197.


 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 197.


 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 197.


 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 197.


 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 197.


 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 197.


 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 197.


 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 197.


 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 197.


 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 197.


 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 197.


 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 197.


 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 197.


 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 197.


 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 197.


 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 197.

team_policy eval average step individual rewards of agent0: -0.016143181442060683
team_policy eval average team episode rewards of agent0: 7.5
team_policy eval idv catch total num of agent0: 2
team_policy eval team catch total num: 3
team_policy eval average step individual rewards of agent1: -0.01814109887305043
team_policy eval average team episode rewards of agent1: 7.5
team_policy eval idv catch total num of agent1: 2
team_policy eval team catch total num: 3
team_policy eval average step individual rewards of agent2: -0.01675344607194928
team_policy eval average team episode rewards of agent2: 7.5
team_policy eval idv catch total num of agent2: 2
team_policy eval team catch total num: 3
team_policy eval average step individual rewards of agent3: 0.024303614381022846
team_policy eval average team episode rewards of agent3: 7.5
team_policy eval idv catch total num of agent3: 4
team_policy eval team catch total num: 3
team_policy eval average step individual rewards of agent4: 0.0770015709870989
team_policy eval average team episode rewards of agent4: 7.5
team_policy eval idv catch total num of agent4: 6
team_policy eval team catch total num: 3
idv_policy eval average step individual rewards of agent0: 0.10507871344534493
idv_policy eval average team episode rewards of agent0: 10.0
idv_policy eval idv catch total num of agent0: 7
idv_policy eval team catch total num: 4
idv_policy eval average step individual rewards of agent1: 0.023164305117876104
idv_policy eval average team episode rewards of agent1: 10.0
idv_policy eval idv catch total num of agent1: 4
idv_policy eval team catch total num: 4
idv_policy eval average step individual rewards of agent2: 0.10421395191860224
idv_policy eval average team episode rewards of agent2: 10.0
idv_policy eval idv catch total num of agent2: 7
idv_policy eval team catch total num: 4
idv_policy eval average step individual rewards of agent3: 0.000264466468207889
idv_policy eval average team episode rewards of agent3: 10.0
idv_policy eval idv catch total num of agent3: 3
idv_policy eval team catch total num: 4
idv_policy eval average step individual rewards of agent4: -0.024190226619245466
idv_policy eval average team episode rewards of agent4: 10.0
idv_policy eval idv catch total num of agent4: 2
idv_policy eval team catch total num: 4

 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 197.


 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 197.


 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 198.


 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 198.


 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 198.


 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 198.


 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 198.


 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 198.


 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 198.


 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 198.


 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 198.


 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 198.


 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 198.


 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 198.


 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 198.


 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 198.


 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 198.


 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 198.


 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 198.


 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 198.


 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 198.


 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 198.


 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 198.


 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 198.


 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 198.

team_policy eval average step individual rewards of agent0: 0.067422372666703
team_policy eval average team episode rewards of agent0: 22.5
team_policy eval idv catch total num of agent0: 5
team_policy eval team catch total num: 9
team_policy eval average step individual rewards of agent1: 0.07400394236057975
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: 0.17808344243360352
team_policy eval average team episode rewards of agent2: 22.5
team_policy eval idv catch total num of agent2: 9
team_policy eval team catch total num: 9
team_policy eval average step individual rewards of agent3: 0.172228753512625
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: 0.2514019719799414
team_policy eval average team episode rewards of agent4: 22.5
team_policy eval idv catch total num of agent4: 12
team_policy eval team catch total num: 9
idv_policy eval average step individual rewards of agent0: 0.2462094058618712
idv_policy eval average team episode rewards of agent0: 27.5
idv_policy eval idv catch total num of agent0: 12
idv_policy eval team catch total num: 11
idv_policy eval average step individual rewards of agent1: 0.16736437065202495
idv_policy eval average team episode rewards of agent1: 27.5
idv_policy eval idv catch total num of agent1: 9
idv_policy eval team catch total num: 11
idv_policy eval average step individual rewards of agent2: 0.20250116123007744
idv_policy eval average team episode rewards of agent2: 27.5
idv_policy eval idv catch total num of agent2: 10
idv_policy eval team catch total num: 11
idv_policy eval average step individual rewards of agent3: 0.21973034977405945
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: 0.1757711650752275
idv_policy eval average team episode rewards of agent4: 27.5
idv_policy eval idv catch total num of agent4: 9
idv_policy eval team catch total num: 11

 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 198.


 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 198.


 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 198.


 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 198.


 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 198.


 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 198.


 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 198.


 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 198.


 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 198.


 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 198.


 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 198.


 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 198.


 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 198.


 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 198.


 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 198.


 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 198.


 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 198.


 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 198.


 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 198.


 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 198.


 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 198.


 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 198.


 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 198.


 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 198.


 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 198.

team_policy eval average step individual rewards of agent0: 0.11676553377628275
team_policy eval average team episode rewards of agent0: 15.0
team_policy eval idv catch total num of agent0: 7
team_policy eval team catch total num: 6
team_policy eval average step individual rewards of agent1: 0.12022249698213233
team_policy eval average team episode rewards of agent1: 15.0
team_policy eval idv catch total num of agent1: 7
team_policy eval team catch total num: 6
team_policy eval average step individual rewards of agent2: 0.12047374788093207
team_policy eval average team episode rewards of agent2: 15.0
team_policy eval idv catch total num of agent2: 7
team_policy eval team catch total num: 6
team_policy eval average step individual rewards of agent3: 0.04217132292646521
team_policy eval average team episode rewards of agent3: 15.0
team_policy eval idv catch total num of agent3: 4
team_policy eval team catch total num: 6
team_policy eval average step individual rewards of agent4: 0.087315393268846
team_policy eval average team episode rewards of agent4: 15.0
team_policy eval idv catch total num of agent4: 6
team_policy eval team catch total num: 6
idv_policy eval average step individual rewards of agent0: -0.004152007815895225
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: -0.01902166789051071
idv_policy eval average team episode rewards of agent1: 7.5
idv_policy eval idv catch total num of agent1: 2
idv_policy eval team catch total num: 3
idv_policy eval average step individual rewards of agent2: 0.004679678593288852
idv_policy eval average team episode rewards of agent2: 7.5
idv_policy eval idv catch total num of agent2: 3
idv_policy eval team catch total num: 3
idv_policy eval average step individual rewards of agent3: -0.019484391492479318
idv_policy eval average team episode rewards of agent3: 7.5
idv_policy eval idv catch total num of agent3: 2
idv_policy eval team catch total num: 3
idv_policy eval average step individual rewards of agent4: 0.003564888320582169
idv_policy eval average team episode rewards of agent4: 7.5
idv_policy eval idv catch total num of agent4: 3
idv_policy eval team catch total num: 3

 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 198.


 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 198.


 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 198.


 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 198.


 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 198.


 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 198.


 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 198.


 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 198.


 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 198.


 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 198.


 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 198.


 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 198.


 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 198.


 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 198.


 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 198.


 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 198.


 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 199.


 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 199.


 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 199.


 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 199.


 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 199.


 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 199.


 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 199.


 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 199.


 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 199.

team_policy eval average step individual rewards of agent0: 0.11145544499417155
team_policy eval average team episode rewards of agent0: 15.0
team_policy eval idv catch total num of agent0: 7
team_policy eval team catch total num: 6
team_policy eval average step individual rewards of agent1: 0.03440619770266588
team_policy eval average team episode rewards of agent1: 15.0
team_policy eval idv catch total num of agent1: 4
team_policy eval team catch total num: 6
team_policy eval average step individual rewards of agent2: 0.1346406798313532
team_policy eval average team episode rewards of agent2: 15.0
team_policy eval idv catch total num of agent2: 8
team_policy eval team catch total num: 6
team_policy eval average step individual rewards of agent3: -0.014272675908824122
team_policy eval average team episode rewards of agent3: 15.0
team_policy eval idv catch total num of agent3: 2
team_policy eval team catch total num: 6
team_policy eval average step individual rewards of agent4: 0.08177961939536907
team_policy eval average team episode rewards of agent4: 15.0
team_policy eval idv catch total num of agent4: 6
team_policy eval team catch total num: 6
idv_policy eval average step individual rewards of agent0: 0.1466677662847483
idv_policy eval average team episode rewards of agent0: 35.0
idv_policy eval idv catch total num of agent0: 8
idv_policy eval team catch total num: 14
idv_policy eval average step individual rewards of agent1: 0.05959606540798795
idv_policy eval average team episode rewards of agent1: 35.0
idv_policy eval idv catch total num of agent1: 5
idv_policy eval team catch total num: 14
idv_policy eval average step individual rewards of agent2: 0.29320171257121874
idv_policy eval average team episode rewards of agent2: 35.0
idv_policy eval idv catch total num of agent2: 14
idv_policy eval team catch total num: 14
idv_policy eval average step individual rewards of agent3: 0.3185022175249188
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: 0.14765497851968623
idv_policy eval average team episode rewards of agent4: 35.0
idv_policy eval idv catch total num of agent4: 8
idv_policy eval team catch total num: 14

 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 199.


 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 199.


 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 199.


 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 199.


 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 199.


 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 199.


 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 199.


 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 199.


 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 199.


 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 199.


 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 199.


 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 199.


 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 199.


 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 199.


 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 199.


 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 199.


 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 199.


 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 199.


 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 199.


 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 199.


 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 199.


 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 199.


 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 199.


 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 199.


 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 199.

team_policy eval average step individual rewards of agent0: -0.014907095813321072
team_policy eval average team episode rewards of agent0: 7.5
team_policy eval idv catch total num of agent0: 2
team_policy eval team catch total num: 3
team_policy eval average step individual rewards of agent1: -0.04458211851585556
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: 0.01043159745860753
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: 0.08385227852057739
team_policy eval average team episode rewards of agent3: 7.5
team_policy eval idv catch total num of agent3: 6
team_policy eval team catch total num: 3
team_policy eval average step individual rewards of agent4: -0.017041382430004443
team_policy eval average team episode rewards of agent4: 7.5
team_policy eval idv catch total num of agent4: 2
team_policy eval team catch total num: 3
idv_policy eval average step individual rewards of agent0: 0.03213837178202573
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: -0.045199042318927025
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: -0.01756392398303617
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: -0.04243739952574739
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: -0.022861105020537634
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 1576/10000 episodes, total num timesteps 315400/2000000, FPS 199.


 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 199.


 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 199.


 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 199.


 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 199.


 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 199.


 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 199.


 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 199.


 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 199.


 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 199.


 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 199.


 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 199.


 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 199.


 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 199.


 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 199.


 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 199.


 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 199.


 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 199.


 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 199.


 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 199.


 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 199.


 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 199.


 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 199.


 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 199.


 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 199.

team_policy eval average step individual rewards of agent0: 0.18813745991035
team_policy eval average team episode rewards of agent0: 10.0
team_policy eval idv catch total num of agent0: 10
team_policy eval team catch total num: 4
team_policy eval average step individual rewards of agent1: -0.01666349192809991
team_policy eval average team episode rewards of agent1: 10.0
team_policy eval idv catch total num of agent1: 2
team_policy eval team catch total num: 4
team_policy eval average step individual rewards of agent2: -0.0174807670242045
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: 0.0367229862075781
team_policy eval average team episode rewards of agent3: 10.0
team_policy eval idv catch total num of agent3: 4
team_policy eval team catch total num: 4
team_policy eval average step individual rewards of agent4: 0.03724988576746981
team_policy eval average team episode rewards of agent4: 10.0
team_policy eval idv catch total num of agent4: 4
team_policy eval team catch total num: 4
idv_policy eval average step individual rewards of agent0: 0.16583765288197
idv_policy eval average team episode rewards of agent0: 27.5
idv_policy eval idv catch total num of agent0: 9
idv_policy eval team catch total num: 11
idv_policy eval average step individual rewards of agent1: 0.11401010598211693
idv_policy eval average team episode rewards of agent1: 27.5
idv_policy eval idv catch total num of agent1: 7
idv_policy eval team catch total num: 11
idv_policy eval average step individual rewards of agent2: 0.061509066096742365
idv_policy eval average team episode rewards of agent2: 27.5
idv_policy eval idv catch total num of agent2: 5
idv_policy eval team catch total num: 11
idv_policy eval average step individual rewards of agent3: 0.24675795635874287
idv_policy eval average team episode rewards of agent3: 27.5
idv_policy eval idv catch total num of agent3: 12
idv_policy eval team catch total num: 11
idv_policy eval average step individual rewards of agent4: 0.18827095707758834
idv_policy eval average team episode rewards of agent4: 27.5
idv_policy eval idv catch total num of agent4: 10
idv_policy eval team catch total num: 11

 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 199.


 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 199.


 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 199.


 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 199.


 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 199.


 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 199.


 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 199.


 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 200.


 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 200.


 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 200.


 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 200.


 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 200.


 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 200.


 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 200.


 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 200.


 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 200.


 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 200.


 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 200.


 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 200.


 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 200.


 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 200.


 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 200.


 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 200.


 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 200.


 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 200.

team_policy eval average step individual rewards of agent0: 0.20003265705660922
team_policy eval average team episode rewards of agent0: 45.0
team_policy eval idv catch total num of agent0: 10
team_policy eval team catch total num: 18
team_policy eval average step individual rewards of agent1: 0.4012040866908009
team_policy eval average team episode rewards of agent1: 45.0
team_policy eval idv catch total num of agent1: 18
team_policy eval team catch total num: 18
team_policy eval average step individual rewards of agent2: 0.19551413859013103
team_policy eval average team episode rewards of agent2: 45.0
team_policy eval idv catch total num of agent2: 10
team_policy eval team catch total num: 18
team_policy eval average step individual rewards of agent3: 0.22712501153584042
team_policy eval average team episode rewards of agent3: 45.0
team_policy eval idv catch total num of agent3: 11
team_policy eval team catch total num: 18
team_policy eval average step individual rewards of agent4: 0.3258393449266888
team_policy eval average team episode rewards of agent4: 45.0
team_policy eval idv catch total num of agent4: 15
team_policy eval team catch total num: 18
idv_policy eval average step individual rewards of agent0: 0.088346688197844
idv_policy eval average team episode rewards of agent0: 27.5
idv_policy eval idv catch total num of agent0: 6
idv_policy eval team catch total num: 11
idv_policy eval average step individual rewards of agent1: 0.14509669429988786
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: 0.17011646780272172
idv_policy eval average team episode rewards of agent2: 27.5
idv_policy eval idv catch total num of agent2: 9
idv_policy eval team catch total num: 11
idv_policy eval average step individual rewards of agent3: 0.0960895105538643
idv_policy eval average team episode rewards of agent3: 27.5
idv_policy eval idv catch total num of agent3: 6
idv_policy eval team catch total num: 11
idv_policy eval average step individual rewards of agent4: 0.12304165221634243
idv_policy eval average team episode rewards of agent4: 27.5
idv_policy eval idv catch total num of agent4: 7
idv_policy eval team catch total num: 11

 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 200.


 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 200.


 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 200.


 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 200.


 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 200.


 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 200.


 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 200.


 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 200.


 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 200.


 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 200.


 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 200.


 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 200.


 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 200.


 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 200.


 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 200.


 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 200.


 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 200.


 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 200.


 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 200.


 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 200.


 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 200.


 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 200.


 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 200.


 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 200.


 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 200.

team_policy eval average step individual rewards of agent0: 0.0731690031763316
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: 0.11447612874403948
team_policy eval average team episode rewards of agent1: 20.0
team_policy eval idv catch total num of agent1: 7
team_policy eval team catch total num: 8
team_policy eval average step individual rewards of agent2: 0.1643370529399487
team_policy eval average team episode rewards of agent2: 20.0
team_policy eval idv catch total num of agent2: 9
team_policy eval team catch total num: 8
team_policy eval average step individual rewards of agent3: 0.07233144906326278
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: 0.17095407197095544
team_policy eval average team episode rewards of agent4: 20.0
team_policy eval idv catch total num of agent4: 9
team_policy eval team catch total num: 8
idv_policy eval average step individual rewards of agent0: 0.13400879724870857
idv_policy eval average team episode rewards of agent0: 17.5
idv_policy eval idv catch total num of agent0: 8
idv_policy eval team catch total num: 7
idv_policy eval average step individual rewards of agent1: 0.10536872450588058
idv_policy eval average team episode rewards of agent1: 17.5
idv_policy eval idv catch total num of agent1: 7
idv_policy eval team catch total num: 7
idv_policy eval average step individual rewards of agent2: 0.23934281582796185
idv_policy eval average team episode rewards of agent2: 17.5
idv_policy eval idv catch total num of agent2: 12
idv_policy eval team catch total num: 7
idv_policy eval average step individual rewards of agent3: 0.06545834813170263
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: 0.13810691217564228
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 1651/10000 episodes, total num timesteps 330400/2000000, FPS 200.


 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 200.


 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 200.


 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 200.


 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 200.


 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 200.


 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 200.


 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 200.


 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 200.


 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 200.


 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 200.


 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 200.


 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 200.


 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 200.


 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 200.


 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 200.


 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 200.


 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 200.


 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 200.


 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 200.


 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 200.


 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 200.


 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 200.


 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 200.


 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 200.

team_policy eval average step individual rewards of agent0: 0.04477254330847194
team_policy eval average team episode rewards of agent0: 12.5
team_policy eval idv catch total num of agent0: 4
team_policy eval team catch total num: 5
team_policy eval average step individual rewards of agent1: 0.11578606520492793
team_policy eval average team episode rewards of agent1: 12.5
team_policy eval idv catch total num of agent1: 7
team_policy eval team catch total num: 5
team_policy eval average step individual rewards of agent2: 0.016260984982713545
team_policy eval average team episode rewards of agent2: 12.5
team_policy eval idv catch total num of agent2: 3
team_policy eval team catch total num: 5
team_policy eval average step individual rewards of agent3: 0.014433720218022037
team_policy eval average team episode rewards of agent3: 12.5
team_policy eval idv catch total num of agent3: 3
team_policy eval team catch total num: 5
team_policy eval average step individual rewards of agent4: 0.04438685309439805
team_policy eval average team episode rewards of agent4: 12.5
team_policy eval idv catch total num of agent4: 4
team_policy eval team catch total num: 5
idv_policy eval average step individual rewards of agent0: 0.012212061055684387
idv_policy eval average team episode rewards of agent0: 10.0
idv_policy eval idv catch total num of agent0: 3
idv_policy eval team catch total num: 4
idv_policy eval average step individual rewards of agent1: 0.13840583264825676
idv_policy eval average team episode rewards of agent1: 10.0
idv_policy eval idv catch total num of agent1: 8
idv_policy eval team catch total num: 4
idv_policy eval average step individual rewards of agent2: 0.03456600562444513
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: 0.08905551881479794
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: -0.014408191431685893
idv_policy eval average team episode rewards of agent4: 10.0
idv_policy eval idv catch total num of agent4: 2
idv_policy eval team catch total num: 4

 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 200.


 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 200.


 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 200.


 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 200.


 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 200.


 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 200.


 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 201.


 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 201.


 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 200.


 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 201.


 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 201.


 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 201.


 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 201.


 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 201.


 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 201.


 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 201.


 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 201.


 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 201.


 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 201.


 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 201.


 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 201.


 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 201.


 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 201.


 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 201.


 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 201.

team_policy eval average step individual rewards of agent0: 0.1258016228575827
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: 0.24059731676324703
team_policy eval average team episode rewards of agent1: 30.0
team_policy eval idv catch total num of agent1: 12
team_policy eval team catch total num: 12
team_policy eval average step individual rewards of agent2: 0.11958804306281907
team_policy eval average team episode rewards of agent2: 30.0
team_policy eval idv catch total num of agent2: 7
team_policy eval team catch total num: 12
team_policy eval average step individual rewards of agent3: 0.17585695118431657
team_policy eval average team episode rewards of agent3: 30.0
team_policy eval idv catch total num of agent3: 9
team_policy eval team catch total num: 12
team_policy eval average step individual rewards of agent4: 0.14374023307277548
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: 0.04024518327856372
idv_policy eval average team episode rewards of agent0: 7.5
idv_policy eval idv catch total num of agent0: 4
idv_policy eval team catch total num: 3
idv_policy eval average step individual rewards of agent1: 0.1633067495874677
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: 0.010022473615558792
idv_policy eval average team episode rewards of agent2: 7.5
idv_policy eval idv catch total num of agent2: 3
idv_policy eval team catch total num: 3
idv_policy eval average step individual rewards of agent3: -0.018194300514330734
idv_policy eval average team episode rewards of agent3: 7.5
idv_policy eval idv catch total num of agent3: 2
idv_policy eval team catch total num: 3
idv_policy eval average step individual rewards of agent4: -0.012567286299235848
idv_policy eval average team episode rewards of agent4: 7.5
idv_policy eval idv catch total num of agent4: 2
idv_policy eval team catch total num: 3

 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 201.


 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 201.


 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 201.


 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 201.


 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 201.


 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 201.


 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 201.


 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 201.


 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 201.


 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 201.


 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 201.


 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 201.


 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 201.


 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 201.


 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 201.


 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 201.


 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 201.


 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 201.


 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 201.


 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 201.


 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 201.


 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 201.


 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 201.


 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 201.


 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 201.

team_policy eval average step individual rewards of agent0: 0.1431214449772301
team_policy eval average team episode rewards of agent0: 27.5
team_policy eval idv catch total num of agent0: 8
team_policy eval team catch total num: 11
team_policy eval average step individual rewards of agent1: 0.1368262507306985
team_policy eval average team episode rewards of agent1: 27.5
team_policy eval idv catch total num of agent1: 8
team_policy eval team catch total num: 11
team_policy eval average step individual rewards of agent2: 0.19580517548227505
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: 0.24294317383837202
team_policy eval average team episode rewards of agent3: 27.5
team_policy eval idv catch total num of agent3: 12
team_policy eval team catch total num: 11
team_policy eval average step individual rewards of agent4: 0.11380576614007624
team_policy eval average team episode rewards of agent4: 27.5
team_policy eval idv catch total num of agent4: 7
team_policy eval team catch total num: 11
idv_policy eval average step individual rewards of agent0: 0.2958246020241075
idv_policy eval average team episode rewards of agent0: 12.5
idv_policy eval idv catch total num of agent0: 14
idv_policy eval team catch total num: 5
idv_policy eval average step individual rewards of agent1: 0.09337009677320537
idv_policy eval average team episode rewards of agent1: 12.5
idv_policy eval idv catch total num of agent1: 6
idv_policy eval team catch total num: 5
idv_policy eval average step individual rewards of agent2: 0.09354512418728707
idv_policy eval average team episode rewards of agent2: 12.5
idv_policy eval idv catch total num of agent2: 6
idv_policy eval team catch total num: 5
idv_policy eval average step individual rewards of agent3: 0.04206426654947167
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: 0.2903843126459353
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 1726/10000 episodes, total num timesteps 345400/2000000, FPS 201.


 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 201.


 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 201.


 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 201.


 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 201.


 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 201.


 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 201.


 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 201.


 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 201.


 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 201.


 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 201.


 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 201.


 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 201.


 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 201.


 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 201.


 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 201.


 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 201.


 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 201.


 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 201.


 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 201.


 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 201.


 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 201.


 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 201.


 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 201.


 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 201.

team_policy eval average step individual rewards of agent0: 0.16612063617063336
team_policy eval average team episode rewards of agent0: 22.5
team_policy eval idv catch total num of agent0: 9
team_policy eval team catch total num: 9
team_policy eval average step individual rewards of agent1: 0.19821685914448162
team_policy eval average team episode rewards of agent1: 22.5
team_policy eval idv catch total num of agent1: 10
team_policy eval team catch total num: 9
team_policy eval average step individual rewards of agent2: 0.1724182576572978
team_policy eval average team episode rewards of agent2: 22.5
team_policy eval idv catch total num of agent2: 9
team_policy eval team catch total num: 9
team_policy eval average step individual rewards of agent3: 0.04481559576178935
team_policy eval average team episode rewards of agent3: 22.5
team_policy eval idv catch total num of agent3: 4
team_policy eval team catch total num: 9
team_policy eval average step individual rewards of agent4: 0.12268314072381098
team_policy eval average team episode rewards of agent4: 22.5
team_policy eval idv catch total num of agent4: 7
team_policy eval team catch total num: 9
idv_policy eval average step individual rewards of agent0: -0.019774696022703063
idv_policy eval average team episode rewards of agent0: 12.5
idv_policy eval idv catch total num of agent0: 2
idv_policy eval team catch total num: 5
idv_policy eval average step individual rewards of agent1: -0.018010201749780026
idv_policy eval average team episode rewards of agent1: 12.5
idv_policy eval idv catch total num of agent1: 2
idv_policy eval team catch total num: 5
idv_policy eval average step individual rewards of agent2: 0.0072323370225859706
idv_policy eval average team episode rewards of agent2: 12.5
idv_policy eval idv catch total num of agent2: 3
idv_policy eval team catch total num: 5
idv_policy eval average step individual rewards of agent3: 0.15710517877365843
idv_policy eval average team episode rewards of agent3: 12.5
idv_policy eval idv catch total num of agent3: 9
idv_policy eval team catch total num: 5
idv_policy eval average step individual rewards of agent4: 0.008356970979511444
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 1751/10000 episodes, total num timesteps 350400/2000000, FPS 201.


 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 201.


 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 201.


 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 201.


 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 201.


 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 201.


 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 201.


 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 201.


 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 201.


 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 201.


 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 201.


 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 201.


 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 201.


 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 202.


 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 202.


 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 202.


 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 202.


 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 202.


 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 202.


 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 202.


 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 202.


 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 202.


 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 202.


 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 202.


 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 202.

team_policy eval average step individual rewards of agent0: -0.007675826508938482
team_policy eval average team episode rewards of agent0: 10.0
team_policy eval idv catch total num of agent0: 2
team_policy eval team catch total num: 4
team_policy eval average step individual rewards of agent1: 0.06412687415146034
team_policy eval average team episode rewards of agent1: 10.0
team_policy eval idv catch total num of agent1: 5
team_policy eval team catch total num: 4
team_policy eval average step individual rewards of agent2: -0.020296812160541963
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: 0.043700365641609375
team_policy eval average team episode rewards of agent3: 10.0
team_policy eval idv catch total num of agent3: 4
team_policy eval team catch total num: 4
team_policy eval average step individual rewards of agent4: 0.011434525756010696
team_policy eval average team episode rewards of agent4: 10.0
team_policy eval idv catch total num of agent4: 3
team_policy eval team catch total num: 4
idv_policy eval average step individual rewards of agent0: 0.21616104810005501
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: 0.34954613109702243
idv_policy eval average team episode rewards of agent1: 32.5
idv_policy eval idv catch total num of agent1: 16
idv_policy eval team catch total num: 13
idv_policy eval average step individual rewards of agent2: 0.24680242061225272
idv_policy eval average team episode rewards of agent2: 32.5
idv_policy eval idv catch total num of agent2: 12
idv_policy eval team catch total num: 13
idv_policy eval average step individual rewards of agent3: 0.19536374957791286
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: 0.1446999401276448
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 1776/10000 episodes, total num timesteps 355400/2000000, FPS 202.


 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 202.


 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 202.


 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 202.


 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 202.


 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 202.


 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 202.


 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 202.


 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 202.


 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 202.


 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 202.


 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 202.


 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 202.


 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 202.


 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 202.


 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 202.


 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 202.


 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 202.


 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 202.


 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 202.


 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 202.


 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 202.


 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 202.


 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 202.


 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 202.

team_policy eval average step individual rewards of agent0: 0.12136384434282657
team_policy eval average team episode rewards of agent0: 37.5
team_policy eval idv catch total num of agent0: 7
team_policy eval team catch total num: 15
team_policy eval average step individual rewards of agent1: 0.24841123457439307
team_policy eval average team episode rewards of agent1: 37.5
team_policy eval idv catch total num of agent1: 12
team_policy eval team catch total num: 15
team_policy eval average step individual rewards of agent2: 0.22579097104634122
team_policy eval average team episode rewards of agent2: 37.5
team_policy eval idv catch total num of agent2: 11
team_policy eval team catch total num: 15
team_policy eval average step individual rewards of agent3: 0.4277048614024568
team_policy eval average team episode rewards of agent3: 37.5
team_policy eval idv catch total num of agent3: 19
team_policy eval team catch total num: 15
team_policy eval average step individual rewards of agent4: 0.04842929256655268
team_policy eval average team episode rewards of agent4: 37.5
team_policy eval idv catch total num of agent4: 4
team_policy eval team catch total num: 15
idv_policy eval average step individual rewards of agent0: 0.0010815882516181285
idv_policy eval average team episode rewards of agent0: 32.5
idv_policy eval idv catch total num of agent0: 2
idv_policy eval team catch total num: 13
idv_policy eval average step individual rewards of agent1: 0.0998371967071944
idv_policy eval average team episode rewards of agent1: 32.5
idv_policy eval idv catch total num of agent1: 6
idv_policy eval team catch total num: 13
idv_policy eval average step individual rewards of agent2: 0.3495497255337338
idv_policy eval average team episode rewards of agent2: 32.5
idv_policy eval idv catch total num of agent2: 16
idv_policy eval team catch total num: 13
idv_policy eval average step individual rewards of agent3: 0.1733324484376248
idv_policy eval average team episode rewards of agent3: 32.5
idv_policy eval idv catch total num of agent3: 9
idv_policy eval team catch total num: 13
idv_policy eval average step individual rewards of agent4: 0.19097129254476586
idv_policy eval average team episode rewards of agent4: 32.5
idv_policy eval idv catch total num of agent4: 10
idv_policy eval team catch total num: 13

 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 202.


 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 202.


 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 202.


 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 202.


 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 202.


 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 202.


 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 202.


 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 202.


 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 202.


 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 202.


 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 202.


 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 202.


 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 202.


 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 202.


 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 202.


 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 202.


 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 202.


 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 202.


 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 202.


 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 202.


 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 202.


 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 202.


 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 202.


 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 202.


 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 202.

team_policy eval average step individual rewards of agent0: 0.3002404511695684
team_policy eval average team episode rewards of agent0: 35.0
team_policy eval idv catch total num of agent0: 14
team_policy eval team catch total num: 14
team_policy eval average step individual rewards of agent1: 0.30328110337791664
team_policy eval average team episode rewards of agent1: 35.0
team_policy eval idv catch total num of agent1: 14
team_policy eval team catch total num: 14
team_policy eval average step individual rewards of agent2: 0.2751948037659106
team_policy eval average team episode rewards of agent2: 35.0
team_policy eval idv catch total num of agent2: 13
team_policy eval team catch total num: 14
team_policy eval average step individual rewards of agent3: 0.2008278036487699
team_policy eval average team episode rewards of agent3: 35.0
team_policy eval idv catch total num of agent3: 10
team_policy eval team catch total num: 14
team_policy eval average step individual rewards of agent4: 0.30258772193446554
team_policy eval average team episode rewards of agent4: 35.0
team_policy eval idv catch total num of agent4: 14
team_policy eval team catch total num: 14
idv_policy eval average step individual rewards of agent0: 0.06510192174050884
idv_policy eval average team episode rewards of agent0: 15.0
idv_policy eval idv catch total num of agent0: 5
idv_policy eval team catch total num: 6
idv_policy eval average step individual rewards of agent1: 0.21567212163195726
idv_policy eval average team episode rewards of agent1: 15.0
idv_policy eval idv catch total num of agent1: 11
idv_policy eval team catch total num: 6
idv_policy eval average step individual rewards of agent2: 0.03499918461618564
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: 0.012983317272733656
idv_policy eval average team episode rewards of agent3: 15.0
idv_policy eval idv catch total num of agent3: 3
idv_policy eval team catch total num: 6
idv_policy eval average step individual rewards of agent4: 0.09133968141492979
idv_policy eval average team episode rewards of agent4: 15.0
idv_policy eval idv catch total num of agent4: 6
idv_policy eval team catch total num: 6

 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 202.


 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 202.


 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 202.


 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 202.


 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 202.


 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 202.


 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 202.


 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 202.


 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 202.


 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 202.


 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 202.


 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 202.


 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 202.


 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 202.


 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 202.


 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 202.


 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 202.


 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 202.


 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 202.


 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 202.


 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 202.


 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 202.


 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 202.


 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 203.


 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 203.

team_policy eval average step individual rewards of agent0: 0.26861769765107363
team_policy eval average team episode rewards of agent0: 30.0
team_policy eval idv catch total num of agent0: 13
team_policy eval team catch total num: 12
team_policy eval average step individual rewards of agent1: 0.1454040342124051
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: 0.06819956099469487
team_policy eval average team episode rewards of agent2: 30.0
team_policy eval idv catch total num of agent2: 5
team_policy eval team catch total num: 12
team_policy eval average step individual rewards of agent3: 0.24590767085739387
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: 0.14529193384484113
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: 0.225514372654619
idv_policy eval average team episode rewards of agent0: 35.0
idv_policy eval idv catch total num of agent0: 11
idv_policy eval team catch total num: 14
idv_policy eval average step individual rewards of agent1: 0.14427849728969158
idv_policy eval average team episode rewards of agent1: 35.0
idv_policy eval idv catch total num of agent1: 8
idv_policy eval team catch total num: 14
idv_policy eval average step individual rewards of agent2: 0.21433735302817258
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: 0.2056000262330611
idv_policy eval average team episode rewards of agent3: 35.0
idv_policy eval idv catch total num of agent3: 10
idv_policy eval team catch total num: 14
idv_policy eval average step individual rewards of agent4: 0.20010525425515013
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 1851/10000 episodes, total num timesteps 370400/2000000, FPS 203.


 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 203.


 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 203.


 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 203.


 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 203.


 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 203.


 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 203.


 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 203.


 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 203.


 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 203.


 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 203.


 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 203.


 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 203.


 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 203.


 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 203.


 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 203.


 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 203.


 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 203.


 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 203.


 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 203.


 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 203.


 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 203.


 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 203.


 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 203.


 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 203.

team_policy eval average step individual rewards of agent0: 0.47371192285252806
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: 0.29896336330781925
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: 0.25327224984606195
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: -0.008816237644310523
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: 0.3251000561061902
team_policy eval average team episode rewards of agent4: 30.0
team_policy eval idv catch total num of agent4: 15
team_policy eval team catch total num: 12
idv_policy eval average step individual rewards of agent0: 0.13421510690520777
idv_policy eval average team episode rewards of agent0: 17.5
idv_policy eval idv catch total num of agent0: 8
idv_policy eval team catch total num: 7
idv_policy eval average step individual rewards of agent1: -0.03830579615557238
idv_policy eval average team episode rewards of agent1: 17.5
idv_policy eval idv catch total num of agent1: 1
idv_policy eval team catch total num: 7
idv_policy eval average step individual rewards of agent2: 0.189996670222431
idv_policy eval average team episode rewards of agent2: 17.5
idv_policy eval idv catch total num of agent2: 10
idv_policy eval team catch total num: 7
idv_policy eval average step individual rewards of agent3: 0.05888066126604267
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: 0.16335162605043854
idv_policy eval average team episode rewards of agent4: 17.5
idv_policy eval idv catch total num of agent4: 9
idv_policy eval team catch total num: 7

 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 203.


 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 203.


 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 203.


 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 203.


 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 203.


 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 203.


 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 203.


 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 203.


 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 203.


 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 203.


 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 203.


 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 203.


 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 203.


 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 203.


 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 203.


 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 203.


 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 203.


 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 203.


 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 203.


 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 203.


 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 203.


 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 203.


 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 203.


 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 203.


 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 203.

team_policy eval average step individual rewards of agent0: 0.28405131211454726
team_policy eval average team episode rewards of agent0: 35.0
team_policy eval idv catch total num of agent0: 14
team_policy eval team catch total num: 14
team_policy eval average step individual rewards of agent1: 0.18724411269612673
team_policy eval average team episode rewards of agent1: 35.0
team_policy eval idv catch total num of agent1: 10
team_policy eval team catch total num: 14
team_policy eval average step individual rewards of agent2: 0.26137580306139635
team_policy eval average team episode rewards of agent2: 35.0
team_policy eval idv catch total num of agent2: 13
team_policy eval team catch total num: 14
team_policy eval average step individual rewards of agent3: 0.20967379272249914
team_policy eval average team episode rewards of agent3: 35.0
team_policy eval idv catch total num of agent3: 11
team_policy eval team catch total num: 14
team_policy eval average step individual rewards of agent4: 0.16253902757431413
team_policy eval average team episode rewards of agent4: 35.0
team_policy eval idv catch total num of agent4: 9
team_policy eval team catch total num: 14
idv_policy eval average step individual rewards of agent0: -0.04331277615016246
idv_policy eval average team episode rewards of agent0: 17.5
idv_policy eval idv catch total num of agent0: 1
idv_policy eval team catch total num: 7
idv_policy eval average step individual rewards of agent1: 0.08265311566684712
idv_policy eval average team episode rewards of agent1: 17.5
idv_policy eval idv catch total num of agent1: 6
idv_policy eval team catch total num: 7
idv_policy eval average step individual rewards of agent2: 0.0867646981340537
idv_policy eval average team episode rewards of agent2: 17.5
idv_policy eval idv catch total num of agent2: 6
idv_policy eval team catch total num: 7
idv_policy eval average step individual rewards of agent3: 0.0639013323774159
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: 0.11727150599963332
idv_policy eval average team episode rewards of agent4: 17.5
idv_policy eval idv catch total num of agent4: 7
idv_policy eval team catch total num: 7

 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 203.


 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 203.


 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 203.


 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 203.


 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 203.


 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 203.


 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 203.


 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 203.


 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 203.


 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 203.


 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 203.


 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 203.


 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 203.


 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 203.


 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 203.


 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 203.


 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 203.


 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 203.


 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 203.


 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 203.


 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 203.


 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 203.


 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 203.


 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 203.


 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 203.

team_policy eval average step individual rewards of agent0: 0.11904057232640007
team_policy eval average team episode rewards of agent0: 25.0
team_policy eval idv catch total num of agent0: 7
team_policy eval team catch total num: 10
team_policy eval average step individual rewards of agent1: 0.14734924107866146
team_policy eval average team episode rewards of agent1: 25.0
team_policy eval idv catch total num of agent1: 8
team_policy eval team catch total num: 10
team_policy eval average step individual rewards of agent2: 0.07634829164813754
team_policy eval average team episode rewards of agent2: 25.0
team_policy eval idv catch total num of agent2: 5
team_policy eval team catch total num: 10
team_policy eval average step individual rewards of agent3: 0.1287051297352417
team_policy eval average team episode rewards of agent3: 25.0
team_policy eval idv catch total num of agent3: 7
team_policy eval team catch total num: 10
team_policy eval average step individual rewards of agent4: 0.09688258846116185
team_policy eval average team episode rewards of agent4: 25.0
team_policy eval idv catch total num of agent4: 6
team_policy eval team catch total num: 10
idv_policy eval average step individual rewards of agent0: 0.29135949049374205
idv_policy eval average team episode rewards of agent0: 35.0
idv_policy eval idv catch total num of agent0: 14
idv_policy eval team catch total num: 14
idv_policy eval average step individual rewards of agent1: 0.061952336277761935
idv_policy eval average team episode rewards of agent1: 35.0
idv_policy eval idv catch total num of agent1: 5
idv_policy eval team catch total num: 14
idv_policy eval average step individual rewards of agent2: 0.20358279281741246
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: 0.21506294300775466
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: 0.29266332956412716
idv_policy eval average team episode rewards of agent4: 35.0
idv_policy eval idv catch total num of agent4: 14
idv_policy eval team catch total num: 14

 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 203.


 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 203.


 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 203.


 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 203.


 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 203.


 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 203.


 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 203.


 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 203.


 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 203.


 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 203.


 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 203.


 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 203.


 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 203.


 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 203.


 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 203.


 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 203.


 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 203.


 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 203.


 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 203.


 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 203.


 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 203.


 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 203.


 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 203.


 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 203.


 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 203.

team_policy eval average step individual rewards of agent0: 0.25025395000115724
team_policy eval average team episode rewards of agent0: 27.5
team_policy eval idv catch total num of agent0: 12
team_policy eval team catch total num: 11
team_policy eval average step individual rewards of agent1: 0.1480260235563088
team_policy eval average team episode rewards of agent1: 27.5
team_policy eval idv catch total num of agent1: 8
team_policy eval team catch total num: 11
team_policy eval average step individual rewards of agent2: 0.1032651806922617
team_policy eval average team episode rewards of agent2: 27.5
team_policy eval idv catch total num of agent2: 6
team_policy eval team catch total num: 11
team_policy eval average step individual rewards of agent3: 0.05380639243615315
team_policy eval average team episode rewards of agent3: 27.5
team_policy eval idv catch total num of agent3: 4
team_policy eval team catch total num: 11
team_policy eval average step individual rewards of agent4: 0.327559572282126
team_policy eval average team episode rewards of agent4: 27.5
team_policy eval idv catch total num of agent4: 15
team_policy eval team catch total num: 11
idv_policy eval average step individual rewards of agent0: 0.05713866641281763
idv_policy eval average team episode rewards of agent0: 15.0
idv_policy eval idv catch total num of agent0: 5
idv_policy eval team catch total num: 6
idv_policy eval average step individual rewards of agent1: -0.02762242633867917
idv_policy eval average team episode rewards of agent1: 15.0
idv_policy eval idv catch total num of agent1: 2
idv_policy eval team catch total num: 6
idv_policy eval average step individual rewards of agent2: 0.1641083393192435
idv_policy eval average team episode rewards of agent2: 15.0
idv_policy eval idv catch total num of agent2: 9
idv_policy eval team catch total num: 6
idv_policy eval average step individual rewards of agent3: 0.05891313626031916
idv_policy eval average team episode rewards of agent3: 15.0
idv_policy eval idv catch total num of agent3: 5
idv_policy eval team catch total num: 6
idv_policy eval average step individual rewards of agent4: 0.05332659374546096
idv_policy eval average team episode rewards of agent4: 15.0
idv_policy eval idv catch total num of agent4: 5
idv_policy eval team catch total num: 6

 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 203.


 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 204.


 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 203.


 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 203.


 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 203.


 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 203.


 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 203.


 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 203.


 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 204.


 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 204.


 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 204.


 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 204.


 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 204.


 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 204.


 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 204.


 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 204.


 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 204.


 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 204.


 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 204.


 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 204.


 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 204.


 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 204.


 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 204.


 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 204.


 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 204.

team_policy eval average step individual rewards of agent0: 0.5056603110428334
team_policy eval average team episode rewards of agent0: 30.0
team_policy eval idv catch total num of agent0: 22
team_policy eval team catch total num: 12
team_policy eval average step individual rewards of agent1: 0.12468722715102283
team_policy eval average team episode rewards of agent1: 30.0
team_policy eval idv catch total num of agent1: 7
team_policy eval team catch total num: 12
team_policy eval average step individual rewards of agent2: 0.10006975353765349
team_policy eval average team episode rewards of agent2: 30.0
team_policy eval idv catch total num of agent2: 6
team_policy eval team catch total num: 12
team_policy eval average step individual rewards of agent3: 0.14718100077100366
team_policy eval average team episode rewards of agent3: 30.0
team_policy eval idv catch total num of agent3: 8
team_policy eval team catch total num: 12
team_policy eval average step individual rewards of agent4: 0.2715798638856309
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: 0.12940034110015705
idv_policy eval average team episode rewards of agent0: 35.0
idv_policy eval idv catch total num of agent0: 8
idv_policy eval team catch total num: 14
idv_policy eval average step individual rewards of agent1: 0.3348930570760039
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: 0.17714085614718272
idv_policy eval average team episode rewards of agent2: 35.0
idv_policy eval idv catch total num of agent2: 10
idv_policy eval team catch total num: 14
idv_policy eval average step individual rewards of agent3: 0.18716056386457464
idv_policy eval average team episode rewards of agent3: 35.0
idv_policy eval idv catch total num of agent3: 10
idv_policy eval team catch total num: 14
idv_policy eval average step individual rewards of agent4: 0.3661839843705403
idv_policy eval average team episode rewards of agent4: 35.0
idv_policy eval idv catch total num of agent4: 17
idv_policy eval team catch total num: 14

 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 204.


 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 204.


 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 204.


 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 204.


 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 204.


 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 204.


 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 204.


 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 204.


 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 204.


 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 204.


 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 204.


 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 204.


 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 204.


 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 204.


 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 204.


 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 204.


 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 204.


 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 204.


 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 204.


 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 204.


 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 204.


 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 204.


 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 204.


 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 204.


 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 204.

team_policy eval average step individual rewards of agent0: 0.15115364288394276
team_policy eval average team episode rewards of agent0: 22.5
team_policy eval idv catch total num of agent0: 8
team_policy eval team catch total num: 9
team_policy eval average step individual rewards of agent1: 0.14612562238537882
team_policy eval average team episode rewards of agent1: 22.5
team_policy eval idv catch total num of agent1: 8
team_policy eval team catch total num: 9
team_policy eval average step individual rewards of agent2: 0.2999139317299814
team_policy eval average team episode rewards of agent2: 22.5
team_policy eval idv catch total num of agent2: 14
team_policy eval team catch total num: 9
team_policy eval average step individual rewards of agent3: 0.11790402610752757
team_policy eval average team episode rewards of agent3: 22.5
team_policy eval idv catch total num of agent3: 7
team_policy eval team catch total num: 9
team_policy eval average step individual rewards of agent4: 0.1787980332815721
team_policy eval average team episode rewards of agent4: 22.5
team_policy eval idv catch total num of agent4: 9
team_policy eval team catch total num: 9
idv_policy eval average step individual rewards of agent0: -0.021507752579929488
idv_policy eval average team episode rewards of agent0: 15.0
idv_policy eval idv catch total num of agent0: 2
idv_policy eval team catch total num: 6
idv_policy eval average step individual rewards of agent1: 0.24556544448351114
idv_policy eval average team episode rewards of agent1: 15.0
idv_policy eval idv catch total num of agent1: 12
idv_policy eval team catch total num: 6
idv_policy eval average step individual rewards of agent2: 0.13676510584631024
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: -0.04329231502654284
idv_policy eval average team episode rewards of agent3: 15.0
idv_policy eval idv catch total num of agent3: 1
idv_policy eval team catch total num: 6
idv_policy eval average step individual rewards of agent4: 0.060596205219948865
idv_policy eval average team episode rewards of agent4: 15.0
idv_policy eval idv catch total num of agent4: 5
idv_policy eval team catch total num: 6

 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 204.


 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 204.


 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 204.


 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 204.


 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 204.


 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 204.


 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 204.


 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 204.


 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 204.


 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 204.


 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 204.


 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 204.


 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 204.


 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 204.


 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 204.


 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 204.


 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 204.


 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 204.


 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 204.


 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 204.


 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 204.


 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 204.


 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 204.


 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 204.


 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 204.

team_policy eval average step individual rewards of agent0: -0.012512043137763787
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: -0.0355275851852472
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: 0.03974193079771737
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: -0.06916717875735552
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: 0.00990546669031743
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: 0.15269325807839715
idv_policy eval average team episode rewards of agent0: 22.5
idv_policy eval idv catch total num of agent0: 8
idv_policy eval team catch total num: 9
idv_policy eval average step individual rewards of agent1: 0.11133354805554861
idv_policy eval average team episode rewards of agent1: 22.5
idv_policy eval idv catch total num of agent1: 7
idv_policy eval team catch total num: 9
idv_policy eval average step individual rewards of agent2: 0.20437793126974704
idv_policy eval average team episode rewards of agent2: 22.5
idv_policy eval idv catch total num of agent2: 10
idv_policy eval team catch total num: 9
idv_policy eval average step individual rewards of agent3: 0.07467520875277792
idv_policy eval average team episode rewards of agent3: 22.5
idv_policy eval idv catch total num of agent3: 5
idv_policy eval team catch total num: 9
idv_policy eval average step individual rewards of agent4: 0.219662539309573
idv_policy eval average team episode rewards of agent4: 22.5
idv_policy eval idv catch total num of agent4: 11
idv_policy eval team catch total num: 9

 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 204.


 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 204.


 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 204.


 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 204.


 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 204.


 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 204.


 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 204.


 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 204.


 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 204.


 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 204.


 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 204.


 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 204.


 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 204.


 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 204.


 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 204.


 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 204.


 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 204.


 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 204.


 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 204.


 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 204.


 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 204.


 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 204.


 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 204.


 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 204.


 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 204.

team_policy eval average step individual rewards of agent0: 0.06894147123341374
team_policy eval average team episode rewards of agent0: 32.5
team_policy eval idv catch total num of agent0: 5
team_policy eval team catch total num: 13
team_policy eval average step individual rewards of agent1: 0.3576611643104644
team_policy eval average team episode rewards of agent1: 32.5
team_policy eval idv catch total num of agent1: 16
team_policy eval team catch total num: 13
team_policy eval average step individual rewards of agent2: 0.1760196137850356
team_policy eval average team episode rewards of agent2: 32.5
team_policy eval idv catch total num of agent2: 9
team_policy eval team catch total num: 13
team_policy eval average step individual rewards of agent3: 0.277687087696175
team_policy eval average team episode rewards of agent3: 32.5
team_policy eval idv catch total num of agent3: 13
team_policy eval team catch total num: 13
team_policy eval average step individual rewards of agent4: 0.256348031151389
team_policy eval average team episode rewards of agent4: 32.5
team_policy eval idv catch total num of agent4: 12
team_policy eval team catch total num: 13
idv_policy eval average step individual rewards of agent0: 0.19288132570458685
idv_policy eval average team episode rewards of agent0: 25.0
idv_policy eval idv catch total num of agent0: 10
idv_policy eval team catch total num: 10
idv_policy eval average step individual rewards of agent1: 0.12128986694391942
idv_policy eval average team episode rewards of agent1: 25.0
idv_policy eval idv catch total num of agent1: 7
idv_policy eval team catch total num: 10
idv_policy eval average step individual rewards of agent2: 0.16709076433349793
idv_policy eval average team episode rewards of agent2: 25.0
idv_policy eval idv catch total num of agent2: 9
idv_policy eval team catch total num: 10
idv_policy eval average step individual rewards of agent3: 0.12154354720984338
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: 0.14498503521818745
idv_policy eval average team episode rewards of agent4: 25.0
idv_policy eval idv catch total num of agent4: 8
idv_policy eval team catch total num: 10

 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 204.


 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 204.


 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 204.


 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 204.


 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 204.


 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 204.


 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 204.


 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 204.


 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 204.


 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 204.


 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 204.


 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 204.


 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 204.


 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 205.


 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 205.


 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 205.


 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 205.


 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 205.


 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 205.


 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 205.


 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 205.


 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 205.


 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 205.


 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 205.


 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 205.

team_policy eval average step individual rewards of agent0: 0.09501793622004459
team_policy eval average team episode rewards of agent0: 25.0
team_policy eval idv catch total num of agent0: 6
team_policy eval team catch total num: 10
team_policy eval average step individual rewards of agent1: 0.22459224958698165
team_policy eval average team episode rewards of agent1: 25.0
team_policy eval idv catch total num of agent1: 11
team_policy eval team catch total num: 10
team_policy eval average step individual rewards of agent2: 0.04529517830421092
team_policy eval average team episode rewards of agent2: 25.0
team_policy eval idv catch total num of agent2: 4
team_policy eval team catch total num: 10
team_policy eval average step individual rewards of agent3: 0.22420044400034722
team_policy eval average team episode rewards of agent3: 25.0
team_policy eval idv catch total num of agent3: 11
team_policy eval team catch total num: 10
team_policy eval average step individual rewards of agent4: 0.39156065125143186
team_policy eval average team episode rewards of agent4: 25.0
team_policy eval idv catch total num of agent4: 18
team_policy eval team catch total num: 10
idv_policy eval average step individual rewards of agent0: 0.193012471574296
idv_policy eval average team episode rewards of agent0: 32.5
idv_policy eval idv catch total num of agent0: 10
idv_policy eval team catch total num: 13
idv_policy eval average step individual rewards of agent1: 0.09554563180453032
idv_policy eval average team episode rewards of agent1: 32.5
idv_policy eval idv catch total num of agent1: 6
idv_policy eval team catch total num: 13
idv_policy eval average step individual rewards of agent2: 0.11892872879115668
idv_policy eval average team episode rewards of agent2: 32.5
idv_policy eval idv catch total num of agent2: 7
idv_policy eval team catch total num: 13
idv_policy eval average step individual rewards of agent3: 0.3449003545153236
idv_policy eval average team episode rewards of agent3: 32.5
idv_policy eval idv catch total num of agent3: 16
idv_policy eval team catch total num: 13
idv_policy eval average step individual rewards of agent4: 0.19108859278188825
idv_policy eval average team episode rewards of agent4: 32.5
idv_policy eval idv catch total num of agent4: 10
idv_policy eval team catch total num: 13

 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 205.


 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 205.


 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 205.


 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 205.


 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 205.


 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 205.


 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 205.


 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 205.


 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 205.


 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 205.


 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 205.


 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 205.


 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 205.


 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 205.


 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 205.


 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 205.


 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 205.


 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 205.


 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 205.


 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 205.


 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 205.


 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 205.


 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 205.


 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 205.


 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 205.

team_policy eval average step individual rewards of agent0: 0.1659709553872616
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: 0.15061173886297094
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: 0.16363442393364555
team_policy eval average team episode rewards of agent2: 17.5
team_policy eval idv catch total num of agent2: 9
team_policy eval team catch total num: 7
team_policy eval average step individual rewards of agent3: 0.06000084401532518
team_policy eval average team episode rewards of agent3: 17.5
team_policy eval idv catch total num of agent3: 5
team_policy eval team catch total num: 7
team_policy eval average step individual rewards of agent4: 0.01523473959819369
team_policy eval average team episode rewards of agent4: 17.5
team_policy eval idv catch total num of agent4: 3
team_policy eval team catch total num: 7
idv_policy eval average step individual rewards of agent0: 0.33178175803433274
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: 0.2950944524298466
idv_policy eval average team episode rewards of agent1: 32.5
idv_policy eval idv catch total num of agent1: 14
idv_policy eval team catch total num: 13
idv_policy eval average step individual rewards of agent2: 0.16400616764882442
idv_policy eval average team episode rewards of agent2: 32.5
idv_policy eval idv catch total num of agent2: 9
idv_policy eval team catch total num: 13
idv_policy eval average step individual rewards of agent3: 0.22673946777805887
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: 0.17125984526159577
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 2101/10000 episodes, total num timesteps 420400/2000000, FPS 205.


 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 205.


 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 205.


 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 205.


 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 205.


 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 205.


 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 205.


 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 205.


 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 205.


 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 205.


 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 205.


 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 205.


 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 205.


 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 205.


 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 205.


 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 205.


 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 205.


 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 205.


 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 205.


 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 205.


 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 205.


 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 205.


 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 205.


 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 205.


 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 205.

team_policy eval average step individual rewards of agent0: 0.2352655392307496
team_policy eval average team episode rewards of agent0: 27.5
team_policy eval idv catch total num of agent0: 12
team_policy eval team catch total num: 11
team_policy eval average step individual rewards of agent1: 0.1681503582963787
team_policy eval average team episode rewards of agent1: 27.5
team_policy eval idv catch total num of agent1: 9
team_policy eval team catch total num: 11
team_policy eval average step individual rewards of agent2: 0.16151003270809564
team_policy eval average team episode rewards of agent2: 27.5
team_policy eval idv catch total num of agent2: 9
team_policy eval team catch total num: 11
team_policy eval average step individual rewards of agent3: 0.19330356661389153
team_policy eval average team episode rewards of agent3: 27.5
team_policy eval idv catch total num of agent3: 10
team_policy eval team catch total num: 11
team_policy eval average step individual rewards of agent4: 0.14414677544550364
team_policy eval average team episode rewards of agent4: 27.5
team_policy eval idv catch total num of agent4: 8
team_policy eval team catch total num: 11
idv_policy eval average step individual rewards of agent0: 0.25033161278655425
idv_policy eval average team episode rewards of agent0: 57.5
idv_policy eval idv catch total num of agent0: 12
idv_policy eval team catch total num: 23
idv_policy eval average step individual rewards of agent1: 0.25543294503202535
idv_policy eval average team episode rewards of agent1: 57.5
idv_policy eval idv catch total num of agent1: 12
idv_policy eval team catch total num: 23
idv_policy eval average step individual rewards of agent2: 0.48638976263740036
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: 0.5827693292865461
idv_policy eval average team episode rewards of agent3: 57.5
idv_policy eval idv catch total num of agent3: 25
idv_policy eval team catch total num: 23
idv_policy eval average step individual rewards of agent4: 0.4628095942671704
idv_policy eval average team episode rewards of agent4: 57.5
idv_policy eval idv catch total num of agent4: 20
idv_policy eval team catch total num: 23

 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 205.


 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 205.


 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 205.


 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 205.


 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 205.


 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 205.


 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 205.


 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 205.


 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 205.


 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 205.


 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 205.


 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 205.


 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 205.


 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 205.


 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 205.


 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 205.


 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 205.


 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 205.


 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 205.


 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 205.


 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 205.


 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 205.


 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 205.


 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 205.


 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 205.

team_policy eval average step individual rewards of agent0: 0.2774392352890226
team_policy eval average team episode rewards of agent0: 30.0
team_policy eval idv catch total num of agent0: 13
team_policy eval team catch total num: 12
team_policy eval average step individual rewards of agent1: 0.20004698300706805
team_policy eval average team episode rewards of agent1: 30.0
team_policy eval idv catch total num of agent1: 10
team_policy eval team catch total num: 12
team_policy eval average step individual rewards of agent2: 0.1007411610818153
team_policy eval average team episode rewards of agent2: 30.0
team_policy eval idv catch total num of agent2: 6
team_policy eval team catch total num: 12
team_policy eval average step individual rewards of agent3: 0.22376280275467952
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: 0.2202325151505184
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: 0.07337610866965204
idv_policy eval average team episode rewards of agent0: 37.5
idv_policy eval idv catch total num of agent0: 5
idv_policy eval team catch total num: 15
idv_policy eval average step individual rewards of agent1: 0.3266261043863966
idv_policy eval average team episode rewards of agent1: 37.5
idv_policy eval idv catch total num of agent1: 15
idv_policy eval team catch total num: 15
idv_policy eval average step individual rewards of agent2: 0.20159553864743845
idv_policy eval average team episode rewards of agent2: 37.5
idv_policy eval idv catch total num of agent2: 10
idv_policy eval team catch total num: 15
idv_policy eval average step individual rewards of agent3: 0.26949682441395495
idv_policy eval average team episode rewards of agent3: 37.5
idv_policy eval idv catch total num of agent3: 13
idv_policy eval team catch total num: 15
idv_policy eval average step individual rewards of agent4: 0.24783786032043054
idv_policy eval average team episode rewards of agent4: 37.5
idv_policy eval idv catch total num of agent4: 12
idv_policy eval team catch total num: 15

 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 205.


 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 205.


 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 205.


 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 205.


 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 205.


 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 205.


 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 205.


 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 205.


 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 205.


 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 205.


 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 205.


 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 205.


 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 205.


 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 205.


 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 205.


 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 205.


 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 205.


 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 205.


 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 205.


 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 205.


 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 205.


 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 205.


 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 205.


 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 205.


 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 205.

team_policy eval average step individual rewards of agent0: 0.24363358399112175
team_policy eval average team episode rewards of agent0: 40.0
team_policy eval idv catch total num of agent0: 12
team_policy eval team catch total num: 16
team_policy eval average step individual rewards of agent1: 0.01374611744584357
team_policy eval average team episode rewards of agent1: 40.0
team_policy eval idv catch total num of agent1: 3
team_policy eval team catch total num: 16
team_policy eval average step individual rewards of agent2: 0.3734627278588992
team_policy eval average team episode rewards of agent2: 40.0
team_policy eval idv catch total num of agent2: 17
team_policy eval team catch total num: 16
team_policy eval average step individual rewards of agent3: 0.2721127032773636
team_policy eval average team episode rewards of agent3: 40.0
team_policy eval idv catch total num of agent3: 13
team_policy eval team catch total num: 16
team_policy eval average step individual rewards of agent4: 0.21699571842465779
team_policy eval average team episode rewards of agent4: 40.0
team_policy eval idv catch total num of agent4: 11
team_policy eval team catch total num: 16
idv_policy eval average step individual rewards of agent0: 0.20458336062389415
idv_policy eval average team episode rewards of agent0: 30.0
idv_policy eval idv catch total num of agent0: 10
idv_policy eval team catch total num: 12
idv_policy eval average step individual rewards of agent1: 0.12751822541258384
idv_policy eval average team episode rewards of agent1: 30.0
idv_policy eval idv catch total num of agent1: 7
idv_policy eval team catch total num: 12
idv_policy eval average step individual rewards of agent2: 0.228631068638077
idv_policy eval average team episode rewards of agent2: 30.0
idv_policy eval idv catch total num of agent2: 11
idv_policy eval team catch total num: 12
idv_policy eval average step individual rewards of agent3: 0.2525846349420045
idv_policy eval average team episode rewards of agent3: 30.0
idv_policy eval idv catch total num of agent3: 12
idv_policy eval team catch total num: 12
idv_policy eval average step individual rewards of agent4: 0.10324505179191071
idv_policy eval average team episode rewards of agent4: 30.0
idv_policy eval idv catch total num of agent4: 6
idv_policy eval team catch total num: 12

 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 205.


 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 205.


 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 205.


 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 205.


 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 205.


 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 205.


 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 206.


 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 205.


 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 206.


 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 206.


 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 206.


 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 206.


 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 206.


 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 206.


 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 206.


 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 206.


 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 206.


 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 206.


 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 206.


 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 206.


 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 206.


 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 206.


 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 206.


 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 206.


 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 206.

team_policy eval average step individual rewards of agent0: 0.2749658656874954
team_policy eval average team episode rewards of agent0: 32.5
team_policy eval idv catch total num of agent0: 13
team_policy eval team catch total num: 13
team_policy eval average step individual rewards of agent1: 0.3046268126504436
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: 0.14676595872254727
team_policy eval average team episode rewards of agent2: 32.5
team_policy eval idv catch total num of agent2: 8
team_policy eval team catch total num: 13
team_policy eval average step individual rewards of agent3: 0.3788013741363995
team_policy eval average team episode rewards of agent3: 32.5
team_policy eval idv catch total num of agent3: 17
team_policy eval team catch total num: 13
team_policy eval average step individual rewards of agent4: 0.37656730338735045
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: 0.16985048292572505
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: 0.042298750589703726
idv_policy eval average team episode rewards of agent1: 25.0
idv_policy eval idv catch total num of agent1: 4
idv_policy eval team catch total num: 10
idv_policy eval average step individual rewards of agent2: 0.14570264996965088
idv_policy eval average team episode rewards of agent2: 25.0
idv_policy eval idv catch total num of agent2: 8
idv_policy eval team catch total num: 10
idv_policy eval average step individual rewards of agent3: 0.19493357028701871
idv_policy eval average team episode rewards of agent3: 25.0
idv_policy eval idv catch total num of agent3: 10
idv_policy eval team catch total num: 10
idv_policy eval average step individual rewards of agent4: 0.22254263416655096
idv_policy eval average team episode rewards of agent4: 25.0
idv_policy eval idv catch total num of agent4: 11
idv_policy eval team catch total num: 10

 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 206.


 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 206.


 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 206.


 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 206.


 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 206.


 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 206.


 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 206.


 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 206.


 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 206.


 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 206.


 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 206.


 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 206.


 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 206.


 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 206.


 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 206.


 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 206.


 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 206.


 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 206.


 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 206.


 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 206.


 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 206.


 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 206.


 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 206.


 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 206.


 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 206.

team_policy eval average step individual rewards of agent0: 0.11535574794351816
team_policy eval average team episode rewards of agent0: 22.5
team_policy eval idv catch total num of agent0: 7
team_policy eval team catch total num: 9
team_policy eval average step individual rewards of agent1: 0.11148424358435782
team_policy eval average team episode rewards of agent1: 22.5
team_policy eval idv catch total num of agent1: 7
team_policy eval team catch total num: 9
team_policy eval average step individual rewards of agent2: 0.062150669940022905
team_policy eval average team episode rewards of agent2: 22.5
team_policy eval idv catch total num of agent2: 5
team_policy eval team catch total num: 9
team_policy eval average step individual rewards of agent3: 0.11512494692422547
team_policy eval average team episode rewards of agent3: 22.5
team_policy eval idv catch total num of agent3: 7
team_policy eval team catch total num: 9
team_policy eval average step individual rewards of agent4: 0.16571155612683974
team_policy eval average team episode rewards of agent4: 22.5
team_policy eval idv catch total num of agent4: 9
team_policy eval team catch total num: 9
idv_policy eval average step individual rewards of agent0: 0.23361650317961818
idv_policy eval average team episode rewards of agent0: 40.0
idv_policy eval idv catch total num of agent0: 11
idv_policy eval team catch total num: 16
idv_policy eval average step individual rewards of agent1: 0.2170103692153509
idv_policy eval average team episode rewards of agent1: 40.0
idv_policy eval idv catch total num of agent1: 11
idv_policy eval team catch total num: 16
idv_policy eval average step individual rewards of agent2: 0.24920256833629537
idv_policy eval average team episode rewards of agent2: 40.0
idv_policy eval idv catch total num of agent2: 12
idv_policy eval team catch total num: 16
idv_policy eval average step individual rewards of agent3: 0.32703479111289124
idv_policy eval average team episode rewards of agent3: 40.0
idv_policy eval idv catch total num of agent3: 15
idv_policy eval team catch total num: 16
idv_policy eval average step individual rewards of agent4: 0.15038617902120535
idv_policy eval average team episode rewards of agent4: 40.0
idv_policy eval idv catch total num of agent4: 8
idv_policy eval team catch total num: 16

 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 206.


 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 206.


 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 206.


 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 206.


 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 206.


 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 206.


 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 206.


 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 206.


 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 206.


 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 206.


 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 206.


 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 206.


 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 206.


 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 206.


 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 206.


 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 206.


 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 206.


 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 206.


 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 206.


 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 206.


 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 206.


 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 206.


 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 206.


 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 206.


 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 206.

team_policy eval average step individual rewards of agent0: 0.281153475322175
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: 0.121605694986069
team_policy eval average team episode rewards of agent1: 22.5
team_policy eval idv catch total num of agent1: 7
team_policy eval team catch total num: 9
team_policy eval average step individual rewards of agent2: 0.14866069343743818
team_policy eval average team episode rewards of agent2: 22.5
team_policy eval idv catch total num of agent2: 8
team_policy eval team catch total num: 9
team_policy eval average step individual rewards of agent3: 0.120543972787621
team_policy eval average team episode rewards of agent3: 22.5
team_policy eval idv catch total num of agent3: 7
team_policy eval team catch total num: 9
team_policy eval average step individual rewards of agent4: 0.22830276726910576
team_policy eval average team episode rewards of agent4: 22.5
team_policy eval idv catch total num of agent4: 11
team_policy eval team catch total num: 9
idv_policy eval average step individual rewards of agent0: 0.4333513834125894
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: 0.406453201701534
idv_policy eval average team episode rewards of agent1: 65.0
idv_policy eval idv catch total num of agent1: 18
idv_policy eval team catch total num: 26
idv_policy eval average step individual rewards of agent2: 0.527617832418187
idv_policy eval average team episode rewards of agent2: 65.0
idv_policy eval idv catch total num of agent2: 23
idv_policy eval team catch total num: 26
idv_policy eval average step individual rewards of agent3: 0.4543671896973283
idv_policy eval average team episode rewards of agent3: 65.0
idv_policy eval idv catch total num of agent3: 20
idv_policy eval team catch total num: 26
idv_policy eval average step individual rewards of agent4: 0.3985818676891825
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 2251/10000 episodes, total num timesteps 450400/2000000, FPS 206.


 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 206.


 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 206.


 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 206.


 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 206.


 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 206.


 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 206.


 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 206.


 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 206.


 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 206.


 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 206.


 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 206.


 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 206.


 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 206.


 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 206.


 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 206.


 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 206.


 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 206.


 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 206.


 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 206.


 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 206.


 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 206.


 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 206.


 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 206.


 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 206.

team_policy eval average step individual rewards of agent0: 0.024061126844494865
team_policy eval average team episode rewards of agent0: 12.5
team_policy eval idv catch total num of agent0: 4
team_policy eval team catch total num: 5
team_policy eval average step individual rewards of agent1: 0.0591153477215284
team_policy eval average team episode rewards of agent1: 12.5
team_policy eval idv catch total num of agent1: 5
team_policy eval team catch total num: 5
team_policy eval average step individual rewards of agent2: 0.006644727596998365
team_policy eval average team episode rewards of agent2: 12.5
team_policy eval idv catch total num of agent2: 3
team_policy eval team catch total num: 5
team_policy eval average step individual rewards of agent3: 0.031141518939404308
team_policy eval average team episode rewards of agent3: 12.5
team_policy eval idv catch total num of agent3: 4
team_policy eval team catch total num: 5
team_policy eval average step individual rewards of agent4: -0.019599700785230628
team_policy eval average team episode rewards of agent4: 12.5
team_policy eval idv catch total num of agent4: 2
team_policy eval team catch total num: 5
idv_policy eval average step individual rewards of agent0: 0.45954741714285413
idv_policy eval average team episode rewards of agent0: 50.0
idv_policy eval idv catch total num of agent0: 20
idv_policy eval team catch total num: 20
idv_policy eval average step individual rewards of agent1: 0.4360105682947812
idv_policy eval average team episode rewards of agent1: 50.0
idv_policy eval idv catch total num of agent1: 19
idv_policy eval team catch total num: 20
idv_policy eval average step individual rewards of agent2: 0.24951726704442534
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: 0.2973570029865777
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: 0.2544004201951082
idv_policy eval average team episode rewards of agent4: 50.0
idv_policy eval idv catch total num of agent4: 12
idv_policy eval team catch total num: 20

 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 206.


 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 206.


 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 206.


 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 206.


 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 206.


 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 206.


 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 206.


 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 206.


 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 206.


 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 206.


 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 206.


 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 206.


 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 206.


 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 206.


 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 206.


 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 206.


 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 206.


 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 206.


 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 206.


 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 206.


 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 206.


 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 206.


 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 206.


 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 206.


 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 206.

team_policy eval average step individual rewards of agent0: 0.2607821866530312
team_policy eval average team episode rewards of agent0: 45.0
team_policy eval idv catch total num of agent0: 12
team_policy eval team catch total num: 18
team_policy eval average step individual rewards of agent1: 0.3627026913668238
team_policy eval average team episode rewards of agent1: 45.0
team_policy eval idv catch total num of agent1: 16
team_policy eval team catch total num: 18
team_policy eval average step individual rewards of agent2: 0.2610481901362929
team_policy eval average team episode rewards of agent2: 45.0
team_policy eval idv catch total num of agent2: 12
team_policy eval team catch total num: 18
team_policy eval average step individual rewards of agent3: 0.5346602740365519
team_policy eval average team episode rewards of agent3: 45.0
team_policy eval idv catch total num of agent3: 23
team_policy eval team catch total num: 18
team_policy eval average step individual rewards of agent4: 0.19785608878045877
team_policy eval average team episode rewards of agent4: 45.0
team_policy eval idv catch total num of agent4: 10
team_policy eval team catch total num: 18
idv_policy eval average step individual rewards of agent0: 0.20293281704627644
idv_policy eval average team episode rewards of agent0: 42.5
idv_policy eval idv catch total num of agent0: 10
idv_policy eval team catch total num: 17
idv_policy eval average step individual rewards of agent1: 0.3753550941825805
idv_policy eval average team episode rewards of agent1: 42.5
idv_policy eval idv catch total num of agent1: 17
idv_policy eval team catch total num: 17
idv_policy eval average step individual rewards of agent2: 0.32705669416441985
idv_policy eval average team episode rewards of agent2: 42.5
idv_policy eval idv catch total num of agent2: 15
idv_policy eval team catch total num: 17
idv_policy eval average step individual rewards of agent3: 0.15007847149823428
idv_policy eval average team episode rewards of agent3: 42.5
idv_policy eval idv catch total num of agent3: 8
idv_policy eval team catch total num: 17
idv_policy eval average step individual rewards of agent4: 0.30015931215131314
idv_policy eval average team episode rewards of agent4: 42.5
idv_policy eval idv catch total num of agent4: 14
idv_policy eval team catch total num: 17

 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 206.


 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 206.


 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 206.


 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 206.


 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 206.


 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 206.


 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 206.


 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 206.


 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 206.


 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 206.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.

team_policy eval average step individual rewards of agent0: 0.5491946425714291
team_policy eval average team episode rewards of agent0: 72.5
team_policy eval idv catch total num of agent0: 24
team_policy eval team catch total num: 29
team_policy eval average step individual rewards of agent1: 0.5257550459608412
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: 0.42469299587141324
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: 0.5224151550215368
team_policy eval average team episode rewards of agent3: 72.5
team_policy eval idv catch total num of agent3: 23
team_policy eval team catch total num: 29
team_policy eval average step individual rewards of agent4: 0.4690306148941354
team_policy eval average team episode rewards of agent4: 72.5
team_policy eval idv catch total num of agent4: 21
team_policy eval team catch total num: 29
idv_policy eval average step individual rewards of agent0: 0.11239776348330181
idv_policy eval average team episode rewards of agent0: 35.0
idv_policy eval idv catch total num of agent0: 7
idv_policy eval team catch total num: 14
idv_policy eval average step individual rewards of agent1: 0.19318879380890863
idv_policy eval average team episode rewards of agent1: 35.0
idv_policy eval idv catch total num of agent1: 10
idv_policy eval team catch total num: 14
idv_policy eval average step individual rewards of agent2: 0.2935004230321993
idv_policy eval average team episode rewards of agent2: 35.0
idv_policy eval idv catch total num of agent2: 14
idv_policy eval team catch total num: 14
idv_policy eval average step individual rewards of agent3: 0.2662234895745895
idv_policy eval average team episode rewards of agent3: 35.0
idv_policy eval idv catch total num of agent3: 13
idv_policy eval team catch total num: 14
idv_policy eval average step individual rewards of agent4: 0.34266568514292656
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 2326/10000 episodes, total num timesteps 465400/2000000, FPS 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.

team_policy eval average step individual rewards of agent0: 0.43126379779307333
team_policy eval average team episode rewards of agent0: 45.0
team_policy eval idv catch total num of agent0: 19
team_policy eval team catch total num: 18
team_policy eval average step individual rewards of agent1: 0.2317700023969239
team_policy eval average team episode rewards of agent1: 45.0
team_policy eval idv catch total num of agent1: 11
team_policy eval team catch total num: 18
team_policy eval average step individual rewards of agent2: 0.3084973697547192
team_policy eval average team episode rewards of agent2: 45.0
team_policy eval idv catch total num of agent2: 14
team_policy eval team catch total num: 18
team_policy eval average step individual rewards of agent3: 0.2777324152289751
team_policy eval average team episode rewards of agent3: 45.0
team_policy eval idv catch total num of agent3: 13
team_policy eval team catch total num: 18
team_policy eval average step individual rewards of agent4: 0.2505289827310517
team_policy eval average team episode rewards of agent4: 45.0
team_policy eval idv catch total num of agent4: 12
team_policy eval team catch total num: 18
idv_policy eval average step individual rewards of agent0: 0.29792529911586063
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: 0.5514069496863376
idv_policy eval average team episode rewards of agent1: 55.0
idv_policy eval idv catch total num of agent1: 24
idv_policy eval team catch total num: 22
idv_policy eval average step individual rewards of agent2: 0.25072877148438916
idv_policy eval average team episode rewards of agent2: 55.0
idv_policy eval idv catch total num of agent2: 12
idv_policy eval team catch total num: 22
idv_policy eval average step individual rewards of agent3: 0.5630030085465125
idv_policy eval average team episode rewards of agent3: 55.0
idv_policy eval idv catch total num of agent3: 24
idv_policy eval team catch total num: 22
idv_policy eval average step individual rewards of agent4: 0.24655883850737564
idv_policy eval average team episode rewards of agent4: 55.0
idv_policy eval idv catch total num of agent4: 12
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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.

team_policy eval average step individual rewards of agent0: 0.14106640274124327
team_policy eval average team episode rewards of agent0: 42.5
team_policy eval idv catch total num of agent0: 8
team_policy eval team catch total num: 17
team_policy eval average step individual rewards of agent1: 0.26700920215513946
team_policy eval average team episode rewards of agent1: 42.5
team_policy eval idv catch total num of agent1: 13
team_policy eval team catch total num: 17
team_policy eval average step individual rewards of agent2: 0.4149697424879071
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: 0.22088941115913355
team_policy eval average team episode rewards of agent3: 42.5
team_policy eval idv catch total num of agent3: 11
team_policy eval team catch total num: 17
team_policy eval average step individual rewards of agent4: 0.26790188855596303
team_policy eval average team episode rewards of agent4: 42.5
team_policy eval idv catch total num of agent4: 13
team_policy eval team catch total num: 17
idv_policy eval average step individual rewards of agent0: 0.353706343961078
idv_policy eval average team episode rewards of agent0: 37.5
idv_policy eval idv catch total num of agent0: 16
idv_policy eval team catch total num: 15
idv_policy eval average step individual rewards of agent1: 0.3084078457973781
idv_policy eval average team episode rewards of agent1: 37.5
idv_policy eval idv catch total num of agent1: 14
idv_policy eval team catch total num: 15
idv_policy eval average step individual rewards of agent2: 0.373922777645344
idv_policy eval average team episode rewards of agent2: 37.5
idv_policy eval idv catch total num of agent2: 17
idv_policy eval team catch total num: 15
idv_policy eval average step individual rewards of agent3: 0.26552697446415974
idv_policy eval average team episode rewards of agent3: 37.5
idv_policy eval idv catch total num of agent3: 13
idv_policy eval team catch total num: 15
idv_policy eval average step individual rewards of agent4: 0.1514763812921456
idv_policy eval average team episode rewards of agent4: 37.5
idv_policy eval idv catch total num of agent4: 8
idv_policy eval team catch total num: 15

 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.

team_policy eval average step individual rewards of agent0: 0.22333442191931624
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: 0.2538799173672531
team_policy eval average team episode rewards of agent1: 30.0
team_policy eval idv catch total num of agent1: 12
team_policy eval team catch total num: 12
team_policy eval average step individual rewards of agent2: 0.28052625753826965
team_policy eval average team episode rewards of agent2: 30.0
team_policy eval idv catch total num of agent2: 13
team_policy eval team catch total num: 12
team_policy eval average step individual rewards of agent3: 0.3005894217954263
team_policy eval average team episode rewards of agent3: 30.0
team_policy eval idv catch total num of agent3: 14
team_policy eval team catch total num: 12
team_policy eval average step individual rewards of agent4: 0.20151914261774015
team_policy eval average team episode rewards of agent4: 30.0
team_policy eval idv catch total num of agent4: 10
team_policy eval team catch total num: 12
idv_policy eval average step individual rewards of agent0: 0.4328431917402952
idv_policy eval average team episode rewards of agent0: 62.5
idv_policy eval idv catch total num of agent0: 19
idv_policy eval team catch total num: 25
idv_policy eval average step individual rewards of agent1: 0.5075707272384055
idv_policy eval average team episode rewards of agent1: 62.5
idv_policy eval idv catch total num of agent1: 22
idv_policy eval team catch total num: 25
idv_policy eval average step individual rewards of agent2: 0.3627794846932075
idv_policy eval average team episode rewards of agent2: 62.5
idv_policy eval idv catch total num of agent2: 16
idv_policy eval team catch total num: 25
idv_policy eval average step individual rewards of agent3: 0.6236999380393186
idv_policy eval average team episode rewards of agent3: 62.5
idv_policy eval idv catch total num of agent3: 27
idv_policy eval team catch total num: 25
idv_policy eval average step individual rewards of agent4: 0.5521021508787595
idv_policy eval average team episode rewards of agent4: 62.5
idv_policy eval idv catch total num of agent4: 24
idv_policy eval team catch total num: 25

 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.

team_policy eval average step individual rewards of agent0: 0.33125141945919423
team_policy eval average team episode rewards of agent0: 32.5
team_policy eval idv catch total num of agent0: 15
team_policy eval team catch total num: 13
team_policy eval average step individual rewards of agent1: 0.08844965275012232
team_policy eval average team episode rewards of agent1: 32.5
team_policy eval idv catch total num of agent1: 6
team_policy eval team catch total num: 13
team_policy eval average step individual rewards of agent2: 0.13814074421146608
team_policy eval average team episode rewards of agent2: 32.5
team_policy eval idv catch total num of agent2: 8
team_policy eval team catch total num: 13
team_policy eval average step individual rewards of agent3: 0.38405898239946556
team_policy eval average team episode rewards of agent3: 32.5
team_policy eval idv catch total num of agent3: 17
team_policy eval team catch total num: 13
team_policy eval average step individual rewards of agent4: 0.34770290857224906
team_policy eval average team episode rewards of agent4: 32.5
team_policy eval idv catch total num of agent4: 16
team_policy eval team catch total num: 13
idv_policy eval average step individual rewards of agent0: 0.4432373760973667
idv_policy eval average team episode rewards of agent0: 77.5
idv_policy eval idv catch total num of agent0: 20
idv_policy eval team catch total num: 31
idv_policy eval average step individual rewards of agent1: 0.5513002452403478
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: 0.42131569616696685
idv_policy eval average team episode rewards of agent2: 77.5
idv_policy eval idv catch total num of agent2: 19
idv_policy eval team catch total num: 31
idv_policy eval average step individual rewards of agent3: 0.6204096581518516
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: 0.6976796791543275
idv_policy eval average team episode rewards of agent4: 77.5
idv_policy eval idv catch total num of agent4: 30
idv_policy eval team catch total num: 31

 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.

team_policy eval average step individual rewards of agent0: 0.3785872123167042
team_policy eval average team episode rewards of agent0: 57.5
team_policy eval idv catch total num of agent0: 17
team_policy eval team catch total num: 23
team_policy eval average step individual rewards of agent1: 0.6090236401767767
team_policy eval average team episode rewards of agent1: 57.5
team_policy eval idv catch total num of agent1: 26
team_policy eval team catch total num: 23
team_policy eval average step individual rewards of agent2: 0.3024801591703155
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: 0.5567478591701112
team_policy eval average team episode rewards of agent3: 57.5
team_policy eval idv catch total num of agent3: 24
team_policy eval team catch total num: 23
team_policy eval average step individual rewards of agent4: 0.3556192549352021
team_policy eval average team episode rewards of agent4: 57.5
team_policy eval idv catch total num of agent4: 16
team_policy eval team catch total num: 23
idv_policy eval average step individual rewards of agent0: 0.40811536938118836
idv_policy eval average team episode rewards of agent0: 50.0
idv_policy eval idv catch total num of agent0: 18
idv_policy eval team catch total num: 20
idv_policy eval average step individual rewards of agent1: 0.4797067765442896
idv_policy eval average team episode rewards of agent1: 50.0
idv_policy eval idv catch total num of agent1: 21
idv_policy eval team catch total num: 20
idv_policy eval average step individual rewards of agent2: 0.48413415080433864
idv_policy eval average team episode rewards of agent2: 50.0
idv_policy eval idv catch total num of agent2: 21
idv_policy eval team catch total num: 20
idv_policy eval average step individual rewards of agent3: 0.293122092092772
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: 0.22786483853362058
idv_policy eval average team episode rewards of agent4: 50.0
idv_policy eval idv catch total num of agent4: 11
idv_policy eval team catch total num: 20

 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.

team_policy eval average step individual rewards of agent0: 0.2779019260250132
team_policy eval average team episode rewards of agent0: 45.0
team_policy eval idv catch total num of agent0: 13
team_policy eval team catch total num: 18
team_policy eval average step individual rewards of agent1: 0.22266716623726832
team_policy eval average team episode rewards of agent1: 45.0
team_policy eval idv catch total num of agent1: 11
team_policy eval team catch total num: 18
team_policy eval average step individual rewards of agent2: 0.1928112832687362
team_policy eval average team episode rewards of agent2: 45.0
team_policy eval idv catch total num of agent2: 10
team_policy eval team catch total num: 18
team_policy eval average step individual rewards of agent3: 0.30298043699596156
team_policy eval average team episode rewards of agent3: 45.0
team_policy eval idv catch total num of agent3: 14
team_policy eval team catch total num: 18
team_policy eval average step individual rewards of agent4: 0.37772240261506795
team_policy eval average team episode rewards of agent4: 45.0
team_policy eval idv catch total num of agent4: 17
team_policy eval team catch total num: 18
idv_policy eval average step individual rewards of agent0: 0.5832007727827176
idv_policy eval average team episode rewards of agent0: 42.5
idv_policy eval idv catch total num of agent0: 25
idv_policy eval team catch total num: 17
idv_policy eval average step individual rewards of agent1: 0.3347329151614463
idv_policy eval average team episode rewards of agent1: 42.5
idv_policy eval idv catch total num of agent1: 15
idv_policy eval team catch total num: 17
idv_policy eval average step individual rewards of agent2: 0.21040411872015338
idv_policy eval average team episode rewards of agent2: 42.5
idv_policy eval idv catch total num of agent2: 10
idv_policy eval team catch total num: 17
idv_policy eval average step individual rewards of agent3: 0.3879952873425884
idv_policy eval average team episode rewards of agent3: 42.5
idv_policy eval idv catch total num of agent3: 17
idv_policy eval team catch total num: 17
idv_policy eval average step individual rewards of agent4: 0.5021177826657575
idv_policy eval average team episode rewards of agent4: 42.5
idv_policy eval idv catch total num of agent4: 22
idv_policy eval team catch total num: 17

 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.


 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 207.

team_policy eval average step individual rewards of agent0: 0.6552790543712836
team_policy eval average team episode rewards of agent0: 65.0
team_policy eval idv catch total num of agent0: 28
team_policy eval team catch total num: 26
team_policy eval average step individual rewards of agent1: 0.427286941120021
team_policy eval average team episode rewards of agent1: 65.0
team_policy eval idv catch total num of agent1: 19
team_policy eval team catch total num: 26
team_policy eval average step individual rewards of agent2: 0.36018091706786776
team_policy eval average team episode rewards of agent2: 65.0
team_policy eval idv catch total num of agent2: 16
team_policy eval team catch total num: 26
team_policy eval average step individual rewards of agent3: 0.3271590531408657
team_policy eval average team episode rewards of agent3: 65.0
team_policy eval idv catch total num of agent3: 15
team_policy eval team catch total num: 26
team_policy eval average step individual rewards of agent4: 0.3818586747761813
team_policy eval average team episode rewards of agent4: 65.0
team_policy eval idv catch total num of agent4: 17
team_policy eval team catch total num: 26
idv_policy eval average step individual rewards of agent0: 0.7315368366421233
idv_policy eval average team episode rewards of agent0: 75.0
idv_policy eval idv catch total num of agent0: 31
idv_policy eval team catch total num: 30
idv_policy eval average step individual rewards of agent1: 0.6005617030310688
idv_policy eval average team episode rewards of agent1: 75.0
idv_policy eval idv catch total num of agent1: 26
idv_policy eval team catch total num: 30
idv_policy eval average step individual rewards of agent2: 0.4773552649409296
idv_policy eval average team episode rewards of agent2: 75.0
idv_policy eval idv catch total num of agent2: 21
idv_policy eval team catch total num: 30
idv_policy eval average step individual rewards of agent3: 0.3498461684984415
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: 0.5806204545497454
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 2501/10000 episodes, total num timesteps 500400/2000000, FPS 207.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.

team_policy eval average step individual rewards of agent0: 0.8565702415169533
team_policy eval average team episode rewards of agent0: 85.0
team_policy eval idv catch total num of agent0: 36
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent1: 0.6089622383300933
team_policy eval average team episode rewards of agent1: 85.0
team_policy eval idv catch total num of agent1: 26
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent2: 0.582250927974167
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: 0.7089183344745639
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: 0.47350931829122395
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: 0.17619114440103428
idv_policy eval average team episode rewards of agent0: 52.5
idv_policy eval idv catch total num of agent0: 9
idv_policy eval team catch total num: 21
idv_policy eval average step individual rewards of agent1: 0.38607234526425005
idv_policy eval average team episode rewards of agent1: 52.5
idv_policy eval idv catch total num of agent1: 17
idv_policy eval team catch total num: 21
idv_policy eval average step individual rewards of agent2: 0.30485036965489526
idv_policy eval average team episode rewards of agent2: 52.5
idv_policy eval idv catch total num of agent2: 14
idv_policy eval team catch total num: 21
idv_policy eval average step individual rewards of agent3: 0.5129663307161232
idv_policy eval average team episode rewards of agent3: 52.5
idv_policy eval idv catch total num of agent3: 22
idv_policy eval team catch total num: 21
idv_policy eval average step individual rewards of agent4: 0.2268252687126945
idv_policy eval average team episode rewards of agent4: 52.5
idv_policy eval idv catch total num of agent4: 11
idv_policy eval team catch total num: 21

 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.

team_policy eval average step individual rewards of agent0: 0.06272789620573718
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: 0.23347618987214186
team_policy eval average team episode rewards of agent1: 20.0
team_policy eval idv catch total num of agent1: 12
team_policy eval team catch total num: 8
team_policy eval average step individual rewards of agent2: 0.06550659198171607
team_policy eval average team episode rewards of agent2: 20.0
team_policy eval idv catch total num of agent2: 5
team_policy eval team catch total num: 8
team_policy eval average step individual rewards of agent3: 0.033312950070042
team_policy eval average team episode rewards of agent3: 20.0
team_policy eval idv catch total num of agent3: 4
team_policy eval team catch total num: 8
team_policy eval average step individual rewards of agent4: 0.034115906596316366
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: 0.4056209459019501
idv_policy eval average team episode rewards of agent0: 70.0
idv_policy eval idv catch total num of agent0: 18
idv_policy eval team catch total num: 28
idv_policy eval average step individual rewards of agent1: 0.5685065548002107
idv_policy eval average team episode rewards of agent1: 70.0
idv_policy eval idv catch total num of agent1: 24
idv_policy eval team catch total num: 28
idv_policy eval average step individual rewards of agent2: 0.6854511051444381
idv_policy eval average team episode rewards of agent2: 70.0
idv_policy eval idv catch total num of agent2: 29
idv_policy eval team catch total num: 28
idv_policy eval average step individual rewards of agent3: 0.632171838330099
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: 0.482504536680528
idv_policy eval average team episode rewards of agent4: 70.0
idv_policy eval idv catch total num of agent4: 21
idv_policy eval team catch total num: 28

 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.

team_policy eval average step individual rewards of agent0: 0.5103593123108482
team_policy eval average team episode rewards of agent0: 55.0
team_policy eval idv catch total num of agent0: 22
team_policy eval team catch total num: 22
team_policy eval average step individual rewards of agent1: 0.4122105579428802
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: 0.4858951652129179
team_policy eval average team episode rewards of agent2: 55.0
team_policy eval idv catch total num of agent2: 21
team_policy eval team catch total num: 22
team_policy eval average step individual rewards of agent3: 0.4373555316551711
team_policy eval average team episode rewards of agent3: 55.0
team_policy eval idv catch total num of agent3: 19
team_policy eval team catch total num: 22
team_policy eval average step individual rewards of agent4: 0.28361010836585626
team_policy eval average team episode rewards of agent4: 55.0
team_policy eval idv catch total num of agent4: 13
team_policy eval team catch total num: 22
idv_policy eval average step individual rewards of agent0: 0.27871284290208764
idv_policy eval average team episode rewards of agent0: 75.0
idv_policy eval idv catch total num of agent0: 13
idv_policy eval team catch total num: 30
idv_policy eval average step individual rewards of agent1: 0.5894994943745525
idv_policy eval average team episode rewards of agent1: 75.0
idv_policy eval idv catch total num of agent1: 25
idv_policy eval team catch total num: 30
idv_policy eval average step individual rewards of agent2: 0.7428416685596307
idv_policy eval average team episode rewards of agent2: 75.0
idv_policy eval idv catch total num of agent2: 31
idv_policy eval team catch total num: 30
idv_policy eval average step individual rewards of agent3: 0.4497307005031276
idv_policy eval average team episode rewards of agent3: 75.0
idv_policy eval idv catch total num of agent3: 20
idv_policy eval team catch total num: 30
idv_policy eval average step individual rewards of agent4: 0.5599300857501117
idv_policy eval average team episode rewards of agent4: 75.0
idv_policy eval idv catch total num of agent4: 24
idv_policy eval team catch total num: 30

 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.

team_policy eval average step individual rewards of agent0: 0.8218397698756966
team_policy eval average team episode rewards of agent0: 85.0
team_policy eval idv catch total num of agent0: 34
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent1: 0.5457610919729784
team_policy eval average team episode rewards of agent1: 85.0
team_policy eval idv catch total num of agent1: 24
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent2: 0.4790176726984889
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: 0.5324753763162777
team_policy eval average team episode rewards of agent3: 85.0
team_policy eval idv catch total num of agent3: 23
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent4: 0.45089135829693716
team_policy eval average team episode rewards of agent4: 85.0
team_policy eval idv catch total num of agent4: 20
team_policy eval team catch total num: 34
idv_policy eval average step individual rewards of agent0: 0.7801913499364705
idv_policy eval average team episode rewards of agent0: 67.5
idv_policy eval idv catch total num of agent0: 33
idv_policy eval team catch total num: 27
idv_policy eval average step individual rewards of agent1: 0.7060621402664634
idv_policy eval average team episode rewards of agent1: 67.5
idv_policy eval idv catch total num of agent1: 30
idv_policy eval team catch total num: 27
idv_policy eval average step individual rewards of agent2: 0.5584610169854537
idv_policy eval average team episode rewards of agent2: 67.5
idv_policy eval idv catch total num of agent2: 24
idv_policy eval team catch total num: 27
idv_policy eval average step individual rewards of agent3: 0.34677980581588264
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: 0.48171159683380027
idv_policy eval average team episode rewards of agent4: 67.5
idv_policy eval idv catch total num of agent4: 21
idv_policy eval team catch total num: 27

 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.

team_policy eval average step individual rewards of agent0: 0.4546113691097531
team_policy eval average team episode rewards of agent0: 57.5
team_policy eval idv catch total num of agent0: 20
team_policy eval team catch total num: 23
team_policy eval average step individual rewards of agent1: 0.8098890147247993
team_policy eval average team episode rewards of agent1: 57.5
team_policy eval idv catch total num of agent1: 34
team_policy eval team catch total num: 23
team_policy eval average step individual rewards of agent2: 0.762327789735415
team_policy eval average team episode rewards of agent2: 57.5
team_policy eval idv catch total num of agent2: 32
team_policy eval team catch total num: 23
team_policy eval average step individual rewards of agent3: 0.22535705604620815
team_policy eval average team episode rewards of agent3: 57.5
team_policy eval idv catch total num of agent3: 11
team_policy eval team catch total num: 23
team_policy eval average step individual rewards of agent4: 0.3778588391878827
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: 0.20195379234288618
idv_policy eval average team episode rewards of agent0: 67.5
idv_policy eval idv catch total num of agent0: 10
idv_policy eval team catch total num: 27
idv_policy eval average step individual rewards of agent1: 0.5347104075908199
idv_policy eval average team episode rewards of agent1: 67.5
idv_policy eval idv catch total num of agent1: 23
idv_policy eval team catch total num: 27
idv_policy eval average step individual rewards of agent2: 0.6065614318840414
idv_policy eval average team episode rewards of agent2: 67.5
idv_policy eval idv catch total num of agent2: 26
idv_policy eval team catch total num: 27
idv_policy eval average step individual rewards of agent3: 0.5824287947832287
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: 0.5119946588809657
idv_policy eval average team episode rewards of agent4: 67.5
idv_policy eval idv catch total num of agent4: 22
idv_policy eval team catch total num: 27

 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.

team_policy eval average step individual rewards of agent0: 0.7165797855074616
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: 0.7859432969703911
team_policy eval average team episode rewards of agent1: 77.5
team_policy eval idv catch total num of agent1: 33
team_policy eval team catch total num: 31
team_policy eval average step individual rewards of agent2: 0.4116461200975565
team_policy eval average team episode rewards of agent2: 77.5
team_policy eval idv catch total num of agent2: 18
team_policy eval team catch total num: 31
team_policy eval average step individual rewards of agent3: 0.5227223900356959
team_policy eval average team episode rewards of agent3: 77.5
team_policy eval idv catch total num of agent3: 23
team_policy eval team catch total num: 31
team_policy eval average step individual rewards of agent4: 0.3366583133801141
team_policy eval average team episode rewards of agent4: 77.5
team_policy eval idv catch total num of agent4: 15
team_policy eval team catch total num: 31
idv_policy eval average step individual rewards of agent0: 0.66715182252657
idv_policy eval average team episode rewards of agent0: 52.5
idv_policy eval idv catch total num of agent0: 28
idv_policy eval team catch total num: 21
idv_policy eval average step individual rewards of agent1: 0.6111413646795677
idv_policy eval average team episode rewards of agent1: 52.5
idv_policy eval idv catch total num of agent1: 26
idv_policy eval team catch total num: 21
idv_policy eval average step individual rewards of agent2: 0.5417710440639512
idv_policy eval average team episode rewards of agent2: 52.5
idv_policy eval idv catch total num of agent2: 23
idv_policy eval team catch total num: 21
idv_policy eval average step individual rewards of agent3: 0.31292833335548753
idv_policy eval average team episode rewards of agent3: 52.5
idv_policy eval idv catch total num of agent3: 14
idv_policy eval team catch total num: 21
idv_policy eval average step individual rewards of agent4: 0.18122901870139416
idv_policy eval average team episode rewards of agent4: 52.5
idv_policy eval idv catch total num of agent4: 9
idv_policy eval team catch total num: 21

 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.

team_policy eval average step individual rewards of agent0: 0.3742901232651418
team_policy eval average team episode rewards of agent0: 70.0
team_policy eval idv catch total num of agent0: 17
team_policy eval team catch total num: 28
team_policy eval average step individual rewards of agent1: 0.7809392555964423
team_policy eval average team episode rewards of agent1: 70.0
team_policy eval idv catch total num of agent1: 33
team_policy eval team catch total num: 28
team_policy eval average step individual rewards of agent2: 0.357260540929491
team_policy eval average team episode rewards of agent2: 70.0
team_policy eval idv catch total num of agent2: 16
team_policy eval team catch total num: 28
team_policy eval average step individual rewards of agent3: 0.4623572105769185
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: 0.34039972071249686
team_policy eval average team episode rewards of agent4: 70.0
team_policy eval idv catch total num of agent4: 15
team_policy eval team catch total num: 28
idv_policy eval average step individual rewards of agent0: 0.5785581875729026
idv_policy eval average team episode rewards of agent0: 60.0
idv_policy eval idv catch total num of agent0: 25
idv_policy eval team catch total num: 24
idv_policy eval average step individual rewards of agent1: 0.3843108227729606
idv_policy eval average team episode rewards of agent1: 60.0
idv_policy eval idv catch total num of agent1: 17
idv_policy eval team catch total num: 24
idv_policy eval average step individual rewards of agent2: 0.3855938907955327
idv_policy eval average team episode rewards of agent2: 60.0
idv_policy eval idv catch total num of agent2: 17
idv_policy eval team catch total num: 24
idv_policy eval average step individual rewards of agent3: 0.37722381202572647
idv_policy eval average team episode rewards of agent3: 60.0
idv_policy eval idv catch total num of agent3: 17
idv_policy eval team catch total num: 24
idv_policy eval average step individual rewards of agent4: 0.08281688333899108
idv_policy eval average team episode rewards of agent4: 60.0
idv_policy eval idv catch total num of agent4: 5
idv_policy eval team catch total num: 24

 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.


 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 208.

team_policy eval average step individual rewards of agent0: 0.487742421486611
team_policy eval average team episode rewards of agent0: 77.5
team_policy eval idv catch total num of agent0: 21
team_policy eval team catch total num: 31
team_policy eval average step individual rewards of agent1: 0.7644347683205476
team_policy eval average team episode rewards of agent1: 77.5
team_policy eval idv catch total num of agent1: 32
team_policy eval team catch total num: 31
team_policy eval average step individual rewards of agent2: 0.247803091799113
team_policy eval average team episode rewards of agent2: 77.5
team_policy eval idv catch total num of agent2: 12
team_policy eval team catch total num: 31
team_policy eval average step individual rewards of agent3: 0.5856673079908886
team_policy eval average team episode rewards of agent3: 77.5
team_policy eval idv catch total num of agent3: 25
team_policy eval team catch total num: 31
team_policy eval average step individual rewards of agent4: 0.44860998324535695
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: 0.4258810556272757
idv_policy eval average team episode rewards of agent0: 95.0
idv_policy eval idv catch total num of agent0: 19
idv_policy eval team catch total num: 38
idv_policy eval average step individual rewards of agent1: 0.5798763268945951
idv_policy eval average team episode rewards of agent1: 95.0
idv_policy eval idv catch total num of agent1: 25
idv_policy eval team catch total num: 38
idv_policy eval average step individual rewards of agent2: 0.6064300911390202
idv_policy eval average team episode rewards of agent2: 95.0
idv_policy eval idv catch total num of agent2: 26
idv_policy eval team catch total num: 38
idv_policy eval average step individual rewards of agent3: 0.9496175076962052
idv_policy eval average team episode rewards of agent3: 95.0
idv_policy eval idv catch total num of agent3: 39
idv_policy eval team catch total num: 38
idv_policy eval average step individual rewards of agent4: 0.8147690705095263
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 2701/10000 episodes, total num timesteps 540400/2000000, FPS 208.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.

team_policy eval average step individual rewards of agent0: 0.34336786539908354
team_policy eval average team episode rewards of agent0: 47.5
team_policy eval idv catch total num of agent0: 16
team_policy eval team catch total num: 19
team_policy eval average step individual rewards of agent1: 0.5612497890002769
team_policy eval average team episode rewards of agent1: 47.5
team_policy eval idv catch total num of agent1: 24
team_policy eval team catch total num: 19
team_policy eval average step individual rewards of agent2: 0.3577731538974645
team_policy eval average team episode rewards of agent2: 47.5
team_policy eval idv catch total num of agent2: 16
team_policy eval team catch total num: 19
team_policy eval average step individual rewards of agent3: 0.3534153249342394
team_policy eval average team episode rewards of agent3: 47.5
team_policy eval idv catch total num of agent3: 16
team_policy eval team catch total num: 19
team_policy eval average step individual rewards of agent4: 0.1928500896562791
team_policy eval average team episode rewards of agent4: 47.5
team_policy eval idv catch total num of agent4: 10
team_policy eval team catch total num: 19
idv_policy eval average step individual rewards of agent0: 0.4790775730353822
idv_policy eval average team episode rewards of agent0: 47.5
idv_policy eval idv catch total num of agent0: 21
idv_policy eval team catch total num: 19
idv_policy eval average step individual rewards of agent1: 0.3145970078323076
idv_policy eval average team episode rewards of agent1: 47.5
idv_policy eval idv catch total num of agent1: 14
idv_policy eval team catch total num: 19
idv_policy eval average step individual rewards of agent2: 0.5411699799335452
idv_policy eval average team episode rewards of agent2: 47.5
idv_policy eval idv catch total num of agent2: 23
idv_policy eval team catch total num: 19
idv_policy eval average step individual rewards of agent3: 0.5617338899677761
idv_policy eval average team episode rewards of agent3: 47.5
idv_policy eval idv catch total num of agent3: 24
idv_policy eval team catch total num: 19
idv_policy eval average step individual rewards of agent4: 0.16215227231171173
idv_policy eval average team episode rewards of agent4: 47.5
idv_policy eval idv catch total num of agent4: 8
idv_policy eval team catch total num: 19

 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.

team_policy eval average step individual rewards of agent0: 0.6910229746208671
team_policy eval average team episode rewards of agent0: 80.0
team_policy eval idv catch total num of agent0: 29
team_policy eval team catch total num: 32
team_policy eval average step individual rewards of agent1: 0.5891743487937336
team_policy eval average team episode rewards of agent1: 80.0
team_policy eval idv catch total num of agent1: 25
team_policy eval team catch total num: 32
team_policy eval average step individual rewards of agent2: 0.5834293093511951
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: 0.5358729899478571
team_policy eval average team episode rewards of agent3: 80.0
team_policy eval idv catch total num of agent3: 23
team_policy eval team catch total num: 32
team_policy eval average step individual rewards of agent4: 0.4160325137237457
team_policy eval average team episode rewards of agent4: 80.0
team_policy eval idv catch total num of agent4: 18
team_policy eval team catch total num: 32
idv_policy eval average step individual rewards of agent0: 0.8202495740922459
idv_policy eval average team episode rewards of agent0: 127.5
idv_policy eval idv catch total num of agent0: 34
idv_policy eval team catch total num: 51
idv_policy eval average step individual rewards of agent1: 0.8889520444667522
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: 0.9180697740810777
idv_policy eval average team episode rewards of agent2: 127.5
idv_policy eval idv catch total num of agent2: 38
idv_policy eval team catch total num: 51
idv_policy eval average step individual rewards of agent3: 1.1442597159639292
idv_policy eval average team episode rewards of agent3: 127.5
idv_policy eval idv catch total num of agent3: 47
idv_policy eval team catch total num: 51
idv_policy eval average step individual rewards of agent4: 0.9668937282219605
idv_policy eval average team episode rewards of agent4: 127.5
idv_policy eval idv catch total num of agent4: 40
idv_policy eval team catch total num: 51

 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.

team_policy eval average step individual rewards of agent0: 0.7912645768595239
team_policy eval average team episode rewards of agent0: 60.0
team_policy eval idv catch total num of agent0: 33
team_policy eval team catch total num: 24
team_policy eval average step individual rewards of agent1: 0.2595988858247133
team_policy eval average team episode rewards of agent1: 60.0
team_policy eval idv catch total num of agent1: 12
team_policy eval team catch total num: 24
team_policy eval average step individual rewards of agent2: 0.4840930888370744
team_policy eval average team episode rewards of agent2: 60.0
team_policy eval idv catch total num of agent2: 21
team_policy eval team catch total num: 24
team_policy eval average step individual rewards of agent3: 0.2769241548767633
team_policy eval average team episode rewards of agent3: 60.0
team_policy eval idv catch total num of agent3: 13
team_policy eval team catch total num: 24
team_policy eval average step individual rewards of agent4: 0.6661701264191664
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: 0.8375785931548779
idv_policy eval average team episode rewards of agent0: 100.0
idv_policy eval idv catch total num of agent0: 35
idv_policy eval team catch total num: 40
idv_policy eval average step individual rewards of agent1: 0.5010036880788686
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: 0.60213370744127
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: 0.5595981992622621
idv_policy eval average team episode rewards of agent3: 100.0
idv_policy eval idv catch total num of agent3: 24
idv_policy eval team catch total num: 40
idv_policy eval average step individual rewards of agent4: 0.5897315698882705
idv_policy eval average team episode rewards of agent4: 100.0
idv_policy eval idv catch total num of agent4: 25
idv_policy eval team catch total num: 40

 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.

team_policy eval average step individual rewards of agent0: 0.6845548729592081
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: 0.8180765263899391
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: 0.44969744563647146
team_policy eval average team episode rewards of agent2: 85.0
team_policy eval idv catch total num of agent2: 20
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent3: 0.7623027820606074
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: 0.6626476269596472
team_policy eval average team episode rewards of agent4: 85.0
team_policy eval idv catch total num of agent4: 28
team_policy eval team catch total num: 34
idv_policy eval average step individual rewards of agent0: 0.5146744389863344
idv_policy eval average team episode rewards of agent0: 92.5
idv_policy eval idv catch total num of agent0: 22
idv_policy eval team catch total num: 37
idv_policy eval average step individual rewards of agent1: 0.6300565821766942
idv_policy eval average team episode rewards of agent1: 92.5
idv_policy eval idv catch total num of agent1: 27
idv_policy eval team catch total num: 37
idv_policy eval average step individual rewards of agent2: 0.42490871333067
idv_policy eval average team episode rewards of agent2: 92.5
idv_policy eval idv catch total num of agent2: 19
idv_policy eval team catch total num: 37
idv_policy eval average step individual rewards of agent3: 1.0185633904732976
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: 1.0196163302277184
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 2801/10000 episodes, total num timesteps 560400/2000000, FPS 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.

team_policy eval average step individual rewards of agent0: 0.5510329102298217
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: 0.7886653688358005
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: 1.3730474641783088
team_policy eval average team episode rewards of agent2: 92.5
team_policy eval idv catch total num of agent2: 56
team_policy eval team catch total num: 37
team_policy eval average step individual rewards of agent3: 0.48717782916621033
team_policy eval average team episode rewards of agent3: 92.5
team_policy eval idv catch total num of agent3: 21
team_policy eval team catch total num: 37
team_policy eval average step individual rewards of agent4: 0.4494325110458011
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: 0.5182090794587859
idv_policy eval average team episode rewards of agent0: 130.0
idv_policy eval idv catch total num of agent0: 22
idv_policy eval team catch total num: 52
idv_policy eval average step individual rewards of agent1: 1.0178216289927848
idv_policy eval average team episode rewards of agent1: 130.0
idv_policy eval idv catch total num of agent1: 42
idv_policy eval team catch total num: 52
idv_policy eval average step individual rewards of agent2: 0.7419026391607525
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: 1.1642472493502438
idv_policy eval average team episode rewards of agent3: 130.0
idv_policy eval idv catch total num of agent3: 48
idv_policy eval team catch total num: 52
idv_policy eval average step individual rewards of agent4: 1.1489102592137486
idv_policy eval average team episode rewards of agent4: 130.0
idv_policy eval idv catch total num of agent4: 47
idv_policy eval team catch total num: 52

 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.

team_policy eval average step individual rewards of agent0: 0.20303500932043647
team_policy eval average team episode rewards of agent0: 65.0
team_policy eval idv catch total num of agent0: 10
team_policy eval team catch total num: 26
team_policy eval average step individual rewards of agent1: 0.23006235281326062
team_policy eval average team episode rewards of agent1: 65.0
team_policy eval idv catch total num of agent1: 11
team_policy eval team catch total num: 26
team_policy eval average step individual rewards of agent2: 0.6610394235203786
team_policy eval average team episode rewards of agent2: 65.0
team_policy eval idv catch total num of agent2: 28
team_policy eval team catch total num: 26
team_policy eval average step individual rewards of agent3: 0.5879507123101516
team_policy eval average team episode rewards of agent3: 65.0
team_policy eval idv catch total num of agent3: 25
team_policy eval team catch total num: 26
team_policy eval average step individual rewards of agent4: 0.5854092838594408
team_policy eval average team episode rewards of agent4: 65.0
team_policy eval idv catch total num of agent4: 25
team_policy eval team catch total num: 26
idv_policy eval average step individual rewards of agent0: 0.8156613003378793
idv_policy eval average team episode rewards of agent0: 75.0
idv_policy eval idv catch total num of agent0: 34
idv_policy eval team catch total num: 30
idv_policy eval average step individual rewards of agent1: 0.48652527006558616
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: 0.6604068071453579
idv_policy eval average team episode rewards of agent2: 75.0
idv_policy eval idv catch total num of agent2: 28
idv_policy eval team catch total num: 30
idv_policy eval average step individual rewards of agent3: 0.5523753354693827
idv_policy eval average team episode rewards of agent3: 75.0
idv_policy eval idv catch total num of agent3: 24
idv_policy eval team catch total num: 30
idv_policy eval average step individual rewards of agent4: 0.39883210261392116
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 2851/10000 episodes, total num timesteps 570400/2000000, FPS 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.

team_policy eval average step individual rewards of agent0: 0.6702586695759599
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: 0.9929549080649949
team_policy eval average team episode rewards of agent1: 115.0
team_policy eval idv catch total num of agent1: 41
team_policy eval team catch total num: 46
team_policy eval average step individual rewards of agent2: 1.0197618198260188
team_policy eval average team episode rewards of agent2: 115.0
team_policy eval idv catch total num of agent2: 42
team_policy eval team catch total num: 46
team_policy eval average step individual rewards of agent3: 0.6582913956247802
team_policy eval average team episode rewards of agent3: 115.0
team_policy eval idv catch total num of agent3: 28
team_policy eval team catch total num: 46
team_policy eval average step individual rewards of agent4: 0.6622364576273134
team_policy eval average team episode rewards of agent4: 115.0
team_policy eval idv catch total num of agent4: 28
team_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent0: 0.47865270798326165
idv_policy eval average team episode rewards of agent0: 67.5
idv_policy eval idv catch total num of agent0: 21
idv_policy eval team catch total num: 27
idv_policy eval average step individual rewards of agent1: 0.9207670271277847
idv_policy eval average team episode rewards of agent1: 67.5
idv_policy eval idv catch total num of agent1: 38
idv_policy eval team catch total num: 27
idv_policy eval average step individual rewards of agent2: 0.6353200167491299
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: 0.386729480304282
idv_policy eval average team episode rewards of agent3: 67.5
idv_policy eval idv catch total num of agent3: 17
idv_policy eval team catch total num: 27
idv_policy eval average step individual rewards of agent4: 0.4996102366909277
idv_policy eval average team episode rewards of agent4: 67.5
idv_policy eval idv catch total num of agent4: 22
idv_policy eval team catch total num: 27

 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.

team_policy eval average step individual rewards of agent0: 1.090978914122931
team_policy eval average team episode rewards of agent0: 135.0
team_policy eval idv catch total num of agent0: 45
team_policy eval team catch total num: 54
team_policy eval average step individual rewards of agent1: 0.8121838796937769
team_policy eval average team episode rewards of agent1: 135.0
team_policy eval idv catch total num of agent1: 34
team_policy eval team catch total num: 54
team_policy eval average step individual rewards of agent2: 0.585196287449513
team_policy eval average team episode rewards of agent2: 135.0
team_policy eval idv catch total num of agent2: 25
team_policy eval team catch total num: 54
team_policy eval average step individual rewards of agent3: 1.0469139852739362
team_policy eval average team episode rewards of agent3: 135.0
team_policy eval idv catch total num of agent3: 43
team_policy eval team catch total num: 54
team_policy eval average step individual rewards of agent4: 0.9649120118291593
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: 0.3686888610226564
idv_policy eval average team episode rewards of agent0: 62.5
idv_policy eval idv catch total num of agent0: 17
idv_policy eval team catch total num: 25
idv_policy eval average step individual rewards of agent1: 0.4188262540090168
idv_policy eval average team episode rewards of agent1: 62.5
idv_policy eval idv catch total num of agent1: 19
idv_policy eval team catch total num: 25
idv_policy eval average step individual rewards of agent2: 0.5767219102265527
idv_policy eval average team episode rewards of agent2: 62.5
idv_policy eval idv catch total num of agent2: 25
idv_policy eval team catch total num: 25
idv_policy eval average step individual rewards of agent3: 0.4683774020358614
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: 0.5774289453785468
idv_policy eval average team episode rewards of agent4: 62.5
idv_policy eval idv catch total num of agent4: 25
idv_policy eval team catch total num: 25

 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 210.


 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 209.

team_policy eval average step individual rewards of agent0: 0.40947573759411676
team_policy eval average team episode rewards of agent0: 112.5
team_policy eval idv catch total num of agent0: 18
team_policy eval team catch total num: 45
team_policy eval average step individual rewards of agent1: 0.9225735055680957
team_policy eval average team episode rewards of agent1: 112.5
team_policy eval idv catch total num of agent1: 38
team_policy eval team catch total num: 45
team_policy eval average step individual rewards of agent2: 1.2515128633415105
team_policy eval average team episode rewards of agent2: 112.5
team_policy eval idv catch total num of agent2: 51
team_policy eval team catch total num: 45
team_policy eval average step individual rewards of agent3: 0.8171071174406244
team_policy eval average team episode rewards of agent3: 112.5
team_policy eval idv catch total num of agent3: 34
team_policy eval team catch total num: 45
team_policy eval average step individual rewards of agent4: 0.5906174918462751
team_policy eval average team episode rewards of agent4: 112.5
team_policy eval idv catch total num of agent4: 25
team_policy eval team catch total num: 45
idv_policy eval average step individual rewards of agent0: 0.8933975731489529
idv_policy eval average team episode rewards of agent0: 122.5
idv_policy eval idv catch total num of agent0: 37
idv_policy eval team catch total num: 49
idv_policy eval average step individual rewards of agent1: 0.9406228447214591
idv_policy eval average team episode rewards of agent1: 122.5
idv_policy eval idv catch total num of agent1: 39
idv_policy eval team catch total num: 49
idv_policy eval average step individual rewards of agent2: 1.0667846426309706
idv_policy eval average team episode rewards of agent2: 122.5
idv_policy eval idv catch total num of agent2: 44
idv_policy eval team catch total num: 49
idv_policy eval average step individual rewards of agent3: 0.7370027613720762
idv_policy eval average team episode rewards of agent3: 122.5
idv_policy eval idv catch total num of agent3: 31
idv_policy eval team catch total num: 49
idv_policy eval average step individual rewards of agent4: 0.6639660451515244
idv_policy eval average team episode rewards of agent4: 122.5
idv_policy eval idv catch total num of agent4: 28
idv_policy eval team catch total num: 49

 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 209.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.

team_policy eval average step individual rewards of agent0: 0.9445771390560558
team_policy eval average team episode rewards of agent0: 100.0
team_policy eval idv catch total num of agent0: 40
team_policy eval team catch total num: 40
team_policy eval average step individual rewards of agent1: 1.0391887993543714
team_policy eval average team episode rewards of agent1: 100.0
team_policy eval idv catch total num of agent1: 43
team_policy eval team catch total num: 40
team_policy eval average step individual rewards of agent2: 0.761550954495879
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: 0.6553090296075623
team_policy eval average team episode rewards of agent3: 100.0
team_policy eval idv catch total num of agent3: 28
team_policy eval team catch total num: 40
team_policy eval average step individual rewards of agent4: 0.6621006116699846
team_policy eval average team episode rewards of agent4: 100.0
team_policy eval idv catch total num of agent4: 28
team_policy eval team catch total num: 40
idv_policy eval average step individual rewards of agent0: 0.8024092621552845
idv_policy eval average team episode rewards of agent0: 107.5
idv_policy eval idv catch total num of agent0: 34
idv_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent1: 0.9502140059680619
idv_policy eval average team episode rewards of agent1: 107.5
idv_policy eval idv catch total num of agent1: 39
idv_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent2: 0.6834409722282065
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: 0.7392734555747944
idv_policy eval average team episode rewards of agent3: 107.5
idv_policy eval idv catch total num of agent3: 31
idv_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent4: 0.7710610526475042
idv_policy eval average team episode rewards of agent4: 107.5
idv_policy eval idv catch total num of agent4: 32
idv_policy eval team catch total num: 43

 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.

team_policy eval average step individual rewards of agent0: 0.5189879665661676
team_policy eval average team episode rewards of agent0: 67.5
team_policy eval idv catch total num of agent0: 23
team_policy eval team catch total num: 27
team_policy eval average step individual rewards of agent1: 0.3208876262102159
team_policy eval average team episode rewards of agent1: 67.5
team_policy eval idv catch total num of agent1: 15
team_policy eval team catch total num: 27
team_policy eval average step individual rewards of agent2: 0.41700336799753834
team_policy eval average team episode rewards of agent2: 67.5
team_policy eval idv catch total num of agent2: 19
team_policy eval team catch total num: 27
team_policy eval average step individual rewards of agent3: 0.45586090302519067
team_policy eval average team episode rewards of agent3: 67.5
team_policy eval idv catch total num of agent3: 20
team_policy eval team catch total num: 27
team_policy eval average step individual rewards of agent4: 0.7536950047338677
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: 1.1640253021093618
idv_policy eval average team episode rewards of agent0: 130.0
idv_policy eval idv catch total num of agent0: 48
idv_policy eval team catch total num: 52
idv_policy eval average step individual rewards of agent1: 0.7254788961649481
idv_policy eval average team episode rewards of agent1: 130.0
idv_policy eval idv catch total num of agent1: 31
idv_policy eval team catch total num: 52
idv_policy eval average step individual rewards of agent2: 0.9447057469567351
idv_policy eval average team episode rewards of agent2: 130.0
idv_policy eval idv catch total num of agent2: 39
idv_policy eval team catch total num: 52
idv_policy eval average step individual rewards of agent3: 0.7437640293025107
idv_policy eval average team episode rewards of agent3: 130.0
idv_policy eval idv catch total num of agent3: 31
idv_policy eval team catch total num: 52
idv_policy eval average step individual rewards of agent4: 1.1692315363232295
idv_policy eval average team episode rewards of agent4: 130.0
idv_policy eval idv catch total num of agent4: 48
idv_policy eval team catch total num: 52

 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.

team_policy eval average step individual rewards of agent0: 1.2481653674575934
team_policy eval average team episode rewards of agent0: 100.0
team_policy eval idv catch total num of agent0: 51
team_policy eval team catch total num: 40
team_policy eval average step individual rewards of agent1: 0.5849469923073457
team_policy eval average team episode rewards of agent1: 100.0
team_policy eval idv catch total num of agent1: 25
team_policy eval team catch total num: 40
team_policy eval average step individual rewards of agent2: 0.6797065284273163
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: 0.7093729996455471
team_policy eval average team episode rewards of agent3: 100.0
team_policy eval idv catch total num of agent3: 30
team_policy eval team catch total num: 40
team_policy eval average step individual rewards of agent4: 0.5782689962151494
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: 0.5333456817657359
idv_policy eval average team episode rewards of agent0: 80.0
idv_policy eval idv catch total num of agent0: 23
idv_policy eval team catch total num: 32
idv_policy eval average step individual rewards of agent1: 0.8288157154672142
idv_policy eval average team episode rewards of agent1: 80.0
idv_policy eval idv catch total num of agent1: 35
idv_policy eval team catch total num: 32
idv_policy eval average step individual rewards of agent2: 0.4042811732564847
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: 0.5585934520833747
idv_policy eval average team episode rewards of agent3: 80.0
idv_policy eval idv catch total num of agent3: 24
idv_policy eval team catch total num: 32
idv_policy eval average step individual rewards of agent4: 0.27604910311514225
idv_policy eval average team episode rewards of agent4: 80.0
idv_policy eval idv catch total num of agent4: 13
idv_policy eval team catch total num: 32

 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.

team_policy eval average step individual rewards of agent0: 0.6684633626889684
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: 0.3086253320155989
team_policy eval average team episode rewards of agent1: 87.5
team_policy eval idv catch total num of agent1: 14
team_policy eval team catch total num: 35
team_policy eval average step individual rewards of agent2: 0.919119447084978
team_policy eval average team episode rewards of agent2: 87.5
team_policy eval idv catch total num of agent2: 38
team_policy eval team catch total num: 35
team_policy eval average step individual rewards of agent3: 0.8938566369334653
team_policy eval average team episode rewards of agent3: 87.5
team_policy eval idv catch total num of agent3: 37
team_policy eval team catch total num: 35
team_policy eval average step individual rewards of agent4: 0.6571681465139946
team_policy eval average team episode rewards of agent4: 87.5
team_policy eval idv catch total num of agent4: 28
team_policy eval team catch total num: 35
idv_policy eval average step individual rewards of agent0: 0.399418334147438
idv_policy eval average team episode rewards of agent0: 40.0
idv_policy eval idv catch total num of agent0: 18
idv_policy eval team catch total num: 16
idv_policy eval average step individual rewards of agent1: 0.3708545129504995
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: 0.6530679635509935
idv_policy eval average team episode rewards of agent2: 40.0
idv_policy eval idv catch total num of agent2: 28
idv_policy eval team catch total num: 16
idv_policy eval average step individual rewards of agent3: 0.03435379865324558
idv_policy eval average team episode rewards of agent3: 40.0
idv_policy eval idv catch total num of agent3: 4
idv_policy eval team catch total num: 16
idv_policy eval average step individual rewards of agent4: 0.4142657997660742
idv_policy eval average team episode rewards of agent4: 40.0
idv_policy eval idv catch total num of agent4: 19
idv_policy eval team catch total num: 16

 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.

team_policy eval average step individual rewards of agent0: 0.740464987846637
team_policy eval average team episode rewards of agent0: 97.5
team_policy eval idv catch total num of agent0: 31
team_policy eval team catch total num: 39
team_policy eval average step individual rewards of agent1: 0.7304669894810921
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: 0.9686687782202503
team_policy eval average team episode rewards of agent2: 97.5
team_policy eval idv catch total num of agent2: 40
team_policy eval team catch total num: 39
team_policy eval average step individual rewards of agent3: 0.8602418421535246
team_policy eval average team episode rewards of agent3: 97.5
team_policy eval idv catch total num of agent3: 36
team_policy eval team catch total num: 39
team_policy eval average step individual rewards of agent4: 0.6038461976950231
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: 0.9010955566970213
idv_policy eval average team episode rewards of agent0: 147.5
idv_policy eval idv catch total num of agent0: 38
idv_policy eval team catch total num: 59
idv_policy eval average step individual rewards of agent1: 0.8841418142066079
idv_policy eval average team episode rewards of agent1: 147.5
idv_policy eval idv catch total num of agent1: 37
idv_policy eval team catch total num: 59
idv_policy eval average step individual rewards of agent2: 1.0913905766319905
idv_policy eval average team episode rewards of agent2: 147.5
idv_policy eval idv catch total num of agent2: 45
idv_policy eval team catch total num: 59
idv_policy eval average step individual rewards of agent3: 0.8673194911683255
idv_policy eval average team episode rewards of agent3: 147.5
idv_policy eval idv catch total num of agent3: 36
idv_policy eval team catch total num: 59
idv_policy eval average step individual rewards of agent4: 0.9420387655164392
idv_policy eval average team episode rewards of agent4: 147.5
idv_policy eval idv catch total num of agent4: 39
idv_policy eval team catch total num: 59

 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.

team_policy eval average step individual rewards of agent0: 0.3574016874425771
team_policy eval average team episode rewards of agent0: 85.0
team_policy eval idv catch total num of agent0: 16
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent1: 0.8178337013588383
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: 0.5874515749630231
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: 0.7700444338059494
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: 0.5359448038971477
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: 1.2860142282618767
idv_policy eval average team episode rewards of agent0: 130.0
idv_policy eval idv catch total num of agent0: 53
idv_policy eval team catch total num: 52
idv_policy eval average step individual rewards of agent1: 0.714043440522855
idv_policy eval average team episode rewards of agent1: 130.0
idv_policy eval idv catch total num of agent1: 30
idv_policy eval team catch total num: 52
idv_policy eval average step individual rewards of agent2: 0.8430518412139401
idv_policy eval average team episode rewards of agent2: 130.0
idv_policy eval idv catch total num of agent2: 35
idv_policy eval team catch total num: 52
idv_policy eval average step individual rewards of agent3: 1.0889205652974077
idv_policy eval average team episode rewards of agent3: 130.0
idv_policy eval idv catch total num of agent3: 45
idv_policy eval team catch total num: 52
idv_policy eval average step individual rewards of agent4: 0.9166983902830494
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 3076/10000 episodes, total num timesteps 615400/2000000, FPS 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.

team_policy eval average step individual rewards of agent0: 1.061885489826895
team_policy eval average team episode rewards of agent0: 102.5
team_policy eval idv catch total num of agent0: 44
team_policy eval team catch total num: 41
team_policy eval average step individual rewards of agent1: 0.7644024767725827
team_policy eval average team episode rewards of agent1: 102.5
team_policy eval idv catch total num of agent1: 32
team_policy eval team catch total num: 41
team_policy eval average step individual rewards of agent2: 0.5111759417941448
team_policy eval average team episode rewards of agent2: 102.5
team_policy eval idv catch total num of agent2: 22
team_policy eval team catch total num: 41
team_policy eval average step individual rewards of agent3: 0.5120015620379186
team_policy eval average team episode rewards of agent3: 102.5
team_policy eval idv catch total num of agent3: 22
team_policy eval team catch total num: 41
team_policy eval average step individual rewards of agent4: 0.7158310640282605
team_policy eval average team episode rewards of agent4: 102.5
team_policy eval idv catch total num of agent4: 30
team_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent0: 1.0465219384236237
idv_policy eval average team episode rewards of agent0: 95.0
idv_policy eval idv catch total num of agent0: 43
idv_policy eval team catch total num: 38
idv_policy eval average step individual rewards of agent1: 0.3225374459452856
idv_policy eval average team episode rewards of agent1: 95.0
idv_policy eval idv catch total num of agent1: 15
idv_policy eval team catch total num: 38
idv_policy eval average step individual rewards of agent2: 0.6120759607493327
idv_policy eval average team episode rewards of agent2: 95.0
idv_policy eval idv catch total num of agent2: 26
idv_policy eval team catch total num: 38
idv_policy eval average step individual rewards of agent3: 0.7542787520691018
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: 0.639903465414515
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 3101/10000 episodes, total num timesteps 620400/2000000, FPS 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.

team_policy eval average step individual rewards of agent0: 1.173144761620288
team_policy eval average team episode rewards of agent0: 105.0
team_policy eval idv catch total num of agent0: 48
team_policy eval team catch total num: 42
team_policy eval average step individual rewards of agent1: 0.5916661243408722
team_policy eval average team episode rewards of agent1: 105.0
team_policy eval idv catch total num of agent1: 25
team_policy eval team catch total num: 42
team_policy eval average step individual rewards of agent2: 0.6836038000114815
team_policy eval average team episode rewards of agent2: 105.0
team_policy eval idv catch total num of agent2: 29
team_policy eval team catch total num: 42
team_policy eval average step individual rewards of agent3: 0.8462160055403646
team_policy eval average team episode rewards of agent3: 105.0
team_policy eval idv catch total num of agent3: 35
team_policy eval team catch total num: 42
team_policy eval average step individual rewards of agent4: 0.9089304050312557
team_policy eval average team episode rewards of agent4: 105.0
team_policy eval idv catch total num of agent4: 38
team_policy eval team catch total num: 42
idv_policy eval average step individual rewards of agent0: 0.6089376017193167
idv_policy eval average team episode rewards of agent0: 85.0
idv_policy eval idv catch total num of agent0: 26
idv_policy eval team catch total num: 34
idv_policy eval average step individual rewards of agent1: 0.7101662066033964
idv_policy eval average team episode rewards of agent1: 85.0
idv_policy eval idv catch total num of agent1: 30
idv_policy eval team catch total num: 34
idv_policy eval average step individual rewards of agent2: 1.0421239995494584
idv_policy eval average team episode rewards of agent2: 85.0
idv_policy eval idv catch total num of agent2: 43
idv_policy eval team catch total num: 34
idv_policy eval average step individual rewards of agent3: 0.4362481260430303
idv_policy eval average team episode rewards of agent3: 85.0
idv_policy eval idv catch total num of agent3: 19
idv_policy eval team catch total num: 34
idv_policy eval average step individual rewards of agent4: 0.4570528443497952
idv_policy eval average team episode rewards of agent4: 85.0
idv_policy eval idv catch total num of agent4: 20
idv_policy eval team catch total num: 34

 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.

team_policy eval average step individual rewards of agent0: 0.8563085574265898
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: 0.6648883081372549
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: 0.5888987084469299
team_policy eval average team episode rewards of agent2: 115.0
team_policy eval idv catch total num of agent2: 25
team_policy eval team catch total num: 46
team_policy eval average step individual rewards of agent3: 0.6868145235920443
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: 0.8718237050542359
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: 0.7618178206518047
idv_policy eval average team episode rewards of agent0: 60.0
idv_policy eval idv catch total num of agent0: 32
idv_policy eval team catch total num: 24
idv_policy eval average step individual rewards of agent1: 0.5566551313164938
idv_policy eval average team episode rewards of agent1: 60.0
idv_policy eval idv catch total num of agent1: 24
idv_policy eval team catch total num: 24
idv_policy eval average step individual rewards of agent2: 0.6315540366725341
idv_policy eval average team episode rewards of agent2: 60.0
idv_policy eval idv catch total num of agent2: 27
idv_policy eval team catch total num: 24
idv_policy eval average step individual rewards of agent3: 0.4082536811175947
idv_policy eval average team episode rewards of agent3: 60.0
idv_policy eval idv catch total num of agent3: 18
idv_policy eval team catch total num: 24
idv_policy eval average step individual rewards of agent4: 0.5609525207262748
idv_policy eval average team episode rewards of agent4: 60.0
idv_policy eval idv catch total num of agent4: 24
idv_policy eval team catch total num: 24

 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.

team_policy eval average step individual rewards of agent0: 0.96444150778517
team_policy eval average team episode rewards of agent0: 165.0
team_policy eval idv catch total num of agent0: 40
team_policy eval team catch total num: 66
team_policy eval average step individual rewards of agent1: 1.2008812630119152
team_policy eval average team episode rewards of agent1: 165.0
team_policy eval idv catch total num of agent1: 49
team_policy eval team catch total num: 66
team_policy eval average step individual rewards of agent2: 0.8636646178740349
team_policy eval average team episode rewards of agent2: 165.0
team_policy eval idv catch total num of agent2: 36
team_policy eval team catch total num: 66
team_policy eval average step individual rewards of agent3: 1.4472252854911192
team_policy eval average team episode rewards of agent3: 165.0
team_policy eval idv catch total num of agent3: 59
team_policy eval team catch total num: 66
team_policy eval average step individual rewards of agent4: 1.398881546611368
team_policy eval average team episode rewards of agent4: 165.0
team_policy eval idv catch total num of agent4: 57
team_policy eval team catch total num: 66
idv_policy eval average step individual rewards of agent0: 0.7858910212420895
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: 1.1386647501599307
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: 0.9172754985365471
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: 0.5813062004091377
idv_policy eval average team episode rewards of agent3: 120.0
idv_policy eval idv catch total num of agent3: 25
idv_policy eval team catch total num: 48
idv_policy eval average step individual rewards of agent4: 1.101653265223668
idv_policy eval average team episode rewards of agent4: 120.0
idv_policy eval idv catch total num of agent4: 45
idv_policy eval team catch total num: 48

 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.

team_policy eval average step individual rewards of agent0: 0.9287176426607132
team_policy eval average team episode rewards of agent0: 57.5
team_policy eval idv catch total num of agent0: 39
team_policy eval team catch total num: 23
team_policy eval average step individual rewards of agent1: 0.6339764409458447
team_policy eval average team episode rewards of agent1: 57.5
team_policy eval idv catch total num of agent1: 27
team_policy eval team catch total num: 23
team_policy eval average step individual rewards of agent2: 0.12309980017512484
team_policy eval average team episode rewards of agent2: 57.5
team_policy eval idv catch total num of agent2: 7
team_policy eval team catch total num: 23
team_policy eval average step individual rewards of agent3: 0.5089402130691848
team_policy eval average team episode rewards of agent3: 57.5
team_policy eval idv catch total num of agent3: 22
team_policy eval team catch total num: 23
team_policy eval average step individual rewards of agent4: 0.6623172547432649
team_policy eval average team episode rewards of agent4: 57.5
team_policy eval idv catch total num of agent4: 28
team_policy eval team catch total num: 23
idv_policy eval average step individual rewards of agent0: 0.5318867930271844
idv_policy eval average team episode rewards of agent0: 80.0
idv_policy eval idv catch total num of agent0: 23
idv_policy eval team catch total num: 32
idv_policy eval average step individual rewards of agent1: 0.5546377239336734
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: 0.9201404140810445
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: 0.4287277444413929
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: 0.24103214839868567
idv_policy eval average team episode rewards of agent4: 80.0
idv_policy eval idv catch total num of agent4: 12
idv_policy eval team catch total num: 32

 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 211.


 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 211.


 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 211.


 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 211.

team_policy eval average step individual rewards of agent0: 1.6041197200577033
team_policy eval average team episode rewards of agent0: 142.5
team_policy eval idv catch total num of agent0: 65
team_policy eval team catch total num: 57
team_policy eval average step individual rewards of agent1: 1.3496390323689451
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: 0.9226412744132085
team_policy eval average team episode rewards of agent2: 142.5
team_policy eval idv catch total num of agent2: 38
team_policy eval team catch total num: 57
team_policy eval average step individual rewards of agent3: 0.5549887739747081
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: 0.585906000142693
team_policy eval average team episode rewards of agent4: 142.5
team_policy eval idv catch total num of agent4: 25
team_policy eval team catch total num: 57
idv_policy eval average step individual rewards of agent0: 0.5882710211966519
idv_policy eval average team episode rewards of agent0: 112.5
idv_policy eval idv catch total num of agent0: 25
idv_policy eval team catch total num: 45
idv_policy eval average step individual rewards of agent1: 0.7619878861796809
idv_policy eval average team episode rewards of agent1: 112.5
idv_policy eval idv catch total num of agent1: 32
idv_policy eval team catch total num: 45
idv_policy eval average step individual rewards of agent2: 0.5539039000465017
idv_policy eval average team episode rewards of agent2: 112.5
idv_policy eval idv catch total num of agent2: 24
idv_policy eval team catch total num: 45
idv_policy eval average step individual rewards of agent3: 0.7122954469332698
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: 0.9171358695199154
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 3226/10000 episodes, total num timesteps 645400/2000000, FPS 210.


 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 210.


 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 210.


 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 210.


 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 210.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.

team_policy eval average step individual rewards of agent0: 0.4612735099118986
team_policy eval average team episode rewards of agent0: 107.5
team_policy eval idv catch total num of agent0: 20
team_policy eval team catch total num: 43
team_policy eval average step individual rewards of agent1: 0.945924448133526
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: 1.0700497526505808
team_policy eval average team episode rewards of agent2: 107.5
team_policy eval idv catch total num of agent2: 44
team_policy eval team catch total num: 43
team_policy eval average step individual rewards of agent3: 0.8653901881045978
team_policy eval average team episode rewards of agent3: 107.5
team_policy eval idv catch total num of agent3: 36
team_policy eval team catch total num: 43
team_policy eval average step individual rewards of agent4: 0.8406459748140339
team_policy eval average team episode rewards of agent4: 107.5
team_policy eval idv catch total num of agent4: 35
team_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent0: 0.8654289101460455
idv_policy eval average team episode rewards of agent0: 115.0
idv_policy eval idv catch total num of agent0: 36
idv_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent1: 0.6705651219657214
idv_policy eval average team episode rewards of agent1: 115.0
idv_policy eval idv catch total num of agent1: 28
idv_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent2: 0.7198436645596579
idv_policy eval average team episode rewards of agent2: 115.0
idv_policy eval idv catch total num of agent2: 30
idv_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent3: 0.919272499423399
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: 0.8401734767207409
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 3251/10000 episodes, total num timesteps 650400/2000000, FPS 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.

team_policy eval average step individual rewards of agent0: 0.5634456128242519
team_policy eval average team episode rewards of agent0: 67.5
team_policy eval idv catch total num of agent0: 24
team_policy eval team catch total num: 27
team_policy eval average step individual rewards of agent1: 0.3531734885002682
team_policy eval average team episode rewards of agent1: 67.5
team_policy eval idv catch total num of agent1: 16
team_policy eval team catch total num: 27
team_policy eval average step individual rewards of agent2: 0.47906896863269205
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: 0.5678528183527958
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: 0.6119619324330436
team_policy eval average team episode rewards of agent4: 67.5
team_policy eval idv catch total num of agent4: 26
team_policy eval team catch total num: 27
idv_policy eval average step individual rewards of agent0: 0.5325650439802864
idv_policy eval average team episode rewards of agent0: 97.5
idv_policy eval idv catch total num of agent0: 23
idv_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent1: 0.4804431298118566
idv_policy eval average team episode rewards of agent1: 97.5
idv_policy eval idv catch total num of agent1: 21
idv_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent2: 0.4329643344302829
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: 0.704876421537999
idv_policy eval average team episode rewards of agent3: 97.5
idv_policy eval idv catch total num of agent3: 30
idv_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent4: 1.1711502795719837
idv_policy eval average team episode rewards of agent4: 97.5
idv_policy eval idv catch total num of agent4: 48
idv_policy eval team catch total num: 39

 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.

team_policy eval average step individual rewards of agent0: 0.7439559396653486
team_policy eval average team episode rewards of agent0: 97.5
team_policy eval idv catch total num of agent0: 31
team_policy eval team catch total num: 39
team_policy eval average step individual rewards of agent1: 0.8197372880465369
team_policy eval average team episode rewards of agent1: 97.5
team_policy eval idv catch total num of agent1: 34
team_policy eval team catch total num: 39
team_policy eval average step individual rewards of agent2: 0.45171567475386737
team_policy eval average team episode rewards of agent2: 97.5
team_policy eval idv catch total num of agent2: 20
team_policy eval team catch total num: 39
team_policy eval average step individual rewards of agent3: 0.3832224932099252
team_policy eval average team episode rewards of agent3: 97.5
team_policy eval idv catch total num of agent3: 17
team_policy eval team catch total num: 39
team_policy eval average step individual rewards of agent4: 0.8177209889782407
team_policy eval average team episode rewards of agent4: 97.5
team_policy eval idv catch total num of agent4: 34
team_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent0: 1.2717238780170312
idv_policy eval average team episode rewards of agent0: 130.0
idv_policy eval idv catch total num of agent0: 52
idv_policy eval team catch total num: 52
idv_policy eval average step individual rewards of agent1: 0.5665512976388556
idv_policy eval average team episode rewards of agent1: 130.0
idv_policy eval idv catch total num of agent1: 24
idv_policy eval team catch total num: 52
idv_policy eval average step individual rewards of agent2: 1.0225924237283004
idv_policy eval average team episode rewards of agent2: 130.0
idv_policy eval idv catch total num of agent2: 42
idv_policy eval team catch total num: 52
idv_policy eval average step individual rewards of agent3: 0.6587832345837561
idv_policy eval average team episode rewards of agent3: 130.0
idv_policy eval idv catch total num of agent3: 28
idv_policy eval team catch total num: 52
idv_policy eval average step individual rewards of agent4: 1.017349306900632
idv_policy eval average team episode rewards of agent4: 130.0
idv_policy eval idv catch total num of agent4: 42
idv_policy eval team catch total num: 52

 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.

team_policy eval average step individual rewards of agent0: 1.0117260468554716
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: 0.33240811126664044
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: 0.21952821855645296
team_policy eval average team episode rewards of agent2: 85.0
team_policy eval idv catch total num of agent2: 11
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent3: 0.3758669300348852
team_policy eval average team episode rewards of agent3: 85.0
team_policy eval idv catch total num of agent3: 17
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent4: 0.5897513535025583
team_policy eval average team episode rewards of agent4: 85.0
team_policy eval idv catch total num of agent4: 25
team_policy eval team catch total num: 34
idv_policy eval average step individual rewards of agent0: 0.8152402517155228
idv_policy eval average team episode rewards of agent0: 87.5
idv_policy eval idv catch total num of agent0: 34
idv_policy eval team catch total num: 35
idv_policy eval average step individual rewards of agent1: 0.40431878878722116
idv_policy eval average team episode rewards of agent1: 87.5
idv_policy eval idv catch total num of agent1: 18
idv_policy eval team catch total num: 35
idv_policy eval average step individual rewards of agent2: 0.6150965288169143
idv_policy eval average team episode rewards of agent2: 87.5
idv_policy eval idv catch total num of agent2: 26
idv_policy eval team catch total num: 35
idv_policy eval average step individual rewards of agent3: 0.6007762434131713
idv_policy eval average team episode rewards of agent3: 87.5
idv_policy eval idv catch total num of agent3: 26
idv_policy eval team catch total num: 35
idv_policy eval average step individual rewards of agent4: 0.7895374307019732
idv_policy eval average team episode rewards of agent4: 87.5
idv_policy eval idv catch total num of agent4: 33
idv_policy eval team catch total num: 35

 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.

team_policy eval average step individual rewards of agent0: 0.32613462347479905
team_policy eval average team episode rewards of agent0: 70.0
team_policy eval idv catch total num of agent0: 15
team_policy eval team catch total num: 28
team_policy eval average step individual rewards of agent1: 0.6067176450090855
team_policy eval average team episode rewards of agent1: 70.0
team_policy eval idv catch total num of agent1: 26
team_policy eval team catch total num: 28
team_policy eval average step individual rewards of agent2: 0.632912407881497
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: 0.19998136617335965
team_policy eval average team episode rewards of agent3: 70.0
team_policy eval idv catch total num of agent3: 10
team_policy eval team catch total num: 28
team_policy eval average step individual rewards of agent4: 0.6139675342392663
team_policy eval average team episode rewards of agent4: 70.0
team_policy eval idv catch total num of agent4: 26
team_policy eval team catch total num: 28
idv_policy eval average step individual rewards of agent0: 0.6680266777825941
idv_policy eval average team episode rewards of agent0: 107.5
idv_policy eval idv catch total num of agent0: 28
idv_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent1: 0.30587280976604175
idv_policy eval average team episode rewards of agent1: 107.5
idv_policy eval idv catch total num of agent1: 14
idv_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent2: 0.7860260700709025
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: 0.9342252194027932
idv_policy eval average team episode rewards of agent3: 107.5
idv_policy eval idv catch total num of agent3: 39
idv_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent4: 0.9144251194949957
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 3351/10000 episodes, total num timesteps 670400/2000000, FPS 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.

team_policy eval average step individual rewards of agent0: 0.864618592553235
team_policy eval average team episode rewards of agent0: 90.0
team_policy eval idv catch total num of agent0: 36
team_policy eval team catch total num: 36
team_policy eval average step individual rewards of agent1: 0.3991592219418715
team_policy eval average team episode rewards of agent1: 90.0
team_policy eval idv catch total num of agent1: 18
team_policy eval team catch total num: 36
team_policy eval average step individual rewards of agent2: 0.8032713687286942
team_policy eval average team episode rewards of agent2: 90.0
team_policy eval idv catch total num of agent2: 34
team_policy eval team catch total num: 36
team_policy eval average step individual rewards of agent3: 0.605136810766059
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: 0.4747787605577184
team_policy eval average team episode rewards of agent4: 90.0
team_policy eval idv catch total num of agent4: 21
team_policy eval team catch total num: 36
idv_policy eval average step individual rewards of agent0: 0.9375236560579484
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: 0.9912502893527857
idv_policy eval average team episode rewards of agent1: 127.5
idv_policy eval idv catch total num of agent1: 41
idv_policy eval team catch total num: 51
idv_policy eval average step individual rewards of agent2: 0.6869619167124139
idv_policy eval average team episode rewards of agent2: 127.5
idv_policy eval idv catch total num of agent2: 29
idv_policy eval team catch total num: 51
idv_policy eval average step individual rewards of agent3: 1.0402694683680311
idv_policy eval average team episode rewards of agent3: 127.5
idv_policy eval idv catch total num of agent3: 43
idv_policy eval team catch total num: 51
idv_policy eval average step individual rewards of agent4: 0.8681767680155691
idv_policy eval average team episode rewards of agent4: 127.5
idv_policy eval idv catch total num of agent4: 36
idv_policy eval team catch total num: 51

 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.

team_policy eval average step individual rewards of agent0: 0.3788460223042879
team_policy eval average team episode rewards of agent0: 145.0
team_policy eval idv catch total num of agent0: 17
team_policy eval team catch total num: 58
team_policy eval average step individual rewards of agent1: 1.1245148212309752
team_policy eval average team episode rewards of agent1: 145.0
team_policy eval idv catch total num of agent1: 46
team_policy eval team catch total num: 58
team_policy eval average step individual rewards of agent2: 1.196326271806191
team_policy eval average team episode rewards of agent2: 145.0
team_policy eval idv catch total num of agent2: 49
team_policy eval team catch total num: 58
team_policy eval average step individual rewards of agent3: 1.2712377643510926
team_policy eval average team episode rewards of agent3: 145.0
team_policy eval idv catch total num of agent3: 52
team_policy eval team catch total num: 58
team_policy eval average step individual rewards of agent4: 0.7814128964996178
team_policy eval average team episode rewards of agent4: 145.0
team_policy eval idv catch total num of agent4: 33
team_policy eval team catch total num: 58
idv_policy eval average step individual rewards of agent0: 0.7393221574313519
idv_policy eval average team episode rewards of agent0: 82.5
idv_policy eval idv catch total num of agent0: 31
idv_policy eval team catch total num: 33
idv_policy eval average step individual rewards of agent1: 0.5121480962335175
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: 0.6689527499885548
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: 0.7398265867511334
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: 0.4078316162840215
idv_policy eval average team episode rewards of agent4: 82.5
idv_policy eval idv catch total num of agent4: 18
idv_policy eval team catch total num: 33

 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.

team_policy eval average step individual rewards of agent0: 1.075438612462839
team_policy eval average team episode rewards of agent0: 115.0
team_policy eval idv catch total num of agent0: 44
team_policy eval team catch total num: 46
team_policy eval average step individual rewards of agent1: 0.9126637545007642
team_policy eval average team episode rewards of agent1: 115.0
team_policy eval idv catch total num of agent1: 38
team_policy eval team catch total num: 46
team_policy eval average step individual rewards of agent2: 0.5572762959729054
team_policy eval average team episode rewards of agent2: 115.0
team_policy eval idv catch total num of agent2: 24
team_policy eval team catch total num: 46
team_policy eval average step individual rewards of agent3: 1.093182459804795
team_policy eval average team episode rewards of agent3: 115.0
team_policy eval idv catch total num of agent3: 45
team_policy eval team catch total num: 46
team_policy eval average step individual rewards of agent4: 0.8597992490751928
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: 1.219406415066021
idv_policy eval average team episode rewards of agent0: 120.0
idv_policy eval idv catch total num of agent0: 50
idv_policy eval team catch total num: 48
idv_policy eval average step individual rewards of agent1: 0.35211516735015236
idv_policy eval average team episode rewards of agent1: 120.0
idv_policy eval idv catch total num of agent1: 16
idv_policy eval team catch total num: 48
idv_policy eval average step individual rewards of agent2: 1.2197977336283188
idv_policy eval average team episode rewards of agent2: 120.0
idv_policy eval idv catch total num of agent2: 50
idv_policy eval team catch total num: 48
idv_policy eval average step individual rewards of agent3: 0.6135869647486316
idv_policy eval average team episode rewards of agent3: 120.0
idv_policy eval idv catch total num of agent3: 26
idv_policy eval team catch total num: 48
idv_policy eval average step individual rewards of agent4: 0.9416942741862829
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 3426/10000 episodes, total num timesteps 685400/2000000, FPS 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.

team_policy eval average step individual rewards of agent0: 1.1697819715316546
team_policy eval average team episode rewards of agent0: 107.5
team_policy eval idv catch total num of agent0: 48
team_policy eval team catch total num: 43
team_policy eval average step individual rewards of agent1: 0.7149938173556939
team_policy eval average team episode rewards of agent1: 107.5
team_policy eval idv catch total num of agent1: 30
team_policy eval team catch total num: 43
team_policy eval average step individual rewards of agent2: 0.7422766460456074
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: 0.5909579581827241
team_policy eval average team episode rewards of agent3: 107.5
team_policy eval idv catch total num of agent3: 25
team_policy eval team catch total num: 43
team_policy eval average step individual rewards of agent4: 0.6552090903398551
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: 0.8900314325085645
idv_policy eval average team episode rewards of agent0: 85.0
idv_policy eval idv catch total num of agent0: 37
idv_policy eval team catch total num: 34
idv_policy eval average step individual rewards of agent1: 0.6541638732029091
idv_policy eval average team episode rewards of agent1: 85.0
idv_policy eval idv catch total num of agent1: 28
idv_policy eval team catch total num: 34
idv_policy eval average step individual rewards of agent2: 0.5204869044111797
idv_policy eval average team episode rewards of agent2: 85.0
idv_policy eval idv catch total num of agent2: 23
idv_policy eval team catch total num: 34
idv_policy eval average step individual rewards of agent3: 0.8834549763826809
idv_policy eval average team episode rewards of agent3: 85.0
idv_policy eval idv catch total num of agent3: 37
idv_policy eval team catch total num: 34
idv_policy eval average step individual rewards of agent4: 0.20138589148293842
idv_policy eval average team episode rewards of agent4: 85.0
idv_policy eval idv catch total num of agent4: 10
idv_policy eval team catch total num: 34

 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 211.


 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 212.


 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 212.


 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 212.


 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 211.


 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 212.


 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 212.


 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 212.


 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 212.


 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 212.


 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 212.


 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 212.


 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 212.


 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 212.


 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 212.


 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 212.

team_policy eval average step individual rewards of agent0: 0.835618231816347
team_policy eval average team episode rewards of agent0: 92.5
team_policy eval idv catch total num of agent0: 35
team_policy eval team catch total num: 37
team_policy eval average step individual rewards of agent1: 0.7533066448592691
team_policy eval average team episode rewards of agent1: 92.5
team_policy eval idv catch total num of agent1: 32
team_policy eval team catch total num: 37
team_policy eval average step individual rewards of agent2: 0.9872780039512915
team_policy eval average team episode rewards of agent2: 92.5
team_policy eval idv catch total num of agent2: 41
team_policy eval team catch total num: 37
team_policy eval average step individual rewards of agent3: 0.6811809825696937
team_policy eval average team episode rewards of agent3: 92.5
team_policy eval idv catch total num of agent3: 29
team_policy eval team catch total num: 37
team_policy eval average step individual rewards of agent4: 0.6593160144526121
team_policy eval average team episode rewards of agent4: 92.5
team_policy eval idv catch total num of agent4: 28
team_policy eval team catch total num: 37
idv_policy eval average step individual rewards of agent0: 0.6892643820529085
idv_policy eval average team episode rewards of agent0: 112.5
idv_policy eval idv catch total num of agent0: 29
idv_policy eval team catch total num: 45
idv_policy eval average step individual rewards of agent1: 1.3225536075479583
idv_policy eval average team episode rewards of agent1: 112.5
idv_policy eval idv catch total num of agent1: 54
idv_policy eval team catch total num: 45
idv_policy eval average step individual rewards of agent2: 0.6939814974521095
idv_policy eval average team episode rewards of agent2: 112.5
idv_policy eval idv catch total num of agent2: 29
idv_policy eval team catch total num: 45
idv_policy eval average step individual rewards of agent3: 0.7482178682643033
idv_policy eval average team episode rewards of agent3: 112.5
idv_policy eval idv catch total num of agent3: 31
idv_policy eval team catch total num: 45
idv_policy eval average step individual rewards of agent4: 0.6906976984501874
idv_policy eval average team episode rewards of agent4: 112.5
idv_policy eval idv catch total num of agent4: 29
idv_policy eval team catch total num: 45

 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 212.


 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 212.


 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 212.


 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 212.


 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 212.


 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 212.


 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 212.


 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 212.


 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 212.


 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 212.


 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 212.


 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 212.


 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 212.


 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 212.


 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 212.


 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 212.


 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 212.


 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 212.


 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 212.


 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 212.


 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 212.


 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 212.


 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 212.


 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 212.


 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 212.

team_policy eval average step individual rewards of agent0: 0.7871065383029083
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: 0.5878726962692721
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: 0.7934151361819495
team_policy eval average team episode rewards of agent2: 102.5
team_policy eval idv catch total num of agent2: 33
team_policy eval team catch total num: 41
team_policy eval average step individual rewards of agent3: 0.6874967802231374
team_policy eval average team episode rewards of agent3: 102.5
team_policy eval idv catch total num of agent3: 29
team_policy eval team catch total num: 41
team_policy eval average step individual rewards of agent4: 0.8848277346096185
team_policy eval average team episode rewards of agent4: 102.5
team_policy eval idv catch total num of agent4: 37
team_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent0: 0.9174418239225541
idv_policy eval average team episode rewards of agent0: 145.0
idv_policy eval idv catch total num of agent0: 38
idv_policy eval team catch total num: 58
idv_policy eval average step individual rewards of agent1: 0.861752188204073
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: 0.9220030074729177
idv_policy eval average team episode rewards of agent2: 145.0
idv_policy eval idv catch total num of agent2: 38
idv_policy eval team catch total num: 58
idv_policy eval average step individual rewards of agent3: 1.224411584329684
idv_policy eval average team episode rewards of agent3: 145.0
idv_policy eval idv catch total num of agent3: 50
idv_policy eval team catch total num: 58
idv_policy eval average step individual rewards of agent4: 0.8192614439142473
idv_policy eval average team episode rewards of agent4: 145.0
idv_policy eval idv catch total num of agent4: 34
idv_policy eval team catch total num: 58

 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 212.


 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 212.


 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 212.


 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 212.


 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 212.


 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 212.


 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 212.


 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 212.


 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 212.


 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 212.


 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 212.


 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 212.


 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 212.


 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 212.


 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 212.


 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 212.


 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 212.


 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 212.


 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 212.


 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 212.


 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 212.


 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 212.


 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 212.


 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 212.


 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 212.

team_policy eval average step individual rewards of agent0: 1.0655626611727316
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: 0.5270107409223509
team_policy eval average team episode rewards of agent1: 117.5
team_policy eval idv catch total num of agent1: 23
team_policy eval team catch total num: 47
team_policy eval average step individual rewards of agent2: 0.8162103393949866
team_policy eval average team episode rewards of agent2: 117.5
team_policy eval idv catch total num of agent2: 34
team_policy eval team catch total num: 47
team_policy eval average step individual rewards of agent3: 1.1213048931857648
team_policy eval average team episode rewards of agent3: 117.5
team_policy eval idv catch total num of agent3: 46
team_policy eval team catch total num: 47
team_policy eval average step individual rewards of agent4: 0.584320636586407
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: 1.625334201601361
idv_policy eval average team episode rewards of agent0: 117.5
idv_policy eval idv catch total num of agent0: 66
idv_policy eval team catch total num: 47
idv_policy eval average step individual rewards of agent1: 1.1947640276026297
idv_policy eval average team episode rewards of agent1: 117.5
idv_policy eval idv catch total num of agent1: 49
idv_policy eval team catch total num: 47
idv_policy eval average step individual rewards of agent2: 0.5339122434056227
idv_policy eval average team episode rewards of agent2: 117.5
idv_policy eval idv catch total num of agent2: 23
idv_policy eval team catch total num: 47
idv_policy eval average step individual rewards of agent3: 0.664097251838135
idv_policy eval average team episode rewards of agent3: 117.5
idv_policy eval idv catch total num of agent3: 28
idv_policy eval team catch total num: 47
idv_policy eval average step individual rewards of agent4: 0.9079546315420248
idv_policy eval average team episode rewards of agent4: 117.5
idv_policy eval idv catch total num of agent4: 38
idv_policy eval team catch total num: 47

 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 212.


 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 212.


 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 212.


 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 212.


 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 212.


 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 212.


 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 212.


 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 212.


 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 212.


 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 212.


 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 212.


 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 212.


 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 212.


 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 212.


 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 212.


 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 212.


 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 212.


 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 212.


 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 212.


 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 212.


 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 212.


 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 212.


 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 212.


 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 212.


 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 212.

team_policy eval average step individual rewards of agent0: 0.4639044173819455
team_policy eval average team episode rewards of agent0: 47.5
team_policy eval idv catch total num of agent0: 20
team_policy eval team catch total num: 19
team_policy eval average step individual rewards of agent1: 0.2826264422625322
team_policy eval average team episode rewards of agent1: 47.5
team_policy eval idv catch total num of agent1: 13
team_policy eval team catch total num: 19
team_policy eval average step individual rewards of agent2: 0.5310638322567813
team_policy eval average team episode rewards of agent2: 47.5
team_policy eval idv catch total num of agent2: 23
team_policy eval team catch total num: 19
team_policy eval average step individual rewards of agent3: 0.30892251576619684
team_policy eval average team episode rewards of agent3: 47.5
team_policy eval idv catch total num of agent3: 14
team_policy eval team catch total num: 19
team_policy eval average step individual rewards of agent4: 0.3085496495687117
team_policy eval average team episode rewards of agent4: 47.5
team_policy eval idv catch total num of agent4: 14
team_policy eval team catch total num: 19
idv_policy eval average step individual rewards of agent0: 0.27672738901246796
idv_policy eval average team episode rewards of agent0: 67.5
idv_policy eval idv catch total num of agent0: 13
idv_policy eval team catch total num: 27
idv_policy eval average step individual rewards of agent1: 0.6600882231609098
idv_policy eval average team episode rewards of agent1: 67.5
idv_policy eval idv catch total num of agent1: 28
idv_policy eval team catch total num: 27
idv_policy eval average step individual rewards of agent2: 0.6326472655352738
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: 0.6353362389387656
idv_policy eval average team episode rewards of agent3: 67.5
idv_policy eval idv catch total num of agent3: 27
idv_policy eval team catch total num: 27
idv_policy eval average step individual rewards of agent4: 0.45745917108830986
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 3551/10000 episodes, total num timesteps 710400/2000000, FPS 212.


 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 212.


 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 212.


 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 212.


 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 212.


 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 212.


 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 212.


 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 212.


 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 212.


 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 212.


 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 212.


 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 212.


 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 212.


 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 212.


 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 212.


 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 213.


 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 213.


 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 213.


 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 213.


 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 213.


 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 213.


 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 213.


 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 213.


 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 213.


 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 213.

team_policy eval average step individual rewards of agent0: 0.696666378182158
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: 0.7940367634855988
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: 0.894365643917283
team_policy eval average team episode rewards of agent2: 92.5
team_policy eval idv catch total num of agent2: 37
team_policy eval team catch total num: 37
team_policy eval average step individual rewards of agent3: 1.0473481222497787
team_policy eval average team episode rewards of agent3: 92.5
team_policy eval idv catch total num of agent3: 43
team_policy eval team catch total num: 37
team_policy eval average step individual rewards of agent4: 0.6819758311695279
team_policy eval average team episode rewards of agent4: 92.5
team_policy eval idv catch total num of agent4: 29
team_policy eval team catch total num: 37
idv_policy eval average step individual rewards of agent0: 0.7859926634816204
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: 0.7467149032357464
idv_policy eval average team episode rewards of agent1: 102.5
idv_policy eval idv catch total num of agent1: 31
idv_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent2: 0.8409432054769769
idv_policy eval average team episode rewards of agent2: 102.5
idv_policy eval idv catch total num of agent2: 35
idv_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent3: 0.6697392068691991
idv_policy eval average team episode rewards of agent3: 102.5
idv_policy eval idv catch total num of agent3: 28
idv_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent4: 0.8140895789811853
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 3576/10000 episodes, total num timesteps 715400/2000000, FPS 213.


 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 213.


 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 213.


 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 213.


 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 213.


 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 213.


 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 213.


 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 213.


 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 213.


 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 213.


 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 213.


 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 213.


 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 213.


 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 213.


 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 213.


 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 213.


 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 213.


 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 213.


 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 213.


 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 213.


 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 213.


 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 213.


 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 213.


 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 213.


 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 213.

team_policy eval average step individual rewards of agent0: 0.6389676771975114
team_policy eval average team episode rewards of agent0: 65.0
team_policy eval idv catch total num of agent0: 27
team_policy eval team catch total num: 26
team_policy eval average step individual rewards of agent1: 0.45676577157180864
team_policy eval average team episode rewards of agent1: 65.0
team_policy eval idv catch total num of agent1: 20
team_policy eval team catch total num: 26
team_policy eval average step individual rewards of agent2: 0.5334202889071933
team_policy eval average team episode rewards of agent2: 65.0
team_policy eval idv catch total num of agent2: 23
team_policy eval team catch total num: 26
team_policy eval average step individual rewards of agent3: 0.3719162182320096
team_policy eval average team episode rewards of agent3: 65.0
team_policy eval idv catch total num of agent3: 17
team_policy eval team catch total num: 26
team_policy eval average step individual rewards of agent4: 0.6678434514376225
team_policy eval average team episode rewards of agent4: 65.0
team_policy eval idv catch total num of agent4: 28
team_policy eval team catch total num: 26
idv_policy eval average step individual rewards of agent0: 0.6083739492038346
idv_policy eval average team episode rewards of agent0: 95.0
idv_policy eval idv catch total num of agent0: 26
idv_policy eval team catch total num: 38
idv_policy eval average step individual rewards of agent1: 0.6610908991172382
idv_policy eval average team episode rewards of agent1: 95.0
idv_policy eval idv catch total num of agent1: 28
idv_policy eval team catch total num: 38
idv_policy eval average step individual rewards of agent2: 0.8419665779545812
idv_policy eval average team episode rewards of agent2: 95.0
idv_policy eval idv catch total num of agent2: 35
idv_policy eval team catch total num: 38
idv_policy eval average step individual rewards of agent3: 1.0212787758462663
idv_policy eval average team episode rewards of agent3: 95.0
idv_policy eval idv catch total num of agent3: 42
idv_policy eval team catch total num: 38
idv_policy eval average step individual rewards of agent4: 0.4450041687770475
idv_policy eval average team episode rewards of agent4: 95.0
idv_policy eval idv catch total num of agent4: 19
idv_policy eval team catch total num: 38

 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 213.


 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 213.


 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 213.


 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 213.


 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 213.


 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 213.


 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 213.


 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 213.


 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 213.


 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 213.


 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 213.


 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 213.


 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 213.


 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 213.


 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 213.


 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 213.


 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 213.


 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 213.


 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 213.


 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 213.


 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 213.


 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 213.


 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 213.


 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 213.


 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 213.

team_policy eval average step individual rewards of agent0: 0.4584199856399762
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: 0.6205476806398263
team_policy eval average team episode rewards of agent1: 92.5
team_policy eval idv catch total num of agent1: 26
team_policy eval team catch total num: 37
team_policy eval average step individual rewards of agent2: 0.7155265405619075
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: 0.792825870683126
team_policy eval average team episode rewards of agent3: 92.5
team_policy eval idv catch total num of agent3: 33
team_policy eval team catch total num: 37
team_policy eval average step individual rewards of agent4: 0.6065123693976899
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: 0.9450679334115459
idv_policy eval average team episode rewards of agent0: 107.5
idv_policy eval idv catch total num of agent0: 39
idv_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent1: 0.7390001068909055
idv_policy eval average team episode rewards of agent1: 107.5
idv_policy eval idv catch total num of agent1: 31
idv_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent2: 0.7142553882032284
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: 0.9987774080177598
idv_policy eval average team episode rewards of agent3: 107.5
idv_policy eval idv catch total num of agent3: 41
idv_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent4: 0.791027781498566
idv_policy eval average team episode rewards of agent4: 107.5
idv_policy eval idv catch total num of agent4: 33
idv_policy eval team catch total num: 43

 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 213.


 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 213.


 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 213.


 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 213.


 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 213.


 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 213.


 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 213.


 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 213.


 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 213.


 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 213.


 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 213.


 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 213.


 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 214.


 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 214.


 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 214.


 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 214.


 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 214.


 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 214.


 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 214.


 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 214.


 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 214.


 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 214.


 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 214.


 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 214.


 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 214.

team_policy eval average step individual rewards of agent0: 0.7804677709318213
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: 0.9112032206377622
team_policy eval average team episode rewards of agent1: 122.5
team_policy eval idv catch total num of agent1: 38
team_policy eval team catch total num: 49
team_policy eval average step individual rewards of agent2: 0.8571325274782486
team_policy eval average team episode rewards of agent2: 122.5
team_policy eval idv catch total num of agent2: 36
team_policy eval team catch total num: 49
team_policy eval average step individual rewards of agent3: 0.7391347616707246
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: 0.8860722065865361
team_policy eval average team episode rewards of agent4: 122.5
team_policy eval idv catch total num of agent4: 37
team_policy eval team catch total num: 49
idv_policy eval average step individual rewards of agent0: 0.5276563626025751
idv_policy eval average team episode rewards of agent0: 75.0
idv_policy eval idv catch total num of agent0: 23
idv_policy eval team catch total num: 30
idv_policy eval average step individual rewards of agent1: 0.6202542330864589
idv_policy eval average team episode rewards of agent1: 75.0
idv_policy eval idv catch total num of agent1: 26
idv_policy eval team catch total num: 30
idv_policy eval average step individual rewards of agent2: 0.5653071187179287
idv_policy eval average team episode rewards of agent2: 75.0
idv_policy eval idv catch total num of agent2: 24
idv_policy eval team catch total num: 30
idv_policy eval average step individual rewards of agent3: 0.4832107458500801
idv_policy eval average team episode rewards of agent3: 75.0
idv_policy eval idv catch total num of agent3: 21
idv_policy eval team catch total num: 30
idv_policy eval average step individual rewards of agent4: 0.5897879453211161
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 3651/10000 episodes, total num timesteps 730400/2000000, FPS 214.


 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 214.


 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 214.


 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 214.


 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 214.


 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 214.


 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 214.


 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 214.


 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 214.


 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 214.


 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 214.


 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 214.


 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 214.


 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 214.


 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 214.


 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 214.


 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 214.


 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 214.


 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 214.


 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 214.


 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 214.


 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 214.


 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 214.


 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 214.


 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 214.

team_policy eval average step individual rewards of agent0: 0.639086230199975
team_policy eval average team episode rewards of agent0: 105.0
team_policy eval idv catch total num of agent0: 27
team_policy eval team catch total num: 42
team_policy eval average step individual rewards of agent1: 0.7159146929879128
team_policy eval average team episode rewards of agent1: 105.0
team_policy eval idv catch total num of agent1: 30
team_policy eval team catch total num: 42
team_policy eval average step individual rewards of agent2: 0.9381186137406624
team_policy eval average team episode rewards of agent2: 105.0
team_policy eval idv catch total num of agent2: 39
team_policy eval team catch total num: 42
team_policy eval average step individual rewards of agent3: 0.8138133141281694
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: 0.6333553471987337
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: 1.1002611094651904
idv_policy eval average team episode rewards of agent0: 142.5
idv_policy eval idv catch total num of agent0: 45
idv_policy eval team catch total num: 57
idv_policy eval average step individual rewards of agent1: 1.0882681526526623
idv_policy eval average team episode rewards of agent1: 142.5
idv_policy eval idv catch total num of agent1: 45
idv_policy eval team catch total num: 57
idv_policy eval average step individual rewards of agent2: 1.2088222884791522
idv_policy eval average team episode rewards of agent2: 142.5
idv_policy eval idv catch total num of agent2: 50
idv_policy eval team catch total num: 57
idv_policy eval average step individual rewards of agent3: 0.8059841166113952
idv_policy eval average team episode rewards of agent3: 142.5
idv_policy eval idv catch total num of agent3: 34
idv_policy eval team catch total num: 57
idv_policy eval average step individual rewards of agent4: 0.7417568233200348
idv_policy eval average team episode rewards of agent4: 142.5
idv_policy eval idv catch total num of agent4: 31
idv_policy eval team catch total num: 57

 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 214.


 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 214.


 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 214.


 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 214.


 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 214.


 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 214.


 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 214.


 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 214.


 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 214.


 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 214.


 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 214.


 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 214.


 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 214.


 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 214.


 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 214.


 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 214.


 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 214.


 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 214.


 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 214.


 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 214.


 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 214.


 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 214.


 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 214.


 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 214.


 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 214.

team_policy eval average step individual rewards of agent0: 0.5631730505595436
team_policy eval average team episode rewards of agent0: 127.5
team_policy eval idv catch total num of agent0: 24
team_policy eval team catch total num: 51
team_policy eval average step individual rewards of agent1: 1.1635808454480272
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: 0.8666157600399744
team_policy eval average team episode rewards of agent2: 127.5
team_policy eval idv catch total num of agent2: 36
team_policy eval team catch total num: 51
team_policy eval average step individual rewards of agent3: 1.0925062055556483
team_policy eval average team episode rewards of agent3: 127.5
team_policy eval idv catch total num of agent3: 45
team_policy eval team catch total num: 51
team_policy eval average step individual rewards of agent4: 0.9943578755364365
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: 0.862940654337684
idv_policy eval average team episode rewards of agent0: 117.5
idv_policy eval idv catch total num of agent0: 36
idv_policy eval team catch total num: 47
idv_policy eval average step individual rewards of agent1: 0.9173506258644707
idv_policy eval average team episode rewards of agent1: 117.5
idv_policy eval idv catch total num of agent1: 38
idv_policy eval team catch total num: 47
idv_policy eval average step individual rewards of agent2: 0.9124259548521598
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: 0.6878408126153173
idv_policy eval average team episode rewards of agent3: 117.5
idv_policy eval idv catch total num of agent3: 29
idv_policy eval team catch total num: 47
idv_policy eval average step individual rewards of agent4: 0.7374277828810731
idv_policy eval average team episode rewards of agent4: 117.5
idv_policy eval idv catch total num of agent4: 31
idv_policy eval team catch total num: 47

 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 214.


 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 214.


 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 214.


 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 214.


 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 214.


 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 214.


 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 214.


 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 214.


 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 214.


 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 214.


 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 214.


 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 214.


 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 215.


 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 215.


 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 215.


 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 215.


 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 215.


 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 215.


 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 215.


 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 215.


 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 215.


 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 215.


 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 215.


 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 215.


 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 215.

team_policy eval average step individual rewards of agent0: 0.6129723993934723
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: 0.806752506585956
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: 1.3179609829729406
team_policy eval average team episode rewards of agent2: 115.0
team_policy eval idv catch total num of agent2: 54
team_policy eval team catch total num: 46
team_policy eval average step individual rewards of agent3: 0.8635633376288786
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: 0.8616314926825984
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: 0.5816948422284667
idv_policy eval average team episode rewards of agent0: 85.0
idv_policy eval idv catch total num of agent0: 25
idv_policy eval team catch total num: 34
idv_policy eval average step individual rewards of agent1: 0.9656017539943934
idv_policy eval average team episode rewards of agent1: 85.0
idv_policy eval idv catch total num of agent1: 40
idv_policy eval team catch total num: 34
idv_policy eval average step individual rewards of agent2: 0.4787242809886952
idv_policy eval average team episode rewards of agent2: 85.0
idv_policy eval idv catch total num of agent2: 21
idv_policy eval team catch total num: 34
idv_policy eval average step individual rewards of agent3: 0.8180945090927572
idv_policy eval average team episode rewards of agent3: 85.0
idv_policy eval idv catch total num of agent3: 34
idv_policy eval team catch total num: 34
idv_policy eval average step individual rewards of agent4: 0.5908796978848847
idv_policy eval average team episode rewards of agent4: 85.0
idv_policy eval idv catch total num of agent4: 25
idv_policy eval team catch total num: 34

 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 215.


 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 215.


 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 215.


 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 215.


 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 215.


 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 215.


 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 215.


 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 215.


 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 215.


 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 215.


 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 215.


 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 215.


 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 215.


 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 215.


 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 215.


 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 215.


 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 215.


 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 215.


 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 215.


 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 215.


 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 215.


 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 215.


 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 215.


 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 215.


 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 215.

team_policy eval average step individual rewards of agent0: 1.1992960115891886
team_policy eval average team episode rewards of agent0: 125.0
team_policy eval idv catch total num of agent0: 49
team_policy eval team catch total num: 50
team_policy eval average step individual rewards of agent1: 1.0205645111126762
team_policy eval average team episode rewards of agent1: 125.0
team_policy eval idv catch total num of agent1: 42
team_policy eval team catch total num: 50
team_policy eval average step individual rewards of agent2: 0.9113342949779083
team_policy eval average team episode rewards of agent2: 125.0
team_policy eval idv catch total num of agent2: 38
team_policy eval team catch total num: 50
team_policy eval average step individual rewards of agent3: 0.9159459572074701
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: 0.8417088685430882
team_policy eval average team episode rewards of agent4: 125.0
team_policy eval idv catch total num of agent4: 35
team_policy eval team catch total num: 50
idv_policy eval average step individual rewards of agent0: 0.7716613314919835
idv_policy eval average team episode rewards of agent0: 97.5
idv_policy eval idv catch total num of agent0: 32
idv_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent1: 0.7963261191402861
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: 0.9144287554302446
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: 0.8403029446844744
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: 0.7786473731466086
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 3751/10000 episodes, total num timesteps 750400/2000000, FPS 215.


 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 215.


 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 215.


 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 215.


 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 215.


 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 215.


 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 215.


 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 215.


 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 215.


 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 215.


 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 215.


 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 215.


 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 215.


 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 215.


 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 215.


 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 215.


 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 215.


 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 215.


 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 215.


 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 215.


 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 215.


 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 215.


 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 215.


 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 215.


 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 215.

team_policy eval average step individual rewards of agent0: 0.6649457327197821
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: 0.9822304765921307
team_policy eval average team episode rewards of agent1: 107.5
team_policy eval idv catch total num of agent1: 41
team_policy eval team catch total num: 43
team_policy eval average step individual rewards of agent2: 0.7194906899497354
team_policy eval average team episode rewards of agent2: 107.5
team_policy eval idv catch total num of agent2: 30
team_policy eval team catch total num: 43
team_policy eval average step individual rewards of agent3: 0.42750611822815054
team_policy eval average team episode rewards of agent3: 107.5
team_policy eval idv catch total num of agent3: 19
team_policy eval team catch total num: 43
team_policy eval average step individual rewards of agent4: 1.214473245860083
team_policy eval average team episode rewards of agent4: 107.5
team_policy eval idv catch total num of agent4: 50
team_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent0: 0.4029165943614308
idv_policy eval average team episode rewards of agent0: 95.0
idv_policy eval idv catch total num of agent0: 18
idv_policy eval team catch total num: 38
idv_policy eval average step individual rewards of agent1: 0.6869885255188336
idv_policy eval average team episode rewards of agent1: 95.0
idv_policy eval idv catch total num of agent1: 29
idv_policy eval team catch total num: 38
idv_policy eval average step individual rewards of agent2: 0.890216584782139
idv_policy eval average team episode rewards of agent2: 95.0
idv_policy eval idv catch total num of agent2: 37
idv_policy eval team catch total num: 38
idv_policy eval average step individual rewards of agent3: 0.837932352209426
idv_policy eval average team episode rewards of agent3: 95.0
idv_policy eval idv catch total num of agent3: 35
idv_policy eval team catch total num: 38
idv_policy eval average step individual rewards of agent4: 0.6815916551476658
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 3776/10000 episodes, total num timesteps 755400/2000000, FPS 215.


 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 215.


 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 215.


 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 215.


 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 216.


 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 216.


 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 216.


 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 216.


 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 216.


 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 216.


 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 216.


 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 216.


 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 216.


 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 216.


 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 216.


 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 216.


 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 216.


 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 216.


 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 216.


 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 216.


 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 216.


 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 216.


 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 216.


 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 216.


 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 216.

team_policy eval average step individual rewards of agent0: 0.503690108146888
team_policy eval average team episode rewards of agent0: 115.0
team_policy eval idv catch total num of agent0: 22
team_policy eval team catch total num: 46
team_policy eval average step individual rewards of agent1: 1.1959031788545398
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: 0.6070353944848592
team_policy eval average team episode rewards of agent2: 115.0
team_policy eval idv catch total num of agent2: 26
team_policy eval team catch total num: 46
team_policy eval average step individual rewards of agent3: 0.9156738455112512
team_policy eval average team episode rewards of agent3: 115.0
team_policy eval idv catch total num of agent3: 38
team_policy eval team catch total num: 46
team_policy eval average step individual rewards of agent4: 0.9899879807139422
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: 0.6222357978809185
idv_policy eval average team episode rewards of agent0: 112.5
idv_policy eval idv catch total num of agent0: 26
idv_policy eval team catch total num: 45
idv_policy eval average step individual rewards of agent1: 0.9376307307561587
idv_policy eval average team episode rewards of agent1: 112.5
idv_policy eval idv catch total num of agent1: 39
idv_policy eval team catch total num: 45
idv_policy eval average step individual rewards of agent2: 0.9097377648681132
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: 1.0124053472311245
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: 0.7711502817673633
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 3801/10000 episodes, total num timesteps 760400/2000000, FPS 216.


 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 216.


 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 216.


 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 216.


 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 216.


 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 216.


 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 216.


 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 216.


 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 216.


 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 216.


 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 216.


 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 216.


 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 216.


 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 216.


 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 216.


 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 216.


 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 216.


 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 216.


 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 216.


 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 216.


 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 216.


 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 216.


 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 216.


 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 216.


 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 216.

team_policy eval average step individual rewards of agent0: 0.8092689785383377
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: 0.7366100656920864
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: 0.5796577570786089
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: 0.7576769535973181
team_policy eval average team episode rewards of agent3: 80.0
team_policy eval idv catch total num of agent3: 32
team_policy eval team catch total num: 32
team_policy eval average step individual rewards of agent4: 0.48559003886282975
team_policy eval average team episode rewards of agent4: 80.0
team_policy eval idv catch total num of agent4: 21
team_policy eval team catch total num: 32
idv_policy eval average step individual rewards of agent0: 0.7569689244194621
idv_policy eval average team episode rewards of agent0: 82.5
idv_policy eval idv catch total num of agent0: 32
idv_policy eval team catch total num: 33
idv_policy eval average step individual rewards of agent1: 0.8714228045454356
idv_policy eval average team episode rewards of agent1: 82.5
idv_policy eval idv catch total num of agent1: 36
idv_policy eval team catch total num: 33
idv_policy eval average step individual rewards of agent2: 0.9645618817784407
idv_policy eval average team episode rewards of agent2: 82.5
idv_policy eval idv catch total num of agent2: 40
idv_policy eval team catch total num: 33
idv_policy eval average step individual rewards of agent3: 0.2480077930315673
idv_policy eval average team episode rewards of agent3: 82.5
idv_policy eval idv catch total num of agent3: 12
idv_policy eval team catch total num: 33
idv_policy eval average step individual rewards of agent4: 0.603013138042908
idv_policy eval average team episode rewards of agent4: 82.5
idv_policy eval idv catch total num of agent4: 26
idv_policy eval team catch total num: 33

 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 216.


 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 216.


 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 216.


 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 216.


 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 216.


 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 216.


 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 216.


 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 216.


 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 216.


 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 216.


 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 216.


 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 216.


 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 216.


 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 216.


 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 216.


 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 216.


 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 216.


 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 216.


 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 216.


 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 216.


 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 216.


 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 216.


 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 217.


 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 217.


 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 217.

team_policy eval average step individual rewards of agent0: 0.8186396204981673
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: 1.0912667444415751
team_policy eval average team episode rewards of agent1: 115.0
team_policy eval idv catch total num of agent1: 45
team_policy eval team catch total num: 46
team_policy eval average step individual rewards of agent2: 0.7367927317685437
team_policy eval average team episode rewards of agent2: 115.0
team_policy eval idv catch total num of agent2: 31
team_policy eval team catch total num: 46
team_policy eval average step individual rewards of agent3: 0.6016729884602308
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: 0.8902661450743871
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: 0.5626656632599104
idv_policy eval average team episode rewards of agent0: 75.0
idv_policy eval idv catch total num of agent0: 24
idv_policy eval team catch total num: 30
idv_policy eval average step individual rewards of agent1: 0.7560160953947888
idv_policy eval average team episode rewards of agent1: 75.0
idv_policy eval idv catch total num of agent1: 32
idv_policy eval team catch total num: 30
idv_policy eval average step individual rewards of agent2: 0.42360728660111474
idv_policy eval average team episode rewards of agent2: 75.0
idv_policy eval idv catch total num of agent2: 19
idv_policy eval team catch total num: 30
idv_policy eval average step individual rewards of agent3: 0.8164532881921709
idv_policy eval average team episode rewards of agent3: 75.0
idv_policy eval idv catch total num of agent3: 34
idv_policy eval team catch total num: 30
idv_policy eval average step individual rewards of agent4: 0.4573671857485015
idv_policy eval average team episode rewards of agent4: 75.0
idv_policy eval idv catch total num of agent4: 20
idv_policy eval team catch total num: 30

 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 217.


 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 217.


 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 217.


 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 217.


 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 217.


 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 217.


 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 217.


 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 217.


 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 217.


 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 217.


 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 217.


 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 217.


 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 217.


 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 217.


 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 217.


 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 217.


 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 217.


 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 217.


 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 217.


 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 217.


 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 217.


 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 217.


 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 217.


 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 217.


 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 217.

team_policy eval average step individual rewards of agent0: 0.7458267074294038
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: 1.3724744812166298
team_policy eval average team episode rewards of agent1: 137.5
team_policy eval idv catch total num of agent1: 56
team_policy eval team catch total num: 55
team_policy eval average step individual rewards of agent2: 0.6124287148037791
team_policy eval average team episode rewards of agent2: 137.5
team_policy eval idv catch total num of agent2: 26
team_policy eval team catch total num: 55
team_policy eval average step individual rewards of agent3: 0.9128896300625402
team_policy eval average team episode rewards of agent3: 137.5
team_policy eval idv catch total num of agent3: 38
team_policy eval team catch total num: 55
team_policy eval average step individual rewards of agent4: 1.3190644010837167
team_policy eval average team episode rewards of agent4: 137.5
team_policy eval idv catch total num of agent4: 54
team_policy eval team catch total num: 55
idv_policy eval average step individual rewards of agent0: 0.8721190330875672
idv_policy eval average team episode rewards of agent0: 127.5
idv_policy eval idv catch total num of agent0: 36
idv_policy eval team catch total num: 51
idv_policy eval average step individual rewards of agent1: 0.9726051885802588
idv_policy eval average team episode rewards of agent1: 127.5
idv_policy eval idv catch total num of agent1: 40
idv_policy eval team catch total num: 51
idv_policy eval average step individual rewards of agent2: 0.9188323851046988
idv_policy eval average team episode rewards of agent2: 127.5
idv_policy eval idv catch total num of agent2: 38
idv_policy eval team catch total num: 51
idv_policy eval average step individual rewards of agent3: 0.6955732418885949
idv_policy eval average team episode rewards of agent3: 127.5
idv_policy eval idv catch total num of agent3: 29
idv_policy eval team catch total num: 51
idv_policy eval average step individual rewards of agent4: 1.024350447735428
idv_policy eval average team episode rewards of agent4: 127.5
idv_policy eval idv catch total num of agent4: 42
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 217.


 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 217.


 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 217.


 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 217.


 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 217.


 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 217.


 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 217.


 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 217.


 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 217.


 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 217.


 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 217.


 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 217.


 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 217.


 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 217.


 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 217.


 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 217.


 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 217.


 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 217.


 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 217.


 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 217.


 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 217.


 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 217.


 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 217.


 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 217.


 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 217.

team_policy eval average step individual rewards of agent0: 0.5367860418870161
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: 0.3837982355185446
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: 0.5134218507962901
team_policy eval average team episode rewards of agent2: 75.0
team_policy eval idv catch total num of agent2: 22
team_policy eval team catch total num: 30
team_policy eval average step individual rewards of agent3: 0.7398040998884562
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: 0.5848661667318622
team_policy eval average team episode rewards of agent4: 75.0
team_policy eval idv catch total num of agent4: 25
team_policy eval team catch total num: 30
idv_policy eval average step individual rewards of agent0: 0.5110009623424693
idv_policy eval average team episode rewards of agent0: 105.0
idv_policy eval idv catch total num of agent0: 22
idv_policy eval team catch total num: 42
idv_policy eval average step individual rewards of agent1: 0.7131892113440172
idv_policy eval average team episode rewards of agent1: 105.0
idv_policy eval idv catch total num of agent1: 30
idv_policy eval team catch total num: 42
idv_policy eval average step individual rewards of agent2: 0.9932014941284473
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: 0.6448917408208508
idv_policy eval average team episode rewards of agent3: 105.0
idv_policy eval idv catch total num of agent3: 27
idv_policy eval team catch total num: 42
idv_policy eval average step individual rewards of agent4: 0.6881360653687539
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 3901/10000 episodes, total num timesteps 780400/2000000, FPS 217.


 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 217.


 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 217.


 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 217.


 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 217.


 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 217.


 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 217.


 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 217.


 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 217.


 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 217.


 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 217.


 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 217.


 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 217.


 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 217.


 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 217.


 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 217.


 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 217.


 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 217.


 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 217.


 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 217.


 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 217.


 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 218.


 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 218.


 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 218.


 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 218.

team_policy eval average step individual rewards of agent0: 1.0703039894319528
team_policy eval average team episode rewards of agent0: 135.0
team_policy eval idv catch total num of agent0: 44
team_policy eval team catch total num: 54
team_policy eval average step individual rewards of agent1: 0.6685556663934846
team_policy eval average team episode rewards of agent1: 135.0
team_policy eval idv catch total num of agent1: 28
team_policy eval team catch total num: 54
team_policy eval average step individual rewards of agent2: 1.1015556252787453
team_policy eval average team episode rewards of agent2: 135.0
team_policy eval idv catch total num of agent2: 45
team_policy eval team catch total num: 54
team_policy eval average step individual rewards of agent3: 0.8468097820787657
team_policy eval average team episode rewards of agent3: 135.0
team_policy eval idv catch total num of agent3: 35
team_policy eval team catch total num: 54
team_policy eval average step individual rewards of agent4: 0.8241976399225933
team_policy eval average team episode rewards of agent4: 135.0
team_policy eval idv catch total num of agent4: 34
team_policy eval team catch total num: 54
idv_policy eval average step individual rewards of agent0: 0.3826566907042758
idv_policy eval average team episode rewards of agent0: 65.0
idv_policy eval idv catch total num of agent0: 17
idv_policy eval team catch total num: 26
idv_policy eval average step individual rewards of agent1: 0.5588338131184835
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: 0.48275527805038543
idv_policy eval average team episode rewards of agent2: 65.0
idv_policy eval idv catch total num of agent2: 21
idv_policy eval team catch total num: 26
idv_policy eval average step individual rewards of agent3: 0.4627321400171165
idv_policy eval average team episode rewards of agent3: 65.0
idv_policy eval idv catch total num of agent3: 20
idv_policy eval team catch total num: 26
idv_policy eval average step individual rewards of agent4: 0.2797829795516883
idv_policy eval average team episode rewards of agent4: 65.0
idv_policy eval idv catch total num of agent4: 13
idv_policy eval team catch total num: 26

 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 218.


 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 218.


 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 218.


 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 218.


 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 218.


 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 218.


 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 218.


 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 218.


 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 218.


 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 218.


 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 218.


 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 218.


 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 218.


 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 218.


 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 218.


 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 218.


 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 218.


 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 218.


 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 218.


 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 218.


 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 218.


 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 218.


 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 218.


 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 218.


 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 218.

team_policy eval average step individual rewards of agent0: 0.6326094840775571
team_policy eval average team episode rewards of agent0: 75.0
team_policy eval idv catch total num of agent0: 27
team_policy eval team catch total num: 30
team_policy eval average step individual rewards of agent1: 0.19323576086344257
team_policy eval average team episode rewards of agent1: 75.0
team_policy eval idv catch total num of agent1: 10
team_policy eval team catch total num: 30
team_policy eval average step individual rewards of agent2: 0.6020524029016568
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: 0.5698654794137791
team_policy eval average team episode rewards of agent3: 75.0
team_policy eval idv catch total num of agent3: 25
team_policy eval team catch total num: 30
team_policy eval average step individual rewards of agent4: 0.4299868309086151
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: 0.400870256441356
idv_policy eval average team episode rewards of agent0: 117.5
idv_policy eval idv catch total num of agent0: 18
idv_policy eval team catch total num: 47
idv_policy eval average step individual rewards of agent1: 1.068655562078001
idv_policy eval average team episode rewards of agent1: 117.5
idv_policy eval idv catch total num of agent1: 44
idv_policy eval team catch total num: 47
idv_policy eval average step individual rewards of agent2: 1.0148158470766875
idv_policy eval average team episode rewards of agent2: 117.5
idv_policy eval idv catch total num of agent2: 42
idv_policy eval team catch total num: 47
idv_policy eval average step individual rewards of agent3: 1.0825737824220973
idv_policy eval average team episode rewards of agent3: 117.5
idv_policy eval idv catch total num of agent3: 45
idv_policy eval team catch total num: 47
idv_policy eval average step individual rewards of agent4: 1.0148503953285302
idv_policy eval average team episode rewards of agent4: 117.5
idv_policy eval idv catch total num of agent4: 42
idv_policy eval team catch total num: 47

 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 218.


 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 218.


 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 218.


 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 218.


 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 218.


 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 218.


 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 218.


 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 218.


 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 218.


 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 218.


 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 218.


 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 218.


 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 218.


 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 218.


 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 218.


 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 218.


 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 218.


 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 218.


 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 218.


 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 218.


 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 218.


 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 218.


 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 218.


 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 218.


 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 218.

team_policy eval average step individual rewards of agent0: 0.7359228314480692
team_policy eval average team episode rewards of agent0: 92.5
team_policy eval idv catch total num of agent0: 31
team_policy eval team catch total num: 37
team_policy eval average step individual rewards of agent1: 0.9165252718406893
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: 0.7084030481692739
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: 0.8178233284603613
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: 0.5097398457772252
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: 0.7912330141679224
idv_policy eval average team episode rewards of agent0: 95.0
idv_policy eval idv catch total num of agent0: 33
idv_policy eval team catch total num: 38
idv_policy eval average step individual rewards of agent1: 0.7639168565262432
idv_policy eval average team episode rewards of agent1: 95.0
idv_policy eval idv catch total num of agent1: 32
idv_policy eval team catch total num: 38
idv_policy eval average step individual rewards of agent2: 0.555277478686035
idv_policy eval average team episode rewards of agent2: 95.0
idv_policy eval idv catch total num of agent2: 24
idv_policy eval team catch total num: 38
idv_policy eval average step individual rewards of agent3: 0.5071735842816144
idv_policy eval average team episode rewards of agent3: 95.0
idv_policy eval idv catch total num of agent3: 22
idv_policy eval team catch total num: 38
idv_policy eval average step individual rewards of agent4: 0.7826259180474535
idv_policy eval average team episode rewards of agent4: 95.0
idv_policy eval idv catch total num of agent4: 33
idv_policy eval team catch total num: 38

 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 218.


 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 218.


 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 218.


 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 218.


 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 218.


 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 218.


 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 218.


 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 218.


 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 218.


 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 218.


 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 218.


 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 218.


 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 218.


 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 218.


 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 218.


 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 218.


 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 218.


 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 218.


 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 218.


 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 218.


 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 219.


 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 219.


 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 219.


 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 219.


 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 219.

team_policy eval average step individual rewards of agent0: 0.8637744038664289
team_policy eval average team episode rewards of agent0: 95.0
team_policy eval idv catch total num of agent0: 36
team_policy eval team catch total num: 38
team_policy eval average step individual rewards of agent1: 0.4817786986561724
team_policy eval average team episode rewards of agent1: 95.0
team_policy eval idv catch total num of agent1: 21
team_policy eval team catch total num: 38
team_policy eval average step individual rewards of agent2: 0.6102061247756115
team_policy eval average team episode rewards of agent2: 95.0
team_policy eval idv catch total num of agent2: 26
team_policy eval team catch total num: 38
team_policy eval average step individual rewards of agent3: 0.8199852008718925
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: 0.7848002199709033
team_policy eval average team episode rewards of agent4: 95.0
team_policy eval idv catch total num of agent4: 33
team_policy eval team catch total num: 38
idv_policy eval average step individual rewards of agent0: 0.5066425403737201
idv_policy eval average team episode rewards of agent0: 92.5
idv_policy eval idv catch total num of agent0: 22
idv_policy eval team catch total num: 37
idv_policy eval average step individual rewards of agent1: 0.8397909370620392
idv_policy eval average team episode rewards of agent1: 92.5
idv_policy eval idv catch total num of agent1: 35
idv_policy eval team catch total num: 37
idv_policy eval average step individual rewards of agent2: 0.5795590542387891
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: 0.5348205393645727
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: 0.5731753080155719
idv_policy eval average team episode rewards of agent4: 92.5
idv_policy eval idv catch total num of agent4: 25
idv_policy eval team catch total num: 37

 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 219.


 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 219.


 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 219.


 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 219.


 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 219.


 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 219.


 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 219.


 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 219.


 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 219.


 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 219.


 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 219.


 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 219.


 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 219.


 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 219.


 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 219.


 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 219.


 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 219.


 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 219.


 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 219.


 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 219.


 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 219.


 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 219.


 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 219.


 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 219.


 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 219.

team_policy eval average step individual rewards of agent0: 0.42058228074774023
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: 0.34363111886218367
team_policy eval average team episode rewards of agent1: 55.0
team_policy eval idv catch total num of agent1: 16
team_policy eval team catch total num: 22
team_policy eval average step individual rewards of agent2: 0.8308839101125397
team_policy eval average team episode rewards of agent2: 55.0
team_policy eval idv catch total num of agent2: 35
team_policy eval team catch total num: 22
team_policy eval average step individual rewards of agent3: 0.45161604012224094
team_policy eval average team episode rewards of agent3: 55.0
team_policy eval idv catch total num of agent3: 20
team_policy eval team catch total num: 22
team_policy eval average step individual rewards of agent4: 0.47752545730419826
team_policy eval average team episode rewards of agent4: 55.0
team_policy eval idv catch total num of agent4: 21
team_policy eval team catch total num: 22
idv_policy eval average step individual rewards of agent0: 0.7465852462282053
idv_policy eval average team episode rewards of agent0: 130.0
idv_policy eval idv catch total num of agent0: 31
idv_policy eval team catch total num: 52
idv_policy eval average step individual rewards of agent1: 0.9376463420285495
idv_policy eval average team episode rewards of agent1: 130.0
idv_policy eval idv catch total num of agent1: 39
idv_policy eval team catch total num: 52
idv_policy eval average step individual rewards of agent2: 1.193537576579786
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: 0.7851834607318746
idv_policy eval average team episode rewards of agent3: 130.0
idv_policy eval idv catch total num of agent3: 33
idv_policy eval team catch total num: 52
idv_policy eval average step individual rewards of agent4: 0.9709717702645787
idv_policy eval average team episode rewards of agent4: 130.0
idv_policy eval idv catch total num of agent4: 40
idv_policy eval team catch total num: 52

 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 219.


 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 219.


 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 219.


 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 219.


 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 219.


 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 219.


 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 219.


 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 219.


 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 219.


 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 219.


 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 219.


 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 219.


 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 219.


 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 219.


 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 219.


 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 219.


 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 219.


 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 219.


 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 219.


 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 219.


 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 219.


 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 219.


 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 219.


 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 219.


 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 219.

team_policy eval average step individual rewards of agent0: 0.3508273150401846
team_policy eval average team episode rewards of agent0: 65.0
team_policy eval idv catch total num of agent0: 16
team_policy eval team catch total num: 26
team_policy eval average step individual rewards of agent1: 0.6850487186599997
team_policy eval average team episode rewards of agent1: 65.0
team_policy eval idv catch total num of agent1: 29
team_policy eval team catch total num: 26
team_policy eval average step individual rewards of agent2: 0.5683346611583393
team_policy eval average team episode rewards of agent2: 65.0
team_policy eval idv catch total num of agent2: 25
team_policy eval team catch total num: 26
team_policy eval average step individual rewards of agent3: 0.5753904230736892
team_policy eval average team episode rewards of agent3: 65.0
team_policy eval idv catch total num of agent3: 25
team_policy eval team catch total num: 26
team_policy eval average step individual rewards of agent4: 0.46863854479633704
team_policy eval average team episode rewards of agent4: 65.0
team_policy eval idv catch total num of agent4: 21
team_policy eval team catch total num: 26
idv_policy eval average step individual rewards of agent0: 1.0607316788841705
idv_policy eval average team episode rewards of agent0: 120.0
idv_policy eval idv catch total num of agent0: 44
idv_policy eval team catch total num: 48
idv_policy eval average step individual rewards of agent1: 0.9756348487552573
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: 0.5627556954162053
idv_policy eval average team episode rewards of agent2: 120.0
idv_policy eval idv catch total num of agent2: 24
idv_policy eval team catch total num: 48
idv_policy eval average step individual rewards of agent3: 0.705484989105156
idv_policy eval average team episode rewards of agent3: 120.0
idv_policy eval idv catch total num of agent3: 30
idv_policy eval team catch total num: 48
idv_policy eval average step individual rewards of agent4: 1.1945488300014118
idv_policy eval average team episode rewards of agent4: 120.0
idv_policy eval idv catch total num of agent4: 49
idv_policy eval team catch total num: 48

 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 219.


 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 219.


 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 219.


 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 219.


 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 219.


 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 219.


 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 219.


 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 219.


 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 219.


 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 219.


 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 219.


 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 219.


 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 219.


 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 219.


 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 219.


 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 219.


 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 219.


 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 219.


 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 220.


 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 220.


 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 220.


 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 220.


 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 220.


 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 220.


 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 220.

team_policy eval average step individual rewards of agent0: 1.0693194545000633
team_policy eval average team episode rewards of agent0: 115.0
team_policy eval idv catch total num of agent0: 44
team_policy eval team catch total num: 46
team_policy eval average step individual rewards of agent1: 0.8467264796949653
team_policy eval average team episode rewards of agent1: 115.0
team_policy eval idv catch total num of agent1: 35
team_policy eval team catch total num: 46
team_policy eval average step individual rewards of agent2: 0.6900778511222455
team_policy eval average team episode rewards of agent2: 115.0
team_policy eval idv catch total num of agent2: 29
team_policy eval team catch total num: 46
team_policy eval average step individual rewards of agent3: 0.6459766496327345
team_policy eval average team episode rewards of agent3: 115.0
team_policy eval idv catch total num of agent3: 28
team_policy eval team catch total num: 46
team_policy eval average step individual rewards of agent4: 0.8104294499391191
team_policy eval average team episode rewards of agent4: 115.0
team_policy eval idv catch total num of agent4: 34
team_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent0: 1.1467714337034614
idv_policy eval average team episode rewards of agent0: 95.0
idv_policy eval idv catch total num of agent0: 47
idv_policy eval team catch total num: 38
idv_policy eval average step individual rewards of agent1: 0.43471460617370544
idv_policy eval average team episode rewards of agent1: 95.0
idv_policy eval idv catch total num of agent1: 19
idv_policy eval team catch total num: 38
idv_policy eval average step individual rewards of agent2: 0.7403874599992151
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: 0.7340968079463832
idv_policy eval average team episode rewards of agent3: 95.0
idv_policy eval idv catch total num of agent3: 31
idv_policy eval team catch total num: 38
idv_policy eval average step individual rewards of agent4: 0.6053885558519557
idv_policy eval average team episode rewards of agent4: 95.0
idv_policy eval idv catch total num of agent4: 26
idv_policy eval team catch total num: 38

 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 220.


 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 220.


 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 220.


 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 220.


 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 220.


 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 220.


 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 220.


 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 220.


 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 220.


 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 220.


 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 220.


 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 220.


 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 220.


 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 220.


 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 220.


 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 220.


 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 220.


 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 220.


 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 220.


 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 220.


 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 220.


 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 220.


 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 220.


 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 220.


 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 220.

team_policy eval average step individual rewards of agent0: 0.9188475625458632
team_policy eval average team episode rewards of agent0: 117.5
team_policy eval idv catch total num of agent0: 38
team_policy eval team catch total num: 47
team_policy eval average step individual rewards of agent1: 1.1152277484994684
team_policy eval average team episode rewards of agent1: 117.5
team_policy eval idv catch total num of agent1: 46
team_policy eval team catch total num: 47
team_policy eval average step individual rewards of agent2: 1.0656667595831175
team_policy eval average team episode rewards of agent2: 117.5
team_policy eval idv catch total num of agent2: 44
team_policy eval team catch total num: 47
team_policy eval average step individual rewards of agent3: 0.6047673420825267
team_policy eval average team episode rewards of agent3: 117.5
team_policy eval idv catch total num of agent3: 26
team_policy eval team catch total num: 47
team_policy eval average step individual rewards of agent4: 0.6306011390386453
team_policy eval average team episode rewards of agent4: 117.5
team_policy eval idv catch total num of agent4: 27
team_policy eval team catch total num: 47
idv_policy eval average step individual rewards of agent0: 1.2253234374792734
idv_policy eval average team episode rewards of agent0: 170.0
idv_policy eval idv catch total num of agent0: 50
idv_policy eval team catch total num: 68
idv_policy eval average step individual rewards of agent1: 1.4769607779976133
idv_policy eval average team episode rewards of agent1: 170.0
idv_policy eval idv catch total num of agent1: 60
idv_policy eval team catch total num: 68
idv_policy eval average step individual rewards of agent2: 0.6886271469694779
idv_policy eval average team episode rewards of agent2: 170.0
idv_policy eval idv catch total num of agent2: 29
idv_policy eval team catch total num: 68
idv_policy eval average step individual rewards of agent3: 1.1448031751347052
idv_policy eval average team episode rewards of agent3: 170.0
idv_policy eval idv catch total num of agent3: 47
idv_policy eval team catch total num: 68
idv_policy eval average step individual rewards of agent4: 1.1972584853387074
idv_policy eval average team episode rewards of agent4: 170.0
idv_policy eval idv catch total num of agent4: 49
idv_policy eval team catch total num: 68

 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 220.


 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 220.


 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 220.


 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 220.


 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 220.


 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 220.


 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 220.


 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 220.


 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 220.


 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 220.


 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 220.


 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 220.


 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 220.


 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 220.


 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 220.


 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 220.


 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 220.


 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 220.


 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 220.


 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 220.


 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 220.


 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 220.


 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 220.


 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 220.


 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 220.

team_policy eval average step individual rewards of agent0: 0.9388214041447153
team_policy eval average team episode rewards of agent0: 115.0
team_policy eval idv catch total num of agent0: 39
team_policy eval team catch total num: 46
team_policy eval average step individual rewards of agent1: 0.6638980614970862
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: 0.8386352396356878
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: 0.9192260141459098
team_policy eval average team episode rewards of agent3: 115.0
team_policy eval idv catch total num of agent3: 38
team_policy eval team catch total num: 46
team_policy eval average step individual rewards of agent4: 0.6287689621180417
team_policy eval average team episode rewards of agent4: 115.0
team_policy eval idv catch total num of agent4: 27
team_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent0: 1.0163745111581812
idv_policy eval average team episode rewards of agent0: 122.5
idv_policy eval idv catch total num of agent0: 42
idv_policy eval team catch total num: 49
idv_policy eval average step individual rewards of agent1: 0.6696855020549853
idv_policy eval average team episode rewards of agent1: 122.5
idv_policy eval idv catch total num of agent1: 28
idv_policy eval team catch total num: 49
idv_policy eval average step individual rewards of agent2: 1.1137435233956818
idv_policy eval average team episode rewards of agent2: 122.5
idv_policy eval idv catch total num of agent2: 46
idv_policy eval team catch total num: 49
idv_policy eval average step individual rewards of agent3: 0.7398669467614722
idv_policy eval average team episode rewards of agent3: 122.5
idv_policy eval idv catch total num of agent3: 31
idv_policy eval team catch total num: 49
idv_policy eval average step individual rewards of agent4: 0.5594685931209138
idv_policy eval average team episode rewards of agent4: 122.5
idv_policy eval idv catch total num of agent4: 24
idv_policy eval team catch total num: 49

 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 220.


 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 220.


 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 220.


 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 220.


 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 220.


 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 220.


 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 220.


 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 220.


 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 220.


 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 220.


 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 220.


 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 220.


 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 220.


 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 220.


 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 220.


 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 220.


 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 220.


 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 220.


 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 220.


 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 220.


 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 220.


 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 220.


 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 220.


 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 220.


 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 221.

team_policy eval average step individual rewards of agent0: 0.6635754878810368
team_policy eval average team episode rewards of agent0: 100.0
team_policy eval idv catch total num of agent0: 28
team_policy eval team catch total num: 40
team_policy eval average step individual rewards of agent1: 0.7663827741814481
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: 0.8599646960824622
team_policy eval average team episode rewards of agent2: 100.0
team_policy eval idv catch total num of agent2: 36
team_policy eval team catch total num: 40
team_policy eval average step individual rewards of agent3: 0.6381537795200937
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: 0.6428653808018763
team_policy eval average team episode rewards of agent4: 100.0
team_policy eval idv catch total num of agent4: 27
team_policy eval team catch total num: 40
idv_policy eval average step individual rewards of agent0: 0.9636432405218113
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: 1.0164001037233399
idv_policy eval average team episode rewards of agent1: 142.5
idv_policy eval idv catch total num of agent1: 42
idv_policy eval team catch total num: 57
idv_policy eval average step individual rewards of agent2: 1.5207484506731288
idv_policy eval average team episode rewards of agent2: 142.5
idv_policy eval idv catch total num of agent2: 62
idv_policy eval team catch total num: 57
idv_policy eval average step individual rewards of agent3: 0.7412066168374943
idv_policy eval average team episode rewards of agent3: 142.5
idv_policy eval idv catch total num of agent3: 31
idv_policy eval team catch total num: 57
idv_policy eval average step individual rewards of agent4: 0.8120236078648699
idv_policy eval average team episode rewards of agent4: 142.5
idv_policy eval idv catch total num of agent4: 34
idv_policy eval team catch total num: 57

 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 221.


 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 221.


 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 221.


 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 221.


 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 221.


 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 221.


 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 221.


 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 221.


 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 221.


 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 221.


 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 221.


 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 221.


 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 221.


 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 221.


 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 221.


 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 221.


 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 221.


 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 221.


 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 221.


 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 221.


 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 221.


 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 221.


 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 221.


 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 221.


 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 221.

team_policy eval average step individual rewards of agent0: 0.5520842065047961
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: 0.4429575648309514
team_policy eval average team episode rewards of agent1: 82.5
team_policy eval idv catch total num of agent1: 20
team_policy eval team catch total num: 33
team_policy eval average step individual rewards of agent2: 0.8429703352392306
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: 0.6892953189958171
team_policy eval average team episode rewards of agent3: 82.5
team_policy eval idv catch total num of agent3: 29
team_policy eval team catch total num: 33
team_policy eval average step individual rewards of agent4: 0.3466305509852667
team_policy eval average team episode rewards of agent4: 82.5
team_policy eval idv catch total num of agent4: 16
team_policy eval team catch total num: 33
idv_policy eval average step individual rewards of agent0: 0.8729440824080436
idv_policy eval average team episode rewards of agent0: 145.0
idv_policy eval idv catch total num of agent0: 36
idv_policy eval team catch total num: 58
idv_policy eval average step individual rewards of agent1: 0.9174872715140642
idv_policy eval average team episode rewards of agent1: 145.0
idv_policy eval idv catch total num of agent1: 38
idv_policy eval team catch total num: 58
idv_policy eval average step individual rewards of agent2: 1.1478548898550003
idv_policy eval average team episode rewards of agent2: 145.0
idv_policy eval idv catch total num of agent2: 47
idv_policy eval team catch total num: 58
idv_policy eval average step individual rewards of agent3: 0.9733467084766765
idv_policy eval average team episode rewards of agent3: 145.0
idv_policy eval idv catch total num of agent3: 40
idv_policy eval team catch total num: 58
idv_policy eval average step individual rewards of agent4: 1.0391596683016884
idv_policy eval average team episode rewards of agent4: 145.0
idv_policy eval idv catch total num of agent4: 43
idv_policy eval team catch total num: 58

 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 221.


 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 221.


 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 221.


 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 221.


 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 221.


 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 221.


 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 221.


 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 221.


 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 221.


 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 221.


 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 221.


 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 221.


 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 221.


 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 221.


 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 221.


 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 221.


 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 221.


 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 221.


 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 221.


 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 221.


 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 221.


 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 221.


 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 221.


 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 221.


 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 221.

team_policy eval average step individual rewards of agent0: 0.7898022646914916
team_policy eval average team episode rewards of agent0: 75.0
team_policy eval idv catch total num of agent0: 33
team_policy eval team catch total num: 30
team_policy eval average step individual rewards of agent1: 0.39721610981390554
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: 0.7906859957855242
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: 0.4457772528859875
team_policy eval average team episode rewards of agent3: 75.0
team_policy eval idv catch total num of agent3: 20
team_policy eval team catch total num: 30
team_policy eval average step individual rewards of agent4: 0.45072839284169525
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: 0.9910769328379504
idv_policy eval average team episode rewards of agent0: 110.0
idv_policy eval idv catch total num of agent0: 41
idv_policy eval team catch total num: 44
idv_policy eval average step individual rewards of agent1: 0.5757139704194413
idv_policy eval average team episode rewards of agent1: 110.0
idv_policy eval idv catch total num of agent1: 25
idv_policy eval team catch total num: 44
idv_policy eval average step individual rewards of agent2: 1.1474911157886993
idv_policy eval average team episode rewards of agent2: 110.0
idv_policy eval idv catch total num of agent2: 47
idv_policy eval team catch total num: 44
idv_policy eval average step individual rewards of agent3: 0.8415424993789559
idv_policy eval average team episode rewards of agent3: 110.0
idv_policy eval idv catch total num of agent3: 35
idv_policy eval team catch total num: 44
idv_policy eval average step individual rewards of agent4: 0.4755894549130445
idv_policy eval average team episode rewards of agent4: 110.0
idv_policy eval idv catch total num of agent4: 21
idv_policy eval team catch total num: 44

 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 221.


 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 221.


 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 221.


 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 221.


 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 221.


 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 221.


 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 221.


 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 221.


 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 221.


 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 221.


 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 221.


 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 221.


 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 221.


 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 221.


 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 221.


 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 221.


 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 221.


 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 221.


 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 221.


 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 221.


 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 221.


 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 221.


 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 221.


 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 221.


 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 221.

team_policy eval average step individual rewards of agent0: 0.8954244297643044
team_policy eval average team episode rewards of agent0: 105.0
team_policy eval idv catch total num of agent0: 37
team_policy eval team catch total num: 42
team_policy eval average step individual rewards of agent1: 0.8099432625442161
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: 0.842140309116579
team_policy eval average team episode rewards of agent2: 105.0
team_policy eval idv catch total num of agent2: 35
team_policy eval team catch total num: 42
team_policy eval average step individual rewards of agent3: 0.43803305509091955
team_policy eval average team episode rewards of agent3: 105.0
team_policy eval idv catch total num of agent3: 19
team_policy eval team catch total num: 42
team_policy eval average step individual rewards of agent4: 0.6830871483301738
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: 0.6276153778035668
idv_policy eval average team episode rewards of agent0: 110.0
idv_policy eval idv catch total num of agent0: 27
idv_policy eval team catch total num: 44
idv_policy eval average step individual rewards of agent1: 0.689403184476102
idv_policy eval average team episode rewards of agent1: 110.0
idv_policy eval idv catch total num of agent1: 29
idv_policy eval team catch total num: 44
idv_policy eval average step individual rewards of agent2: 0.9218610011540386
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: 0.8607553665975312
idv_policy eval average team episode rewards of agent3: 110.0
idv_policy eval idv catch total num of agent3: 36
idv_policy eval team catch total num: 44
idv_policy eval average step individual rewards of agent4: 0.7439626947589036
idv_policy eval average team episode rewards of agent4: 110.0
idv_policy eval idv catch total num of agent4: 31
idv_policy eval team catch total num: 44

 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 221.


 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 221.


 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 221.


 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 221.


 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 221.


 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 221.


 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 222.


 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 222.


 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 222.


 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 222.


 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 222.


 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 222.


 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 222.


 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 222.


 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 222.


 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 222.


 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 222.


 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 222.


 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 222.


 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 222.


 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 222.


 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 222.


 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 222.


 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 222.


 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 222.

team_policy eval average step individual rewards of agent0: 0.9635360850491236
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: 1.1383469727291982
team_policy eval average team episode rewards of agent1: 140.0
team_policy eval idv catch total num of agent1: 47
team_policy eval team catch total num: 56
team_policy eval average step individual rewards of agent2: 0.8914188996674542
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: 0.9198181793288913
team_policy eval average team episode rewards of agent3: 140.0
team_policy eval idv catch total num of agent3: 38
team_policy eval team catch total num: 56
team_policy eval average step individual rewards of agent4: 1.0496596107096456
team_policy eval average team episode rewards of agent4: 140.0
team_policy eval idv catch total num of agent4: 43
team_policy eval team catch total num: 56
idv_policy eval average step individual rewards of agent0: 1.0416090223717804
idv_policy eval average team episode rewards of agent0: 132.5
idv_policy eval idv catch total num of agent0: 43
idv_policy eval team catch total num: 53
idv_policy eval average step individual rewards of agent1: 0.9131561688801536
idv_policy eval average team episode rewards of agent1: 132.5
idv_policy eval idv catch total num of agent1: 38
idv_policy eval team catch total num: 53
idv_policy eval average step individual rewards of agent2: 0.7885925545783603
idv_policy eval average team episode rewards of agent2: 132.5
idv_policy eval idv catch total num of agent2: 33
idv_policy eval team catch total num: 53
idv_policy eval average step individual rewards of agent3: 0.7802488001179085
idv_policy eval average team episode rewards of agent3: 132.5
idv_policy eval idv catch total num of agent3: 33
idv_policy eval team catch total num: 53
idv_policy eval average step individual rewards of agent4: 1.1638633772647027
idv_policy eval average team episode rewards of agent4: 132.5
idv_policy eval idv catch total num of agent4: 48
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 222.


 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 222.


 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 222.


 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 222.


 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 222.


 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 222.


 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 222.


 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 222.


 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 222.


 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 222.


 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 222.


 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 222.


 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 222.


 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 222.


 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 222.


 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 222.


 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 222.


 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 222.


 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 222.


 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 222.


 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 222.


 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 222.


 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 222.


 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 222.


 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 222.

team_policy eval average step individual rewards of agent0: 0.4280519541057292
team_policy eval average team episode rewards of agent0: 75.0
team_policy eval idv catch total num of agent0: 19
team_policy eval team catch total num: 30
team_policy eval average step individual rewards of agent1: 0.6620285223614615
team_policy eval average team episode rewards of agent1: 75.0
team_policy eval idv catch total num of agent1: 28
team_policy eval team catch total num: 30
team_policy eval average step individual rewards of agent2: 0.7881306020965694
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: 0.7768062810236223
team_policy eval average team episode rewards of agent3: 75.0
team_policy eval idv catch total num of agent3: 33
team_policy eval team catch total num: 30
team_policy eval average step individual rewards of agent4: 0.47834690549055714
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: 0.6077537859107583
idv_policy eval average team episode rewards of agent0: 102.5
idv_policy eval idv catch total num of agent0: 26
idv_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent1: 1.0647316368089816
idv_policy eval average team episode rewards of agent1: 102.5
idv_policy eval idv catch total num of agent1: 44
idv_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent2: 0.7606265198260022
idv_policy eval average team episode rewards of agent2: 102.5
idv_policy eval idv catch total num of agent2: 32
idv_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent3: 0.6794678733456149
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: 0.8889762785111518
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 4276/10000 episodes, total num timesteps 855400/2000000, FPS 222.


 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 222.


 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 222.


 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 222.


 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 222.


 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 222.


 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 222.


 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 222.


 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 222.


 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 222.


 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 222.


 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 222.


 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 222.


 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 222.


 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 222.


 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 222.


 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 222.


 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 222.


 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 222.


 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 222.


 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 222.


 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 222.


 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 222.


 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 222.


 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 222.

team_policy eval average step individual rewards of agent0: 0.8945876516008638
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: 1.1163604583647229
team_policy eval average team episode rewards of agent1: 120.0
team_policy eval idv catch total num of agent1: 46
team_policy eval team catch total num: 48
team_policy eval average step individual rewards of agent2: 0.891504342588236
team_policy eval average team episode rewards of agent2: 120.0
team_policy eval idv catch total num of agent2: 37
team_policy eval team catch total num: 48
team_policy eval average step individual rewards of agent3: 0.7760827518628086
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: 0.7610446975764227
team_policy eval average team episode rewards of agent4: 120.0
team_policy eval idv catch total num of agent4: 32
team_policy eval team catch total num: 48
idv_policy eval average step individual rewards of agent0: 0.7339000848785868
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: 0.46125979991626886
idv_policy eval average team episode rewards of agent1: 80.0
idv_policy eval idv catch total num of agent1: 20
idv_policy eval team catch total num: 32
idv_policy eval average step individual rewards of agent2: 0.6344611697560792
idv_policy eval average team episode rewards of agent2: 80.0
idv_policy eval idv catch total num of agent2: 27
idv_policy eval team catch total num: 32
idv_policy eval average step individual rewards of agent3: 0.7323690695128675
idv_policy eval average team episode rewards of agent3: 80.0
idv_policy eval idv catch total num of agent3: 31
idv_policy eval team catch total num: 32
idv_policy eval average step individual rewards of agent4: 0.3300496479053525
idv_policy eval average team episode rewards of agent4: 80.0
idv_policy eval idv catch total num of agent4: 15
idv_policy eval team catch total num: 32

 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 222.


 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 222.


 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 222.


 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 222.


 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 222.


 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 222.


 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 222.


 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 222.


 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 222.


 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 223.


 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 223.


 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 223.


 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 223.


 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 223.


 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 223.


 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 223.


 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 223.


 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 223.


 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 223.


 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 223.


 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 223.


 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 223.


 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 223.


 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 223.


 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 223.

team_policy eval average step individual rewards of agent0: 0.24897977865363138
team_policy eval average team episode rewards of agent0: 85.0
team_policy eval idv catch total num of agent0: 12
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent1: 0.6046040577542515
team_policy eval average team episode rewards of agent1: 85.0
team_policy eval idv catch total num of agent1: 26
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent2: 0.2821035615723797
team_policy eval average team episode rewards of agent2: 85.0
team_policy eval idv catch total num of agent2: 13
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent3: 1.1983504245672503
team_policy eval average team episode rewards of agent3: 85.0
team_policy eval idv catch total num of agent3: 49
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent4: 0.633564784719859
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: 0.6643713098994775
idv_policy eval average team episode rewards of agent0: 115.0
idv_policy eval idv catch total num of agent0: 28
idv_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent1: 0.589754492584725
idv_policy eval average team episode rewards of agent1: 115.0
idv_policy eval idv catch total num of agent1: 25
idv_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent2: 0.9443753617990259
idv_policy eval average team episode rewards of agent2: 115.0
idv_policy eval idv catch total num of agent2: 39
idv_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent3: 1.0658390409730858
idv_policy eval average team episode rewards of agent3: 115.0
idv_policy eval idv catch total num of agent3: 44
idv_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent4: 0.8686425590424176
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 4326/10000 episodes, total num timesteps 865400/2000000, FPS 223.


 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 223.


 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 223.


 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 223.


 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 223.


 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 223.


 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 223.


 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 223.


 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 223.


 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 223.


 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 223.


 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 223.


 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 223.


 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 223.


 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 223.


 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 223.


 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 223.


 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 223.


 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 223.


 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 223.


 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 223.


 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 223.


 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 223.


 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 223.


 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 223.

team_policy eval average step individual rewards of agent0: 0.9986547514786633
team_policy eval average team episode rewards of agent0: 92.5
team_policy eval idv catch total num of agent0: 41
team_policy eval team catch total num: 37
team_policy eval average step individual rewards of agent1: 0.7342487634884898
team_policy eval average team episode rewards of agent1: 92.5
team_policy eval idv catch total num of agent1: 31
team_policy eval team catch total num: 37
team_policy eval average step individual rewards of agent2: 0.8403829488874175
team_policy eval average team episode rewards of agent2: 92.5
team_policy eval idv catch total num of agent2: 35
team_policy eval team catch total num: 37
team_policy eval average step individual rewards of agent3: 0.6884059060737175
team_policy eval average team episode rewards of agent3: 92.5
team_policy eval idv catch total num of agent3: 29
team_policy eval team catch total num: 37
team_policy eval average step individual rewards of agent4: 0.48559114644113194
team_policy eval average team episode rewards of agent4: 92.5
team_policy eval idv catch total num of agent4: 21
team_policy eval team catch total num: 37
idv_policy eval average step individual rewards of agent0: 0.39891119928153096
idv_policy eval average team episode rewards of agent0: 60.0
idv_policy eval idv catch total num of agent0: 18
idv_policy eval team catch total num: 24
idv_policy eval average step individual rewards of agent1: 0.6837873261429028
idv_policy eval average team episode rewards of agent1: 60.0
idv_policy eval idv catch total num of agent1: 29
idv_policy eval team catch total num: 24
idv_policy eval average step individual rewards of agent2: 0.6807456254174988
idv_policy eval average team episode rewards of agent2: 60.0
idv_policy eval idv catch total num of agent2: 29
idv_policy eval team catch total num: 24
idv_policy eval average step individual rewards of agent3: 0.3759911288521057
idv_policy eval average team episode rewards of agent3: 60.0
idv_policy eval idv catch total num of agent3: 17
idv_policy eval team catch total num: 24
idv_policy eval average step individual rewards of agent4: 0.6000537602139321
idv_policy eval average team episode rewards of agent4: 60.0
idv_policy eval idv catch total num of agent4: 26
idv_policy eval team catch total num: 24

 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 223.


 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 223.


 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 223.


 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 223.


 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 223.


 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 223.


 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 223.


 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 223.


 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 223.


 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 223.


 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 223.


 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 223.


 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 223.


 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 223.


 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 223.


 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 223.


 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 223.


 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 223.


 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 223.


 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 223.


 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 223.


 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 223.


 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 223.


 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 223.


 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 223.

team_policy eval average step individual rewards of agent0: 0.716805328767669
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: 0.861149074767915
team_policy eval average team episode rewards of agent1: 102.5
team_policy eval idv catch total num of agent1: 36
team_policy eval team catch total num: 41
team_policy eval average step individual rewards of agent2: 0.6145304380910916
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: 0.505543540501876
team_policy eval average team episode rewards of agent3: 102.5
team_policy eval idv catch total num of agent3: 22
team_policy eval team catch total num: 41
team_policy eval average step individual rewards of agent4: 1.0412576785791332
team_policy eval average team episode rewards of agent4: 102.5
team_policy eval idv catch total num of agent4: 43
team_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent0: 0.7607919471589519
idv_policy eval average team episode rewards of agent0: 90.0
idv_policy eval idv catch total num of agent0: 32
idv_policy eval team catch total num: 36
idv_policy eval average step individual rewards of agent1: 0.9181553760830142
idv_policy eval average team episode rewards of agent1: 90.0
idv_policy eval idv catch total num of agent1: 38
idv_policy eval team catch total num: 36
idv_policy eval average step individual rewards of agent2: 0.5508730331086844
idv_policy eval average team episode rewards of agent2: 90.0
idv_policy eval idv catch total num of agent2: 24
idv_policy eval team catch total num: 36
idv_policy eval average step individual rewards of agent3: 0.8170809816630381
idv_policy eval average team episode rewards of agent3: 90.0
idv_policy eval idv catch total num of agent3: 34
idv_policy eval team catch total num: 36
idv_policy eval average step individual rewards of agent4: 0.5086356635354243
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 4376/10000 episodes, total num timesteps 875400/2000000, FPS 223.


 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 223.


 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 223.


 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 223.


 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 223.


 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 223.


 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 223.


 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 223.


 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 223.


 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 223.


 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 223.


 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 223.


 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 223.


 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 223.


 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 223.


 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 223.


 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 223.


 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 223.


 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 223.


 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 224.


 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 224.


 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 224.


 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 224.


 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 224.


 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 224.

team_policy eval average step individual rewards of agent0: 0.5351317617658164
team_policy eval average team episode rewards of agent0: 110.0
team_policy eval idv catch total num of agent0: 23
team_policy eval team catch total num: 44
team_policy eval average step individual rewards of agent1: 1.0721938895996006
team_policy eval average team episode rewards of agent1: 110.0
team_policy eval idv catch total num of agent1: 44
team_policy eval team catch total num: 44
team_policy eval average step individual rewards of agent2: 0.732126944920916
team_policy eval average team episode rewards of agent2: 110.0
team_policy eval idv catch total num of agent2: 31
team_policy eval team catch total num: 44
team_policy eval average step individual rewards of agent3: 0.8841683449335478
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: 0.8197706032383968
team_policy eval average team episode rewards of agent4: 110.0
team_policy eval idv catch total num of agent4: 34
team_policy eval team catch total num: 44
idv_policy eval average step individual rewards of agent0: 0.7163200631214892
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: 0.9627682933230733
idv_policy eval average team episode rewards of agent1: 117.5
idv_policy eval idv catch total num of agent1: 40
idv_policy eval team catch total num: 47
idv_policy eval average step individual rewards of agent2: 0.7152187921265544
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: 0.9532464120349702
idv_policy eval average team episode rewards of agent3: 117.5
idv_policy eval idv catch total num of agent3: 40
idv_policy eval team catch total num: 47
idv_policy eval average step individual rewards of agent4: 1.0409594577459267
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 4401/10000 episodes, total num timesteps 880400/2000000, FPS 224.


 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 224.


 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 224.


 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 224.


 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 224.


 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 224.


 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 224.


 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 224.


 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 224.


 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 224.


 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 224.


 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 224.


 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 224.


 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 224.


 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 224.


 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 224.


 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 224.


 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 224.


 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 224.


 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 224.


 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 224.


 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 224.


 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 224.


 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 224.


 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 224.

team_policy eval average step individual rewards of agent0: 0.9851434719391979
team_policy eval average team episode rewards of agent0: 167.5
team_policy eval idv catch total num of agent0: 41
team_policy eval team catch total num: 67
team_policy eval average step individual rewards of agent1: 1.1691068265237923
team_policy eval average team episode rewards of agent1: 167.5
team_policy eval idv catch total num of agent1: 48
team_policy eval team catch total num: 67
team_policy eval average step individual rewards of agent2: 0.9628624720655011
team_policy eval average team episode rewards of agent2: 167.5
team_policy eval idv catch total num of agent2: 40
team_policy eval team catch total num: 67
team_policy eval average step individual rewards of agent3: 1.348072233930587
team_policy eval average team episode rewards of agent3: 167.5
team_policy eval idv catch total num of agent3: 55
team_policy eval team catch total num: 67
team_policy eval average step individual rewards of agent4: 1.3878745156724823
team_policy eval average team episode rewards of agent4: 167.5
team_policy eval idv catch total num of agent4: 57
team_policy eval team catch total num: 67
idv_policy eval average step individual rewards of agent0: 1.36597719604912
idv_policy eval average team episode rewards of agent0: 192.5
idv_policy eval idv catch total num of agent0: 56
idv_policy eval team catch total num: 77
idv_policy eval average step individual rewards of agent1: 1.0116715331928732
idv_policy eval average team episode rewards of agent1: 192.5
idv_policy eval idv catch total num of agent1: 42
idv_policy eval team catch total num: 77
idv_policy eval average step individual rewards of agent2: 1.1688580497795211
idv_policy eval average team episode rewards of agent2: 192.5
idv_policy eval idv catch total num of agent2: 48
idv_policy eval team catch total num: 77
idv_policy eval average step individual rewards of agent3: 1.4545498212021732
idv_policy eval average team episode rewards of agent3: 192.5
idv_policy eval idv catch total num of agent3: 59
idv_policy eval team catch total num: 77
idv_policy eval average step individual rewards of agent4: 1.0000614181980958
idv_policy eval average team episode rewards of agent4: 192.5
idv_policy eval idv catch total num of agent4: 41
idv_policy eval team catch total num: 77

 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 224.


 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 224.


 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 224.


 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 224.


 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 224.


 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 224.


 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 224.


 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 224.


 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 224.


 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 224.


 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 224.


 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 224.


 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 224.


 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 224.


 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 224.


 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 224.


 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 224.


 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 224.


 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 224.


 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 224.


 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 224.


 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 224.


 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 224.


 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 224.


 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 224.

team_policy eval average step individual rewards of agent0: 0.6139975082116466
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: 0.7020019156809826
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: 0.6762963138935005
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: 0.7650542409675845
team_policy eval average team episode rewards of agent3: 112.5
team_policy eval idv catch total num of agent3: 32
team_policy eval team catch total num: 45
team_policy eval average step individual rewards of agent4: 0.7621877212780261
team_policy eval average team episode rewards of agent4: 112.5
team_policy eval idv catch total num of agent4: 32
team_policy eval team catch total num: 45
idv_policy eval average step individual rewards of agent0: 0.7828156561681591
idv_policy eval average team episode rewards of agent0: 145.0
idv_policy eval idv catch total num of agent0: 33
idv_policy eval team catch total num: 58
idv_policy eval average step individual rewards of agent1: 0.8626353872905602
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: 0.8183545866850647
idv_policy eval average team episode rewards of agent2: 145.0
idv_policy eval idv catch total num of agent2: 34
idv_policy eval team catch total num: 58
idv_policy eval average step individual rewards of agent3: 1.2630822690406993
idv_policy eval average team episode rewards of agent3: 145.0
idv_policy eval idv catch total num of agent3: 52
idv_policy eval team catch total num: 58
idv_policy eval average step individual rewards of agent4: 1.2948247145436402
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 4451/10000 episodes, total num timesteps 890400/2000000, FPS 224.


 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 224.


 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 224.


 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 224.


 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 224.


 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 224.


 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 224.


 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 224.


 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 224.


 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 224.


 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 224.


 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 224.


 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 224.


 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 224.


 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 224.


 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 224.


 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 224.


 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 224.


 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 224.


 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 224.


 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 224.


 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 224.


 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 224.


 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 224.


 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 224.

team_policy eval average step individual rewards of agent0: 0.5077969188989946
team_policy eval average team episode rewards of agent0: 45.0
team_policy eval idv catch total num of agent0: 22
team_policy eval team catch total num: 18
team_policy eval average step individual rewards of agent1: 0.29953850410899446
team_policy eval average team episode rewards of agent1: 45.0
team_policy eval idv catch total num of agent1: 14
team_policy eval team catch total num: 18
team_policy eval average step individual rewards of agent2: 0.32242081511085635
team_policy eval average team episode rewards of agent2: 45.0
team_policy eval idv catch total num of agent2: 15
team_policy eval team catch total num: 18
team_policy eval average step individual rewards of agent3: 0.3275083886279373
team_policy eval average team episode rewards of agent3: 45.0
team_policy eval idv catch total num of agent3: 15
team_policy eval team catch total num: 18
team_policy eval average step individual rewards of agent4: 0.35835356075872693
team_policy eval average team episode rewards of agent4: 45.0
team_policy eval idv catch total num of agent4: 16
team_policy eval team catch total num: 18
idv_policy eval average step individual rewards of agent0: 0.8659595238706999
idv_policy eval average team episode rewards of agent0: 137.5
idv_policy eval idv catch total num of agent0: 36
idv_policy eval team catch total num: 55
idv_policy eval average step individual rewards of agent1: 0.8923108230047039
idv_policy eval average team episode rewards of agent1: 137.5
idv_policy eval idv catch total num of agent1: 37
idv_policy eval team catch total num: 55
idv_policy eval average step individual rewards of agent2: 1.1728426198801127
idv_policy eval average team episode rewards of agent2: 137.5
idv_policy eval idv catch total num of agent2: 48
idv_policy eval team catch total num: 55
idv_policy eval average step individual rewards of agent3: 0.8690680720073228
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: 1.1126552169915072
idv_policy eval average team episode rewards of agent4: 137.5
idv_policy eval idv catch total num of agent4: 46
idv_policy eval team catch total num: 55

 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 224.


 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 224.


 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 224.


 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 224.


 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 224.


 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 224.


 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 224.


 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 225.


 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 225.


 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 225.


 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 225.


 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 225.


 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 225.


 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 225.


 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 225.


 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 225.


 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 225.


 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 225.


 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 225.


 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 225.


 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 225.


 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 225.


 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 225.


 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 225.


 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 225.

team_policy eval average step individual rewards of agent0: 0.7380270300280941
team_policy eval average team episode rewards of agent0: 90.0
team_policy eval idv catch total num of agent0: 31
team_policy eval team catch total num: 36
team_policy eval average step individual rewards of agent1: 0.7812158382902143
team_policy eval average team episode rewards of agent1: 90.0
team_policy eval idv catch total num of agent1: 33
team_policy eval team catch total num: 36
team_policy eval average step individual rewards of agent2: 0.4325381716216822
team_policy eval average team episode rewards of agent2: 90.0
team_policy eval idv catch total num of agent2: 19
team_policy eval team catch total num: 36
team_policy eval average step individual rewards of agent3: 0.7626518833916539
team_policy eval average team episode rewards of agent3: 90.0
team_policy eval idv catch total num of agent3: 32
team_policy eval team catch total num: 36
team_policy eval average step individual rewards of agent4: 0.5793161334577396
team_policy eval average team episode rewards of agent4: 90.0
team_policy eval idv catch total num of agent4: 25
team_policy eval team catch total num: 36
idv_policy eval average step individual rewards of agent0: 1.0220358019601394
idv_policy eval average team episode rewards of agent0: 127.5
idv_policy eval idv catch total num of agent0: 42
idv_policy eval team catch total num: 51
idv_policy eval average step individual rewards of agent1: 0.5509447799337983
idv_policy eval average team episode rewards of agent1: 127.5
idv_policy eval idv catch total num of agent1: 24
idv_policy eval team catch total num: 51
idv_policy eval average step individual rewards of agent2: 0.9706443112535278
idv_policy eval average team episode rewards of agent2: 127.5
idv_policy eval idv catch total num of agent2: 40
idv_policy eval team catch total num: 51
idv_policy eval average step individual rewards of agent3: 0.7876276362055699
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: 1.4777947564273226
idv_policy eval average team episode rewards of agent4: 127.5
idv_policy eval idv catch total num of agent4: 60
idv_policy eval team catch total num: 51

 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 225.


 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 225.


 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 225.


 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 225.


 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 225.


 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 225.


 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 225.


 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 225.


 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 225.


 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 225.


 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 225.


 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 225.


 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 225.


 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 225.


 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 225.


 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 225.


 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 225.


 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 225.


 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 225.


 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 225.


 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 225.


 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 225.


 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 225.


 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 225.


 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 225.

team_policy eval average step individual rewards of agent0: 1.084086273180539
team_policy eval average team episode rewards of agent0: 130.0
team_policy eval idv catch total num of agent0: 45
team_policy eval team catch total num: 52
team_policy eval average step individual rewards of agent1: 0.8847454159326893
team_policy eval average team episode rewards of agent1: 130.0
team_policy eval idv catch total num of agent1: 37
team_policy eval team catch total num: 52
team_policy eval average step individual rewards of agent2: 1.034198573076631
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: 0.9412258744760615
team_policy eval average team episode rewards of agent3: 130.0
team_policy eval idv catch total num of agent3: 39
team_policy eval team catch total num: 52
team_policy eval average step individual rewards of agent4: 0.8312359646578235
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: 0.9137625793675818
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: 0.8305817271790907
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: 0.6072075056271606
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: 0.8341271670408521
idv_policy eval average team episode rewards of agent3: 110.0
idv_policy eval idv catch total num of agent3: 35
idv_policy eval team catch total num: 44
idv_policy eval average step individual rewards of agent4: 0.795106182653099
idv_policy eval average team episode rewards of agent4: 110.0
idv_policy eval idv catch total num of agent4: 33
idv_policy eval team catch total num: 44

 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 225.


 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 225.


 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 225.


 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 225.


 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 225.


 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 225.


 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 225.


 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 225.


 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 225.


 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 225.


 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 225.


 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 225.


 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 225.


 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 225.


 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 225.


 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 225.


 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 225.


 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 225.


 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 225.


 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 225.


 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 225.


 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 225.


 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 225.


 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 225.


 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 225.

team_policy eval average step individual rewards of agent0: 0.8370938630926452
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: 0.4014286631856904
team_policy eval average team episode rewards of agent1: 100.0
team_policy eval idv catch total num of agent1: 18
team_policy eval team catch total num: 40
team_policy eval average step individual rewards of agent2: 0.8102224428338916
team_policy eval average team episode rewards of agent2: 100.0
team_policy eval idv catch total num of agent2: 34
team_policy eval team catch total num: 40
team_policy eval average step individual rewards of agent3: 0.6543560641002187
team_policy eval average team episode rewards of agent3: 100.0
team_policy eval idv catch total num of agent3: 28
team_policy eval team catch total num: 40
team_policy eval average step individual rewards of agent4: 0.8071765254828843
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: 0.9917321164991927
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: 0.6771266848539418
idv_policy eval average team episode rewards of agent1: 115.0
idv_policy eval idv catch total num of agent1: 29
idv_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent2: 0.7787808634750604
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: 0.7751850198932897
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: 0.8826906861292662
idv_policy eval average team episode rewards of agent4: 115.0
idv_policy eval idv catch total num of agent4: 37
idv_policy eval team catch total num: 46

 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 225.


 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 225.


 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 225.


 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 225.


 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 225.


 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 225.


 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 225.


 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 225.


 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 225.


 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 225.


 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 225.


 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 225.


 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 225.


 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 225.


 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 225.


 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 225.


 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 225.


 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 225.


 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 225.


 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 225.


 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 225.


 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 225.


 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 225.


 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 225.


 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 226.

team_policy eval average step individual rewards of agent0: 0.8637247834527291
team_policy eval average team episode rewards of agent0: 137.5
team_policy eval idv catch total num of agent0: 36
team_policy eval team catch total num: 55
team_policy eval average step individual rewards of agent1: 0.5846729699857133
team_policy eval average team episode rewards of agent1: 137.5
team_policy eval idv catch total num of agent1: 25
team_policy eval team catch total num: 55
team_policy eval average step individual rewards of agent2: 0.7388984130409701
team_policy eval average team episode rewards of agent2: 137.5
team_policy eval idv catch total num of agent2: 31
team_policy eval team catch total num: 55
team_policy eval average step individual rewards of agent3: 1.3720719487353505
team_policy eval average team episode rewards of agent3: 137.5
team_policy eval idv catch total num of agent3: 56
team_policy eval team catch total num: 55
team_policy eval average step individual rewards of agent4: 1.2921477513031374
team_policy eval average team episode rewards of agent4: 137.5
team_policy eval idv catch total num of agent4: 53
team_policy eval team catch total num: 55
idv_policy eval average step individual rewards of agent0: 1.092566180402009
idv_policy eval average team episode rewards of agent0: 110.0
idv_policy eval idv catch total num of agent0: 45
idv_policy eval team catch total num: 44
idv_policy eval average step individual rewards of agent1: 0.6517169679206767
idv_policy eval average team episode rewards of agent1: 110.0
idv_policy eval idv catch total num of agent1: 28
idv_policy eval team catch total num: 44
idv_policy eval average step individual rewards of agent2: 0.9378694908898214
idv_policy eval average team episode rewards of agent2: 110.0
idv_policy eval idv catch total num of agent2: 39
idv_policy eval team catch total num: 44
idv_policy eval average step individual rewards of agent3: 0.9103727693204356
idv_policy eval average team episode rewards of agent3: 110.0
idv_policy eval idv catch total num of agent3: 38
idv_policy eval team catch total num: 44
idv_policy eval average step individual rewards of agent4: 1.0864430153124485
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 4576/10000 episodes, total num timesteps 915400/2000000, FPS 225.


 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 226.


 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 226.


 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 226.


 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 226.


 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 226.


 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 226.


 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 226.


 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 226.


 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 226.


 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 226.


 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 226.


 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 226.


 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 226.


 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 226.


 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 226.


 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 226.


 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 226.


 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 226.


 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 226.


 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 226.


 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 226.


 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 226.


 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 226.


 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 226.

team_policy eval average step individual rewards of agent0: 1.009196637318911
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: 0.8935676979918362
team_policy eval average team episode rewards of agent1: 120.0
team_policy eval idv catch total num of agent1: 37
team_policy eval team catch total num: 48
team_policy eval average step individual rewards of agent2: 0.7365615166276457
team_policy eval average team episode rewards of agent2: 120.0
team_policy eval idv catch total num of agent2: 31
team_policy eval team catch total num: 48
team_policy eval average step individual rewards of agent3: 0.791317836147434
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: 1.1933106025827096
team_policy eval average team episode rewards of agent4: 120.0
team_policy eval idv catch total num of agent4: 49
team_policy eval team catch total num: 48
idv_policy eval average step individual rewards of agent0: 0.581710676495501
idv_policy eval average team episode rewards of agent0: 82.5
idv_policy eval idv catch total num of agent0: 25
idv_policy eval team catch total num: 33
idv_policy eval average step individual rewards of agent1: 0.6892762528292533
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: 0.3358713972522228
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: 0.8688603623172493
idv_policy eval average team episode rewards of agent3: 82.5
idv_policy eval idv catch total num of agent3: 36
idv_policy eval team catch total num: 33
idv_policy eval average step individual rewards of agent4: 0.6647039942939776
idv_policy eval average team episode rewards of agent4: 82.5
idv_policy eval idv catch total num of agent4: 28
idv_policy eval team catch total num: 33

 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 226.


 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 226.


 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 226.


 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 226.


 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 226.


 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 226.


 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 226.


 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 226.


 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 226.


 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 226.


 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 226.


 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 226.


 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 226.


 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 226.


 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 226.


 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 226.


 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 226.


 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 226.


 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 226.


 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 226.


 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 226.


 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 226.


 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 226.


 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 226.


 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 226.

team_policy eval average step individual rewards of agent0: 0.9120066199196488
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: 0.31203260716283665
team_policy eval average team episode rewards of agent1: 107.5
team_policy eval idv catch total num of agent1: 14
team_policy eval team catch total num: 43
team_policy eval average step individual rewards of agent2: 0.8183025335427366
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: 0.9390799139742699
team_policy eval average team episode rewards of agent3: 107.5
team_policy eval idv catch total num of agent3: 39
team_policy eval team catch total num: 43
team_policy eval average step individual rewards of agent4: 0.8613693429732245
team_policy eval average team episode rewards of agent4: 107.5
team_policy eval idv catch total num of agent4: 36
team_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent0: 0.9678355618923637
idv_policy eval average team episode rewards of agent0: 112.5
idv_policy eval idv catch total num of agent0: 40
idv_policy eval team catch total num: 45
idv_policy eval average step individual rewards of agent1: 0.4836932604250173
idv_policy eval average team episode rewards of agent1: 112.5
idv_policy eval idv catch total num of agent1: 21
idv_policy eval team catch total num: 45
idv_policy eval average step individual rewards of agent2: 0.9453782260177662
idv_policy eval average team episode rewards of agent2: 112.5
idv_policy eval idv catch total num of agent2: 39
idv_policy eval team catch total num: 45
idv_policy eval average step individual rewards of agent3: 0.9923317661534885
idv_policy eval average team episode rewards of agent3: 112.5
idv_policy eval idv catch total num of agent3: 41
idv_policy eval team catch total num: 45
idv_policy eval average step individual rewards of agent4: 0.8981182004700747
idv_policy eval average team episode rewards of agent4: 112.5
idv_policy eval idv catch total num of agent4: 37
idv_policy eval team catch total num: 45

 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 226.


 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 226.


 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 226.


 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 226.


 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 226.


 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 226.


 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 226.


 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 226.


 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 226.


 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 226.


 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 226.


 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 226.


 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 226.


 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 226.


 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 226.


 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 226.


 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 226.


 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 226.


 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 226.


 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 226.


 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 226.


 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 226.


 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 226.


 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 226.


 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 226.

team_policy eval average step individual rewards of agent0: 0.6201353841663688
team_policy eval average team episode rewards of agent0: 100.0
team_policy eval idv catch total num of agent0: 26
team_policy eval team catch total num: 40
team_policy eval average step individual rewards of agent1: 0.8858837051229268
team_policy eval average team episode rewards of agent1: 100.0
team_policy eval idv catch total num of agent1: 37
team_policy eval team catch total num: 40
team_policy eval average step individual rewards of agent2: 0.8333394639369288
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: 0.763238880418642
team_policy eval average team episode rewards of agent3: 100.0
team_policy eval idv catch total num of agent3: 32
team_policy eval team catch total num: 40
team_policy eval average step individual rewards of agent4: 0.5332831137173994
team_policy eval average team episode rewards of agent4: 100.0
team_policy eval idv catch total num of agent4: 23
team_policy eval team catch total num: 40
idv_policy eval average step individual rewards of agent0: 0.3427257372649498
idv_policy eval average team episode rewards of agent0: 62.5
idv_policy eval idv catch total num of agent0: 16
idv_policy eval team catch total num: 25
idv_policy eval average step individual rewards of agent1: 0.4952143250579056
idv_policy eval average team episode rewards of agent1: 62.5
idv_policy eval idv catch total num of agent1: 22
idv_policy eval team catch total num: 25
idv_policy eval average step individual rewards of agent2: 0.5602522444790238
idv_policy eval average team episode rewards of agent2: 62.5
idv_policy eval idv catch total num of agent2: 24
idv_policy eval team catch total num: 25
idv_policy eval average step individual rewards of agent3: 0.47888030240621454
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: 0.5730957948416147
idv_policy eval average team episode rewards of agent4: 62.5
idv_policy eval idv catch total num of agent4: 25
idv_policy eval team catch total num: 25

 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 226.


 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 226.


 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 226.


 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 226.


 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 226.


 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 226.


 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 226.


 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 226.


 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 226.


 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 226.


 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 226.


 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 226.


 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 226.


 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 226.


 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 226.


 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 226.


 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 226.


 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 227.


 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 227.


 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 227.


 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 227.


 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 227.


 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 227.


 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 227.


 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 227.

team_policy eval average step individual rewards of agent0: 0.9194590339629555
team_policy eval average team episode rewards of agent0: 110.0
team_policy eval idv catch total num of agent0: 38
team_policy eval team catch total num: 44
team_policy eval average step individual rewards of agent1: 0.8191296334791731
team_policy eval average team episode rewards of agent1: 110.0
team_policy eval idv catch total num of agent1: 34
team_policy eval team catch total num: 44
team_policy eval average step individual rewards of agent2: 1.1484265427329852
team_policy eval average team episode rewards of agent2: 110.0
team_policy eval idv catch total num of agent2: 47
team_policy eval team catch total num: 44
team_policy eval average step individual rewards of agent3: 0.583095467048067
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: 0.20416249448006824
team_policy eval average team episode rewards of agent4: 110.0
team_policy eval idv catch total num of agent4: 10
team_policy eval team catch total num: 44
idv_policy eval average step individual rewards of agent0: 0.5199686581574147
idv_policy eval average team episode rewards of agent0: 122.5
idv_policy eval idv catch total num of agent0: 23
idv_policy eval team catch total num: 49
idv_policy eval average step individual rewards of agent1: 1.0655881642911849
idv_policy eval average team episode rewards of agent1: 122.5
idv_policy eval idv catch total num of agent1: 44
idv_policy eval team catch total num: 49
idv_policy eval average step individual rewards of agent2: 0.4437715024925687
idv_policy eval average team episode rewards of agent2: 122.5
idv_policy eval idv catch total num of agent2: 20
idv_policy eval team catch total num: 49
idv_policy eval average step individual rewards of agent3: 0.7140939126317895
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: 1.0646788196531822
idv_policy eval average team episode rewards of agent4: 122.5
idv_policy eval idv catch total num of agent4: 44
idv_policy eval team catch total num: 49

 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 227.


 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 227.


 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 227.


 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 227.


 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 227.


 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 227.


 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 227.


 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 227.


 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 227.


 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 227.


 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 227.


 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 227.


 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 227.


 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 227.


 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 227.


 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 227.


 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 227.


 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 227.


 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 227.


 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 227.


 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 227.


 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 227.


 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 227.


 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 227.


 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 227.

team_policy eval average step individual rewards of agent0: 0.432233084718382
team_policy eval average team episode rewards of agent0: 105.0
team_policy eval idv catch total num of agent0: 19
team_policy eval team catch total num: 42
team_policy eval average step individual rewards of agent1: 0.7456444917471776
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: 0.9833743982177389
team_policy eval average team episode rewards of agent2: 105.0
team_policy eval idv catch total num of agent2: 41
team_policy eval team catch total num: 42
team_policy eval average step individual rewards of agent3: 1.1502396392163587
team_policy eval average team episode rewards of agent3: 105.0
team_policy eval idv catch total num of agent3: 47
team_policy eval team catch total num: 42
team_policy eval average step individual rewards of agent4: 0.8454498326455638
team_policy eval average team episode rewards of agent4: 105.0
team_policy eval idv catch total num of agent4: 35
team_policy eval team catch total num: 42
idv_policy eval average step individual rewards of agent0: 0.3443748277708179
idv_policy eval average team episode rewards of agent0: 85.0
idv_policy eval idv catch total num of agent0: 16
idv_policy eval team catch total num: 34
idv_policy eval average step individual rewards of agent1: 0.9418881820058388
idv_policy eval average team episode rewards of agent1: 85.0
idv_policy eval idv catch total num of agent1: 39
idv_policy eval team catch total num: 34
idv_policy eval average step individual rewards of agent2: 0.6672541633766723
idv_policy eval average team episode rewards of agent2: 85.0
idv_policy eval idv catch total num of agent2: 28
idv_policy eval team catch total num: 34
idv_policy eval average step individual rewards of agent3: 0.6154945241996813
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: 0.5353052805682772
idv_policy eval average team episode rewards of agent4: 85.0
idv_policy eval idv catch total num of agent4: 23
idv_policy eval team catch total num: 34

 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 227.


 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 227.


 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 227.


 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 227.


 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 227.


 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 227.


 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 227.


 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 227.


 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 227.


 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 227.


 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 227.


 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 227.


 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 227.


 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 227.


 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 227.


 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 227.


 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 227.


 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 227.


 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 227.


 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 227.


 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 227.


 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 227.


 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 227.


 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 227.


 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 227.

team_policy eval average step individual rewards of agent0: 0.7638509957472153
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: 0.5090639161989946
team_policy eval average team episode rewards of agent1: 117.5
team_policy eval idv catch total num of agent1: 22
team_policy eval team catch total num: 47
team_policy eval average step individual rewards of agent2: 1.1638960639989793
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: 0.8875546895887555
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: 1.073784750143392
team_policy eval average team episode rewards of agent4: 117.5
team_policy eval idv catch total num of agent4: 44
team_policy eval team catch total num: 47
idv_policy eval average step individual rewards of agent0: 1.017064616127246
idv_policy eval average team episode rewards of agent0: 122.5
idv_policy eval idv catch total num of agent0: 42
idv_policy eval team catch total num: 49
idv_policy eval average step individual rewards of agent1: 0.6790614060975241
idv_policy eval average team episode rewards of agent1: 122.5
idv_policy eval idv catch total num of agent1: 29
idv_policy eval team catch total num: 49
idv_policy eval average step individual rewards of agent2: 1.0213209022102419
idv_policy eval average team episode rewards of agent2: 122.5
idv_policy eval idv catch total num of agent2: 42
idv_policy eval team catch total num: 49
idv_policy eval average step individual rewards of agent3: 1.2605349130166312
idv_policy eval average team episode rewards of agent3: 122.5
idv_policy eval idv catch total num of agent3: 52
idv_policy eval team catch total num: 49
idv_policy eval average step individual rewards of agent4: 0.9160612671542442
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 4726/10000 episodes, total num timesteps 945400/2000000, FPS 227.


 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 227.


 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 227.


 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 227.


 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 227.


 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 227.


 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 227.


 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 227.


 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 227.


 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 227.


 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 227.


 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 227.


 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 227.


 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 227.


 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 227.


 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 227.


 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 227.


 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 227.


 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 227.


 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 227.


 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 227.


 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 227.


 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 227.


 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 227.


 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 227.

team_policy eval average step individual rewards of agent0: 0.6625038509836793
team_policy eval average team episode rewards of agent0: 102.5
team_policy eval idv catch total num of agent0: 28
team_policy eval team catch total num: 41
team_policy eval average step individual rewards of agent1: 0.9133196811347084
team_policy eval average team episode rewards of agent1: 102.5
team_policy eval idv catch total num of agent1: 38
team_policy eval team catch total num: 41
team_policy eval average step individual rewards of agent2: 0.5605118219893326
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: 0.5068772428208429
team_policy eval average team episode rewards of agent3: 102.5
team_policy eval idv catch total num of agent3: 22
team_policy eval team catch total num: 41
team_policy eval average step individual rewards of agent4: 0.5369508330399914
team_policy eval average team episode rewards of agent4: 102.5
team_policy eval idv catch total num of agent4: 23
team_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent0: 1.3233270321566954
idv_policy eval average team episode rewards of agent0: 160.0
idv_policy eval idv catch total num of agent0: 54
idv_policy eval team catch total num: 64
idv_policy eval average step individual rewards of agent1: 1.4727622905455495
idv_policy eval average team episode rewards of agent1: 160.0
idv_policy eval idv catch total num of agent1: 60
idv_policy eval team catch total num: 64
idv_policy eval average step individual rewards of agent2: 1.3988845666172955
idv_policy eval average team episode rewards of agent2: 160.0
idv_policy eval idv catch total num of agent2: 57
idv_policy eval team catch total num: 64
idv_policy eval average step individual rewards of agent3: 0.7712824046052285
idv_policy eval average team episode rewards of agent3: 160.0
idv_policy eval idv catch total num of agent3: 32
idv_policy eval team catch total num: 64
idv_policy eval average step individual rewards of agent4: 0.8468580785898621
idv_policy eval average team episode rewards of agent4: 160.0
idv_policy eval idv catch total num of agent4: 35
idv_policy eval team catch total num: 64

 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 227.


 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 227.


 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 227.


 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 227.


 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 227.


 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 227.


 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 227.


 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 227.


 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 227.


 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 227.


 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 227.


 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 227.


 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 227.


 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 227.


 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 227.


 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 227.


 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 227.


 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 227.


 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 227.


 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 227.


 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 227.


 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 227.


 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 227.


 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 227.


 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 227.

team_policy eval average step individual rewards of agent0: 0.740388275701083
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: 0.5646738047743338
team_policy eval average team episode rewards of agent1: 87.5
team_policy eval idv catch total num of agent1: 24
team_policy eval team catch total num: 35
team_policy eval average step individual rewards of agent2: 0.4324611371040643
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: 0.5322363021810821
team_policy eval average team episode rewards of agent3: 87.5
team_policy eval idv catch total num of agent3: 23
team_policy eval team catch total num: 35
team_policy eval average step individual rewards of agent4: 0.7096606809973882
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: 0.6858518309721782
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: 0.8543886446578836
idv_policy eval average team episode rewards of agent1: 110.0
idv_policy eval idv catch total num of agent1: 36
idv_policy eval team catch total num: 44
idv_policy eval average step individual rewards of agent2: 0.5082781257518408
idv_policy eval average team episode rewards of agent2: 110.0
idv_policy eval idv catch total num of agent2: 22
idv_policy eval team catch total num: 44
idv_policy eval average step individual rewards of agent3: 1.0352367350661307
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: 0.504982923213566
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 4776/10000 episodes, total num timesteps 955400/2000000, FPS 227.


 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 227.


 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 227.


 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 227.


 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 227.


 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 227.


 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 227.


 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 228.


 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 228.


 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 228.


 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 228.


 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 228.


 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 228.


 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 228.


 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 228.


 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 228.


 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 228.


 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 228.


 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 228.


 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 228.


 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 228.


 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 228.


 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 228.


 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 228.


 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 228.

team_policy eval average step individual rewards of agent0: 1.0195407242217984
team_policy eval average team episode rewards of agent0: 160.0
team_policy eval idv catch total num of agent0: 42
team_policy eval team catch total num: 64
team_policy eval average step individual rewards of agent1: 1.114083845474672
team_policy eval average team episode rewards of agent1: 160.0
team_policy eval idv catch total num of agent1: 46
team_policy eval team catch total num: 64
team_policy eval average step individual rewards of agent2: 0.9331304170409163
team_policy eval average team episode rewards of agent2: 160.0
team_policy eval idv catch total num of agent2: 39
team_policy eval team catch total num: 64
team_policy eval average step individual rewards of agent3: 0.985099591154075
team_policy eval average team episode rewards of agent3: 160.0
team_policy eval idv catch total num of agent3: 41
team_policy eval team catch total num: 64
team_policy eval average step individual rewards of agent4: 1.3226715873406263
team_policy eval average team episode rewards of agent4: 160.0
team_policy eval idv catch total num of agent4: 54
team_policy eval team catch total num: 64
idv_policy eval average step individual rewards of agent0: 0.46416412815270136
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: 0.8204528957333731
idv_policy eval average team episode rewards of agent1: 67.5
idv_policy eval idv catch total num of agent1: 34
idv_policy eval team catch total num: 27
idv_policy eval average step individual rewards of agent2: 0.5666040759857086
idv_policy eval average team episode rewards of agent2: 67.5
idv_policy eval idv catch total num of agent2: 24
idv_policy eval team catch total num: 27
idv_policy eval average step individual rewards of agent3: 0.7929700632733888
idv_policy eval average team episode rewards of agent3: 67.5
idv_policy eval idv catch total num of agent3: 33
idv_policy eval team catch total num: 27
idv_policy eval average step individual rewards of agent4: 0.5044439202942655
idv_policy eval average team episode rewards of agent4: 67.5
idv_policy eval idv catch total num of agent4: 22
idv_policy eval team catch total num: 27

 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 228.


 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 228.


 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 228.


 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 228.


 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 228.


 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 228.


 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 228.


 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 228.


 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 228.


 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 228.


 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 228.


 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 228.


 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 228.


 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 228.


 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 228.


 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 228.


 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 228.


 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 228.


 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 228.


 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 228.


 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 228.


 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 228.


 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 228.


 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 228.


 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 228.

team_policy eval average step individual rewards of agent0: 0.2524062503138723
team_policy eval average team episode rewards of agent0: 85.0
team_policy eval idv catch total num of agent0: 12
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent1: 1.1513962615915605
team_policy eval average team episode rewards of agent1: 85.0
team_policy eval idv catch total num of agent1: 47
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent2: 1.2034973765618258
team_policy eval average team episode rewards of agent2: 85.0
team_policy eval idv catch total num of agent2: 49
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent3: 0.5249970220128329
team_policy eval average team episode rewards of agent3: 85.0
team_policy eval idv catch total num of agent3: 23
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent4: 0.4878167085606043
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: 0.8403643473786424
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: 0.9966946479917308
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: 1.0943600338127002
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: 1.164121362838864
idv_policy eval average team episode rewards of agent3: 132.5
idv_policy eval idv catch total num of agent3: 48
idv_policy eval team catch total num: 53
idv_policy eval average step individual rewards of agent4: 1.0296394342188646
idv_policy eval average team episode rewards of agent4: 132.5
idv_policy eval idv catch total num of agent4: 43
idv_policy eval team catch total num: 53

 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 228.


 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 228.


 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 228.


 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 228.


 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 228.


 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 228.


 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 228.


 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 228.


 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 228.


 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 228.


 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 228.


 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 228.


 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 228.


 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 228.


 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 228.


 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 228.


 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 228.


 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 228.


 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 228.


 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 228.


 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 228.


 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 228.


 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 228.


 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 228.


 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 228.

team_policy eval average step individual rewards of agent0: 0.688279146688266
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: 0.4315467135010181
team_policy eval average team episode rewards of agent1: 87.5
team_policy eval idv catch total num of agent1: 19
team_policy eval team catch total num: 35
team_policy eval average step individual rewards of agent2: 0.8968406950966548
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: 0.7439643698804048
team_policy eval average team episode rewards of agent3: 87.5
team_policy eval idv catch total num of agent3: 31
team_policy eval team catch total num: 35
team_policy eval average step individual rewards of agent4: 0.7176252798824515
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: 0.6662709898211193
idv_policy eval average team episode rewards of agent0: 60.0
idv_policy eval idv catch total num of agent0: 28
idv_policy eval team catch total num: 24
idv_policy eval average step individual rewards of agent1: 0.6078749300888328
idv_policy eval average team episode rewards of agent1: 60.0
idv_policy eval idv catch total num of agent1: 26
idv_policy eval team catch total num: 24
idv_policy eval average step individual rewards of agent2: 0.6062842621998195
idv_policy eval average team episode rewards of agent2: 60.0
idv_policy eval idv catch total num of agent2: 26
idv_policy eval team catch total num: 24
idv_policy eval average step individual rewards of agent3: 0.43329631723739964
idv_policy eval average team episode rewards of agent3: 60.0
idv_policy eval idv catch total num of agent3: 19
idv_policy eval team catch total num: 24
idv_policy eval average step individual rewards of agent4: 0.22638992144312944
idv_policy eval average team episode rewards of agent4: 60.0
idv_policy eval idv catch total num of agent4: 11
idv_policy eval team catch total num: 24

 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 228.


 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 228.


 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 228.


 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 228.


 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 228.


 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 228.


 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 228.


 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 228.


 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 228.


 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 228.


 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 228.


 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 228.


 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 228.


 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 228.


 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 228.


 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 228.


 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 228.


 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 228.


 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 228.


 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 228.


 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 228.


 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 228.


 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 228.


 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 228.


 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 228.

team_policy eval average step individual rewards of agent0: 0.5621451370985575
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: 1.1134111555333248
team_policy eval average team episode rewards of agent1: 82.5
team_policy eval idv catch total num of agent1: 46
team_policy eval team catch total num: 33
team_policy eval average step individual rewards of agent2: 0.6802751946747114
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: 0.45812542091843583
team_policy eval average team episode rewards of agent3: 82.5
team_policy eval idv catch total num of agent3: 20
team_policy eval team catch total num: 33
team_policy eval average step individual rewards of agent4: 0.35442160791256605
team_policy eval average team episode rewards of agent4: 82.5
team_policy eval idv catch total num of agent4: 16
team_policy eval team catch total num: 33
idv_policy eval average step individual rewards of agent0: 0.8969387665412369
idv_policy eval average team episode rewards of agent0: 135.0
idv_policy eval idv catch total num of agent0: 37
idv_policy eval team catch total num: 54
idv_policy eval average step individual rewards of agent1: 1.1485615963803129
idv_policy eval average team episode rewards of agent1: 135.0
idv_policy eval idv catch total num of agent1: 47
idv_policy eval team catch total num: 54
idv_policy eval average step individual rewards of agent2: 0.8648492149860013
idv_policy eval average team episode rewards of agent2: 135.0
idv_policy eval idv catch total num of agent2: 36
idv_policy eval team catch total num: 54
idv_policy eval average step individual rewards of agent3: 0.5900380510486944
idv_policy eval average team episode rewards of agent3: 135.0
idv_policy eval idv catch total num of agent3: 25
idv_policy eval team catch total num: 54
idv_policy eval average step individual rewards of agent4: 1.1938510007650573
idv_policy eval average team episode rewards of agent4: 135.0
idv_policy eval idv catch total num of agent4: 49
idv_policy eval team catch total num: 54

 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 228.


 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 228.


 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 228.


 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 228.


 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 228.


 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 228.


 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 228.


 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 228.


 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 228.


 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 228.


 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 228.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.

team_policy eval average step individual rewards of agent0: 1.57770210278457
team_policy eval average team episode rewards of agent0: 180.0
team_policy eval idv catch total num of agent0: 64
team_policy eval team catch total num: 72
team_policy eval average step individual rewards of agent1: 0.8945800552396531
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: 0.9437287549940659
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: 1.5280845387565773
team_policy eval average team episode rewards of agent3: 180.0
team_policy eval idv catch total num of agent3: 62
team_policy eval team catch total num: 72
team_policy eval average step individual rewards of agent4: 1.5223098608116459
team_policy eval average team episode rewards of agent4: 180.0
team_policy eval idv catch total num of agent4: 62
team_policy eval team catch total num: 72
idv_policy eval average step individual rewards of agent0: 1.0732775310046583
idv_policy eval average team episode rewards of agent0: 137.5
idv_policy eval idv catch total num of agent0: 44
idv_policy eval team catch total num: 55
idv_policy eval average step individual rewards of agent1: 1.2870321117591967
idv_policy eval average team episode rewards of agent1: 137.5
idv_policy eval idv catch total num of agent1: 53
idv_policy eval team catch total num: 55
idv_policy eval average step individual rewards of agent2: 1.119691977044075
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: 0.535736492242832
idv_policy eval average team episode rewards of agent3: 137.5
idv_policy eval idv catch total num of agent3: 23
idv_policy eval team catch total num: 55
idv_policy eval average step individual rewards of agent4: 0.807512574660561
idv_policy eval average team episode rewards of agent4: 137.5
idv_policy eval idv catch total num of agent4: 34
idv_policy eval team catch total num: 55

 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.

team_policy eval average step individual rewards of agent0: 0.5234002323939468
team_policy eval average team episode rewards of agent0: 92.5
team_policy eval idv catch total num of agent0: 23
team_policy eval team catch total num: 37
team_policy eval average step individual rewards of agent1: 0.8086827937834427
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: 0.7662813567645279
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: 0.7592054914527896
team_policy eval average team episode rewards of agent3: 92.5
team_policy eval idv catch total num of agent3: 32
team_policy eval team catch total num: 37
team_policy eval average step individual rewards of agent4: 0.33021170309629894
team_policy eval average team episode rewards of agent4: 92.5
team_policy eval idv catch total num of agent4: 15
team_policy eval team catch total num: 37
idv_policy eval average step individual rewards of agent0: 0.9419799081959198
idv_policy eval average team episode rewards of agent0: 130.0
idv_policy eval idv catch total num of agent0: 39
idv_policy eval team catch total num: 52
idv_policy eval average step individual rewards of agent1: 0.9967616796823799
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: 0.9175787763519803
idv_policy eval average team episode rewards of agent2: 130.0
idv_policy eval idv catch total num of agent2: 38
idv_policy eval team catch total num: 52
idv_policy eval average step individual rewards of agent3: 0.7674320692175735
idv_policy eval average team episode rewards of agent3: 130.0
idv_policy eval idv catch total num of agent3: 32
idv_policy eval team catch total num: 52
idv_policy eval average step individual rewards of agent4: 1.2480535663338201
idv_policy eval average team episode rewards of agent4: 130.0
idv_policy eval idv catch total num of agent4: 51
idv_policy eval team catch total num: 52

 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.

team_policy eval average step individual rewards of agent0: 0.575580499526253
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: 1.1685638146935202
team_policy eval average team episode rewards of agent1: 90.0
team_policy eval idv catch total num of agent1: 48
team_policy eval team catch total num: 36
team_policy eval average step individual rewards of agent2: 0.07511928992851159
team_policy eval average team episode rewards of agent2: 90.0
team_policy eval idv catch total num of agent2: 6
team_policy eval team catch total num: 36
team_policy eval average step individual rewards of agent3: 1.0671048971175954
team_policy eval average team episode rewards of agent3: 90.0
team_policy eval idv catch total num of agent3: 44
team_policy eval team catch total num: 36
team_policy eval average step individual rewards of agent4: 0.5436398309086667
team_policy eval average team episode rewards of agent4: 90.0
team_policy eval idv catch total num of agent4: 24
team_policy eval team catch total num: 36
idv_policy eval average step individual rewards of agent0: 0.5143456254906563
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: 0.5768266267646274
idv_policy eval average team episode rewards of agent1: 70.0
idv_policy eval idv catch total num of agent1: 25
idv_policy eval team catch total num: 28
idv_policy eval average step individual rewards of agent2: 0.5579903090736216
idv_policy eval average team episode rewards of agent2: 70.0
idv_policy eval idv catch total num of agent2: 24
idv_policy eval team catch total num: 28
idv_policy eval average step individual rewards of agent3: 0.3556582132092467
idv_policy eval average team episode rewards of agent3: 70.0
idv_policy eval idv catch total num of agent3: 16
idv_policy eval team catch total num: 28
idv_policy eval average step individual rewards of agent4: 0.7187617430991387
idv_policy eval average team episode rewards of agent4: 70.0
idv_policy eval idv catch total num of agent4: 30
idv_policy eval team catch total num: 28

 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.

team_policy eval average step individual rewards of agent0: 0.871614235069921
team_policy eval average team episode rewards of agent0: 132.5
team_policy eval idv catch total num of agent0: 36
team_policy eval team catch total num: 53
team_policy eval average step individual rewards of agent1: 0.7423694461725114
team_policy eval average team episode rewards of agent1: 132.5
team_policy eval idv catch total num of agent1: 31
team_policy eval team catch total num: 53
team_policy eval average step individual rewards of agent2: 1.0994735582962505
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: 1.3432113647957433
team_policy eval average team episode rewards of agent3: 132.5
team_policy eval idv catch total num of agent3: 55
team_policy eval team catch total num: 53
team_policy eval average step individual rewards of agent4: 0.8400175775912803
team_policy eval average team episode rewards of agent4: 132.5
team_policy eval idv catch total num of agent4: 35
team_policy eval team catch total num: 53
idv_policy eval average step individual rewards of agent0: 0.5951173414137486
idv_policy eval average team episode rewards of agent0: 120.0
idv_policy eval idv catch total num of agent0: 25
idv_policy eval team catch total num: 48
idv_policy eval average step individual rewards of agent1: 1.123741633697454
idv_policy eval average team episode rewards of agent1: 120.0
idv_policy eval idv catch total num of agent1: 46
idv_policy eval team catch total num: 48
idv_policy eval average step individual rewards of agent2: 1.018675974575372
idv_policy eval average team episode rewards of agent2: 120.0
idv_policy eval idv catch total num of agent2: 42
idv_policy eval team catch total num: 48
idv_policy eval average step individual rewards of agent3: 0.5669131555938967
idv_policy eval average team episode rewards of agent3: 120.0
idv_policy eval idv catch total num of agent3: 24
idv_policy eval team catch total num: 48
idv_policy eval average step individual rewards of agent4: 0.6623964603557932
idv_policy eval average team episode rewards of agent4: 120.0
idv_policy eval idv catch total num of agent4: 28
idv_policy eval team catch total num: 48

 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.

team_policy eval average step individual rewards of agent0: 1.015025271213562
team_policy eval average team episode rewards of agent0: 177.5
team_policy eval idv catch total num of agent0: 42
team_policy eval team catch total num: 71
team_policy eval average step individual rewards of agent1: 1.0421846061813014
team_policy eval average team episode rewards of agent1: 177.5
team_policy eval idv catch total num of agent1: 43
team_policy eval team catch total num: 71
team_policy eval average step individual rewards of agent2: 1.498964999869953
team_policy eval average team episode rewards of agent2: 177.5
team_policy eval idv catch total num of agent2: 61
team_policy eval team catch total num: 71
team_policy eval average step individual rewards of agent3: 1.7021600424477097
team_policy eval average team episode rewards of agent3: 177.5
team_policy eval idv catch total num of agent3: 69
team_policy eval team catch total num: 71
team_policy eval average step individual rewards of agent4: 1.1932361320495708
team_policy eval average team episode rewards of agent4: 177.5
team_policy eval idv catch total num of agent4: 49
team_policy eval team catch total num: 71
idv_policy eval average step individual rewards of agent0: 0.9449846466460017
idv_policy eval average team episode rewards of agent0: 152.5
idv_policy eval idv catch total num of agent0: 39
idv_policy eval team catch total num: 61
idv_policy eval average step individual rewards of agent1: 0.8827135185388848
idv_policy eval average team episode rewards of agent1: 152.5
idv_policy eval idv catch total num of agent1: 37
idv_policy eval team catch total num: 61
idv_policy eval average step individual rewards of agent2: 1.1476382534742284
idv_policy eval average team episode rewards of agent2: 152.5
idv_policy eval idv catch total num of agent2: 47
idv_policy eval team catch total num: 61
idv_policy eval average step individual rewards of agent3: 1.3095011170927149
idv_policy eval average team episode rewards of agent3: 152.5
idv_policy eval idv catch total num of agent3: 54
idv_policy eval team catch total num: 61
idv_policy eval average step individual rewards of agent4: 1.0123568076963438
idv_policy eval average team episode rewards of agent4: 152.5
idv_policy eval idv catch total num of agent4: 42
idv_policy eval team catch total num: 61

 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.

team_policy eval average step individual rewards of agent0: 1.2196937810451007
team_policy eval average team episode rewards of agent0: 112.5
team_policy eval idv catch total num of agent0: 50
team_policy eval team catch total num: 45
team_policy eval average step individual rewards of agent1: 0.785076824415221
team_policy eval average team episode rewards of agent1: 112.5
team_policy eval idv catch total num of agent1: 33
team_policy eval team catch total num: 45
team_policy eval average step individual rewards of agent2: 0.5131726582179241
team_policy eval average team episode rewards of agent2: 112.5
team_policy eval idv catch total num of agent2: 22
team_policy eval team catch total num: 45
team_policy eval average step individual rewards of agent3: 0.6370814302468877
team_policy eval average team episode rewards of agent3: 112.5
team_policy eval idv catch total num of agent3: 27
team_policy eval team catch total num: 45
team_policy eval average step individual rewards of agent4: 0.6362751090855445
team_policy eval average team episode rewards of agent4: 112.5
team_policy eval idv catch total num of agent4: 27
team_policy eval team catch total num: 45
idv_policy eval average step individual rewards of agent0: 0.6842006979547706
idv_policy eval average team episode rewards of agent0: 140.0
idv_policy eval idv catch total num of agent0: 29
idv_policy eval team catch total num: 56
idv_policy eval average step individual rewards of agent1: 1.0928179707347683
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: 1.295833147174268
idv_policy eval average team episode rewards of agent2: 140.0
idv_policy eval idv catch total num of agent2: 53
idv_policy eval team catch total num: 56
idv_policy eval average step individual rewards of agent3: 1.2746805517651936
idv_policy eval average team episode rewards of agent3: 140.0
idv_policy eval idv catch total num of agent3: 52
idv_policy eval team catch total num: 56
idv_policy eval average step individual rewards of agent4: 1.2692483609614666
idv_policy eval average team episode rewards of agent4: 140.0
idv_policy eval idv catch total num of agent4: 52
idv_policy eval team catch total num: 56

 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.

team_policy eval average step individual rewards of agent0: 0.48162793733145093
team_policy eval average team episode rewards of agent0: 65.0
team_policy eval idv catch total num of agent0: 21
team_policy eval team catch total num: 26
team_policy eval average step individual rewards of agent1: 0.4286807898627812
team_policy eval average team episode rewards of agent1: 65.0
team_policy eval idv catch total num of agent1: 19
team_policy eval team catch total num: 26
team_policy eval average step individual rewards of agent2: 0.32515679780991064
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: 0.3891367497301155
team_policy eval average team episode rewards of agent3: 65.0
team_policy eval idv catch total num of agent3: 17
team_policy eval team catch total num: 26
team_policy eval average step individual rewards of agent4: 0.9913292855999393
team_policy eval average team episode rewards of agent4: 65.0
team_policy eval idv catch total num of agent4: 41
team_policy eval team catch total num: 26
idv_policy eval average step individual rewards of agent0: 0.4177817036772183
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: 0.8641026257809409
idv_policy eval average team episode rewards of agent1: 67.5
idv_policy eval idv catch total num of agent1: 36
idv_policy eval team catch total num: 27
idv_policy eval average step individual rewards of agent2: 0.5435588297070821
idv_policy eval average team episode rewards of agent2: 67.5
idv_policy eval idv catch total num of agent2: 24
idv_policy eval team catch total num: 27
idv_policy eval average step individual rewards of agent3: 0.5636406740029293
idv_policy eval average team episode rewards of agent3: 67.5
idv_policy eval idv catch total num of agent3: 24
idv_policy eval team catch total num: 27
idv_policy eval average step individual rewards of agent4: 0.45028622427676707
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 5051/10000 episodes, total num timesteps 1010400/2000000, FPS 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.

team_policy eval average step individual rewards of agent0: 0.8172128974729165
team_policy eval average team episode rewards of agent0: 107.5
team_policy eval idv catch total num of agent0: 34
team_policy eval team catch total num: 43
team_policy eval average step individual rewards of agent1: 0.7389233664159316
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: 0.6670966884724495
team_policy eval average team episode rewards of agent2: 107.5
team_policy eval idv catch total num of agent2: 28
team_policy eval team catch total num: 43
team_policy eval average step individual rewards of agent3: 0.8501798135592644
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: 1.020064677492593
team_policy eval average team episode rewards of agent4: 107.5
team_policy eval idv catch total num of agent4: 42
team_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent0: 0.7870380004818854
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: 0.9241161258608134
idv_policy eval average team episode rewards of agent1: 92.5
idv_policy eval idv catch total num of agent1: 39
idv_policy eval team catch total num: 37
idv_policy eval average step individual rewards of agent2: 0.6315269300556002
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: 0.7376398478006694
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: 0.3831874265839079
idv_policy eval average team episode rewards of agent4: 92.5
idv_policy eval idv catch total num of agent4: 17
idv_policy eval team catch total num: 37

 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.

team_policy eval average step individual rewards of agent0: 1.2498044181987784
team_policy eval average team episode rewards of agent0: 175.0
team_policy eval idv catch total num of agent0: 51
team_policy eval team catch total num: 70
team_policy eval average step individual rewards of agent1: 1.2511795823079428
team_policy eval average team episode rewards of agent1: 175.0
team_policy eval idv catch total num of agent1: 51
team_policy eval team catch total num: 70
team_policy eval average step individual rewards of agent2: 1.0478405973890665
team_policy eval average team episode rewards of agent2: 175.0
team_policy eval idv catch total num of agent2: 43
team_policy eval team catch total num: 70
team_policy eval average step individual rewards of agent3: 1.2427871931500678
team_policy eval average team episode rewards of agent3: 175.0
team_policy eval idv catch total num of agent3: 51
team_policy eval team catch total num: 70
team_policy eval average step individual rewards of agent4: 1.3417749742951284
team_policy eval average team episode rewards of agent4: 175.0
team_policy eval idv catch total num of agent4: 55
team_policy eval team catch total num: 70
idv_policy eval average step individual rewards of agent0: 0.8437575026944214
idv_policy eval average team episode rewards of agent0: 112.5
idv_policy eval idv catch total num of agent0: 35
idv_policy eval team catch total num: 45
idv_policy eval average step individual rewards of agent1: 0.8308456304972209
idv_policy eval average team episode rewards of agent1: 112.5
idv_policy eval idv catch total num of agent1: 35
idv_policy eval team catch total num: 45
idv_policy eval average step individual rewards of agent2: 1.0165239596873634
idv_policy eval average team episode rewards of agent2: 112.5
idv_policy eval idv catch total num of agent2: 42
idv_policy eval team catch total num: 45
idv_policy eval average step individual rewards of agent3: 0.9146414728446581
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: 0.459230965285312
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 5101/10000 episodes, total num timesteps 1020400/2000000, FPS 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.

team_policy eval average step individual rewards of agent0: 0.6892208044933631
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: 0.7617823181696882
team_policy eval average team episode rewards of agent1: 142.5
team_policy eval idv catch total num of agent1: 32
team_policy eval team catch total num: 57
team_policy eval average step individual rewards of agent2: 1.4913958296774563
team_policy eval average team episode rewards of agent2: 142.5
team_policy eval idv catch total num of agent2: 61
team_policy eval team catch total num: 57
team_policy eval average step individual rewards of agent3: 0.631873105237567
team_policy eval average team episode rewards of agent3: 142.5
team_policy eval idv catch total num of agent3: 27
team_policy eval team catch total num: 57
team_policy eval average step individual rewards of agent4: 0.7605284554570996
team_policy eval average team episode rewards of agent4: 142.5
team_policy eval idv catch total num of agent4: 32
team_policy eval team catch total num: 57
idv_policy eval average step individual rewards of agent0: 0.5295585008032391
idv_policy eval average team episode rewards of agent0: 100.0
idv_policy eval idv catch total num of agent0: 23
idv_policy eval team catch total num: 40
idv_policy eval average step individual rewards of agent1: 0.5658769854006621
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: 0.6927708774100744
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: 0.5857121094067441
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: 1.4014175217285745
idv_policy eval average team episode rewards of agent4: 100.0
idv_policy eval idv catch total num of agent4: 57
idv_policy eval team catch total num: 40

 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.

team_policy eval average step individual rewards of agent0: 0.8855312808174769
team_policy eval average team episode rewards of agent0: 102.5
team_policy eval idv catch total num of agent0: 37
team_policy eval team catch total num: 41
team_policy eval average step individual rewards of agent1: 0.4662404535611521
team_policy eval average team episode rewards of agent1: 102.5
team_policy eval idv catch total num of agent1: 20
team_policy eval team catch total num: 41
team_policy eval average step individual rewards of agent2: 0.47671917173799494
team_policy eval average team episode rewards of agent2: 102.5
team_policy eval idv catch total num of agent2: 21
team_policy eval team catch total num: 41
team_policy eval average step individual rewards of agent3: 0.7189272916900511
team_policy eval average team episode rewards of agent3: 102.5
team_policy eval idv catch total num of agent3: 30
team_policy eval team catch total num: 41
team_policy eval average step individual rewards of agent4: 1.0421697311027949
team_policy eval average team episode rewards of agent4: 102.5
team_policy eval idv catch total num of agent4: 43
team_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent0: 0.7844855450931089
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: 0.941210344526531
idv_policy eval average team episode rewards of agent1: 120.0
idv_policy eval idv catch total num of agent1: 39
idv_policy eval team catch total num: 48
idv_policy eval average step individual rewards of agent2: 0.766222769582198
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: 0.8629894854505654
idv_policy eval average team episode rewards of agent3: 120.0
idv_policy eval idv catch total num of agent3: 36
idv_policy eval team catch total num: 48
idv_policy eval average step individual rewards of agent4: 0.9650439097201725
idv_policy eval average team episode rewards of agent4: 120.0
idv_policy eval idv catch total num of agent4: 40
idv_policy eval team catch total num: 48

 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.

team_policy eval average step individual rewards of agent0: 1.1263405439935579
team_policy eval average team episode rewards of agent0: 80.0
team_policy eval idv catch total num of agent0: 47
team_policy eval team catch total num: 32
team_policy eval average step individual rewards of agent1: 0.6876020343514412
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: 0.4670397183167424
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: 0.4518033247485581
team_policy eval average team episode rewards of agent3: 80.0
team_policy eval idv catch total num of agent3: 20
team_policy eval team catch total num: 32
team_policy eval average step individual rewards of agent4: 0.45285840825055224
team_policy eval average team episode rewards of agent4: 80.0
team_policy eval idv catch total num of agent4: 20
team_policy eval team catch total num: 32
idv_policy eval average step individual rewards of agent0: 0.9445385916177824
idv_policy eval average team episode rewards of agent0: 137.5
idv_policy eval idv catch total num of agent0: 39
idv_policy eval team catch total num: 55
idv_policy eval average step individual rewards of agent1: 0.4941414194165409
idv_policy eval average team episode rewards of agent1: 137.5
idv_policy eval idv catch total num of agent1: 22
idv_policy eval team catch total num: 55
idv_policy eval average step individual rewards of agent2: 1.0464634530158734
idv_policy eval average team episode rewards of agent2: 137.5
idv_policy eval idv catch total num of agent2: 43
idv_policy eval team catch total num: 55
idv_policy eval average step individual rewards of agent3: 0.7080219165546571
idv_policy eval average team episode rewards of agent3: 137.5
idv_policy eval idv catch total num of agent3: 30
idv_policy eval team catch total num: 55
idv_policy eval average step individual rewards of agent4: 1.0146655720636182
idv_policy eval average team episode rewards of agent4: 137.5
idv_policy eval idv catch total num of agent4: 42
idv_policy eval team catch total num: 55

 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.

team_policy eval average step individual rewards of agent0: 0.4812716988262391
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: 0.2180155037935583
team_policy eval average team episode rewards of agent1: 67.5
team_policy eval idv catch total num of agent1: 11
team_policy eval team catch total num: 27
team_policy eval average step individual rewards of agent2: 0.5132234235362904
team_policy eval average team episode rewards of agent2: 67.5
team_policy eval idv catch total num of agent2: 22
team_policy eval team catch total num: 27
team_policy eval average step individual rewards of agent3: 0.5771758937379134
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: 0.839675033979368
team_policy eval average team episode rewards of agent4: 67.5
team_policy eval idv catch total num of agent4: 35
team_policy eval team catch total num: 27
idv_policy eval average step individual rewards of agent0: 0.44842320301292415
idv_policy eval average team episode rewards of agent0: 125.0
idv_policy eval idv catch total num of agent0: 20
idv_policy eval team catch total num: 50
idv_policy eval average step individual rewards of agent1: 0.6382188285628857
idv_policy eval average team episode rewards of agent1: 125.0
idv_policy eval idv catch total num of agent1: 27
idv_policy eval team catch total num: 50
idv_policy eval average step individual rewards of agent2: 1.16405149862611
idv_policy eval average team episode rewards of agent2: 125.0
idv_policy eval idv catch total num of agent2: 48
idv_policy eval team catch total num: 50
idv_policy eval average step individual rewards of agent3: 1.2214906481564718
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: 0.7820527733847843
idv_policy eval average team episode rewards of agent4: 125.0
idv_policy eval idv catch total num of agent4: 33
idv_policy eval team catch total num: 50

 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.

team_policy eval average step individual rewards of agent0: 0.8628478364023975
team_policy eval average team episode rewards of agent0: 125.0
team_policy eval idv catch total num of agent0: 36
team_policy eval team catch total num: 50
team_policy eval average step individual rewards of agent1: 0.89089115375523
team_policy eval average team episode rewards of agent1: 125.0
team_policy eval idv catch total num of agent1: 37
team_policy eval team catch total num: 50
team_policy eval average step individual rewards of agent2: 0.8408979748044128
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: 1.1211866404221333
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: 0.8951647073288492
team_policy eval average team episode rewards of agent4: 125.0
team_policy eval idv catch total num of agent4: 37
team_policy eval team catch total num: 50
idv_policy eval average step individual rewards of agent0: 0.1920934434656022
idv_policy eval average team episode rewards of agent0: 52.5
idv_policy eval idv catch total num of agent0: 10
idv_policy eval team catch total num: 21
idv_policy eval average step individual rewards of agent1: 0.32491952182276285
idv_policy eval average team episode rewards of agent1: 52.5
idv_policy eval idv catch total num of agent1: 15
idv_policy eval team catch total num: 21
idv_policy eval average step individual rewards of agent2: 0.558228392143038
idv_policy eval average team episode rewards of agent2: 52.5
idv_policy eval idv catch total num of agent2: 24
idv_policy eval team catch total num: 21
idv_policy eval average step individual rewards of agent3: 0.4974045891141721
idv_policy eval average team episode rewards of agent3: 52.5
idv_policy eval idv catch total num of agent3: 22
idv_policy eval team catch total num: 21
idv_policy eval average step individual rewards of agent4: 0.4999108689115653
idv_policy eval average team episode rewards of agent4: 52.5
idv_policy eval idv catch total num of agent4: 22
idv_policy eval team catch total num: 21

 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.

team_policy eval average step individual rewards of agent0: 0.45764539632705015
team_policy eval average team episode rewards of agent0: 112.5
team_policy eval idv catch total num of agent0: 20
team_policy eval team catch total num: 45
team_policy eval average step individual rewards of agent1: 1.0423020204507552
team_policy eval average team episode rewards of agent1: 112.5
team_policy eval idv catch total num of agent1: 43
team_policy eval team catch total num: 45
team_policy eval average step individual rewards of agent2: 0.4649590472565035
team_policy eval average team episode rewards of agent2: 112.5
team_policy eval idv catch total num of agent2: 21
team_policy eval team catch total num: 45
team_policy eval average step individual rewards of agent3: 0.9874193876715712
team_policy eval average team episode rewards of agent3: 112.5
team_policy eval idv catch total num of agent3: 41
team_policy eval team catch total num: 45
team_policy eval average step individual rewards of agent4: 1.0612748851809461
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: 1.1974001268620038
idv_policy eval average team episode rewards of agent0: 167.5
idv_policy eval idv catch total num of agent0: 49
idv_policy eval team catch total num: 67
idv_policy eval average step individual rewards of agent1: 0.8715251094471315
idv_policy eval average team episode rewards of agent1: 167.5
idv_policy eval idv catch total num of agent1: 36
idv_policy eval team catch total num: 67
idv_policy eval average step individual rewards of agent2: 1.4238603240053174
idv_policy eval average team episode rewards of agent2: 167.5
idv_policy eval idv catch total num of agent2: 58
idv_policy eval team catch total num: 67
idv_policy eval average step individual rewards of agent3: 1.1699468231325354
idv_policy eval average team episode rewards of agent3: 167.5
idv_policy eval idv catch total num of agent3: 48
idv_policy eval team catch total num: 67
idv_policy eval average step individual rewards of agent4: 0.9954807097985215
idv_policy eval average team episode rewards of agent4: 167.5
idv_policy eval idv catch total num of agent4: 41
idv_policy eval team catch total num: 67

 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.

team_policy eval average step individual rewards of agent0: 0.8455856067434917
team_policy eval average team episode rewards of agent0: 140.0
team_policy eval idv catch total num of agent0: 35
team_policy eval team catch total num: 56
team_policy eval average step individual rewards of agent1: 0.8665140581056283
team_policy eval average team episode rewards of agent1: 140.0
team_policy eval idv catch total num of agent1: 36
team_policy eval team catch total num: 56
team_policy eval average step individual rewards of agent2: 0.9223156378019541
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: 1.2142052637080132
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: 0.7158274932901254
team_policy eval average team episode rewards of agent4: 140.0
team_policy eval idv catch total num of agent4: 30
team_policy eval team catch total num: 56
idv_policy eval average step individual rewards of agent0: 0.9912709789363987
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: 0.6620616895439068
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: 0.5774862907796207
idv_policy eval average team episode rewards of agent2: 112.5
idv_policy eval idv catch total num of agent2: 25
idv_policy eval team catch total num: 45
idv_policy eval average step individual rewards of agent3: 0.8080802521340587
idv_policy eval average team episode rewards of agent3: 112.5
idv_policy eval idv catch total num of agent3: 34
idv_policy eval team catch total num: 45
idv_policy eval average step individual rewards of agent4: 0.6323949587298578
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 5276/10000 episodes, total num timesteps 1055400/2000000, FPS 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.

team_policy eval average step individual rewards of agent0: 0.788492986159096
team_policy eval average team episode rewards of agent0: 70.0
team_policy eval idv catch total num of agent0: 33
team_policy eval team catch total num: 28
team_policy eval average step individual rewards of agent1: 0.37895982641448833
team_policy eval average team episode rewards of agent1: 70.0
team_policy eval idv catch total num of agent1: 17
team_policy eval team catch total num: 28
team_policy eval average step individual rewards of agent2: 0.6897542849721896
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: 0.35779395793755214
team_policy eval average team episode rewards of agent3: 70.0
team_policy eval idv catch total num of agent3: 16
team_policy eval team catch total num: 28
team_policy eval average step individual rewards of agent4: 0.9439196834683767
team_policy eval average team episode rewards of agent4: 70.0
team_policy eval idv catch total num of agent4: 39
team_policy eval team catch total num: 28
idv_policy eval average step individual rewards of agent0: 0.41908946209248976
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: 0.7389318054818897
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: 1.0927614992727492
idv_policy eval average team episode rewards of agent2: 97.5
idv_policy eval idv catch total num of agent2: 45
idv_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent3: 0.46751133931314515
idv_policy eval average team episode rewards of agent3: 97.5
idv_policy eval idv catch total num of agent3: 21
idv_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent4: 0.8068857639133614
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 5301/10000 episodes, total num timesteps 1060400/2000000, FPS 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.

team_policy eval average step individual rewards of agent0: 0.920330662020133
team_policy eval average team episode rewards of agent0: 117.5
team_policy eval idv catch total num of agent0: 38
team_policy eval team catch total num: 47
team_policy eval average step individual rewards of agent1: 0.6090137348696982
team_policy eval average team episode rewards of agent1: 117.5
team_policy eval idv catch total num of agent1: 26
team_policy eval team catch total num: 47
team_policy eval average step individual rewards of agent2: 0.9877036599569002
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: 0.6329598555413907
team_policy eval average team episode rewards of agent3: 117.5
team_policy eval idv catch total num of agent3: 27
team_policy eval team catch total num: 47
team_policy eval average step individual rewards of agent4: 0.8613953308534831
team_policy eval average team episode rewards of agent4: 117.5
team_policy eval idv catch total num of agent4: 36
team_policy eval team catch total num: 47
idv_policy eval average step individual rewards of agent0: 0.8444770694883209
idv_policy eval average team episode rewards of agent0: 110.0
idv_policy eval idv catch total num of agent0: 35
idv_policy eval team catch total num: 44
idv_policy eval average step individual rewards of agent1: 1.0266058794914066
idv_policy eval average team episode rewards of agent1: 110.0
idv_policy eval idv catch total num of agent1: 42
idv_policy eval team catch total num: 44
idv_policy eval average step individual rewards of agent2: 0.8422032965516024
idv_policy eval average team episode rewards of agent2: 110.0
idv_policy eval idv catch total num of agent2: 35
idv_policy eval team catch total num: 44
idv_policy eval average step individual rewards of agent3: 1.196708664694271
idv_policy eval average team episode rewards of agent3: 110.0
idv_policy eval idv catch total num of agent3: 49
idv_policy eval team catch total num: 44
idv_policy eval average step individual rewards of agent4: 0.5159587581923419
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 5326/10000 episodes, total num timesteps 1065400/2000000, FPS 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.

team_policy eval average step individual rewards of agent0: 0.608369940773854
team_policy eval average team episode rewards of agent0: 82.5
team_policy eval idv catch total num of agent0: 26
team_policy eval team catch total num: 33
team_policy eval average step individual rewards of agent1: 0.3819284603299421
team_policy eval average team episode rewards of agent1: 82.5
team_policy eval idv catch total num of agent1: 17
team_policy eval team catch total num: 33
team_policy eval average step individual rewards of agent2: 0.9917592480660724
team_policy eval average team episode rewards of agent2: 82.5
team_policy eval idv catch total num of agent2: 41
team_policy eval team catch total num: 33
team_policy eval average step individual rewards of agent3: 0.487720882435506
team_policy eval average team episode rewards of agent3: 82.5
team_policy eval idv catch total num of agent3: 21
team_policy eval team catch total num: 33
team_policy eval average step individual rewards of agent4: 0.7415468174006827
team_policy eval average team episode rewards of agent4: 82.5
team_policy eval idv catch total num of agent4: 31
team_policy eval team catch total num: 33
idv_policy eval average step individual rewards of agent0: 1.0154306073806811
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: 0.9943055910308576
idv_policy eval average team episode rewards of agent1: 137.5
idv_policy eval idv catch total num of agent1: 41
idv_policy eval team catch total num: 55
idv_policy eval average step individual rewards of agent2: 1.0938923310514361
idv_policy eval average team episode rewards of agent2: 137.5
idv_policy eval idv catch total num of agent2: 45
idv_policy eval team catch total num: 55
idv_policy eval average step individual rewards of agent3: 1.2411384448181089
idv_policy eval average team episode rewards of agent3: 137.5
idv_policy eval idv catch total num of agent3: 51
idv_policy eval team catch total num: 55
idv_policy eval average step individual rewards of agent4: 0.9648020250294809
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 5351/10000 episodes, total num timesteps 1070400/2000000, FPS 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.

team_policy eval average step individual rewards of agent0: 1.0609964626187698
team_policy eval average team episode rewards of agent0: 105.0
team_policy eval idv catch total num of agent0: 44
team_policy eval team catch total num: 42
team_policy eval average step individual rewards of agent1: 0.6567138537531075
team_policy eval average team episode rewards of agent1: 105.0
team_policy eval idv catch total num of agent1: 28
team_policy eval team catch total num: 42
team_policy eval average step individual rewards of agent2: 1.0174939516934025
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: 0.6038266745209779
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: 0.6541222688539272
team_policy eval average team episode rewards of agent4: 105.0
team_policy eval idv catch total num of agent4: 28
team_policy eval team catch total num: 42
idv_policy eval average step individual rewards of agent0: 0.5323467316551914
idv_policy eval average team episode rewards of agent0: 135.0
idv_policy eval idv catch total num of agent0: 23
idv_policy eval team catch total num: 54
idv_policy eval average step individual rewards of agent1: 0.7590089403841156
idv_policy eval average team episode rewards of agent1: 135.0
idv_policy eval idv catch total num of agent1: 32
idv_policy eval team catch total num: 54
idv_policy eval average step individual rewards of agent2: 1.0114411074799654
idv_policy eval average team episode rewards of agent2: 135.0
idv_policy eval idv catch total num of agent2: 42
idv_policy eval team catch total num: 54
idv_policy eval average step individual rewards of agent3: 0.914663434914449
idv_policy eval average team episode rewards of agent3: 135.0
idv_policy eval idv catch total num of agent3: 38
idv_policy eval team catch total num: 54
idv_policy eval average step individual rewards of agent4: 0.9634923283914678
idv_policy eval average team episode rewards of agent4: 135.0
idv_policy eval idv catch total num of agent4: 40
idv_policy eval team catch total num: 54

 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.

team_policy eval average step individual rewards of agent0: 0.5948638268036829
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: 0.7195452451793741
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: 0.8933844829699847
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: 0.5935921940632191
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: 0.5696765529792417
team_policy eval average team episode rewards of agent4: 85.0
team_policy eval idv catch total num of agent4: 24
team_policy eval team catch total num: 34
idv_policy eval average step individual rewards of agent0: 1.323670081422518
idv_policy eval average team episode rewards of agent0: 127.5
idv_policy eval idv catch total num of agent0: 54
idv_policy eval team catch total num: 51
idv_policy eval average step individual rewards of agent1: 0.8967970932844364
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: 1.071542057314328
idv_policy eval average team episode rewards of agent2: 127.5
idv_policy eval idv catch total num of agent2: 44
idv_policy eval team catch total num: 51
idv_policy eval average step individual rewards of agent3: 0.6302800923079228
idv_policy eval average team episode rewards of agent3: 127.5
idv_policy eval idv catch total num of agent3: 27
idv_policy eval team catch total num: 51
idv_policy eval average step individual rewards of agent4: 0.8626628011047043
idv_policy eval average team episode rewards of agent4: 127.5
idv_policy eval idv catch total num of agent4: 36
idv_policy eval team catch total num: 51

 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.

team_policy eval average step individual rewards of agent0: 0.48653596262671317
team_policy eval average team episode rewards of agent0: 82.5
team_policy eval idv catch total num of agent0: 21
team_policy eval team catch total num: 33
team_policy eval average step individual rewards of agent1: 0.841448195147212
team_policy eval average team episode rewards of agent1: 82.5
team_policy eval idv catch total num of agent1: 35
team_policy eval team catch total num: 33
team_policy eval average step individual rewards of agent2: 0.6576155170270241
team_policy eval average team episode rewards of agent2: 82.5
team_policy eval idv catch total num of agent2: 28
team_policy eval team catch total num: 33
team_policy eval average step individual rewards of agent3: 0.5883915867325169
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: 0.7180788999674556
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: 0.3747681290039662
idv_policy eval average team episode rewards of agent0: 112.5
idv_policy eval idv catch total num of agent0: 17
idv_policy eval team catch total num: 45
idv_policy eval average step individual rewards of agent1: 0.7814767553493311
idv_policy eval average team episode rewards of agent1: 112.5
idv_policy eval idv catch total num of agent1: 33
idv_policy eval team catch total num: 45
idv_policy eval average step individual rewards of agent2: 0.9411786098573355
idv_policy eval average team episode rewards of agent2: 112.5
idv_policy eval idv catch total num of agent2: 39
idv_policy eval team catch total num: 45
idv_policy eval average step individual rewards of agent3: 0.8115722038364862
idv_policy eval average team episode rewards of agent3: 112.5
idv_policy eval idv catch total num of agent3: 34
idv_policy eval team catch total num: 45
idv_policy eval average step individual rewards of agent4: 0.933768852344427
idv_policy eval average team episode rewards of agent4: 112.5
idv_policy eval idv catch total num of agent4: 39
idv_policy eval team catch total num: 45

 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.

team_policy eval average step individual rewards of agent0: 0.7347984738456481
team_policy eval average team episode rewards of agent0: 95.0
team_policy eval idv catch total num of agent0: 31
team_policy eval team catch total num: 38
team_policy eval average step individual rewards of agent1: 0.9155097257736705
team_policy eval average team episode rewards of agent1: 95.0
team_policy eval idv catch total num of agent1: 38
team_policy eval team catch total num: 38
team_policy eval average step individual rewards of agent2: 0.5064346249928141
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: 0.6087777895248319
team_policy eval average team episode rewards of agent3: 95.0
team_policy eval idv catch total num of agent3: 26
team_policy eval team catch total num: 38
team_policy eval average step individual rewards of agent4: 1.0465588363776146
team_policy eval average team episode rewards of agent4: 95.0
team_policy eval idv catch total num of agent4: 43
team_policy eval team catch total num: 38
idv_policy eval average step individual rewards of agent0: 0.9180148115071273
idv_policy eval average team episode rewards of agent0: 130.0
idv_policy eval idv catch total num of agent0: 38
idv_policy eval team catch total num: 52
idv_policy eval average step individual rewards of agent1: 0.9868548985095393
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: 1.1409814683400408
idv_policy eval average team episode rewards of agent2: 130.0
idv_policy eval idv catch total num of agent2: 47
idv_policy eval team catch total num: 52
idv_policy eval average step individual rewards of agent3: 0.7814832431075286
idv_policy eval average team episode rewards of agent3: 130.0
idv_policy eval idv catch total num of agent3: 33
idv_policy eval team catch total num: 52
idv_policy eval average step individual rewards of agent4: 0.7345650813353263
idv_policy eval average team episode rewards of agent4: 130.0
idv_policy eval idv catch total num of agent4: 31
idv_policy eval team catch total num: 52

 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.

team_policy eval average step individual rewards of agent0: 0.7679051635034867
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: 0.8432691462584071
team_policy eval average team episode rewards of agent1: 102.5
team_policy eval idv catch total num of agent1: 35
team_policy eval team catch total num: 41
team_policy eval average step individual rewards of agent2: 0.6098198154933598
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: 0.9053546787123835
team_policy eval average team episode rewards of agent3: 102.5
team_policy eval idv catch total num of agent3: 38
team_policy eval team catch total num: 41
team_policy eval average step individual rewards of agent4: 0.887863531749679
team_policy eval average team episode rewards of agent4: 102.5
team_policy eval idv catch total num of agent4: 37
team_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent0: 0.6549142318456063
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: 0.2787191391874076
idv_policy eval average team episode rewards of agent1: 97.5
idv_policy eval idv catch total num of agent1: 13
idv_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent2: 0.9098962527364293
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: 1.0147348644429983
idv_policy eval average team episode rewards of agent3: 97.5
idv_policy eval idv catch total num of agent3: 42
idv_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent4: 0.9388044940535886
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 5476/10000 episodes, total num timesteps 1095400/2000000, FPS 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.

team_policy eval average step individual rewards of agent0: 0.47843937479309034
team_policy eval average team episode rewards of agent0: 80.0
team_policy eval idv catch total num of agent0: 21
team_policy eval team catch total num: 32
team_policy eval average step individual rewards of agent1: 1.0391322277294537
team_policy eval average team episode rewards of agent1: 80.0
team_policy eval idv catch total num of agent1: 43
team_policy eval team catch total num: 32
team_policy eval average step individual rewards of agent2: 0.5077575830094347
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: 0.5823272416842309
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: 0.5260562483773757
team_policy eval average team episode rewards of agent4: 80.0
team_policy eval idv catch total num of agent4: 23
team_policy eval team catch total num: 32
idv_policy eval average step individual rewards of agent0: 0.7104773006985826
idv_policy eval average team episode rewards of agent0: 110.0
idv_policy eval idv catch total num of agent0: 30
idv_policy eval team catch total num: 44
idv_policy eval average step individual rewards of agent1: 1.0128065095668715
idv_policy eval average team episode rewards of agent1: 110.0
idv_policy eval idv catch total num of agent1: 42
idv_policy eval team catch total num: 44
idv_policy eval average step individual rewards of agent2: 0.5764236216081612
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: 1.0088922086962975
idv_policy eval average team episode rewards of agent3: 110.0
idv_policy eval idv catch total num of agent3: 42
idv_policy eval team catch total num: 44
idv_policy eval average step individual rewards of agent4: 0.7015329471162505
idv_policy eval average team episode rewards of agent4: 110.0
idv_policy eval idv catch total num of agent4: 30
idv_policy eval team catch total num: 44

 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.

team_policy eval average step individual rewards of agent0: 0.8929886689162784
team_policy eval average team episode rewards of agent0: 102.5
team_policy eval idv catch total num of agent0: 37
team_policy eval team catch total num: 41
team_policy eval average step individual rewards of agent1: 0.7195851903795222
team_policy eval average team episode rewards of agent1: 102.5
team_policy eval idv catch total num of agent1: 30
team_policy eval team catch total num: 41
team_policy eval average step individual rewards of agent2: 0.2908923102424566
team_policy eval average team episode rewards of agent2: 102.5
team_policy eval idv catch total num of agent2: 13
team_policy eval team catch total num: 41
team_policy eval average step individual rewards of agent3: 0.8162464610938278
team_policy eval average team episode rewards of agent3: 102.5
team_policy eval idv catch total num of agent3: 34
team_policy eval team catch total num: 41
team_policy eval average step individual rewards of agent4: 0.9204405030653944
team_policy eval average team episode rewards of agent4: 102.5
team_policy eval idv catch total num of agent4: 38
team_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent0: 1.0958391369671956
idv_policy eval average team episode rewards of agent0: 125.0
idv_policy eval idv catch total num of agent0: 45
idv_policy eval team catch total num: 50
idv_policy eval average step individual rewards of agent1: 1.1945542979709054
idv_policy eval average team episode rewards of agent1: 125.0
idv_policy eval idv catch total num of agent1: 49
idv_policy eval team catch total num: 50
idv_policy eval average step individual rewards of agent2: 0.5378331501866348
idv_policy eval average team episode rewards of agent2: 125.0
idv_policy eval idv catch total num of agent2: 23
idv_policy eval team catch total num: 50
idv_policy eval average step individual rewards of agent3: 1.071170020505748
idv_policy eval average team episode rewards of agent3: 125.0
idv_policy eval idv catch total num of agent3: 44
idv_policy eval team catch total num: 50
idv_policy eval average step individual rewards of agent4: 0.5097319166335749
idv_policy eval average team episode rewards of agent4: 125.0
idv_policy eval idv catch total num of agent4: 22
idv_policy eval team catch total num: 50

 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.

team_policy eval average step individual rewards of agent0: 0.8614026767993203
team_policy eval average team episode rewards of agent0: 132.5
team_policy eval idv catch total num of agent0: 36
team_policy eval team catch total num: 53
team_policy eval average step individual rewards of agent1: 1.0862354142453936
team_policy eval average team episode rewards of agent1: 132.5
team_policy eval idv catch total num of agent1: 45
team_policy eval team catch total num: 53
team_policy eval average step individual rewards of agent2: 0.9104531409249509
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: 0.7141088095074514
team_policy eval average team episode rewards of agent3: 132.5
team_policy eval idv catch total num of agent3: 30
team_policy eval team catch total num: 53
team_policy eval average step individual rewards of agent4: 0.9381792571995367
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: 0.7414599240185139
idv_policy eval average team episode rewards of agent0: 127.5
idv_policy eval idv catch total num of agent0: 31
idv_policy eval team catch total num: 51
idv_policy eval average step individual rewards of agent1: 0.9382696804068601
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: 1.2262500697110226
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: 1.1959244391315573
idv_policy eval average team episode rewards of agent3: 127.5
idv_policy eval idv catch total num of agent3: 49
idv_policy eval team catch total num: 51
idv_policy eval average step individual rewards of agent4: 0.747710331545942
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 5551/10000 episodes, total num timesteps 1110400/2000000, FPS 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.

team_policy eval average step individual rewards of agent0: 0.3572902706542304
team_policy eval average team episode rewards of agent0: 37.5
team_policy eval idv catch total num of agent0: 16
team_policy eval team catch total num: 15
team_policy eval average step individual rewards of agent1: 0.4716963194485279
team_policy eval average team episode rewards of agent1: 37.5
team_policy eval idv catch total num of agent1: 21
team_policy eval team catch total num: 15
team_policy eval average step individual rewards of agent2: 0.09674599444751449
team_policy eval average team episode rewards of agent2: 37.5
team_policy eval idv catch total num of agent2: 6
team_policy eval team catch total num: 15
team_policy eval average step individual rewards of agent3: 0.1987971845800937
team_policy eval average team episode rewards of agent3: 37.5
team_policy eval idv catch total num of agent3: 10
team_policy eval team catch total num: 15
team_policy eval average step individual rewards of agent4: 0.7043447941120584
team_policy eval average team episode rewards of agent4: 37.5
team_policy eval idv catch total num of agent4: 30
team_policy eval team catch total num: 15
idv_policy eval average step individual rewards of agent0: 0.8416144052082362
idv_policy eval average team episode rewards of agent0: 125.0
idv_policy eval idv catch total num of agent0: 35
idv_policy eval team catch total num: 50
idv_policy eval average step individual rewards of agent1: 0.5139110312388124
idv_policy eval average team episode rewards of agent1: 125.0
idv_policy eval idv catch total num of agent1: 22
idv_policy eval team catch total num: 50
idv_policy eval average step individual rewards of agent2: 1.0658975917318698
idv_policy eval average team episode rewards of agent2: 125.0
idv_policy eval idv catch total num of agent2: 44
idv_policy eval team catch total num: 50
idv_policy eval average step individual rewards of agent3: 1.1475416650670198
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: 1.0236408292606505
idv_policy eval average team episode rewards of agent4: 125.0
idv_policy eval idv catch total num of agent4: 42
idv_policy eval team catch total num: 50

 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.

team_policy eval average step individual rewards of agent0: 0.8179088733955979
team_policy eval average team episode rewards of agent0: 102.5
team_policy eval idv catch total num of agent0: 34
team_policy eval team catch total num: 41
team_policy eval average step individual rewards of agent1: 0.5011870923224798
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: 1.0167090437316895
team_policy eval average team episode rewards of agent2: 102.5
team_policy eval idv catch total num of agent2: 42
team_policy eval team catch total num: 41
team_policy eval average step individual rewards of agent3: 0.6150492617235339
team_policy eval average team episode rewards of agent3: 102.5
team_policy eval idv catch total num of agent3: 27
team_policy eval team catch total num: 41
team_policy eval average step individual rewards of agent4: 0.7316950453841398
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: 0.8057056502323033
idv_policy eval average team episode rewards of agent0: 102.5
idv_policy eval idv catch total num of agent0: 34
idv_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent1: 0.563419550014511
idv_policy eval average team episode rewards of agent1: 102.5
idv_policy eval idv catch total num of agent1: 24
idv_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent2: 0.987608120907334
idv_policy eval average team episode rewards of agent2: 102.5
idv_policy eval idv catch total num of agent2: 41
idv_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent3: 0.7034428344329094
idv_policy eval average team episode rewards of agent3: 102.5
idv_policy eval idv catch total num of agent3: 30
idv_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent4: 0.7455237763480406
idv_policy eval average team episode rewards of agent4: 102.5
idv_policy eval idv catch total num of agent4: 31
idv_policy eval team catch total num: 41

 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.

team_policy eval average step individual rewards of agent0: 0.7077570305845482
team_policy eval average team episode rewards of agent0: 112.5
team_policy eval idv catch total num of agent0: 30
team_policy eval team catch total num: 45
team_policy eval average step individual rewards of agent1: 0.6511543742512965
team_policy eval average team episode rewards of agent1: 112.5
team_policy eval idv catch total num of agent1: 28
team_policy eval team catch total num: 45
team_policy eval average step individual rewards of agent2: 0.8357772300637006
team_policy eval average team episode rewards of agent2: 112.5
team_policy eval idv catch total num of agent2: 35
team_policy eval team catch total num: 45
team_policy eval average step individual rewards of agent3: 0.9572293535621967
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: 1.4699192779957286
team_policy eval average team episode rewards of agent4: 112.5
team_policy eval idv catch total num of agent4: 60
team_policy eval team catch total num: 45
idv_policy eval average step individual rewards of agent0: 0.529405165885031
idv_policy eval average team episode rewards of agent0: 97.5
idv_policy eval idv catch total num of agent0: 23
idv_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent1: 0.6101590558540151
idv_policy eval average team episode rewards of agent1: 97.5
idv_policy eval idv catch total num of agent1: 26
idv_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent2: 1.0100446870318989
idv_policy eval average team episode rewards of agent2: 97.5
idv_policy eval idv catch total num of agent2: 42
idv_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent3: 0.7249495890576592
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: 0.8333925530160858
idv_policy eval average team episode rewards of agent4: 97.5
idv_policy eval idv catch total num of agent4: 35
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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.

team_policy eval average step individual rewards of agent0: 0.357679471577249
team_policy eval average team episode rewards of agent0: 125.0
team_policy eval idv catch total num of agent0: 16
team_policy eval team catch total num: 50
team_policy eval average step individual rewards of agent1: 0.812536808080746
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: 1.1953534052452826
team_policy eval average team episode rewards of agent2: 125.0
team_policy eval idv catch total num of agent2: 49
team_policy eval team catch total num: 50
team_policy eval average step individual rewards of agent3: 0.6906984994676393
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: 1.0931675536447178
team_policy eval average team episode rewards of agent4: 125.0
team_policy eval idv catch total num of agent4: 45
team_policy eval team catch total num: 50
idv_policy eval average step individual rewards of agent0: 1.0319040015527077
idv_policy eval average team episode rewards of agent0: 65.0
idv_policy eval idv catch total num of agent0: 43
idv_policy eval team catch total num: 26
idv_policy eval average step individual rewards of agent1: 0.6581157118352412
idv_policy eval average team episode rewards of agent1: 65.0
idv_policy eval idv catch total num of agent1: 28
idv_policy eval team catch total num: 26
idv_policy eval average step individual rewards of agent2: 0.37493843727348036
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: 0.15540945258136923
idv_policy eval average team episode rewards of agent3: 65.0
idv_policy eval idv catch total num of agent3: 8
idv_policy eval team catch total num: 26
idv_policy eval average step individual rewards of agent4: 0.43330082603048375
idv_policy eval average team episode rewards of agent4: 65.0
idv_policy eval idv catch total num of agent4: 19
idv_policy eval team catch total num: 26

 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 229.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.

team_policy eval average step individual rewards of agent0: 0.7280182177670378
team_policy eval average team episode rewards of agent0: 135.0
team_policy eval idv catch total num of agent0: 31
team_policy eval team catch total num: 54
team_policy eval average step individual rewards of agent1: 0.8269805272175776
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: 0.963815334061612
team_policy eval average team episode rewards of agent2: 135.0
team_policy eval idv catch total num of agent2: 40
team_policy eval team catch total num: 54
team_policy eval average step individual rewards of agent3: 0.8740841651844309
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: 1.2353689828262073
team_policy eval average team episode rewards of agent4: 135.0
team_policy eval idv catch total num of agent4: 51
team_policy eval team catch total num: 54
idv_policy eval average step individual rewards of agent0: 0.6230775361313744
idv_policy eval average team episode rewards of agent0: 112.5
idv_policy eval idv catch total num of agent0: 27
idv_policy eval team catch total num: 45
idv_policy eval average step individual rewards of agent1: 0.7896396393288383
idv_policy eval average team episode rewards of agent1: 112.5
idv_policy eval idv catch total num of agent1: 33
idv_policy eval team catch total num: 45
idv_policy eval average step individual rewards of agent2: 0.6512654689210273
idv_policy eval average team episode rewards of agent2: 112.5
idv_policy eval idv catch total num of agent2: 28
idv_policy eval team catch total num: 45
idv_policy eval average step individual rewards of agent3: 0.65550673131832
idv_policy eval average team episode rewards of agent3: 112.5
idv_policy eval idv catch total num of agent3: 28
idv_policy eval team catch total num: 45
idv_policy eval average step individual rewards of agent4: 0.7820468151202479
idv_policy eval average team episode rewards of agent4: 112.5
idv_policy eval idv catch total num of agent4: 33
idv_policy eval team catch total num: 45

 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.

team_policy eval average step individual rewards of agent0: 0.7012930005440791
team_policy eval average team episode rewards of agent0: 122.5
team_policy eval idv catch total num of agent0: 30
team_policy eval team catch total num: 49
team_policy eval average step individual rewards of agent1: 1.1342324138754802
team_policy eval average team episode rewards of agent1: 122.5
team_policy eval idv catch total num of agent1: 47
team_policy eval team catch total num: 49
team_policy eval average step individual rewards of agent2: 1.0144768967692601
team_policy eval average team episode rewards of agent2: 122.5
team_policy eval idv catch total num of agent2: 42
team_policy eval team catch total num: 49
team_policy eval average step individual rewards of agent3: 0.7077152668797236
team_policy eval average team episode rewards of agent3: 122.5
team_policy eval idv catch total num of agent3: 30
team_policy eval team catch total num: 49
team_policy eval average step individual rewards of agent4: 0.9572092919652104
team_policy eval average team episode rewards of agent4: 122.5
team_policy eval idv catch total num of agent4: 40
team_policy eval team catch total num: 49
idv_policy eval average step individual rewards of agent0: 0.6367465546311585
idv_policy eval average team episode rewards of agent0: 90.0
idv_policy eval idv catch total num of agent0: 27
idv_policy eval team catch total num: 36
idv_policy eval average step individual rewards of agent1: 0.7156744993641437
idv_policy eval average team episode rewards of agent1: 90.0
idv_policy eval idv catch total num of agent1: 30
idv_policy eval team catch total num: 36
idv_policy eval average step individual rewards of agent2: 0.5551288135748381
idv_policy eval average team episode rewards of agent2: 90.0
idv_policy eval idv catch total num of agent2: 24
idv_policy eval team catch total num: 36
idv_policy eval average step individual rewards of agent3: 0.7939329297183518
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: 0.7514604725106538
idv_policy eval average team episode rewards of agent4: 90.0
idv_policy eval idv catch total num of agent4: 32
idv_policy eval team catch total num: 36

 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.

team_policy eval average step individual rewards of agent0: 0.9392048088464682
team_policy eval average team episode rewards of agent0: 82.5
team_policy eval idv catch total num of agent0: 39
team_policy eval team catch total num: 33
team_policy eval average step individual rewards of agent1: 0.4721343109736791
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: 0.8283041562543899
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: 0.5353914185352623
team_policy eval average team episode rewards of agent3: 82.5
team_policy eval idv catch total num of agent3: 24
team_policy eval team catch total num: 33
team_policy eval average step individual rewards of agent4: 0.7994138033576129
team_policy eval average team episode rewards of agent4: 82.5
team_policy eval idv catch total num of agent4: 33
team_policy eval team catch total num: 33
idv_policy eval average step individual rewards of agent0: 0.4618642950635832
idv_policy eval average team episode rewards of agent0: 75.0
idv_policy eval idv catch total num of agent0: 20
idv_policy eval team catch total num: 30
idv_policy eval average step individual rewards of agent1: 0.7824628041762918
idv_policy eval average team episode rewards of agent1: 75.0
idv_policy eval idv catch total num of agent1: 33
idv_policy eval team catch total num: 30
idv_policy eval average step individual rewards of agent2: 0.4349114434967851
idv_policy eval average team episode rewards of agent2: 75.0
idv_policy eval idv catch total num of agent2: 19
idv_policy eval team catch total num: 30
idv_policy eval average step individual rewards of agent3: 0.5563499822285233
idv_policy eval average team episode rewards of agent3: 75.0
idv_policy eval idv catch total num of agent3: 24
idv_policy eval team catch total num: 30
idv_policy eval average step individual rewards of agent4: 0.6693557173213169
idv_policy eval average team episode rewards of agent4: 75.0
idv_policy eval idv catch total num of agent4: 28
idv_policy eval team catch total num: 30

 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.

team_policy eval average step individual rewards of agent0: 1.0668237913934702
team_policy eval average team episode rewards of agent0: 97.5
team_policy eval idv catch total num of agent0: 44
team_policy eval team catch total num: 39
team_policy eval average step individual rewards of agent1: 0.8703890397314857
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: 0.5076767755854443
team_policy eval average team episode rewards of agent2: 97.5
team_policy eval idv catch total num of agent2: 22
team_policy eval team catch total num: 39
team_policy eval average step individual rewards of agent3: 1.08585902765515
team_policy eval average team episode rewards of agent3: 97.5
team_policy eval idv catch total num of agent3: 45
team_policy eval team catch total num: 39
team_policy eval average step individual rewards of agent4: 0.7407489265640669
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: 1.1697663465999053
idv_policy eval average team episode rewards of agent0: 107.5
idv_policy eval idv catch total num of agent0: 48
idv_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent1: 0.8924047151517288
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: 0.8125146631525246
idv_policy eval average team episode rewards of agent2: 107.5
idv_policy eval idv catch total num of agent2: 34
idv_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent3: 0.9147324795743608
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: 0.48312294900300295
idv_policy eval average team episode rewards of agent4: 107.5
idv_policy eval idv catch total num of agent4: 21
idv_policy eval team catch total num: 43

 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.

team_policy eval average step individual rewards of agent0: 0.7584902645081584
team_policy eval average team episode rewards of agent0: 115.0
team_policy eval idv catch total num of agent0: 32
team_policy eval team catch total num: 46
team_policy eval average step individual rewards of agent1: 0.8430560503520749
team_policy eval average team episode rewards of agent1: 115.0
team_policy eval idv catch total num of agent1: 35
team_policy eval team catch total num: 46
team_policy eval average step individual rewards of agent2: 0.9178450929595686
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: 0.7324259712059142
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: 0.8196934977419085
team_policy eval average team episode rewards of agent4: 115.0
team_policy eval idv catch total num of agent4: 34
team_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent0: 1.0939204242503315
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: 0.7634554362957059
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: 1.1164148819401247
idv_policy eval average team episode rewards of agent2: 132.5
idv_policy eval idv catch total num of agent2: 46
idv_policy eval team catch total num: 53
idv_policy eval average step individual rewards of agent3: 1.0095694142820653
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: 0.835991269924688
idv_policy eval average team episode rewards of agent4: 132.5
idv_policy eval idv catch total num of agent4: 35
idv_policy eval team catch total num: 53

 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.

team_policy eval average step individual rewards of agent0: 1.2160863268856255
team_policy eval average team episode rewards of agent0: 157.5
team_policy eval idv catch total num of agent0: 50
team_policy eval team catch total num: 63
team_policy eval average step individual rewards of agent1: 1.1760949349734604
team_policy eval average team episode rewards of agent1: 157.5
team_policy eval idv catch total num of agent1: 48
team_policy eval team catch total num: 63
team_policy eval average step individual rewards of agent2: 0.8612590010086096
team_policy eval average team episode rewards of agent2: 157.5
team_policy eval idv catch total num of agent2: 36
team_policy eval team catch total num: 63
team_policy eval average step individual rewards of agent3: 1.2693699885781158
team_policy eval average team episode rewards of agent3: 157.5
team_policy eval idv catch total num of agent3: 52
team_policy eval team catch total num: 63
team_policy eval average step individual rewards of agent4: 0.9940922273233644
team_policy eval average team episode rewards of agent4: 157.5
team_policy eval idv catch total num of agent4: 41
team_policy eval team catch total num: 63
idv_policy eval average step individual rewards of agent0: 0.6509581768409889
idv_policy eval average team episode rewards of agent0: 112.5
idv_policy eval idv catch total num of agent0: 28
idv_policy eval team catch total num: 45
idv_policy eval average step individual rewards of agent1: 0.8356765946015553
idv_policy eval average team episode rewards of agent1: 112.5
idv_policy eval idv catch total num of agent1: 35
idv_policy eval team catch total num: 45
idv_policy eval average step individual rewards of agent2: 0.6512449559640927
idv_policy eval average team episode rewards of agent2: 112.5
idv_policy eval idv catch total num of agent2: 28
idv_policy eval team catch total num: 45
idv_policy eval average step individual rewards of agent3: 0.6239025009357944
idv_policy eval average team episode rewards of agent3: 112.5
idv_policy eval idv catch total num of agent3: 27
idv_policy eval team catch total num: 45
idv_policy eval average step individual rewards of agent4: 0.8280982454158088
idv_policy eval average team episode rewards of agent4: 112.5
idv_policy eval idv catch total num of agent4: 35
idv_policy eval team catch total num: 45

 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.

team_policy eval average step individual rewards of agent0: 0.7639789416828165
team_policy eval average team episode rewards of agent0: 80.0
team_policy eval idv catch total num of agent0: 32
team_policy eval team catch total num: 32
team_policy eval average step individual rewards of agent1: 0.8443860310085839
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: 0.45947691397468743
team_policy eval average team episode rewards of agent2: 80.0
team_policy eval idv catch total num of agent2: 20
team_policy eval team catch total num: 32
team_policy eval average step individual rewards of agent3: 0.3266208208605698
team_policy eval average team episode rewards of agent3: 80.0
team_policy eval idv catch total num of agent3: 15
team_policy eval team catch total num: 32
team_policy eval average step individual rewards of agent4: 0.50391617653161
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: 0.9947372344751888
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: 1.1742700746783108
idv_policy eval average team episode rewards of agent1: 147.5
idv_policy eval idv catch total num of agent1: 48
idv_policy eval team catch total num: 59
idv_policy eval average step individual rewards of agent2: 0.9742514228472468
idv_policy eval average team episode rewards of agent2: 147.5
idv_policy eval idv catch total num of agent2: 40
idv_policy eval team catch total num: 59
idv_policy eval average step individual rewards of agent3: 1.1696183535027505
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: 0.993803502812694
idv_policy eval average team episode rewards of agent4: 147.5
idv_policy eval idv catch total num of agent4: 41
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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.

team_policy eval average step individual rewards of agent0: 1.1226401028915416
team_policy eval average team episode rewards of agent0: 130.0
team_policy eval idv catch total num of agent0: 46
team_policy eval team catch total num: 52
team_policy eval average step individual rewards of agent1: 1.0182924975268806
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: 0.9555115697307138
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: 0.7419480787338348
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: 0.8894359682289636
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: 0.5567723645027818
idv_policy eval average team episode rewards of agent0: 102.5
idv_policy eval idv catch total num of agent0: 24
idv_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent1: 1.116575201004979
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: 0.8120152296103403
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: 0.9442617760728881
idv_policy eval average team episode rewards of agent3: 102.5
idv_policy eval idv catch total num of agent3: 39
idv_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent4: 0.38807598427209977
idv_policy eval average team episode rewards of agent4: 102.5
idv_policy eval idv catch total num of agent4: 17
idv_policy eval team catch total num: 41

 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 230.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.

team_policy eval average step individual rewards of agent0: 0.2841748217247011
team_policy eval average team episode rewards of agent0: 82.5
team_policy eval idv catch total num of agent0: 13
team_policy eval team catch total num: 33
team_policy eval average step individual rewards of agent1: 0.38132059046093403
team_policy eval average team episode rewards of agent1: 82.5
team_policy eval idv catch total num of agent1: 17
team_policy eval team catch total num: 33
team_policy eval average step individual rewards of agent2: 1.3013048926005175
team_policy eval average team episode rewards of agent2: 82.5
team_policy eval idv catch total num of agent2: 53
team_policy eval team catch total num: 33
team_policy eval average step individual rewards of agent3: 0.8411707540690528
team_policy eval average team episode rewards of agent3: 82.5
team_policy eval idv catch total num of agent3: 35
team_policy eval team catch total num: 33
team_policy eval average step individual rewards of agent4: 0.6312564557252152
team_policy eval average team episode rewards of agent4: 82.5
team_policy eval idv catch total num of agent4: 27
team_policy eval team catch total num: 33
idv_policy eval average step individual rewards of agent0: 0.5825913768685906
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: 0.7626681942943194
idv_policy eval average team episode rewards of agent1: 102.5
idv_policy eval idv catch total num of agent1: 32
idv_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent2: 0.6897988532917404
idv_policy eval average team episode rewards of agent2: 102.5
idv_policy eval idv catch total num of agent2: 29
idv_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent3: 0.6839699236797191
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: 1.0672120459627599
idv_policy eval average team episode rewards of agent4: 102.5
idv_policy eval idv catch total num of agent4: 44
idv_policy eval team catch total num: 41

 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.

team_policy eval average step individual rewards of agent0: 0.29963369101712106
team_policy eval average team episode rewards of agent0: 85.0
team_policy eval idv catch total num of agent0: 14
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent1: 0.6045820862602963
team_policy eval average team episode rewards of agent1: 85.0
team_policy eval idv catch total num of agent1: 26
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent2: 0.8619968749678563
team_policy eval average team episode rewards of agent2: 85.0
team_policy eval idv catch total num of agent2: 36
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent3: 1.2687750055931775
team_policy eval average team episode rewards of agent3: 85.0
team_policy eval idv catch total num of agent3: 52
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent4: 0.48518277936445947
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: 1.14260372099002
idv_policy eval average team episode rewards of agent0: 175.0
idv_policy eval idv catch total num of agent0: 47
idv_policy eval team catch total num: 70
idv_policy eval average step individual rewards of agent1: 0.8430601978749741
idv_policy eval average team episode rewards of agent1: 175.0
idv_policy eval idv catch total num of agent1: 35
idv_policy eval team catch total num: 70
idv_policy eval average step individual rewards of agent2: 1.020415979707997
idv_policy eval average team episode rewards of agent2: 175.0
idv_policy eval idv catch total num of agent2: 42
idv_policy eval team catch total num: 70
idv_policy eval average step individual rewards of agent3: 1.478983750303525
idv_policy eval average team episode rewards of agent3: 175.0
idv_policy eval idv catch total num of agent3: 60
idv_policy eval team catch total num: 70
idv_policy eval average step individual rewards of agent4: 0.8939853760455615
idv_policy eval average team episode rewards of agent4: 175.0
idv_policy eval idv catch total num of agent4: 37
idv_policy eval team catch total num: 70

 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.

team_policy eval average step individual rewards of agent0: 0.43846432433588733
team_policy eval average team episode rewards of agent0: 45.0
team_policy eval idv catch total num of agent0: 19
team_policy eval team catch total num: 18
team_policy eval average step individual rewards of agent1: 0.7350346848274182
team_policy eval average team episode rewards of agent1: 45.0
team_policy eval idv catch total num of agent1: 31
team_policy eval team catch total num: 18
team_policy eval average step individual rewards of agent2: 0.2804936718165256
team_policy eval average team episode rewards of agent2: 45.0
team_policy eval idv catch total num of agent2: 13
team_policy eval team catch total num: 18
team_policy eval average step individual rewards of agent3: 0.40318953838938504
team_policy eval average team episode rewards of agent3: 45.0
team_policy eval idv catch total num of agent3: 18
team_policy eval team catch total num: 18
team_policy eval average step individual rewards of agent4: 0.5636467247843303
team_policy eval average team episode rewards of agent4: 45.0
team_policy eval idv catch total num of agent4: 24
team_policy eval team catch total num: 18
idv_policy eval average step individual rewards of agent0: 0.4252763439089047
idv_policy eval average team episode rewards of agent0: 52.5
idv_policy eval idv catch total num of agent0: 19
idv_policy eval team catch total num: 21
idv_policy eval average step individual rewards of agent1: 0.5496852117727041
idv_policy eval average team episode rewards of agent1: 52.5
idv_policy eval idv catch total num of agent1: 24
idv_policy eval team catch total num: 21
idv_policy eval average step individual rewards of agent2: 0.6441714675286966
idv_policy eval average team episode rewards of agent2: 52.5
idv_policy eval idv catch total num of agent2: 28
idv_policy eval team catch total num: 21
idv_policy eval average step individual rewards of agent3: 0.4062705134861481
idv_policy eval average team episode rewards of agent3: 52.5
idv_policy eval idv catch total num of agent3: 18
idv_policy eval team catch total num: 21
idv_policy eval average step individual rewards of agent4: 0.5108296608830137
idv_policy eval average team episode rewards of agent4: 52.5
idv_policy eval idv catch total num of agent4: 22
idv_policy eval team catch total num: 21

 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.

team_policy eval average step individual rewards of agent0: 0.8652503569515787
team_policy eval average team episode rewards of agent0: 92.5
team_policy eval idv catch total num of agent0: 36
team_policy eval team catch total num: 37
team_policy eval average step individual rewards of agent1: 0.5614783688752845
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: 0.5414192260870648
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: 0.5907167945912913
team_policy eval average team episode rewards of agent3: 92.5
team_policy eval idv catch total num of agent3: 25
team_policy eval team catch total num: 37
team_policy eval average step individual rewards of agent4: 1.024422920183375
team_policy eval average team episode rewards of agent4: 92.5
team_policy eval idv catch total num of agent4: 42
team_policy eval team catch total num: 37
idv_policy eval average step individual rewards of agent0: 0.43640887334593464
idv_policy eval average team episode rewards of agent0: 105.0
idv_policy eval idv catch total num of agent0: 19
idv_policy eval team catch total num: 42
idv_policy eval average step individual rewards of agent1: 0.9815557825012045
idv_policy eval average team episode rewards of agent1: 105.0
idv_policy eval idv catch total num of agent1: 41
idv_policy eval team catch total num: 42
idv_policy eval average step individual rewards of agent2: 0.9318693479296764
idv_policy eval average team episode rewards of agent2: 105.0
idv_policy eval idv catch total num of agent2: 39
idv_policy eval team catch total num: 42
idv_policy eval average step individual rewards of agent3: 0.17165827805139108
idv_policy eval average team episode rewards of agent3: 105.0
idv_policy eval idv catch total num of agent3: 9
idv_policy eval team catch total num: 42
idv_policy eval average step individual rewards of agent4: 0.9135604183436143
idv_policy eval average team episode rewards of agent4: 105.0
idv_policy eval idv catch total num of agent4: 38
idv_policy eval team catch total num: 42

 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.

team_policy eval average step individual rewards of agent0: 0.8806451616217885
team_policy eval average team episode rewards of agent0: 150.0
team_policy eval idv catch total num of agent0: 37
team_policy eval team catch total num: 60
team_policy eval average step individual rewards of agent1: 0.8346142092555863
team_policy eval average team episode rewards of agent1: 150.0
team_policy eval idv catch total num of agent1: 35
team_policy eval team catch total num: 60
team_policy eval average step individual rewards of agent2: 0.845377599782583
team_policy eval average team episode rewards of agent2: 150.0
team_policy eval idv catch total num of agent2: 35
team_policy eval team catch total num: 60
team_policy eval average step individual rewards of agent3: 1.1858401050067096
team_policy eval average team episode rewards of agent3: 150.0
team_policy eval idv catch total num of agent3: 49
team_policy eval team catch total num: 60
team_policy eval average step individual rewards of agent4: 1.3678912372978465
team_policy eval average team episode rewards of agent4: 150.0
team_policy eval idv catch total num of agent4: 56
team_policy eval team catch total num: 60
idv_policy eval average step individual rewards of agent0: 0.7770933490322
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: 0.7029767725684504
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: 0.6843884243917541
idv_policy eval average team episode rewards of agent2: 92.5
idv_policy eval idv catch total num of agent2: 29
idv_policy eval team catch total num: 37
idv_policy eval average step individual rewards of agent3: 0.4062816291460966
idv_policy eval average team episode rewards of agent3: 92.5
idv_policy eval idv catch total num of agent3: 19
idv_policy eval team catch total num: 37
idv_policy eval average step individual rewards of agent4: 0.7885519570951223
idv_policy eval average team episode rewards of agent4: 92.5
idv_policy eval idv catch total num of agent4: 33
idv_policy eval team catch total num: 37

 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.

team_policy eval average step individual rewards of agent0: 0.5299041647106236
team_policy eval average team episode rewards of agent0: 132.5
team_policy eval idv catch total num of agent0: 23
team_policy eval team catch total num: 53
team_policy eval average step individual rewards of agent1: 1.0451044994456276
team_policy eval average team episode rewards of agent1: 132.5
team_policy eval idv catch total num of agent1: 43
team_policy eval team catch total num: 53
team_policy eval average step individual rewards of agent2: 1.08488374819505
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: 0.9423389621505075
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: 0.8649357931883698
team_policy eval average team episode rewards of agent4: 132.5
team_policy eval idv catch total num of agent4: 36
team_policy eval team catch total num: 53
idv_policy eval average step individual rewards of agent0: 0.8912804174164429
idv_policy eval average team episode rewards of agent0: 140.0
idv_policy eval idv catch total num of agent0: 37
idv_policy eval team catch total num: 56
idv_policy eval average step individual rewards of agent1: 1.038187508819813
idv_policy eval average team episode rewards of agent1: 140.0
idv_policy eval idv catch total num of agent1: 43
idv_policy eval team catch total num: 56
idv_policy eval average step individual rewards of agent2: 1.598812613863207
idv_policy eval average team episode rewards of agent2: 140.0
idv_policy eval idv catch total num of agent2: 65
idv_policy eval team catch total num: 56
idv_policy eval average step individual rewards of agent3: 0.86472310016573
idv_policy eval average team episode rewards of agent3: 140.0
idv_policy eval idv catch total num of agent3: 36
idv_policy eval team catch total num: 56
idv_policy eval average step individual rewards of agent4: 0.5247763422023622
idv_policy eval average team episode rewards of agent4: 140.0
idv_policy eval idv catch total num of agent4: 23
idv_policy eval team catch total num: 56

 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.

team_policy eval average step individual rewards of agent0: 1.083907402648754
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: 0.7863947786082258
team_policy eval average team episode rewards of agent1: 125.0
team_policy eval idv catch total num of agent1: 33
team_policy eval team catch total num: 50
team_policy eval average step individual rewards of agent2: 0.917208485612865
team_policy eval average team episode rewards of agent2: 125.0
team_policy eval idv catch total num of agent2: 38
team_policy eval team catch total num: 50
team_policy eval average step individual rewards of agent3: 0.7622402802867217
team_policy eval average team episode rewards of agent3: 125.0
team_policy eval idv catch total num of agent3: 32
team_policy eval team catch total num: 50
team_policy eval average step individual rewards of agent4: 0.9192257638785775
team_policy eval average team episode rewards of agent4: 125.0
team_policy eval idv catch total num of agent4: 38
team_policy eval team catch total num: 50
idv_policy eval average step individual rewards of agent0: 0.7389288983332141
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: 1.0155472000930956
idv_policy eval average team episode rewards of agent1: 125.0
idv_policy eval idv catch total num of agent1: 42
idv_policy eval team catch total num: 50
idv_policy eval average step individual rewards of agent2: 0.9465023696757474
idv_policy eval average team episode rewards of agent2: 125.0
idv_policy eval idv catch total num of agent2: 39
idv_policy eval team catch total num: 50
idv_policy eval average step individual rewards of agent3: 0.6036211357547564
idv_policy eval average team episode rewards of agent3: 125.0
idv_policy eval idv catch total num of agent3: 26
idv_policy eval team catch total num: 50
idv_policy eval average step individual rewards of agent4: 1.1364946517088632
idv_policy eval average team episode rewards of agent4: 125.0
idv_policy eval idv catch total num of agent4: 47
idv_policy eval team catch total num: 50

 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 231.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.

team_policy eval average step individual rewards of agent0: 0.7747856359247021
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: 0.9752408954221502
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: 0.7335528126146805
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: 1.1630634251751888
team_policy eval average team episode rewards of agent3: 125.0
team_policy eval idv catch total num of agent3: 48
team_policy eval team catch total num: 50
team_policy eval average step individual rewards of agent4: 0.7045336661033454
team_policy eval average team episode rewards of agent4: 125.0
team_policy eval idv catch total num of agent4: 30
team_policy eval team catch total num: 50
idv_policy eval average step individual rewards of agent0: 0.9346285329260988
idv_policy eval average team episode rewards of agent0: 107.5
idv_policy eval idv catch total num of agent0: 39
idv_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent1: 0.9714249000097833
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: 0.7096325192303924
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: 0.6113661331978093
idv_policy eval average team episode rewards of agent3: 107.5
idv_policy eval idv catch total num of agent3: 26
idv_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent4: 0.8375952664633097
idv_policy eval average team episode rewards of agent4: 107.5
idv_policy eval idv catch total num of agent4: 35
idv_policy eval team catch total num: 43

 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.

team_policy eval average step individual rewards of agent0: 1.2976014131472449
team_policy eval average team episode rewards of agent0: 155.0
team_policy eval idv catch total num of agent0: 53
team_policy eval team catch total num: 62
team_policy eval average step individual rewards of agent1: 0.7687143290871898
team_policy eval average team episode rewards of agent1: 155.0
team_policy eval idv catch total num of agent1: 32
team_policy eval team catch total num: 62
team_policy eval average step individual rewards of agent2: 1.0096484249167887
team_policy eval average team episode rewards of agent2: 155.0
team_policy eval idv catch total num of agent2: 42
team_policy eval team catch total num: 62
team_policy eval average step individual rewards of agent3: 1.0651294205007595
team_policy eval average team episode rewards of agent3: 155.0
team_policy eval idv catch total num of agent3: 44
team_policy eval team catch total num: 62
team_policy eval average step individual rewards of agent4: 0.9955939664956182
team_policy eval average team episode rewards of agent4: 155.0
team_policy eval idv catch total num of agent4: 41
team_policy eval team catch total num: 62
idv_policy eval average step individual rewards of agent0: 1.1207006898545406
idv_policy eval average team episode rewards of agent0: 112.5
idv_policy eval idv catch total num of agent0: 46
idv_policy eval team catch total num: 45
idv_policy eval average step individual rewards of agent1: 0.6989365752147547
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: 0.756514969695448
idv_policy eval average team episode rewards of agent2: 112.5
idv_policy eval idv catch total num of agent2: 32
idv_policy eval team catch total num: 45
idv_policy eval average step individual rewards of agent3: 0.8137105950589071
idv_policy eval average team episode rewards of agent3: 112.5
idv_policy eval idv catch total num of agent3: 34
idv_policy eval team catch total num: 45
idv_policy eval average step individual rewards of agent4: 0.6831152389568677
idv_policy eval average team episode rewards of agent4: 112.5
idv_policy eval idv catch total num of agent4: 29
idv_policy eval team catch total num: 45

 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.

team_policy eval average step individual rewards of agent0: 0.9172417486014122
team_policy eval average team episode rewards of agent0: 125.0
team_policy eval idv catch total num of agent0: 38
team_policy eval team catch total num: 50
team_policy eval average step individual rewards of agent1: 1.1457152049460517
team_policy eval average team episode rewards of agent1: 125.0
team_policy eval idv catch total num of agent1: 47
team_policy eval team catch total num: 50
team_policy eval average step individual rewards of agent2: 0.511204651542758
team_policy eval average team episode rewards of agent2: 125.0
team_policy eval idv catch total num of agent2: 22
team_policy eval team catch total num: 50
team_policy eval average step individual rewards of agent3: 0.7890947305895293
team_policy eval average team episode rewards of agent3: 125.0
team_policy eval idv catch total num of agent3: 33
team_policy eval team catch total num: 50
team_policy eval average step individual rewards of agent4: 1.1131049512907358
team_policy eval average team episode rewards of agent4: 125.0
team_policy eval idv catch total num of agent4: 46
team_policy eval team catch total num: 50
idv_policy eval average step individual rewards of agent0: 1.1403211027610338
idv_policy eval average team episode rewards of agent0: 122.5
idv_policy eval idv catch total num of agent0: 47
idv_policy eval team catch total num: 49
idv_policy eval average step individual rewards of agent1: 0.9737442591764033
idv_policy eval average team episode rewards of agent1: 122.5
idv_policy eval idv catch total num of agent1: 40
idv_policy eval team catch total num: 49
idv_policy eval average step individual rewards of agent2: 0.8408585693686842
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: 0.8713653450998483
idv_policy eval average team episode rewards of agent3: 122.5
idv_policy eval idv catch total num of agent3: 36
idv_policy eval team catch total num: 49
idv_policy eval average step individual rewards of agent4: 0.7126157474040602
idv_policy eval average team episode rewards of agent4: 122.5
idv_policy eval idv catch total num of agent4: 30
idv_policy eval team catch total num: 49

 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.

team_policy eval average step individual rewards of agent0: 0.8057510341366239
team_policy eval average team episode rewards of agent0: 137.5
team_policy eval idv catch total num of agent0: 34
team_policy eval team catch total num: 55
team_policy eval average step individual rewards of agent1: 0.9883997539723107
team_policy eval average team episode rewards of agent1: 137.5
team_policy eval idv catch total num of agent1: 41
team_policy eval team catch total num: 55
team_policy eval average step individual rewards of agent2: 1.0437577420955177
team_policy eval average team episode rewards of agent2: 137.5
team_policy eval idv catch total num of agent2: 43
team_policy eval team catch total num: 55
team_policy eval average step individual rewards of agent3: 0.6843674815908345
team_policy eval average team episode rewards of agent3: 137.5
team_policy eval idv catch total num of agent3: 29
team_policy eval team catch total num: 55
team_policy eval average step individual rewards of agent4: 0.9664294357590283
team_policy eval average team episode rewards of agent4: 137.5
team_policy eval idv catch total num of agent4: 40
team_policy eval team catch total num: 55
idv_policy eval average step individual rewards of agent0: 0.760658294238342
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: 0.8421572958828724
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: 0.8669337732680801
idv_policy eval average team episode rewards of agent2: 100.0
idv_policy eval idv catch total num of agent2: 36
idv_policy eval team catch total num: 40
idv_policy eval average step individual rewards of agent3: 0.8155818624062177
idv_policy eval average team episode rewards of agent3: 100.0
idv_policy eval idv catch total num of agent3: 34
idv_policy eval team catch total num: 40
idv_policy eval average step individual rewards of agent4: 0.32777651104837985
idv_policy eval average team episode rewards of agent4: 100.0
idv_policy eval idv catch total num of agent4: 15
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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.

team_policy eval average step individual rewards of agent0: 0.9832364669458267
team_policy eval average team episode rewards of agent0: 117.5
team_policy eval idv catch total num of agent0: 41
team_policy eval team catch total num: 47
team_policy eval average step individual rewards of agent1: 1.0981202367990242
team_policy eval average team episode rewards of agent1: 117.5
team_policy eval idv catch total num of agent1: 45
team_policy eval team catch total num: 47
team_policy eval average step individual rewards of agent2: 0.22115163631672086
team_policy eval average team episode rewards of agent2: 117.5
team_policy eval idv catch total num of agent2: 11
team_policy eval team catch total num: 47
team_policy eval average step individual rewards of agent3: 0.8465100385618309
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: 1.1861033550000124
team_policy eval average team episode rewards of agent4: 117.5
team_policy eval idv catch total num of agent4: 49
team_policy eval team catch total num: 47
idv_policy eval average step individual rewards of agent0: 0.8924806111392688
idv_policy eval average team episode rewards of agent0: 115.0
idv_policy eval idv catch total num of agent0: 37
idv_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent1: 0.6591486428939188
idv_policy eval average team episode rewards of agent1: 115.0
idv_policy eval idv catch total num of agent1: 28
idv_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent2: 0.7332999814925659
idv_policy eval average team episode rewards of agent2: 115.0
idv_policy eval idv catch total num of agent2: 31
idv_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent3: 1.0405882146635492
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: 0.501466874211151
idv_policy eval average team episode rewards of agent4: 115.0
idv_policy eval idv catch total num of agent4: 22
idv_policy eval team catch total num: 46

 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.

team_policy eval average step individual rewards of agent0: 0.8855237011721293
team_policy eval average team episode rewards of agent0: 132.5
team_policy eval idv catch total num of agent0: 37
team_policy eval team catch total num: 53
team_policy eval average step individual rewards of agent1: 0.9908746225663626
team_policy eval average team episode rewards of agent1: 132.5
team_policy eval idv catch total num of agent1: 41
team_policy eval team catch total num: 53
team_policy eval average step individual rewards of agent2: 1.1685066168376175
team_policy eval average team episode rewards of agent2: 132.5
team_policy eval idv catch total num of agent2: 48
team_policy eval team catch total num: 53
team_policy eval average step individual rewards of agent3: 1.0394962437439705
team_policy eval average team episode rewards of agent3: 132.5
team_policy eval idv catch total num of agent3: 43
team_policy eval team catch total num: 53
team_policy eval average step individual rewards of agent4: 0.984495395676335
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: 0.9432622806972109
idv_policy eval average team episode rewards of agent0: 140.0
idv_policy eval idv catch total num of agent0: 39
idv_policy eval team catch total num: 56
idv_policy eval average step individual rewards of agent1: 1.3430083427013966
idv_policy eval average team episode rewards of agent1: 140.0
idv_policy eval idv catch total num of agent1: 55
idv_policy eval team catch total num: 56
idv_policy eval average step individual rewards of agent2: 0.8770682879134979
idv_policy eval average team episode rewards of agent2: 140.0
idv_policy eval idv catch total num of agent2: 37
idv_policy eval team catch total num: 56
idv_policy eval average step individual rewards of agent3: 0.5778682700710213
idv_policy eval average team episode rewards of agent3: 140.0
idv_policy eval idv catch total num of agent3: 25
idv_policy eval team catch total num: 56
idv_policy eval average step individual rewards of agent4: 0.6147954657623969
idv_policy eval average team episode rewards of agent4: 140.0
idv_policy eval idv catch total num of agent4: 26
idv_policy eval team catch total num: 56

 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.

team_policy eval average step individual rewards of agent0: 0.7553852644850281
team_policy eval average team episode rewards of agent0: 105.0
team_policy eval idv catch total num of agent0: 32
team_policy eval team catch total num: 42
team_policy eval average step individual rewards of agent1: 0.45132741282422484
team_policy eval average team episode rewards of agent1: 105.0
team_policy eval idv catch total num of agent1: 20
team_policy eval team catch total num: 42
team_policy eval average step individual rewards of agent2: 0.8908831756023006
team_policy eval average team episode rewards of agent2: 105.0
team_policy eval idv catch total num of agent2: 37
team_policy eval team catch total num: 42
team_policy eval average step individual rewards of agent3: 1.018352345967416
team_policy eval average team episode rewards of agent3: 105.0
team_policy eval idv catch total num of agent3: 42
team_policy eval team catch total num: 42
team_policy eval average step individual rewards of agent4: 0.5733201785423838
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: 0.9171852454578928
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: 0.8845009467528664
idv_policy eval average team episode rewards of agent1: 135.0
idv_policy eval idv catch total num of agent1: 37
idv_policy eval team catch total num: 54
idv_policy eval average step individual rewards of agent2: 0.6314589878019988
idv_policy eval average team episode rewards of agent2: 135.0
idv_policy eval idv catch total num of agent2: 27
idv_policy eval team catch total num: 54
idv_policy eval average step individual rewards of agent3: 0.9897957295562188
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: 1.0917119429011182
idv_policy eval average team episode rewards of agent4: 135.0
idv_policy eval idv catch total num of agent4: 45
idv_policy eval team catch total num: 54

 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 232.


 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 233.


 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 233.


 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 233.


 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 233.


 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 233.

team_policy eval average step individual rewards of agent0: 0.47114281585760615
team_policy eval average team episode rewards of agent0: 87.5
team_policy eval idv catch total num of agent0: 21
team_policy eval team catch total num: 35
team_policy eval average step individual rewards of agent1: 0.632404126347502
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: 0.4842690911965498
team_policy eval average team episode rewards of agent2: 87.5
team_policy eval idv catch total num of agent2: 21
team_policy eval team catch total num: 35
team_policy eval average step individual rewards of agent3: 0.781672859142476
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: 1.0211178955563474
team_policy eval average team episode rewards of agent4: 87.5
team_policy eval idv catch total num of agent4: 42
team_policy eval team catch total num: 35
idv_policy eval average step individual rewards of agent0: 0.8870192012886574
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: 0.4734735744170559
idv_policy eval average team episode rewards of agent1: 97.5
idv_policy eval idv catch total num of agent1: 21
idv_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent2: 0.6244552220034961
idv_policy eval average team episode rewards of agent2: 97.5
idv_policy eval idv catch total num of agent2: 27
idv_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent3: 0.5248412618152599
idv_policy eval average team episode rewards of agent3: 97.5
idv_policy eval idv catch total num of agent3: 23
idv_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent4: 0.7913802567982989
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 6226/10000 episodes, total num timesteps 1245400/2000000, FPS 233.


 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 233.


 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 233.


 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 233.


 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 233.


 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 233.


 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 233.


 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 233.


 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 233.


 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 233.


 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 233.


 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 233.


 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 233.


 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 233.


 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 233.


 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 233.


 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 233.


 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 233.


 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 233.


 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 233.


 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 233.


 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 233.


 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 233.


 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 233.


 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 233.

team_policy eval average step individual rewards of agent0: 1.1665615525014068
team_policy eval average team episode rewards of agent0: 150.0
team_policy eval idv catch total num of agent0: 48
team_policy eval team catch total num: 60
team_policy eval average step individual rewards of agent1: 0.7054071037095965
team_policy eval average team episode rewards of agent1: 150.0
team_policy eval idv catch total num of agent1: 30
team_policy eval team catch total num: 60
team_policy eval average step individual rewards of agent2: 1.043759865617453
team_policy eval average team episode rewards of agent2: 150.0
team_policy eval idv catch total num of agent2: 43
team_policy eval team catch total num: 60
team_policy eval average step individual rewards of agent3: 1.3490860821435533
team_policy eval average team episode rewards of agent3: 150.0
team_policy eval idv catch total num of agent3: 55
team_policy eval team catch total num: 60
team_policy eval average step individual rewards of agent4: 0.9938705529456056
team_policy eval average team episode rewards of agent4: 150.0
team_policy eval idv catch total num of agent4: 41
team_policy eval team catch total num: 60
idv_policy eval average step individual rewards of agent0: 1.0970761627996912
idv_policy eval average team episode rewards of agent0: 105.0
idv_policy eval idv catch total num of agent0: 45
idv_policy eval team catch total num: 42
idv_policy eval average step individual rewards of agent1: 0.6201159675400378
idv_policy eval average team episode rewards of agent1: 105.0
idv_policy eval idv catch total num of agent1: 26
idv_policy eval team catch total num: 42
idv_policy eval average step individual rewards of agent2: 0.8454227572386472
idv_policy eval average team episode rewards of agent2: 105.0
idv_policy eval idv catch total num of agent2: 35
idv_policy eval team catch total num: 42
idv_policy eval average step individual rewards of agent3: 0.8734053661331588
idv_policy eval average team episode rewards of agent3: 105.0
idv_policy eval idv catch total num of agent3: 36
idv_policy eval team catch total num: 42
idv_policy eval average step individual rewards of agent4: 0.8939279032991333
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 6251/10000 episodes, total num timesteps 1250400/2000000, FPS 233.


 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 233.


 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 233.


 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 233.


 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 233.


 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 233.


 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 233.


 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 233.


 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 233.


 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 233.


 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 233.


 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 233.


 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 233.


 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 233.


 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 233.


 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 233.


 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 233.


 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 233.


 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 233.


 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 233.


 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 233.


 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 233.


 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 233.


 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 233.


 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 233.

team_policy eval average step individual rewards of agent0: 0.9055222683263809
team_policy eval average team episode rewards of agent0: 147.5
team_policy eval idv catch total num of agent0: 38
team_policy eval team catch total num: 59
team_policy eval average step individual rewards of agent1: 1.3226777285807783
team_policy eval average team episode rewards of agent1: 147.5
team_policy eval idv catch total num of agent1: 54
team_policy eval team catch total num: 59
team_policy eval average step individual rewards of agent2: 1.0444570247805651
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: 0.7436582136198423
team_policy eval average team episode rewards of agent3: 147.5
team_policy eval idv catch total num of agent3: 31
team_policy eval team catch total num: 59
team_policy eval average step individual rewards of agent4: 1.1203341361506762
team_policy eval average team episode rewards of agent4: 147.5
team_policy eval idv catch total num of agent4: 46
team_policy eval team catch total num: 59
idv_policy eval average step individual rewards of agent0: 0.660585919713258
idv_policy eval average team episode rewards of agent0: 105.0
idv_policy eval idv catch total num of agent0: 28
idv_policy eval team catch total num: 42
idv_policy eval average step individual rewards of agent1: 0.8587265792039186
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: 1.043748701171518
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: 0.7279340727689244
idv_policy eval average team episode rewards of agent3: 105.0
idv_policy eval idv catch total num of agent3: 31
idv_policy eval team catch total num: 42
idv_policy eval average step individual rewards of agent4: 0.6371736783890299
idv_policy eval average team episode rewards of agent4: 105.0
idv_policy eval idv catch total num of agent4: 27
idv_policy eval team catch total num: 42

 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 233.


 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 233.


 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 233.


 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 233.


 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 233.


 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 233.


 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 233.


 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 233.


 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 233.


 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 233.


 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 233.


 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 233.


 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 233.


 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 233.


 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 233.


 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 233.


 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 233.


 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 233.


 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 233.


 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 233.


 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 233.


 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 233.


 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 233.


 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 233.


 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 233.

team_policy eval average step individual rewards of agent0: 1.0172713727038414
team_policy eval average team episode rewards of agent0: 110.0
team_policy eval idv catch total num of agent0: 42
team_policy eval team catch total num: 44
team_policy eval average step individual rewards of agent1: 0.7068041083507046
team_policy eval average team episode rewards of agent1: 110.0
team_policy eval idv catch total num of agent1: 30
team_policy eval team catch total num: 44
team_policy eval average step individual rewards of agent2: 0.9672426360310764
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: 1.0418521490880845
team_policy eval average team episode rewards of agent3: 110.0
team_policy eval idv catch total num of agent3: 43
team_policy eval team catch total num: 44
team_policy eval average step individual rewards of agent4: 0.9880154577949266
team_policy eval average team episode rewards of agent4: 110.0
team_policy eval idv catch total num of agent4: 41
team_policy eval team catch total num: 44
idv_policy eval average step individual rewards of agent0: 0.7881407845810584
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: 0.6859628222677947
idv_policy eval average team episode rewards of agent1: 120.0
idv_policy eval idv catch total num of agent1: 29
idv_policy eval team catch total num: 48
idv_policy eval average step individual rewards of agent2: 1.0226351829189009
idv_policy eval average team episode rewards of agent2: 120.0
idv_policy eval idv catch total num of agent2: 42
idv_policy eval team catch total num: 48
idv_policy eval average step individual rewards of agent3: 0.8659054112557963
idv_policy eval average team episode rewards of agent3: 120.0
idv_policy eval idv catch total num of agent3: 36
idv_policy eval team catch total num: 48
idv_policy eval average step individual rewards of agent4: 0.8801472884184665
idv_policy eval average team episode rewards of agent4: 120.0
idv_policy eval idv catch total num of agent4: 37
idv_policy eval team catch total num: 48

 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 233.


 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 233.


 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 233.


 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 233.


 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 233.


 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 233.


 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 233.


 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 233.


 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 233.


 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 233.


 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 233.


 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 233.


 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 233.


 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 233.


 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 233.


 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 233.


 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 233.


 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 233.


 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 233.


 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 233.


 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 233.


 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 233.


 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 233.


 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 233.


 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 233.

team_policy eval average step individual rewards of agent0: 0.7607912167836965
team_policy eval average team episode rewards of agent0: 120.0
team_policy eval idv catch total num of agent0: 32
team_policy eval team catch total num: 48
team_policy eval average step individual rewards of agent1: 1.02353604316371
team_policy eval average team episode rewards of agent1: 120.0
team_policy eval idv catch total num of agent1: 42
team_policy eval team catch total num: 48
team_policy eval average step individual rewards of agent2: 0.7829190153584374
team_policy eval average team episode rewards of agent2: 120.0
team_policy eval idv catch total num of agent2: 33
team_policy eval team catch total num: 48
team_policy eval average step individual rewards of agent3: 0.7073291368072502
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: 0.8830860744797979
team_policy eval average team episode rewards of agent4: 120.0
team_policy eval idv catch total num of agent4: 37
team_policy eval team catch total num: 48
idv_policy eval average step individual rewards of agent0: 0.9445680602786214
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: 1.2975092370650907
idv_policy eval average team episode rewards of agent1: 127.5
idv_policy eval idv catch total num of agent1: 53
idv_policy eval team catch total num: 51
idv_policy eval average step individual rewards of agent2: 0.7835810075925952
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: 0.8959317208939785
idv_policy eval average team episode rewards of agent3: 127.5
idv_policy eval idv catch total num of agent3: 37
idv_policy eval team catch total num: 51
idv_policy eval average step individual rewards of agent4: 0.07714458300896611
idv_policy eval average team episode rewards of agent4: 127.5
idv_policy eval idv catch total num of agent4: 5
idv_policy eval team catch total num: 51

 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 233.


 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 233.


 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 233.


 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 233.


 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 233.


 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 233.


 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 233.


 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 233.


 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 233.


 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 233.


 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 233.


 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 233.


 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 233.


 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 233.


 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 233.


 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 233.


 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 233.


 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 233.


 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 233.


 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 233.


 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 233.


 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 233.


 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 233.


 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 233.


 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 233.

team_policy eval average step individual rewards of agent0: 0.7229941556295136
team_policy eval average team episode rewards of agent0: 80.0
team_policy eval idv catch total num of agent0: 31
team_policy eval team catch total num: 32
team_policy eval average step individual rewards of agent1: 0.6239497962060259
team_policy eval average team episode rewards of agent1: 80.0
team_policy eval idv catch total num of agent1: 27
team_policy eval team catch total num: 32
team_policy eval average step individual rewards of agent2: 0.3318728170322957
team_policy eval average team episode rewards of agent2: 80.0
team_policy eval idv catch total num of agent2: 16
team_policy eval team catch total num: 32
team_policy eval average step individual rewards of agent3: 0.5523335362001416
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: 0.4287515798360031
team_policy eval average team episode rewards of agent4: 80.0
team_policy eval idv catch total num of agent4: 19
team_policy eval team catch total num: 32
idv_policy eval average step individual rewards of agent0: 0.639376967618366
idv_policy eval average team episode rewards of agent0: 140.0
idv_policy eval idv catch total num of agent0: 27
idv_policy eval team catch total num: 56
idv_policy eval average step individual rewards of agent1: 0.7339181477103994
idv_policy eval average team episode rewards of agent1: 140.0
idv_policy eval idv catch total num of agent1: 31
idv_policy eval team catch total num: 56
idv_policy eval average step individual rewards of agent2: 1.368966775681453
idv_policy eval average team episode rewards of agent2: 140.0
idv_policy eval idv catch total num of agent2: 56
idv_policy eval team catch total num: 56
idv_policy eval average step individual rewards of agent3: 1.2755580450307789
idv_policy eval average team episode rewards of agent3: 140.0
idv_policy eval idv catch total num of agent3: 52
idv_policy eval team catch total num: 56
idv_policy eval average step individual rewards of agent4: 1.0121183220418841
idv_policy eval average team episode rewards of agent4: 140.0
idv_policy eval idv catch total num of agent4: 42
idv_policy eval team catch total num: 56

 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 233.


 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 233.


 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 233.


 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 233.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.

team_policy eval average step individual rewards of agent0: 1.142500639497614
team_policy eval average team episode rewards of agent0: 117.5
team_policy eval idv catch total num of agent0: 47
team_policy eval team catch total num: 47
team_policy eval average step individual rewards of agent1: 0.5811213629823688
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: 1.044795784791931
team_policy eval average team episode rewards of agent2: 117.5
team_policy eval idv catch total num of agent2: 43
team_policy eval team catch total num: 47
team_policy eval average step individual rewards of agent3: 0.9380968389728876
team_policy eval average team episode rewards of agent3: 117.5
team_policy eval idv catch total num of agent3: 39
team_policy eval team catch total num: 47
team_policy eval average step individual rewards of agent4: 0.7674927409969896
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: 0.8638317580422759
idv_policy eval average team episode rewards of agent0: 97.5
idv_policy eval idv catch total num of agent0: 36
idv_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent1: 0.9415416075583238
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: 0.5071038758853266
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: 0.637198974727174
idv_policy eval average team episode rewards of agent3: 97.5
idv_policy eval idv catch total num of agent3: 27
idv_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent4: 0.8373605571900364
idv_policy eval average team episode rewards of agent4: 97.5
idv_policy eval idv catch total num of agent4: 35
idv_policy eval team catch total num: 39

 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.

team_policy eval average step individual rewards of agent0: 0.9903310306568559
team_policy eval average team episode rewards of agent0: 125.0
team_policy eval idv catch total num of agent0: 41
team_policy eval team catch total num: 50
team_policy eval average step individual rewards of agent1: 0.6166961286821967
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: 0.6863940403022584
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: 0.9748704074478521
team_policy eval average team episode rewards of agent3: 125.0
team_policy eval idv catch total num of agent3: 40
team_policy eval team catch total num: 50
team_policy eval average step individual rewards of agent4: 0.9674260783090801
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: 0.5831413957326798
idv_policy eval average team episode rewards of agent0: 110.0
idv_policy eval idv catch total num of agent0: 25
idv_policy eval team catch total num: 44
idv_policy eval average step individual rewards of agent1: 0.7264033560740205
idv_policy eval average team episode rewards of agent1: 110.0
idv_policy eval idv catch total num of agent1: 31
idv_policy eval team catch total num: 44
idv_policy eval average step individual rewards of agent2: 1.0365122311454924
idv_policy eval average team episode rewards of agent2: 110.0
idv_policy eval idv catch total num of agent2: 43
idv_policy eval team catch total num: 44
idv_policy eval average step individual rewards of agent3: 0.8676041375102196
idv_policy eval average team episode rewards of agent3: 110.0
idv_policy eval idv catch total num of agent3: 36
idv_policy eval team catch total num: 44
idv_policy eval average step individual rewards of agent4: 0.9928369407207007
idv_policy eval average team episode rewards of agent4: 110.0
idv_policy eval idv catch total num of agent4: 41
idv_policy eval team catch total num: 44

 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.

team_policy eval average step individual rewards of agent0: 0.7460358458433141
team_policy eval average team episode rewards of agent0: 102.5
team_policy eval idv catch total num of agent0: 31
team_policy eval team catch total num: 41
team_policy eval average step individual rewards of agent1: 0.89531352090187
team_policy eval average team episode rewards of agent1: 102.5
team_policy eval idv catch total num of agent1: 37
team_policy eval team catch total num: 41
team_policy eval average step individual rewards of agent2: 0.971708655118633
team_policy eval average team episode rewards of agent2: 102.5
team_policy eval idv catch total num of agent2: 40
team_policy eval team catch total num: 41
team_policy eval average step individual rewards of agent3: 0.6134263919311019
team_policy eval average team episode rewards of agent3: 102.5
team_policy eval idv catch total num of agent3: 26
team_policy eval team catch total num: 41
team_policy eval average step individual rewards of agent4: 0.6343248079662057
team_policy eval average team episode rewards of agent4: 102.5
team_policy eval idv catch total num of agent4: 27
team_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent0: 0.6846399798397818
idv_policy eval average team episode rewards of agent0: 72.5
idv_policy eval idv catch total num of agent0: 29
idv_policy eval team catch total num: 29
idv_policy eval average step individual rewards of agent1: 0.6825136263078241
idv_policy eval average team episode rewards of agent1: 72.5
idv_policy eval idv catch total num of agent1: 29
idv_policy eval team catch total num: 29
idv_policy eval average step individual rewards of agent2: 0.40274432019966405
idv_policy eval average team episode rewards of agent2: 72.5
idv_policy eval idv catch total num of agent2: 18
idv_policy eval team catch total num: 29
idv_policy eval average step individual rewards of agent3: 0.6370648025826944
idv_policy eval average team episode rewards of agent3: 72.5
idv_policy eval idv catch total num of agent3: 27
idv_policy eval team catch total num: 29
idv_policy eval average step individual rewards of agent4: 0.5148805775587093
idv_policy eval average team episode rewards of agent4: 72.5
idv_policy eval idv catch total num of agent4: 22
idv_policy eval team catch total num: 29

 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.

team_policy eval average step individual rewards of agent0: 0.7849529963941994
team_policy eval average team episode rewards of agent0: 107.5
team_policy eval idv catch total num of agent0: 33
team_policy eval team catch total num: 43
team_policy eval average step individual rewards of agent1: 0.8143416043695191
team_policy eval average team episode rewards of agent1: 107.5
team_policy eval idv catch total num of agent1: 34
team_policy eval team catch total num: 43
team_policy eval average step individual rewards of agent2: 1.0687686659019071
team_policy eval average team episode rewards of agent2: 107.5
team_policy eval idv catch total num of agent2: 44
team_policy eval team catch total num: 43
team_policy eval average step individual rewards of agent3: 0.8139617635359484
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: 0.6650396235622383
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: 0.35983645045927654
idv_policy eval average team episode rewards of agent0: 77.5
idv_policy eval idv catch total num of agent0: 16
idv_policy eval team catch total num: 31
idv_policy eval average step individual rewards of agent1: 0.5778033946098882
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: 0.8642257958720705
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: 0.27685056405150343
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: 0.733573498995419
idv_policy eval average team episode rewards of agent4: 77.5
idv_policy eval idv catch total num of agent4: 31
idv_policy eval team catch total num: 31

 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.

team_policy eval average step individual rewards of agent0: 0.7641825385528337
team_policy eval average team episode rewards of agent0: 112.5
team_policy eval idv catch total num of agent0: 32
team_policy eval team catch total num: 45
team_policy eval average step individual rewards of agent1: 1.0092090735494577
team_policy eval average team episode rewards of agent1: 112.5
team_policy eval idv catch total num of agent1: 42
team_policy eval team catch total num: 45
team_policy eval average step individual rewards of agent2: 0.6675940208813285
team_policy eval average team episode rewards of agent2: 112.5
team_policy eval idv catch total num of agent2: 28
team_policy eval team catch total num: 45
team_policy eval average step individual rewards of agent3: 1.0149019082162523
team_policy eval average team episode rewards of agent3: 112.5
team_policy eval idv catch total num of agent3: 42
team_policy eval team catch total num: 45
team_policy eval average step individual rewards of agent4: 0.9177367191413915
team_policy eval average team episode rewards of agent4: 112.5
team_policy eval idv catch total num of agent4: 38
team_policy eval team catch total num: 45
idv_policy eval average step individual rewards of agent0: 0.811558432955811
idv_policy eval average team episode rewards of agent0: 132.5
idv_policy eval idv catch total num of agent0: 34
idv_policy eval team catch total num: 53
idv_policy eval average step individual rewards of agent1: 0.9931753593837425
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: 0.6012325869470106
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: 0.8211391967854498
idv_policy eval average team episode rewards of agent3: 132.5
idv_policy eval idv catch total num of agent3: 34
idv_policy eval team catch total num: 53
idv_policy eval average step individual rewards of agent4: 1.348316179538241
idv_policy eval average team episode rewards of agent4: 132.5
idv_policy eval idv catch total num of agent4: 55
idv_policy eval team catch total num: 53

 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.

team_policy eval average step individual rewards of agent0: 0.5953571836358159
team_policy eval average team episode rewards of agent0: 92.5
team_policy eval idv catch total num of agent0: 25
team_policy eval team catch total num: 37
team_policy eval average step individual rewards of agent1: 0.6773587278447437
team_policy eval average team episode rewards of agent1: 92.5
team_policy eval idv catch total num of agent1: 29
team_policy eval team catch total num: 37
team_policy eval average step individual rewards of agent2: 0.6614050791850853
team_policy eval average team episode rewards of agent2: 92.5
team_policy eval idv catch total num of agent2: 28
team_policy eval team catch total num: 37
team_policy eval average step individual rewards of agent3: 0.6753919994899227
team_policy eval average team episode rewards of agent3: 92.5
team_policy eval idv catch total num of agent3: 29
team_policy eval team catch total num: 37
team_policy eval average step individual rewards of agent4: 0.7435575756711651
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: 0.8517210240623918
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: 0.810380199334169
idv_policy eval average team episode rewards of agent1: 100.0
idv_policy eval idv catch total num of agent1: 34
idv_policy eval team catch total num: 40
idv_policy eval average step individual rewards of agent2: 0.8096527259351605
idv_policy eval average team episode rewards of agent2: 100.0
idv_policy eval idv catch total num of agent2: 34
idv_policy eval team catch total num: 40
idv_policy eval average step individual rewards of agent3: 0.6854785469955091
idv_policy eval average team episode rewards of agent3: 100.0
idv_policy eval idv catch total num of agent3: 29
idv_policy eval team catch total num: 40
idv_policy eval average step individual rewards of agent4: 0.6914408149949844
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 6501/10000 episodes, total num timesteps 1300400/2000000, FPS 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.

team_policy eval average step individual rewards of agent0: 0.9886782718466324
team_policy eval average team episode rewards of agent0: 102.5
team_policy eval idv catch total num of agent0: 41
team_policy eval team catch total num: 41
team_policy eval average step individual rewards of agent1: 0.6351871957886445
team_policy eval average team episode rewards of agent1: 102.5
team_policy eval idv catch total num of agent1: 27
team_policy eval team catch total num: 41
team_policy eval average step individual rewards of agent2: 0.6107642072153628
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: 0.6930051879838246
team_policy eval average team episode rewards of agent3: 102.5
team_policy eval idv catch total num of agent3: 29
team_policy eval team catch total num: 41
team_policy eval average step individual rewards of agent4: 0.9606642424354558
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: 0.36028068561334287
idv_policy eval average team episode rewards of agent0: 87.5
idv_policy eval idv catch total num of agent0: 16
idv_policy eval team catch total num: 35
idv_policy eval average step individual rewards of agent1: 0.6475722422641624
idv_policy eval average team episode rewards of agent1: 87.5
idv_policy eval idv catch total num of agent1: 28
idv_policy eval team catch total num: 35
idv_policy eval average step individual rewards of agent2: 0.9565480202385488
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: 0.5655635316677612
idv_policy eval average team episode rewards of agent3: 87.5
idv_policy eval idv catch total num of agent3: 24
idv_policy eval team catch total num: 35
idv_policy eval average step individual rewards of agent4: 0.605943614596565
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 6526/10000 episodes, total num timesteps 1305400/2000000, FPS 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 234.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.

team_policy eval average step individual rewards of agent0: 0.502569641912514
team_policy eval average team episode rewards of agent0: 100.0
team_policy eval idv catch total num of agent0: 22
team_policy eval team catch total num: 40
team_policy eval average step individual rewards of agent1: 1.0967616864933212
team_policy eval average team episode rewards of agent1: 100.0
team_policy eval idv catch total num of agent1: 45
team_policy eval team catch total num: 40
team_policy eval average step individual rewards of agent2: 0.6118806208546469
team_policy eval average team episode rewards of agent2: 100.0
team_policy eval idv catch total num of agent2: 26
team_policy eval team catch total num: 40
team_policy eval average step individual rewards of agent3: 0.5330434032057605
team_policy eval average team episode rewards of agent3: 100.0
team_policy eval idv catch total num of agent3: 23
team_policy eval team catch total num: 40
team_policy eval average step individual rewards of agent4: 0.8895386523195531
team_policy eval average team episode rewards of agent4: 100.0
team_policy eval idv catch total num of agent4: 37
team_policy eval team catch total num: 40
idv_policy eval average step individual rewards of agent0: 0.6901910005596436
idv_policy eval average team episode rewards of agent0: 117.5
idv_policy eval idv catch total num of agent0: 29
idv_policy eval team catch total num: 47
idv_policy eval average step individual rewards of agent1: 0.8079729498997484
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: 0.7164689153864177
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: 0.940435174150067
idv_policy eval average team episode rewards of agent3: 117.5
idv_policy eval idv catch total num of agent3: 39
idv_policy eval team catch total num: 47
idv_policy eval average step individual rewards of agent4: 1.1152526929364073
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 6551/10000 episodes, total num timesteps 1310400/2000000, FPS 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.

team_policy eval average step individual rewards of agent0: 0.6906029955868359
team_policy eval average team episode rewards of agent0: 117.5
team_policy eval idv catch total num of agent0: 29
team_policy eval team catch total num: 47
team_policy eval average step individual rewards of agent1: 0.784130035393914
team_policy eval average team episode rewards of agent1: 117.5
team_policy eval idv catch total num of agent1: 33
team_policy eval team catch total num: 47
team_policy eval average step individual rewards of agent2: 0.9904820427784581
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: 0.8446570785759588
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: 1.0130024961405641
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: 1.60281922438861
idv_policy eval average team episode rewards of agent0: 150.0
idv_policy eval idv catch total num of agent0: 65
idv_policy eval team catch total num: 60
idv_policy eval average step individual rewards of agent1: 0.7468052721637228
idv_policy eval average team episode rewards of agent1: 150.0
idv_policy eval idv catch total num of agent1: 31
idv_policy eval team catch total num: 60
idv_policy eval average step individual rewards of agent2: 0.7673284274901166
idv_policy eval average team episode rewards of agent2: 150.0
idv_policy eval idv catch total num of agent2: 32
idv_policy eval team catch total num: 60
idv_policy eval average step individual rewards of agent3: 0.8489800330936625
idv_policy eval average team episode rewards of agent3: 150.0
idv_policy eval idv catch total num of agent3: 35
idv_policy eval team catch total num: 60
idv_policy eval average step individual rewards of agent4: 1.0474220604459616
idv_policy eval average team episode rewards of agent4: 150.0
idv_policy eval idv catch total num of agent4: 43
idv_policy eval team catch total num: 60

 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 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.

team_policy eval average step individual rewards of agent0: 1.0470654301916753
team_policy eval average team episode rewards of agent0: 127.5
team_policy eval idv catch total num of agent0: 43
team_policy eval team catch total num: 51
team_policy eval average step individual rewards of agent1: 0.9924155906097977
team_policy eval average team episode rewards of agent1: 127.5
team_policy eval idv catch total num of agent1: 41
team_policy eval team catch total num: 51
team_policy eval average step individual rewards of agent2: 0.5383857161764418
team_policy eval average team episode rewards of agent2: 127.5
team_policy eval idv catch total num of agent2: 23
team_policy eval team catch total num: 51
team_policy eval average step individual rewards of agent3: 0.6049615137108387
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: 1.152540190521074
team_policy eval average team episode rewards of agent4: 127.5
team_policy eval idv catch total num of agent4: 48
team_policy eval team catch total num: 51
idv_policy eval average step individual rewards of agent0: 0.5087586734531081
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: 0.8895104824449618
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: 0.7957324898729544
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: 0.5067274231509457
idv_policy eval average team episode rewards of agent3: 90.0
idv_policy eval idv catch total num of agent3: 22
idv_policy eval team catch total num: 36
idv_policy eval average step individual rewards of agent4: 0.5391279337018422
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 6601/10000 episodes, total num timesteps 1320400/2000000, FPS 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.

team_policy eval average step individual rewards of agent0: 0.7714411410549735
team_policy eval average team episode rewards of agent0: 105.0
team_policy eval idv catch total num of agent0: 32
team_policy eval team catch total num: 42
team_policy eval average step individual rewards of agent1: 0.8265456883577167
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: 1.0353312856062826
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: 0.5812833679763953
team_policy eval average team episode rewards of agent3: 105.0
team_policy eval idv catch total num of agent3: 25
team_policy eval team catch total num: 42
team_policy eval average step individual rewards of agent4: 1.3196002233183437
team_policy eval average team episode rewards of agent4: 105.0
team_policy eval idv catch total num of agent4: 54
team_policy eval team catch total num: 42
idv_policy eval average step individual rewards of agent0: 0.989947710510828
idv_policy eval average team episode rewards of agent0: 135.0
idv_policy eval idv catch total num of agent0: 41
idv_policy eval team catch total num: 54
idv_policy eval average step individual rewards of agent1: 0.5686875361289069
idv_policy eval average team episode rewards of agent1: 135.0
idv_policy eval idv catch total num of agent1: 24
idv_policy eval team catch total num: 54
idv_policy eval average step individual rewards of agent2: 1.4725811559482518
idv_policy eval average team episode rewards of agent2: 135.0
idv_policy eval idv catch total num of agent2: 60
idv_policy eval team catch total num: 54
idv_policy eval average step individual rewards of agent3: 0.8343010587751829
idv_policy eval average team episode rewards of agent3: 135.0
idv_policy eval idv catch total num of agent3: 35
idv_policy eval team catch total num: 54
idv_policy eval average step individual rewards of agent4: 1.0215346510614973
idv_policy eval average team episode rewards of agent4: 135.0
idv_policy eval idv catch total num of agent4: 42
idv_policy eval team catch total num: 54

 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 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.

team_policy eval average step individual rewards of agent0: 0.6385069998904953
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: 1.0861456770783955
team_policy eval average team episode rewards of agent1: 110.0
team_policy eval idv catch total num of agent1: 45
team_policy eval team catch total num: 44
team_policy eval average step individual rewards of agent2: 0.7884130904099184
team_policy eval average team episode rewards of agent2: 110.0
team_policy eval idv catch total num of agent2: 33
team_policy eval team catch total num: 44
team_policy eval average step individual rewards of agent3: 0.8425597394324893
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: 0.4534385262277386
team_policy eval average team episode rewards of agent4: 110.0
team_policy eval idv catch total num of agent4: 20
team_policy eval team catch total num: 44
idv_policy eval average step individual rewards of agent0: 0.8422624864937842
idv_policy eval average team episode rewards of agent0: 122.5
idv_policy eval idv catch total num of agent0: 35
idv_policy eval team catch total num: 49
idv_policy eval average step individual rewards of agent1: 0.8626355403425053
idv_policy eval average team episode rewards of agent1: 122.5
idv_policy eval idv catch total num of agent1: 36
idv_policy eval team catch total num: 49
idv_policy eval average step individual rewards of agent2: 1.1202266525662439
idv_policy eval average team episode rewards of agent2: 122.5
idv_policy eval idv catch total num of agent2: 46
idv_policy eval team catch total num: 49
idv_policy eval average step individual rewards of agent3: 1.0418315025426184
idv_policy eval average team episode rewards of agent3: 122.5
idv_policy eval idv catch total num of agent3: 43
idv_policy eval team catch total num: 49
idv_policy eval average step individual rewards of agent4: 0.6106186065538456
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 6651/10000 episodes, total num timesteps 1330400/2000000, FPS 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.

team_policy eval average step individual rewards of agent0: 0.28674373288388355
team_policy eval average team episode rewards of agent0: 87.5
team_policy eval idv catch total num of agent0: 14
team_policy eval team catch total num: 35
team_policy eval average step individual rewards of agent1: 0.6777943144078779
team_policy eval average team episode rewards of agent1: 87.5
team_policy eval idv catch total num of agent1: 29
team_policy eval team catch total num: 35
team_policy eval average step individual rewards of agent2: 0.8626568440994056
team_policy eval average team episode rewards of agent2: 87.5
team_policy eval idv catch total num of agent2: 36
team_policy eval team catch total num: 35
team_policy eval average step individual rewards of agent3: 0.709901856125105
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: 0.8244866024852581
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: 1.1705150201777732
idv_policy eval average team episode rewards of agent0: 192.5
idv_policy eval idv catch total num of agent0: 48
idv_policy eval team catch total num: 77
idv_policy eval average step individual rewards of agent1: 1.5785701107409633
idv_policy eval average team episode rewards of agent1: 192.5
idv_policy eval idv catch total num of agent1: 64
idv_policy eval team catch total num: 77
idv_policy eval average step individual rewards of agent2: 1.3952392638986977
idv_policy eval average team episode rewards of agent2: 192.5
idv_policy eval idv catch total num of agent2: 57
idv_policy eval team catch total num: 77
idv_policy eval average step individual rewards of agent3: 1.2234105583551222
idv_policy eval average team episode rewards of agent3: 192.5
idv_policy eval idv catch total num of agent3: 50
idv_policy eval team catch total num: 77
idv_policy eval average step individual rewards of agent4: 1.0428134349142955
idv_policy eval average team episode rewards of agent4: 192.5
idv_policy eval idv catch total num of agent4: 43
idv_policy eval team catch total num: 77

 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 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 235.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.

team_policy eval average step individual rewards of agent0: 0.4927562636510149
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: 0.9028383920453474
team_policy eval average team episode rewards of agent1: 90.0
team_policy eval idv catch total num of agent1: 38
team_policy eval team catch total num: 36
team_policy eval average step individual rewards of agent2: 1.0573184562781899
team_policy eval average team episode rewards of agent2: 90.0
team_policy eval idv catch total num of agent2: 44
team_policy eval team catch total num: 36
team_policy eval average step individual rewards of agent3: 0.6611667867153213
team_policy eval average team episode rewards of agent3: 90.0
team_policy eval idv catch total num of agent3: 28
team_policy eval team catch total num: 36
team_policy eval average step individual rewards of agent4: 0.5541993553524401
team_policy eval average team episode rewards of agent4: 90.0
team_policy eval idv catch total num of agent4: 24
team_policy eval team catch total num: 36
idv_policy eval average step individual rewards of agent0: 1.0885469062951136
idv_policy eval average team episode rewards of agent0: 140.0
idv_policy eval idv catch total num of agent0: 45
idv_policy eval team catch total num: 56
idv_policy eval average step individual rewards of agent1: 0.9698562808836568
idv_policy eval average team episode rewards of agent1: 140.0
idv_policy eval idv catch total num of agent1: 40
idv_policy eval team catch total num: 56
idv_policy eval average step individual rewards of agent2: 1.0390714040674753
idv_policy eval average team episode rewards of agent2: 140.0
idv_policy eval idv catch total num of agent2: 43
idv_policy eval team catch total num: 56
idv_policy eval average step individual rewards of agent3: 0.8082066437775011
idv_policy eval average team episode rewards of agent3: 140.0
idv_policy eval idv catch total num of agent3: 34
idv_policy eval team catch total num: 56
idv_policy eval average step individual rewards of agent4: 0.9640830391798377
idv_policy eval average team episode rewards of agent4: 140.0
idv_policy eval idv catch total num of agent4: 40
idv_policy eval team catch total num: 56

 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.

team_policy eval average step individual rewards of agent0: 0.7661175950132784
team_policy eval average team episode rewards of agent0: 115.0
team_policy eval idv catch total num of agent0: 32
team_policy eval team catch total num: 46
team_policy eval average step individual rewards of agent1: 0.7860884366376419
team_policy eval average team episode rewards of agent1: 115.0
team_policy eval idv catch total num of agent1: 33
team_policy eval team catch total num: 46
team_policy eval average step individual rewards of agent2: 0.8898019562608843
team_policy eval average team episode rewards of agent2: 115.0
team_policy eval idv catch total num of agent2: 37
team_policy eval team catch total num: 46
team_policy eval average step individual rewards of agent3: 0.664328024242815
team_policy eval average team episode rewards of agent3: 115.0
team_policy eval idv catch total num of agent3: 28
team_policy eval team catch total num: 46
team_policy eval average step individual rewards of agent4: 0.7409142095325947
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: 0.9167099196055554
idv_policy eval average team episode rewards of agent0: 90.0
idv_policy eval idv catch total num of agent0: 38
idv_policy eval team catch total num: 36
idv_policy eval average step individual rewards of agent1: 0.6326604064167001
idv_policy eval average team episode rewards of agent1: 90.0
idv_policy eval idv catch total num of agent1: 27
idv_policy eval team catch total num: 36
idv_policy eval average step individual rewards of agent2: 0.6343214746681616
idv_policy eval average team episode rewards of agent2: 90.0
idv_policy eval idv catch total num of agent2: 27
idv_policy eval team catch total num: 36
idv_policy eval average step individual rewards of agent3: 0.7138554223129696
idv_policy eval average team episode rewards of agent3: 90.0
idv_policy eval idv catch total num of agent3: 30
idv_policy eval team catch total num: 36
idv_policy eval average step individual rewards of agent4: 0.8382598719793244
idv_policy eval average team episode rewards of agent4: 90.0
idv_policy eval idv catch total num of agent4: 35
idv_policy eval team catch total num: 36

 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.

team_policy eval average step individual rewards of agent0: 0.7980248956882301
team_policy eval average team episode rewards of agent0: 70.0
team_policy eval idv catch total num of agent0: 33
team_policy eval team catch total num: 28
team_policy eval average step individual rewards of agent1: 0.35868583046610647
team_policy eval average team episode rewards of agent1: 70.0
team_policy eval idv catch total num of agent1: 16
team_policy eval team catch total num: 28
team_policy eval average step individual rewards of agent2: 0.40792438839198625
team_policy eval average team episode rewards of agent2: 70.0
team_policy eval idv catch total num of agent2: 18
team_policy eval team catch total num: 28
team_policy eval average step individual rewards of agent3: 0.9893076198043278
team_policy eval average team episode rewards of agent3: 70.0
team_policy eval idv catch total num of agent3: 41
team_policy eval team catch total num: 28
team_policy eval average step individual rewards of agent4: 0.6387290438697585
team_policy eval average team episode rewards of agent4: 70.0
team_policy eval idv catch total num of agent4: 27
team_policy eval team catch total num: 28
idv_policy eval average step individual rewards of agent0: 0.5077190588986871
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: 0.583839695526988
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: 0.7392601165541203
idv_policy eval average team episode rewards of agent2: 90.0
idv_policy eval idv catch total num of agent2: 31
idv_policy eval team catch total num: 36
idv_policy eval average step individual rewards of agent3: 0.9182905409123685
idv_policy eval average team episode rewards of agent3: 90.0
idv_policy eval idv catch total num of agent3: 38
idv_policy eval team catch total num: 36
idv_policy eval average step individual rewards of agent4: 0.7152985268901447
idv_policy eval average team episode rewards of agent4: 90.0
idv_policy eval idv catch total num of agent4: 30
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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.

team_policy eval average step individual rewards of agent0: 0.7079347829378156
team_policy eval average team episode rewards of agent0: 100.0
team_policy eval idv catch total num of agent0: 30
team_policy eval team catch total num: 40
team_policy eval average step individual rewards of agent1: 1.1219023422945917
team_policy eval average team episode rewards of agent1: 100.0
team_policy eval idv catch total num of agent1: 46
team_policy eval team catch total num: 40
team_policy eval average step individual rewards of agent2: 0.6375402393959855
team_policy eval average team episode rewards of agent2: 100.0
team_policy eval idv catch total num of agent2: 27
team_policy eval team catch total num: 40
team_policy eval average step individual rewards of agent3: 0.5628729810865035
team_policy eval average team episode rewards of agent3: 100.0
team_policy eval idv catch total num of agent3: 24
team_policy eval team catch total num: 40
team_policy eval average step individual rewards of agent4: 1.038339710257948
team_policy eval average team episode rewards of agent4: 100.0
team_policy eval idv catch total num of agent4: 43
team_policy eval team catch total num: 40
idv_policy eval average step individual rewards of agent0: 0.8983615870302405
idv_policy eval average team episode rewards of agent0: 100.0
idv_policy eval idv catch total num of agent0: 37
idv_policy eval team catch total num: 40
idv_policy eval average step individual rewards of agent1: 0.4175286757930992
idv_policy eval average team episode rewards of agent1: 100.0
idv_policy eval idv catch total num of agent1: 18
idv_policy eval team catch total num: 40
idv_policy eval average step individual rewards of agent2: 0.9946744593708915
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: 0.809222544130339
idv_policy eval average team episode rewards of agent3: 100.0
idv_policy eval idv catch total num of agent3: 34
idv_policy eval team catch total num: 40
idv_policy eval average step individual rewards of agent4: 0.8463105935758812
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 6776/10000 episodes, total num timesteps 1355400/2000000, FPS 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.

team_policy eval average step individual rewards of agent0: 1.2732909688857923
team_policy eval average team episode rewards of agent0: 120.0
team_policy eval idv catch total num of agent0: 52
team_policy eval team catch total num: 48
team_policy eval average step individual rewards of agent1: 0.6568650978879441
team_policy eval average team episode rewards of agent1: 120.0
team_policy eval idv catch total num of agent1: 28
team_policy eval team catch total num: 48
team_policy eval average step individual rewards of agent2: 0.9135434714666999
team_policy eval average team episode rewards of agent2: 120.0
team_policy eval idv catch total num of agent2: 38
team_policy eval team catch total num: 48
team_policy eval average step individual rewards of agent3: 0.6361606857651051
team_policy eval average team episode rewards of agent3: 120.0
team_policy eval idv catch total num of agent3: 27
team_policy eval team catch total num: 48
team_policy eval average step individual rewards of agent4: 0.837722471647694
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: 0.7445386534404473
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: 0.80716862084329
idv_policy eval average team episode rewards of agent1: 102.5
idv_policy eval idv catch total num of agent1: 34
idv_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent2: 0.8194960375579204
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: 0.40758865853180304
idv_policy eval average team episode rewards of agent3: 102.5
idv_policy eval idv catch total num of agent3: 18
idv_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent4: 1.015865759459868
idv_policy eval average team episode rewards of agent4: 102.5
idv_policy eval idv catch total num of agent4: 42
idv_policy eval team catch total num: 41

 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.

team_policy eval average step individual rewards of agent0: 0.8167397489755709
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: 0.6606385821908936
team_policy eval average team episode rewards of agent1: 105.0
team_policy eval idv catch total num of agent1: 28
team_policy eval team catch total num: 42
team_policy eval average step individual rewards of agent2: 0.7379349124298125
team_policy eval average team episode rewards of agent2: 105.0
team_policy eval idv catch total num of agent2: 31
team_policy eval team catch total num: 42
team_policy eval average step individual rewards of agent3: 0.4368919980118167
team_policy eval average team episode rewards of agent3: 105.0
team_policy eval idv catch total num of agent3: 19
team_policy eval team catch total num: 42
team_policy eval average step individual rewards of agent4: 0.6893403601173068
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: 0.6382538408412194
idv_policy eval average team episode rewards of agent0: 90.0
idv_policy eval idv catch total num of agent0: 27
idv_policy eval team catch total num: 36
idv_policy eval average step individual rewards of agent1: 0.5730723456706971
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: 0.966637843487026
idv_policy eval average team episode rewards of agent2: 90.0
idv_policy eval idv catch total num of agent2: 40
idv_policy eval team catch total num: 36
idv_policy eval average step individual rewards of agent3: 0.5018292138191665
idv_policy eval average team episode rewards of agent3: 90.0
idv_policy eval idv catch total num of agent3: 22
idv_policy eval team catch total num: 36
idv_policy eval average step individual rewards of agent4: 0.5873344723231131
idv_policy eval average team episode rewards of agent4: 90.0
idv_policy eval idv catch total num of agent4: 25
idv_policy eval team catch total num: 36

 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.

team_policy eval average step individual rewards of agent0: 0.2114420560261516
team_policy eval average team episode rewards of agent0: 80.0
team_policy eval idv catch total num of agent0: 11
team_policy eval team catch total num: 32
team_policy eval average step individual rewards of agent1: 0.5439646627029934
team_policy eval average team episode rewards of agent1: 80.0
team_policy eval idv catch total num of agent1: 24
team_policy eval team catch total num: 32
team_policy eval average step individual rewards of agent2: 0.47408319921594183
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: 1.031370488368215
team_policy eval average team episode rewards of agent3: 80.0
team_policy eval idv catch total num of agent3: 43
team_policy eval team catch total num: 32
team_policy eval average step individual rewards of agent4: 0.6371441240689595
team_policy eval average team episode rewards of agent4: 80.0
team_policy eval idv catch total num of agent4: 27
team_policy eval team catch total num: 32
idv_policy eval average step individual rewards of agent0: 1.1094176032961638
idv_policy eval average team episode rewards of agent0: 130.0
idv_policy eval idv catch total num of agent0: 46
idv_policy eval team catch total num: 52
idv_policy eval average step individual rewards of agent1: 0.7044026389122772
idv_policy eval average team episode rewards of agent1: 130.0
idv_policy eval idv catch total num of agent1: 30
idv_policy eval team catch total num: 52
idv_policy eval average step individual rewards of agent2: 0.47494529714675615
idv_policy eval average team episode rewards of agent2: 130.0
idv_policy eval idv catch total num of agent2: 21
idv_policy eval team catch total num: 52
idv_policy eval average step individual rewards of agent3: 0.9153272259393386
idv_policy eval average team episode rewards of agent3: 130.0
idv_policy eval idv catch total num of agent3: 38
idv_policy eval team catch total num: 52
idv_policy eval average step individual rewards of agent4: 0.8534226389671747
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 6851/10000 episodes, total num timesteps 1370400/2000000, FPS 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 237.


 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 236.


 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 237.

team_policy eval average step individual rewards of agent0: 0.40526285602764406
team_policy eval average team episode rewards of agent0: 62.5
team_policy eval idv catch total num of agent0: 18
team_policy eval team catch total num: 25
team_policy eval average step individual rewards of agent1: 0.5708270793322193
team_policy eval average team episode rewards of agent1: 62.5
team_policy eval idv catch total num of agent1: 25
team_policy eval team catch total num: 25
team_policy eval average step individual rewards of agent2: 0.4347385383071144
team_policy eval average team episode rewards of agent2: 62.5
team_policy eval idv catch total num of agent2: 19
team_policy eval team catch total num: 25
team_policy eval average step individual rewards of agent3: 0.7170024285658957
team_policy eval average team episode rewards of agent3: 62.5
team_policy eval idv catch total num of agent3: 30
team_policy eval team catch total num: 25
team_policy eval average step individual rewards of agent4: 0.610614288326857
team_policy eval average team episode rewards of agent4: 62.5
team_policy eval idv catch total num of agent4: 26
team_policy eval team catch total num: 25
idv_policy eval average step individual rewards of agent0: 0.8707134052438117
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: 0.5798632107720462
idv_policy eval average team episode rewards of agent1: 125.0
idv_policy eval idv catch total num of agent1: 25
idv_policy eval team catch total num: 50
idv_policy eval average step individual rewards of agent2: 0.9922604852141852
idv_policy eval average team episode rewards of agent2: 125.0
idv_policy eval idv catch total num of agent2: 41
idv_policy eval team catch total num: 50
idv_policy eval average step individual rewards of agent3: 0.8455855865143315
idv_policy eval average team episode rewards of agent3: 125.0
idv_policy eval idv catch total num of agent3: 35
idv_policy eval team catch total num: 50
idv_policy eval average step individual rewards of agent4: 1.1185610059986797
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 6876/10000 episodes, total num timesteps 1375400/2000000, FPS 236.


 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 237.


 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 236.


 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 236.


 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 236.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.

team_policy eval average step individual rewards of agent0: 0.672952576814331
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: 0.5800671730467808
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: 0.638216740826602
team_policy eval average team episode rewards of agent2: 90.0
team_policy eval idv catch total num of agent2: 27
team_policy eval team catch total num: 36
team_policy eval average step individual rewards of agent3: 0.3580238880474957
team_policy eval average team episode rewards of agent3: 90.0
team_policy eval idv catch total num of agent3: 16
team_policy eval team catch total num: 36
team_policy eval average step individual rewards of agent4: 1.0616767843393164
team_policy eval average team episode rewards of agent4: 90.0
team_policy eval idv catch total num of agent4: 44
team_policy eval team catch total num: 36
idv_policy eval average step individual rewards of agent0: 1.0877355344418502
idv_policy eval average team episode rewards of agent0: 120.0
idv_policy eval idv catch total num of agent0: 45
idv_policy eval team catch total num: 48
idv_policy eval average step individual rewards of agent1: 0.6332588843650138
idv_policy eval average team episode rewards of agent1: 120.0
idv_policy eval idv catch total num of agent1: 27
idv_policy eval team catch total num: 48
idv_policy eval average step individual rewards of agent2: 0.7794495096204895
idv_policy eval average team episode rewards of agent2: 120.0
idv_policy eval idv catch total num of agent2: 33
idv_policy eval team catch total num: 48
idv_policy eval average step individual rewards of agent3: 1.0387622718347653
idv_policy eval average team episode rewards of agent3: 120.0
idv_policy eval idv catch total num of agent3: 43
idv_policy eval team catch total num: 48
idv_policy eval average step individual rewards of agent4: 0.7466458177404348
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 6901/10000 episodes, total num timesteps 1380400/2000000, FPS 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.

team_policy eval average step individual rewards of agent0: 0.7399148154416421
team_policy eval average team episode rewards of agent0: 75.0
team_policy eval idv catch total num of agent0: 31
team_policy eval team catch total num: 30
team_policy eval average step individual rewards of agent1: 0.6514700274604667
team_policy eval average team episode rewards of agent1: 75.0
team_policy eval idv catch total num of agent1: 28
team_policy eval team catch total num: 30
team_policy eval average step individual rewards of agent2: 0.2754612211618817
team_policy eval average team episode rewards of agent2: 75.0
team_policy eval idv catch total num of agent2: 13
team_policy eval team catch total num: 30
team_policy eval average step individual rewards of agent3: 0.4379346648946714
team_policy eval average team episode rewards of agent3: 75.0
team_policy eval idv catch total num of agent3: 19
team_policy eval team catch total num: 30
team_policy eval average step individual rewards of agent4: 0.7368435060349426
team_policy eval average team episode rewards of agent4: 75.0
team_policy eval idv catch total num of agent4: 31
team_policy eval team catch total num: 30
idv_policy eval average step individual rewards of agent0: 1.0171320303686147
idv_policy eval average team episode rewards of agent0: 87.5
idv_policy eval idv catch total num of agent0: 42
idv_policy eval team catch total num: 35
idv_policy eval average step individual rewards of agent1: 0.8477861836738976
idv_policy eval average team episode rewards of agent1: 87.5
idv_policy eval idv catch total num of agent1: 35
idv_policy eval team catch total num: 35
idv_policy eval average step individual rewards of agent2: 0.3293195258591801
idv_policy eval average team episode rewards of agent2: 87.5
idv_policy eval idv catch total num of agent2: 15
idv_policy eval team catch total num: 35
idv_policy eval average step individual rewards of agent3: 0.5094141323998619
idv_policy eval average team episode rewards of agent3: 87.5
idv_policy eval idv catch total num of agent3: 22
idv_policy eval team catch total num: 35
idv_policy eval average step individual rewards of agent4: 0.4597977736839867
idv_policy eval average team episode rewards of agent4: 87.5
idv_policy eval idv catch total num of agent4: 20
idv_policy eval team catch total num: 35

 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.

team_policy eval average step individual rewards of agent0: 0.3786036925384749
team_policy eval average team episode rewards of agent0: 100.0
team_policy eval idv catch total num of agent0: 17
team_policy eval team catch total num: 40
team_policy eval average step individual rewards of agent1: 0.38558890159756076
team_policy eval average team episode rewards of agent1: 100.0
team_policy eval idv catch total num of agent1: 17
team_policy eval team catch total num: 40
team_policy eval average step individual rewards of agent2: 1.050314532817061
team_policy eval average team episode rewards of agent2: 100.0
team_policy eval idv catch total num of agent2: 43
team_policy eval team catch total num: 40
team_policy eval average step individual rewards of agent3: 1.1396378993966974
team_policy eval average team episode rewards of agent3: 100.0
team_policy eval idv catch total num of agent3: 47
team_policy eval team catch total num: 40
team_policy eval average step individual rewards of agent4: 1.0424483823203232
team_policy eval average team episode rewards of agent4: 100.0
team_policy eval idv catch total num of agent4: 43
team_policy eval team catch total num: 40
idv_policy eval average step individual rewards of agent0: 0.3121283319449867
idv_policy eval average team episode rewards of agent0: 110.0
idv_policy eval idv catch total num of agent0: 14
idv_policy eval team catch total num: 44
idv_policy eval average step individual rewards of agent1: 0.360479040234213
idv_policy eval average team episode rewards of agent1: 110.0
idv_policy eval idv catch total num of agent1: 16
idv_policy eval team catch total num: 44
idv_policy eval average step individual rewards of agent2: 1.2688402916967059
idv_policy eval average team episode rewards of agent2: 110.0
idv_policy eval idv catch total num of agent2: 52
idv_policy eval team catch total num: 44
idv_policy eval average step individual rewards of agent3: 0.8870991214652366
idv_policy eval average team episode rewards of agent3: 110.0
idv_policy eval idv catch total num of agent3: 37
idv_policy eval team catch total num: 44
idv_policy eval average step individual rewards of agent4: 1.0184425549870244
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 6951/10000 episodes, total num timesteps 1390400/2000000, FPS 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.

team_policy eval average step individual rewards of agent0: 0.9471226658476638
team_policy eval average team episode rewards of agent0: 112.5
team_policy eval idv catch total num of agent0: 39
team_policy eval team catch total num: 45
team_policy eval average step individual rewards of agent1: 0.7151556296134437
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: 0.5826047989008686
team_policy eval average team episode rewards of agent2: 112.5
team_policy eval idv catch total num of agent2: 25
team_policy eval team catch total num: 45
team_policy eval average step individual rewards of agent3: 1.4260949207400977
team_policy eval average team episode rewards of agent3: 112.5
team_policy eval idv catch total num of agent3: 58
team_policy eval team catch total num: 45
team_policy eval average step individual rewards of agent4: 1.087566538638134
team_policy eval average team episode rewards of agent4: 112.5
team_policy eval idv catch total num of agent4: 45
team_policy eval team catch total num: 45
idv_policy eval average step individual rewards of agent0: 1.6045155928218149
idv_policy eval average team episode rewards of agent0: 150.0
idv_policy eval idv catch total num of agent0: 65
idv_policy eval team catch total num: 60
idv_policy eval average step individual rewards of agent1: 1.1362547116049384
idv_policy eval average team episode rewards of agent1: 150.0
idv_policy eval idv catch total num of agent1: 47
idv_policy eval team catch total num: 60
idv_policy eval average step individual rewards of agent2: 0.7443944981837771
idv_policy eval average team episode rewards of agent2: 150.0
idv_policy eval idv catch total num of agent2: 31
idv_policy eval team catch total num: 60
idv_policy eval average step individual rewards of agent3: 0.866344068247877
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: 1.0463485078656345
idv_policy eval average team episode rewards of agent4: 150.0
idv_policy eval idv catch total num of agent4: 43
idv_policy eval team catch total num: 60

 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.

team_policy eval average step individual rewards of agent0: 0.2971108720995445
team_policy eval average team episode rewards of agent0: 122.5
team_policy eval idv catch total num of agent0: 14
team_policy eval team catch total num: 49
team_policy eval average step individual rewards of agent1: 0.7087009826341528
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: 1.2704887772275433
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: 0.939038197360998
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: 0.858930746819981
team_policy eval average team episode rewards of agent4: 122.5
team_policy eval idv catch total num of agent4: 36
team_policy eval team catch total num: 49
idv_policy eval average step individual rewards of agent0: 1.0648451407340163
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: 1.0711545175157828
idv_policy eval average team episode rewards of agent1: 107.5
idv_policy eval idv catch total num of agent1: 44
idv_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent2: 0.9185070747619244
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: 0.6339562405074116
idv_policy eval average team episode rewards of agent3: 107.5
idv_policy eval idv catch total num of agent3: 27
idv_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent4: 0.6851129467157251
idv_policy eval average team episode rewards of agent4: 107.5
idv_policy eval idv catch total num of agent4: 29
idv_policy eval team catch total num: 43

 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.

team_policy eval average step individual rewards of agent0: 0.971716286807625
team_policy eval average team episode rewards of agent0: 82.5
team_policy eval idv catch total num of agent0: 40
team_policy eval team catch total num: 33
team_policy eval average step individual rewards of agent1: 0.5821380622537387
team_policy eval average team episode rewards of agent1: 82.5
team_policy eval idv catch total num of agent1: 25
team_policy eval team catch total num: 33
team_policy eval average step individual rewards of agent2: 0.5675600790135281
team_policy eval average team episode rewards of agent2: 82.5
team_policy eval idv catch total num of agent2: 24
team_policy eval team catch total num: 33
team_policy eval average step individual rewards of agent3: 0.48984880657478186
team_policy eval average team episode rewards of agent3: 82.5
team_policy eval idv catch total num of agent3: 21
team_policy eval team catch total num: 33
team_policy eval average step individual rewards of agent4: 0.3583929130018121
team_policy eval average team episode rewards of agent4: 82.5
team_policy eval idv catch total num of agent4: 16
team_policy eval team catch total num: 33
idv_policy eval average step individual rewards of agent0: 0.45955947918536255
idv_policy eval average team episode rewards of agent0: 115.0
idv_policy eval idv catch total num of agent0: 20
idv_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent1: 1.1111309266645721
idv_policy eval average team episode rewards of agent1: 115.0
idv_policy eval idv catch total num of agent1: 46
idv_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent2: 0.97046784958621
idv_policy eval average team episode rewards of agent2: 115.0
idv_policy eval idv catch total num of agent2: 40
idv_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent3: 0.40512402406540055
idv_policy eval average team episode rewards of agent3: 115.0
idv_policy eval idv catch total num of agent3: 18
idv_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent4: 1.0911088429865088
idv_policy eval average team episode rewards of agent4: 115.0
idv_policy eval idv catch total num of agent4: 45
idv_policy eval team catch total num: 46

 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.

team_policy eval average step individual rewards of agent0: 0.45389362087762036
team_policy eval average team episode rewards of agent0: 60.0
team_policy eval idv catch total num of agent0: 20
team_policy eval team catch total num: 24
team_policy eval average step individual rewards of agent1: 0.6894037114163316
team_policy eval average team episode rewards of agent1: 60.0
team_policy eval idv catch total num of agent1: 29
team_policy eval team catch total num: 24
team_policy eval average step individual rewards of agent2: 0.9381284336161868
team_policy eval average team episode rewards of agent2: 60.0
team_policy eval idv catch total num of agent2: 39
team_policy eval team catch total num: 24
team_policy eval average step individual rewards of agent3: 0.8931578045723737
team_policy eval average team episode rewards of agent3: 60.0
team_policy eval idv catch total num of agent3: 37
team_policy eval team catch total num: 24
team_policy eval average step individual rewards of agent4: 0.23461638759620482
team_policy eval average team episode rewards of agent4: 60.0
team_policy eval idv catch total num of agent4: 11
team_policy eval team catch total num: 24
idv_policy eval average step individual rewards of agent0: 0.99533318821965
idv_policy eval average team episode rewards of agent0: 140.0
idv_policy eval idv catch total num of agent0: 41
idv_policy eval team catch total num: 56
idv_policy eval average step individual rewards of agent1: 0.8169602111220208
idv_policy eval average team episode rewards of agent1: 140.0
idv_policy eval idv catch total num of agent1: 34
idv_policy eval team catch total num: 56
idv_policy eval average step individual rewards of agent2: 1.023724775082712
idv_policy eval average team episode rewards of agent2: 140.0
idv_policy eval idv catch total num of agent2: 42
idv_policy eval team catch total num: 56
idv_policy eval average step individual rewards of agent3: 1.2983146911859313
idv_policy eval average team episode rewards of agent3: 140.0
idv_policy eval idv catch total num of agent3: 53
idv_policy eval team catch total num: 56
idv_policy eval average step individual rewards of agent4: 0.9111519785877111
idv_policy eval average team episode rewards of agent4: 140.0
idv_policy eval idv catch total num of agent4: 38
idv_policy eval team catch total num: 56

 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.

team_policy eval average step individual rewards of agent0: 0.9154048567785824
team_policy eval average team episode rewards of agent0: 117.5
team_policy eval idv catch total num of agent0: 38
team_policy eval team catch total num: 47
team_policy eval average step individual rewards of agent1: 0.7400104995978591
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: 0.8151407265403976
team_policy eval average team episode rewards of agent2: 117.5
team_policy eval idv catch total num of agent2: 34
team_policy eval team catch total num: 47
team_policy eval average step individual rewards of agent3: 0.9468138462955199
team_policy eval average team episode rewards of agent3: 117.5
team_policy eval idv catch total num of agent3: 39
team_policy eval team catch total num: 47
team_policy eval average step individual rewards of agent4: 1.0952462457767036
team_policy eval average team episode rewards of agent4: 117.5
team_policy eval idv catch total num of agent4: 45
team_policy eval team catch total num: 47
idv_policy eval average step individual rewards of agent0: 0.6608098395896466
idv_policy eval average team episode rewards of agent0: 120.0
idv_policy eval idv catch total num of agent0: 28
idv_policy eval team catch total num: 48
idv_policy eval average step individual rewards of agent1: 1.0598421575740287
idv_policy eval average team episode rewards of agent1: 120.0
idv_policy eval idv catch total num of agent1: 44
idv_policy eval team catch total num: 48
idv_policy eval average step individual rewards of agent2: 1.0690118948710312
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: 1.1787742690796374
idv_policy eval average team episode rewards of agent3: 120.0
idv_policy eval idv catch total num of agent3: 48
idv_policy eval team catch total num: 48
idv_policy eval average step individual rewards of agent4: 0.7597376126978028
idv_policy eval average team episode rewards of agent4: 120.0
idv_policy eval idv catch total num of agent4: 32
idv_policy eval team catch total num: 48

 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.

team_policy eval average step individual rewards of agent0: 0.45392796184625245
team_policy eval average team episode rewards of agent0: 87.5
team_policy eval idv catch total num of agent0: 20
team_policy eval team catch total num: 35
team_policy eval average step individual rewards of agent1: 0.35486358515764344
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: 0.6139705596580906
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: 1.0894779913069885
team_policy eval average team episode rewards of agent3: 87.5
team_policy eval idv catch total num of agent3: 45
team_policy eval team catch total num: 35
team_policy eval average step individual rewards of agent4: 0.5024512401730582
team_policy eval average team episode rewards of agent4: 87.5
team_policy eval idv catch total num of agent4: 22
team_policy eval team catch total num: 35
idv_policy eval average step individual rewards of agent0: 0.7897188935329075
idv_policy eval average team episode rewards of agent0: 90.0
idv_policy eval idv catch total num of agent0: 33
idv_policy eval team catch total num: 36
idv_policy eval average step individual rewards of agent1: 0.33230034566197586
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: 0.7110479648293694
idv_policy eval average team episode rewards of agent2: 90.0
idv_policy eval idv catch total num of agent2: 30
idv_policy eval team catch total num: 36
idv_policy eval average step individual rewards of agent3: 0.8375147300243012
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: 0.7599427675964711
idv_policy eval average team episode rewards of agent4: 90.0
idv_policy eval idv catch total num of agent4: 32
idv_policy eval team catch total num: 36

 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.

team_policy eval average step individual rewards of agent0: 1.0901093022073427
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: 0.679450271889819
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: 0.37834046299220975
team_policy eval average team episode rewards of agent2: 115.0
team_policy eval idv catch total num of agent2: 17
team_policy eval team catch total num: 46
team_policy eval average step individual rewards of agent3: 0.9630798456001473
team_policy eval average team episode rewards of agent3: 115.0
team_policy eval idv catch total num of agent3: 40
team_policy eval team catch total num: 46
team_policy eval average step individual rewards of agent4: 0.9697760055294532
team_policy eval average team episode rewards of agent4: 115.0
team_policy eval idv catch total num of agent4: 40
team_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent0: 0.8101918995096412
idv_policy eval average team episode rewards of agent0: 125.0
idv_policy eval idv catch total num of agent0: 34
idv_policy eval team catch total num: 50
idv_policy eval average step individual rewards of agent1: 1.3225324246570642
idv_policy eval average team episode rewards of agent1: 125.0
idv_policy eval idv catch total num of agent1: 54
idv_policy eval team catch total num: 50
idv_policy eval average step individual rewards of agent2: 0.9877540852016238
idv_policy eval average team episode rewards of agent2: 125.0
idv_policy eval idv catch total num of agent2: 41
idv_policy eval team catch total num: 50
idv_policy eval average step individual rewards of agent3: 0.624210921582121
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: 0.8893176008575944
idv_policy eval average team episode rewards of agent4: 125.0
idv_policy eval idv catch total num of agent4: 37
idv_policy eval team catch total num: 50

 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.

team_policy eval average step individual rewards of agent0: 0.7686491337347576
team_policy eval average team episode rewards of agent0: 110.0
team_policy eval idv catch total num of agent0: 32
team_policy eval team catch total num: 44
team_policy eval average step individual rewards of agent1: 0.8103077861004252
team_policy eval average team episode rewards of agent1: 110.0
team_policy eval idv catch total num of agent1: 34
team_policy eval team catch total num: 44
team_policy eval average step individual rewards of agent2: 0.9869205311594016
team_policy eval average team episode rewards of agent2: 110.0
team_policy eval idv catch total num of agent2: 41
team_policy eval team catch total num: 44
team_policy eval average step individual rewards of agent3: 0.8133122771001529
team_policy eval average team episode rewards of agent3: 110.0
team_policy eval idv catch total num of agent3: 34
team_policy eval team catch total num: 44
team_policy eval average step individual rewards of agent4: 0.7855349319452813
team_policy eval average team episode rewards of agent4: 110.0
team_policy eval idv catch total num of agent4: 33
team_policy eval team catch total num: 44
idv_policy eval average step individual rewards of agent0: 1.0452743757149612
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: 1.1960159562830257
idv_policy eval average team episode rewards of agent1: 117.5
idv_policy eval idv catch total num of agent1: 49
idv_policy eval team catch total num: 47
idv_policy eval average step individual rewards of agent2: 0.4341654997209241
idv_policy eval average team episode rewards of agent2: 117.5
idv_policy eval idv catch total num of agent2: 19
idv_policy eval team catch total num: 47
idv_policy eval average step individual rewards of agent3: 0.7691917554709714
idv_policy eval average team episode rewards of agent3: 117.5
idv_policy eval idv catch total num of agent3: 32
idv_policy eval team catch total num: 47
idv_policy eval average step individual rewards of agent4: 0.8070044264340193
idv_policy eval average team episode rewards of agent4: 117.5
idv_policy eval idv catch total num of agent4: 34
idv_policy eval team catch total num: 47

 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.

team_policy eval average step individual rewards of agent0: 1.03544562318207
team_policy eval average team episode rewards of agent0: 112.5
team_policy eval idv catch total num of agent0: 43
team_policy eval team catch total num: 45
team_policy eval average step individual rewards of agent1: 0.8441002730976109
team_policy eval average team episode rewards of agent1: 112.5
team_policy eval idv catch total num of agent1: 35
team_policy eval team catch total num: 45
team_policy eval average step individual rewards of agent2: 0.3054771176209269
team_policy eval average team episode rewards of agent2: 112.5
team_policy eval idv catch total num of agent2: 14
team_policy eval team catch total num: 45
team_policy eval average step individual rewards of agent3: 0.7613528573333629
team_policy eval average team episode rewards of agent3: 112.5
team_policy eval idv catch total num of agent3: 32
team_policy eval team catch total num: 45
team_policy eval average step individual rewards of agent4: 0.8189759676410288
team_policy eval average team episode rewards of agent4: 112.5
team_policy eval idv catch total num of agent4: 34
team_policy eval team catch total num: 45
idv_policy eval average step individual rewards of agent0: 0.7950566547572575
idv_policy eval average team episode rewards of agent0: 147.5
idv_policy eval idv catch total num of agent0: 33
idv_policy eval team catch total num: 59
idv_policy eval average step individual rewards of agent1: 0.9187872387800078
idv_policy eval average team episode rewards of agent1: 147.5
idv_policy eval idv catch total num of agent1: 38
idv_policy eval team catch total num: 59
idv_policy eval average step individual rewards of agent2: 0.8846384332181284
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: 1.2957807491793658
idv_policy eval average team episode rewards of agent3: 147.5
idv_policy eval idv catch total num of agent3: 53
idv_policy eval team catch total num: 59
idv_policy eval average step individual rewards of agent4: 1.0652398406342256
idv_policy eval average team episode rewards of agent4: 147.5
idv_policy eval idv catch total num of agent4: 44
idv_policy eval team catch total num: 59

 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.

team_policy eval average step individual rewards of agent0: 0.6151540186635942
team_policy eval average team episode rewards of agent0: 90.0
team_policy eval idv catch total num of agent0: 26
team_policy eval team catch total num: 36
team_policy eval average step individual rewards of agent1: 1.2767328542860938
team_policy eval average team episode rewards of agent1: 90.0
team_policy eval idv catch total num of agent1: 52
team_policy eval team catch total num: 36
team_policy eval average step individual rewards of agent2: 0.2577834151922631
team_policy eval average team episode rewards of agent2: 90.0
team_policy eval idv catch total num of agent2: 12
team_policy eval team catch total num: 36
team_policy eval average step individual rewards of agent3: 0.8967314428645469
team_policy eval average team episode rewards of agent3: 90.0
team_policy eval idv catch total num of agent3: 37
team_policy eval team catch total num: 36
team_policy eval average step individual rewards of agent4: 0.614220466469931
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: 0.7884629056908459
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: 0.9509766449916432
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: 0.6113673279641247
idv_policy eval average team episode rewards of agent2: 102.5
idv_policy eval idv catch total num of agent2: 26
idv_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent3: 0.7566585040698739
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: 0.687852803283787
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 7201/10000 episodes, total num timesteps 1440400/2000000, FPS 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.

team_policy eval average step individual rewards of agent0: 0.9601171228792892
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: 0.5603971293838446
team_policy eval average team episode rewards of agent1: 125.0
team_policy eval idv catch total num of agent1: 24
team_policy eval team catch total num: 50
team_policy eval average step individual rewards of agent2: 0.9704288542672734
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: 1.0139465409048618
team_policy eval average team episode rewards of agent3: 125.0
team_policy eval idv catch total num of agent3: 42
team_policy eval team catch total num: 50
team_policy eval average step individual rewards of agent4: 0.8419995716529456
team_policy eval average team episode rewards of agent4: 125.0
team_policy eval idv catch total num of agent4: 35
team_policy eval team catch total num: 50
idv_policy eval average step individual rewards of agent0: 0.5915272886790219
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: 0.8189211996625345
idv_policy eval average team episode rewards of agent1: 102.5
idv_policy eval idv catch total num of agent1: 34
idv_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent2: 0.8410495510446158
idv_policy eval average team episode rewards of agent2: 102.5
idv_policy eval idv catch total num of agent2: 35
idv_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent3: 1.044339444580573
idv_policy eval average team episode rewards of agent3: 102.5
idv_policy eval idv catch total num of agent3: 43
idv_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent4: 0.5598870388070659
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 7226/10000 episodes, total num timesteps 1445400/2000000, FPS 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.

team_policy eval average step individual rewards of agent0: 1.0112124776900433
team_policy eval average team episode rewards of agent0: 110.0
team_policy eval idv catch total num of agent0: 42
team_policy eval team catch total num: 44
team_policy eval average step individual rewards of agent1: 0.6093972982431426
team_policy eval average team episode rewards of agent1: 110.0
team_policy eval idv catch total num of agent1: 26
team_policy eval team catch total num: 44
team_policy eval average step individual rewards of agent2: 0.5654902796637914
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: 0.7260702011890366
team_policy eval average team episode rewards of agent3: 110.0
team_policy eval idv catch total num of agent3: 31
team_policy eval team catch total num: 44
team_policy eval average step individual rewards of agent4: 0.8888229080553713
team_policy eval average team episode rewards of agent4: 110.0
team_policy eval idv catch total num of agent4: 37
team_policy eval team catch total num: 44
idv_policy eval average step individual rewards of agent0: 0.5888145597720367
idv_policy eval average team episode rewards of agent0: 87.5
idv_policy eval idv catch total num of agent0: 25
idv_policy eval team catch total num: 35
idv_policy eval average step individual rewards of agent1: 0.5098084905110324
idv_policy eval average team episode rewards of agent1: 87.5
idv_policy eval idv catch total num of agent1: 22
idv_policy eval team catch total num: 35
idv_policy eval average step individual rewards of agent2: 1.0207227925174776
idv_policy eval average team episode rewards of agent2: 87.5
idv_policy eval idv catch total num of agent2: 42
idv_policy eval team catch total num: 35
idv_policy eval average step individual rewards of agent3: 0.6964091144243747
idv_policy eval average team episode rewards of agent3: 87.5
idv_policy eval idv catch total num of agent3: 29
idv_policy eval team catch total num: 35
idv_policy eval average step individual rewards of agent4: 0.8619649425674798
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 7251/10000 episodes, total num timesteps 1450400/2000000, FPS 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.

team_policy eval average step individual rewards of agent0: 0.8354517030447977
team_policy eval average team episode rewards of agent0: 85.0
team_policy eval idv catch total num of agent0: 35
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent1: 0.8025674134157919
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: 0.5318983843294367
team_policy eval average team episode rewards of agent2: 85.0
team_policy eval idv catch total num of agent2: 23
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent3: 0.4642110429372821
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: 0.6558490670185239
team_policy eval average team episode rewards of agent4: 85.0
team_policy eval idv catch total num of agent4: 28
team_policy eval team catch total num: 34
idv_policy eval average step individual rewards of agent0: 0.7055212499018905
idv_policy eval average team episode rewards of agent0: 105.0
idv_policy eval idv catch total num of agent0: 30
idv_policy eval team catch total num: 42
idv_policy eval average step individual rewards of agent1: 0.9642757139271638
idv_policy eval average team episode rewards of agent1: 105.0
idv_policy eval idv catch total num of agent1: 40
idv_policy eval team catch total num: 42
idv_policy eval average step individual rewards of agent2: 0.9429169804555295
idv_policy eval average team episode rewards of agent2: 105.0
idv_policy eval idv catch total num of agent2: 39
idv_policy eval team catch total num: 42
idv_policy eval average step individual rewards of agent3: 0.8608531236592296
idv_policy eval average team episode rewards of agent3: 105.0
idv_policy eval idv catch total num of agent3: 36
idv_policy eval team catch total num: 42
idv_policy eval average step individual rewards of agent4: 0.48399950741650843
idv_policy eval average team episode rewards of agent4: 105.0
idv_policy eval idv catch total num of agent4: 21
idv_policy eval team catch total num: 42

 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.

team_policy eval average step individual rewards of agent0: 0.8953820760970354
team_policy eval average team episode rewards of agent0: 112.5
team_policy eval idv catch total num of agent0: 37
team_policy eval team catch total num: 45
team_policy eval average step individual rewards of agent1: 1.07298253471491
team_policy eval average team episode rewards of agent1: 112.5
team_policy eval idv catch total num of agent1: 44
team_policy eval team catch total num: 45
team_policy eval average step individual rewards of agent2: 0.9146141678037145
team_policy eval average team episode rewards of agent2: 112.5
team_policy eval idv catch total num of agent2: 38
team_policy eval team catch total num: 45
team_policy eval average step individual rewards of agent3: 0.5581541222567422
team_policy eval average team episode rewards of agent3: 112.5
team_policy eval idv catch total num of agent3: 24
team_policy eval team catch total num: 45
team_policy eval average step individual rewards of agent4: 0.9489205712256731
team_policy eval average team episode rewards of agent4: 112.5
team_policy eval idv catch total num of agent4: 39
team_policy eval team catch total num: 45
idv_policy eval average step individual rewards of agent0: 0.5260721668830903
idv_policy eval average team episode rewards of agent0: 92.5
idv_policy eval idv catch total num of agent0: 23
idv_policy eval team catch total num: 37
idv_policy eval average step individual rewards of agent1: 0.9803758146630476
idv_policy eval average team episode rewards of agent1: 92.5
idv_policy eval idv catch total num of agent1: 41
idv_policy eval team catch total num: 37
idv_policy eval average step individual rewards of agent2: 0.3532776130678512
idv_policy eval average team episode rewards of agent2: 92.5
idv_policy eval idv catch total num of agent2: 16
idv_policy eval team catch total num: 37
idv_policy eval average step individual rewards of agent3: 0.6799807051529951
idv_policy eval average team episode rewards of agent3: 92.5
idv_policy eval idv catch total num of agent3: 29
idv_policy eval team catch total num: 37
idv_policy eval average step individual rewards of agent4: 0.6968508651275073
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 7301/10000 episodes, total num timesteps 1460400/2000000, FPS 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.

team_policy eval average step individual rewards of agent0: 0.8775506377040457
team_policy eval average team episode rewards of agent0: 142.5
team_policy eval idv catch total num of agent0: 37
team_policy eval team catch total num: 57
team_policy eval average step individual rewards of agent1: 0.9583168844973872
team_policy eval average team episode rewards of agent1: 142.5
team_policy eval idv catch total num of agent1: 40
team_policy eval team catch total num: 57
team_policy eval average step individual rewards of agent2: 0.7593311022828217
team_policy eval average team episode rewards of agent2: 142.5
team_policy eval idv catch total num of agent2: 32
team_policy eval team catch total num: 57
team_policy eval average step individual rewards of agent3: 0.8853361392581133
team_policy eval average team episode rewards of agent3: 142.5
team_policy eval idv catch total num of agent3: 37
team_policy eval team catch total num: 57
team_policy eval average step individual rewards of agent4: 1.1341020361074237
team_policy eval average team episode rewards of agent4: 142.5
team_policy eval idv catch total num of agent4: 47
team_policy eval team catch total num: 57
idv_policy eval average step individual rewards of agent0: 0.7683327676598392
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: 0.564154314299698
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: 0.7861099474849262
idv_policy eval average team episode rewards of agent2: 92.5
idv_policy eval idv catch total num of agent2: 33
idv_policy eval team catch total num: 37
idv_policy eval average step individual rewards of agent3: 0.7603504388368252
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: 0.4178333558699823
idv_policy eval average team episode rewards of agent4: 92.5
idv_policy eval idv catch total num of agent4: 19
idv_policy eval team catch total num: 37

 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.

team_policy eval average step individual rewards of agent0: 0.786782969088579
team_policy eval average team episode rewards of agent0: 70.0
team_policy eval idv catch total num of agent0: 33
team_policy eval team catch total num: 28
team_policy eval average step individual rewards of agent1: 0.5272241335575562
team_policy eval average team episode rewards of agent1: 70.0
team_policy eval idv catch total num of agent1: 23
team_policy eval team catch total num: 28
team_policy eval average step individual rewards of agent2: 0.5869594568579751
team_policy eval average team episode rewards of agent2: 70.0
team_policy eval idv catch total num of agent2: 25
team_policy eval team catch total num: 28
team_policy eval average step individual rewards of agent3: 0.5245588321663069
team_policy eval average team episode rewards of agent3: 70.0
team_policy eval idv catch total num of agent3: 23
team_policy eval team catch total num: 28
team_policy eval average step individual rewards of agent4: 0.3659141421451863
team_policy eval average team episode rewards of agent4: 70.0
team_policy eval idv catch total num of agent4: 17
team_policy eval team catch total num: 28
idv_policy eval average step individual rewards of agent0: 0.8375050342309485
idv_policy eval average team episode rewards of agent0: 87.5
idv_policy eval idv catch total num of agent0: 35
idv_policy eval team catch total num: 35
idv_policy eval average step individual rewards of agent1: 0.6547704188866942
idv_policy eval average team episode rewards of agent1: 87.5
idv_policy eval idv catch total num of agent1: 28
idv_policy eval team catch total num: 35
idv_policy eval average step individual rewards of agent2: 0.9629556117337931
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: 0.3914022659723752
idv_policy eval average team episode rewards of agent3: 87.5
idv_policy eval idv catch total num of agent3: 18
idv_policy eval team catch total num: 35
idv_policy eval average step individual rewards of agent4: 0.5057494681447355
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 7351/10000 episodes, total num timesteps 1470400/2000000, FPS 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.

team_policy eval average step individual rewards of agent0: 1.0860201255503172
team_policy eval average team episode rewards of agent0: 130.0
team_policy eval idv catch total num of agent0: 45
team_policy eval team catch total num: 52
team_policy eval average step individual rewards of agent1: 0.6617930014309207
team_policy eval average team episode rewards of agent1: 130.0
team_policy eval idv catch total num of agent1: 28
team_policy eval team catch total num: 52
team_policy eval average step individual rewards of agent2: 0.7385680400331276
team_policy eval average team episode rewards of agent2: 130.0
team_policy eval idv catch total num of agent2: 31
team_policy eval team catch total num: 52
team_policy eval average step individual rewards of agent3: 1.2184661305954967
team_policy eval average team episode rewards of agent3: 130.0
team_policy eval idv catch total num of agent3: 50
team_policy eval team catch total num: 52
team_policy eval average step individual rewards of agent4: 1.052386980584775
team_policy eval average team episode rewards of agent4: 130.0
team_policy eval idv catch total num of agent4: 43
team_policy eval team catch total num: 52
idv_policy eval average step individual rewards of agent0: 0.9409984964870539
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: 0.9980462224575065
idv_policy eval average team episode rewards of agent1: 102.5
idv_policy eval idv catch total num of agent1: 41
idv_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent2: 0.6293554165495737
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: 0.5589796596712209
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: 0.9965814560245295
idv_policy eval average team episode rewards of agent4: 102.5
idv_policy eval idv catch total num of agent4: 41
idv_policy eval team catch total num: 41

 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.

team_policy eval average step individual rewards of agent0: 0.4890740390022291
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: 1.1229786229006775
team_policy eval average team episode rewards of agent1: 127.5
team_policy eval idv catch total num of agent1: 46
team_policy eval team catch total num: 51
team_policy eval average step individual rewards of agent2: 1.3125123382391708
team_policy eval average team episode rewards of agent2: 127.5
team_policy eval idv catch total num of agent2: 54
team_policy eval team catch total num: 51
team_policy eval average step individual rewards of agent3: 0.5290655699921204
team_policy eval average team episode rewards of agent3: 127.5
team_policy eval idv catch total num of agent3: 23
team_policy eval team catch total num: 51
team_policy eval average step individual rewards of agent4: 1.1393885000977715
team_policy eval average team episode rewards of agent4: 127.5
team_policy eval idv catch total num of agent4: 47
team_policy eval team catch total num: 51
idv_policy eval average step individual rewards of agent0: 1.17125999943889
idv_policy eval average team episode rewards of agent0: 107.5
idv_policy eval idv catch total num of agent0: 48
idv_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent1: 0.5408477981416481
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: 0.5350201382693276
idv_policy eval average team episode rewards of agent2: 107.5
idv_policy eval idv catch total num of agent2: 23
idv_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent3: 0.5671818339808915
idv_policy eval average team episode rewards of agent3: 107.5
idv_policy eval idv catch total num of agent3: 24
idv_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent4: 0.8898049689063227
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 7401/10000 episodes, total num timesteps 1480400/2000000, FPS 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.

team_policy eval average step individual rewards of agent0: 0.6886089291866533
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: 0.689230850458499
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: 0.8416538564407865
team_policy eval average team episode rewards of agent2: 95.0
team_policy eval idv catch total num of agent2: 35
team_policy eval team catch total num: 38
team_policy eval average step individual rewards of agent3: 0.6078591760188577
team_policy eval average team episode rewards of agent3: 95.0
team_policy eval idv catch total num of agent3: 26
team_policy eval team catch total num: 38
team_policy eval average step individual rewards of agent4: 0.6775525635660151
team_policy eval average team episode rewards of agent4: 95.0
team_policy eval idv catch total num of agent4: 29
team_policy eval team catch total num: 38
idv_policy eval average step individual rewards of agent0: 0.805721825020041
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: 0.8625137393675386
idv_policy eval average team episode rewards of agent1: 97.5
idv_policy eval idv catch total num of agent1: 36
idv_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent2: 0.644820207213578
idv_policy eval average team episode rewards of agent2: 97.5
idv_policy eval idv catch total num of agent2: 28
idv_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent3: 0.7320787758677244
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: 0.7392420141085535
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 7426/10000 episodes, total num timesteps 1485400/2000000, FPS 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.

team_policy eval average step individual rewards of agent0: 0.4011443519973112
team_policy eval average team episode rewards of agent0: 65.0
team_policy eval idv catch total num of agent0: 18
team_policy eval team catch total num: 26
team_policy eval average step individual rewards of agent1: 0.2181635540722964
team_policy eval average team episode rewards of agent1: 65.0
team_policy eval idv catch total num of agent1: 11
team_policy eval team catch total num: 26
team_policy eval average step individual rewards of agent2: 0.5076100171411604
team_policy eval average team episode rewards of agent2: 65.0
team_policy eval idv catch total num of agent2: 22
team_policy eval team catch total num: 26
team_policy eval average step individual rewards of agent3: 0.4079442988699937
team_policy eval average team episode rewards of agent3: 65.0
team_policy eval idv catch total num of agent3: 18
team_policy eval team catch total num: 26
team_policy eval average step individual rewards of agent4: 0.7297509410262003
team_policy eval average team episode rewards of agent4: 65.0
team_policy eval idv catch total num of agent4: 31
team_policy eval team catch total num: 26
idv_policy eval average step individual rewards of agent0: 1.241661938896919
idv_policy eval average team episode rewards of agent0: 112.5
idv_policy eval idv catch total num of agent0: 51
idv_policy eval team catch total num: 45
idv_policy eval average step individual rewards of agent1: 1.0660925678502173
idv_policy eval average team episode rewards of agent1: 112.5
idv_policy eval idv catch total num of agent1: 44
idv_policy eval team catch total num: 45
idv_policy eval average step individual rewards of agent2: 0.656617222186322
idv_policy eval average team episode rewards of agent2: 112.5
idv_policy eval idv catch total num of agent2: 28
idv_policy eval team catch total num: 45
idv_policy eval average step individual rewards of agent3: 0.7919550280116857
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: 0.3320951237583257
idv_policy eval average team episode rewards of agent4: 112.5
idv_policy eval idv catch total num of agent4: 15
idv_policy eval team catch total num: 45

 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.

team_policy eval average step individual rewards of agent0: 0.5324970596445529
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: 0.916599382684342
team_policy eval average team episode rewards of agent1: 97.5
team_policy eval idv catch total num of agent1: 38
team_policy eval team catch total num: 39
team_policy eval average step individual rewards of agent2: 0.6389618810147891
team_policy eval average team episode rewards of agent2: 97.5
team_policy eval idv catch total num of agent2: 27
team_policy eval team catch total num: 39
team_policy eval average step individual rewards of agent3: 0.8136842066074422
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: 0.7709875388228207
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: 0.7142734137249258
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: 0.5611908008228764
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: 0.4824715504282886
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: 1.0197951720624059
idv_policy eval average team episode rewards of agent3: 100.0
idv_policy eval idv catch total num of agent3: 42
idv_policy eval team catch total num: 40
idv_policy eval average step individual rewards of agent4: 0.6131800642808478
idv_policy eval average team episode rewards of agent4: 100.0
idv_policy eval idv catch total num of agent4: 26
idv_policy eval team catch total num: 40

 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.

team_policy eval average step individual rewards of agent0: 1.2198085431097794
team_policy eval average team episode rewards of agent0: 115.0
team_policy eval idv catch total num of agent0: 50
team_policy eval team catch total num: 46
team_policy eval average step individual rewards of agent1: 1.1509230781591104
team_policy eval average team episode rewards of agent1: 115.0
team_policy eval idv catch total num of agent1: 47
team_policy eval team catch total num: 46
team_policy eval average step individual rewards of agent2: 0.6147334159050709
team_policy eval average team episode rewards of agent2: 115.0
team_policy eval idv catch total num of agent2: 26
team_policy eval team catch total num: 46
team_policy eval average step individual rewards of agent3: 0.6921898024106646
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: 0.4890909577795221
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: 1.019062550104484
idv_policy eval average team episode rewards of agent0: 110.0
idv_policy eval idv catch total num of agent0: 42
idv_policy eval team catch total num: 44
idv_policy eval average step individual rewards of agent1: 0.8691655286145803
idv_policy eval average team episode rewards of agent1: 110.0
idv_policy eval idv catch total num of agent1: 36
idv_policy eval team catch total num: 44
idv_policy eval average step individual rewards of agent2: 0.6857457359325119
idv_policy eval average team episode rewards of agent2: 110.0
idv_policy eval idv catch total num of agent2: 29
idv_policy eval team catch total num: 44
idv_policy eval average step individual rewards of agent3: 0.891944297846457
idv_policy eval average team episode rewards of agent3: 110.0
idv_policy eval idv catch total num of agent3: 37
idv_policy eval team catch total num: 44
idv_policy eval average step individual rewards of agent4: 0.35790400819330154
idv_policy eval average team episode rewards of agent4: 110.0
idv_policy eval idv catch total num of agent4: 16
idv_policy eval team catch total num: 44

 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.


 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 236.

team_policy eval average step individual rewards of agent0: 0.5879171117442002
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: 0.5853122134892397
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: 0.3718388056176422
team_policy eval average team episode rewards of agent2: 85.0
team_policy eval idv catch total num of agent2: 17
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent3: 0.8068251778320339
team_policy eval average team episode rewards of agent3: 85.0
team_policy eval idv catch total num of agent3: 34
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent4: 0.8120932769270927
team_policy eval average team episode rewards of agent4: 85.0
team_policy eval idv catch total num of agent4: 34
team_policy eval team catch total num: 34
idv_policy eval average step individual rewards of agent0: 0.7095419555861442
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: 1.0018149834236962
idv_policy eval average team episode rewards of agent1: 97.5
idv_policy eval idv catch total num of agent1: 42
idv_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent2: 0.31758489998225004
idv_policy eval average team episode rewards of agent2: 97.5
idv_policy eval idv catch total num of agent2: 15
idv_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent3: 0.6057014553495133
idv_policy eval average team episode rewards of agent3: 97.5
idv_policy eval idv catch total num of agent3: 26
idv_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent4: 0.6070932738082803
idv_policy eval average team episode rewards of agent4: 97.5
idv_policy eval idv catch total num of agent4: 26
idv_policy eval team catch total num: 39

 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 236.


 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 236.


 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 236.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.

team_policy eval average step individual rewards of agent0: 1.2454850196760534
team_policy eval average team episode rewards of agent0: 120.0
team_policy eval idv catch total num of agent0: 51
team_policy eval team catch total num: 48
team_policy eval average step individual rewards of agent1: 0.784500257907142
team_policy eval average team episode rewards of agent1: 120.0
team_policy eval idv catch total num of agent1: 33
team_policy eval team catch total num: 48
team_policy eval average step individual rewards of agent2: 0.8409803052261111
team_policy eval average team episode rewards of agent2: 120.0
team_policy eval idv catch total num of agent2: 35
team_policy eval team catch total num: 48
team_policy eval average step individual rewards of agent3: 1.1909617169892401
team_policy eval average team episode rewards of agent3: 120.0
team_policy eval idv catch total num of agent3: 49
team_policy eval team catch total num: 48
team_policy eval average step individual rewards of agent4: 0.6354056851596761
team_policy eval average team episode rewards of agent4: 120.0
team_policy eval idv catch total num of agent4: 27
team_policy eval team catch total num: 48
idv_policy eval average step individual rewards of agent0: 0.7614976057461189
idv_policy eval average team episode rewards of agent0: 130.0
idv_policy eval idv catch total num of agent0: 32
idv_policy eval team catch total num: 52
idv_policy eval average step individual rewards of agent1: 1.1681903863303953
idv_policy eval average team episode rewards of agent1: 130.0
idv_policy eval idv catch total num of agent1: 48
idv_policy eval team catch total num: 52
idv_policy eval average step individual rewards of agent2: 0.6142764188342137
idv_policy eval average team episode rewards of agent2: 130.0
idv_policy eval idv catch total num of agent2: 26
idv_policy eval team catch total num: 52
idv_policy eval average step individual rewards of agent3: 1.0887917666779392
idv_policy eval average team episode rewards of agent3: 130.0
idv_policy eval idv catch total num of agent3: 45
idv_policy eval team catch total num: 52
idv_policy eval average step individual rewards of agent4: 0.9099801452154985
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 7551/10000 episodes, total num timesteps 1510400/2000000, FPS 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.

team_policy eval average step individual rewards of agent0: 1.0663322569884657
team_policy eval average team episode rewards of agent0: 107.5
team_policy eval idv catch total num of agent0: 44
team_policy eval team catch total num: 43
team_policy eval average step individual rewards of agent1: 0.9176738178863754
team_policy eval average team episode rewards of agent1: 107.5
team_policy eval idv catch total num of agent1: 38
team_policy eval team catch total num: 43
team_policy eval average step individual rewards of agent2: 0.7985170893200089
team_policy eval average team episode rewards of agent2: 107.5
team_policy eval idv catch total num of agent2: 33
team_policy eval team catch total num: 43
team_policy eval average step individual rewards of agent3: 0.6858664162646738
team_policy eval average team episode rewards of agent3: 107.5
team_policy eval idv catch total num of agent3: 29
team_policy eval team catch total num: 43
team_policy eval average step individual rewards of agent4: 0.8146622855859331
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: 0.9673386177540544
idv_policy eval average team episode rewards of agent0: 82.5
idv_policy eval idv catch total num of agent0: 40
idv_policy eval team catch total num: 33
idv_policy eval average step individual rewards of agent1: 0.7419607736706818
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: 0.16144226803213513
idv_policy eval average team episode rewards of agent2: 82.5
idv_policy eval idv catch total num of agent2: 9
idv_policy eval team catch total num: 33
idv_policy eval average step individual rewards of agent3: 0.9406691631198839
idv_policy eval average team episode rewards of agent3: 82.5
idv_policy eval idv catch total num of agent3: 39
idv_policy eval team catch total num: 33
idv_policy eval average step individual rewards of agent4: 0.7073841224862447
idv_policy eval average team episode rewards of agent4: 82.5
idv_policy eval idv catch total num of agent4: 30
idv_policy eval team catch total num: 33

 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.

team_policy eval average step individual rewards of agent0: 0.5879106972365642
team_policy eval average team episode rewards of agent0: 132.5
team_policy eval idv catch total num of agent0: 25
team_policy eval team catch total num: 53
team_policy eval average step individual rewards of agent1: 1.1238607210169977
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: 0.7954002289472746
team_policy eval average team episode rewards of agent2: 132.5
team_policy eval idv catch total num of agent2: 33
team_policy eval team catch total num: 53
team_policy eval average step individual rewards of agent3: 1.3732499211937688
team_policy eval average team episode rewards of agent3: 132.5
team_policy eval idv catch total num of agent3: 56
team_policy eval team catch total num: 53
team_policy eval average step individual rewards of agent4: 0.8463877711544093
team_policy eval average team episode rewards of agent4: 132.5
team_policy eval idv catch total num of agent4: 35
team_policy eval team catch total num: 53
idv_policy eval average step individual rewards of agent0: 0.765770974369615
idv_policy eval average team episode rewards of agent0: 115.0
idv_policy eval idv catch total num of agent0: 32
idv_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent1: 0.7481045191661804
idv_policy eval average team episode rewards of agent1: 115.0
idv_policy eval idv catch total num of agent1: 31
idv_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent2: 0.8413325915547688
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: 0.8162670981410993
idv_policy eval average team episode rewards of agent3: 115.0
idv_policy eval idv catch total num of agent3: 34
idv_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent4: 1.049997136308905
idv_policy eval average team episode rewards of agent4: 115.0
idv_policy eval idv catch total num of agent4: 43
idv_policy eval team catch total num: 46

 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.

team_policy eval average step individual rewards of agent0: 0.7236468845907198
team_policy eval average team episode rewards of agent0: 92.5
team_policy eval idv catch total num of agent0: 31
team_policy eval team catch total num: 37
team_policy eval average step individual rewards of agent1: 0.3926769381178572
team_policy eval average team episode rewards of agent1: 92.5
team_policy eval idv catch total num of agent1: 18
team_policy eval team catch total num: 37
team_policy eval average step individual rewards of agent2: 0.7039693901179714
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: 0.724672604538106
team_policy eval average team episode rewards of agent3: 92.5
team_policy eval idv catch total num of agent3: 31
team_policy eval team catch total num: 37
team_policy eval average step individual rewards of agent4: 0.5244573244438184
team_policy eval average team episode rewards of agent4: 92.5
team_policy eval idv catch total num of agent4: 23
team_policy eval team catch total num: 37
idv_policy eval average step individual rewards of agent0: 0.7931131103060554
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: 0.7390016241238038
idv_policy eval average team episode rewards of agent1: 117.5
idv_policy eval idv catch total num of agent1: 31
idv_policy eval team catch total num: 47
idv_policy eval average step individual rewards of agent2: 1.066811690413224
idv_policy eval average team episode rewards of agent2: 117.5
idv_policy eval idv catch total num of agent2: 44
idv_policy eval team catch total num: 47
idv_policy eval average step individual rewards of agent3: 0.8135802701998301
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: 0.8380472662549586
idv_policy eval average team episode rewards of agent4: 117.5
idv_policy eval idv catch total num of agent4: 35
idv_policy eval team catch total num: 47

 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.

team_policy eval average step individual rewards of agent0: 0.7611993788110034
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: 0.4499372217474912
team_policy eval average team episode rewards of agent1: 95.0
team_policy eval idv catch total num of agent1: 20
team_policy eval team catch total num: 38
team_policy eval average step individual rewards of agent2: 0.7530590120648032
team_policy eval average team episode rewards of agent2: 95.0
team_policy eval idv catch total num of agent2: 32
team_policy eval team catch total num: 38
team_policy eval average step individual rewards of agent3: 1.1621428496379438
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: 0.33335313534160366
team_policy eval average team episode rewards of agent4: 95.0
team_policy eval idv catch total num of agent4: 15
team_policy eval team catch total num: 38
idv_policy eval average step individual rewards of agent0: 0.9342826744957469
idv_policy eval average team episode rewards of agent0: 140.0
idv_policy eval idv catch total num of agent0: 39
idv_policy eval team catch total num: 56
idv_policy eval average step individual rewards of agent1: 0.7664931464114794
idv_policy eval average team episode rewards of agent1: 140.0
idv_policy eval idv catch total num of agent1: 32
idv_policy eval team catch total num: 56
idv_policy eval average step individual rewards of agent2: 0.9476630787372025
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: 0.9968794002592566
idv_policy eval average team episode rewards of agent3: 140.0
idv_policy eval idv catch total num of agent3: 41
idv_policy eval team catch total num: 56
idv_policy eval average step individual rewards of agent4: 1.1455173092463407
idv_policy eval average team episode rewards of agent4: 140.0
idv_policy eval idv catch total num of agent4: 47
idv_policy eval team catch total num: 56

 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.

team_policy eval average step individual rewards of agent0: 0.8687079638316869
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: 1.046366192563701
team_policy eval average team episode rewards of agent1: 127.5
team_policy eval idv catch total num of agent1: 43
team_policy eval team catch total num: 51
team_policy eval average step individual rewards of agent2: 0.9867132498133624
team_policy eval average team episode rewards of agent2: 127.5
team_policy eval idv catch total num of agent2: 41
team_policy eval team catch total num: 51
team_policy eval average step individual rewards of agent3: 0.5397618371873137
team_policy eval average team episode rewards of agent3: 127.5
team_policy eval idv catch total num of agent3: 23
team_policy eval team catch total num: 51
team_policy eval average step individual rewards of agent4: 0.833751253706522
team_policy eval average team episode rewards of agent4: 127.5
team_policy eval idv catch total num of agent4: 35
team_policy eval team catch total num: 51
idv_policy eval average step individual rewards of agent0: 0.5387100955336246
idv_policy eval average team episode rewards of agent0: 92.5
idv_policy eval idv catch total num of agent0: 23
idv_policy eval team catch total num: 37
idv_policy eval average step individual rewards of agent1: 0.8614076499052105
idv_policy eval average team episode rewards of agent1: 92.5
idv_policy eval idv catch total num of agent1: 36
idv_policy eval team catch total num: 37
idv_policy eval average step individual rewards of agent2: 0.5663705243461246
idv_policy eval average team episode rewards of agent2: 92.5
idv_policy eval idv catch total num of agent2: 24
idv_policy eval team catch total num: 37
idv_policy eval average step individual rewards of agent3: 0.8399842364601103
idv_policy eval average team episode rewards of agent3: 92.5
idv_policy eval idv catch total num of agent3: 35
idv_policy eval team catch total num: 37
idv_policy eval average step individual rewards of agent4: 1.0145150035266084
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 7676/10000 episodes, total num timesteps 1535400/2000000, FPS 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.

team_policy eval average step individual rewards of agent0: 0.7994218676810143
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: 0.9107618910912776
team_policy eval average team episode rewards of agent1: 115.0
team_policy eval idv catch total num of agent1: 38
team_policy eval team catch total num: 46
team_policy eval average step individual rewards of agent2: 1.0707019089511534
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: 0.8598057334613426
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: 0.7334337994907572
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: 0.9005251785196008
idv_policy eval average team episode rewards of agent0: 115.0
idv_policy eval idv catch total num of agent0: 37
idv_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent1: 0.8737869003861541
idv_policy eval average team episode rewards of agent1: 115.0
idv_policy eval idv catch total num of agent1: 37
idv_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent2: 0.4510816382729543
idv_policy eval average team episode rewards of agent2: 115.0
idv_policy eval idv catch total num of agent2: 20
idv_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent3: 0.6187008032893009
idv_policy eval average team episode rewards of agent3: 115.0
idv_policy eval idv catch total num of agent3: 27
idv_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent4: 0.6733119559220512
idv_policy eval average team episode rewards of agent4: 115.0
idv_policy eval idv catch total num of agent4: 29
idv_policy eval team catch total num: 46

 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.

team_policy eval average step individual rewards of agent0: 0.38767035700564106
team_policy eval average team episode rewards of agent0: 102.5
team_policy eval idv catch total num of agent0: 17
team_policy eval team catch total num: 41
team_policy eval average step individual rewards of agent1: 0.3786166099750332
team_policy eval average team episode rewards of agent1: 102.5
team_policy eval idv catch total num of agent1: 17
team_policy eval team catch total num: 41
team_policy eval average step individual rewards of agent2: 0.7891025332273256
team_policy eval average team episode rewards of agent2: 102.5
team_policy eval idv catch total num of agent2: 33
team_policy eval team catch total num: 41
team_policy eval average step individual rewards of agent3: 0.8093763074019965
team_policy eval average team episode rewards of agent3: 102.5
team_policy eval idv catch total num of agent3: 34
team_policy eval team catch total num: 41
team_policy eval average step individual rewards of agent4: 1.1956165080718875
team_policy eval average team episode rewards of agent4: 102.5
team_policy eval idv catch total num of agent4: 49
team_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent0: 1.221067103691504
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: 1.3620428395115383
idv_policy eval average team episode rewards of agent1: 142.5
idv_policy eval idv catch total num of agent1: 56
idv_policy eval team catch total num: 57
idv_policy eval average step individual rewards of agent2: 0.7277848342511442
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: 0.8164417800607414
idv_policy eval average team episode rewards of agent3: 142.5
idv_policy eval idv catch total num of agent3: 34
idv_policy eval team catch total num: 57
idv_policy eval average step individual rewards of agent4: 0.9308458262136813
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 7726/10000 episodes, total num timesteps 1545400/2000000, FPS 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.

team_policy eval average step individual rewards of agent0: 1.0506161711454873
team_policy eval average team episode rewards of agent0: 115.0
team_policy eval idv catch total num of agent0: 43
team_policy eval team catch total num: 46
team_policy eval average step individual rewards of agent1: 0.575184419209461
team_policy eval average team episode rewards of agent1: 115.0
team_policy eval idv catch total num of agent1: 25
team_policy eval team catch total num: 46
team_policy eval average step individual rewards of agent2: 0.7523479302329029
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: 0.9129166518321687
team_policy eval average team episode rewards of agent3: 115.0
team_policy eval idv catch total num of agent3: 38
team_policy eval team catch total num: 46
team_policy eval average step individual rewards of agent4: 0.7137822346404196
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: 0.9349221211868581
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: 0.8377036340188667
idv_policy eval average team episode rewards of agent1: 95.0
idv_policy eval idv catch total num of agent1: 35
idv_policy eval team catch total num: 38
idv_policy eval average step individual rewards of agent2: 0.7450632349367293
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: 1.0910866883433412
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: 0.4096339720713893
idv_policy eval average team episode rewards of agent4: 95.0
idv_policy eval idv catch total num of agent4: 18
idv_policy eval team catch total num: 38

 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.

team_policy eval average step individual rewards of agent0: 0.8176721181183829
team_policy eval average team episode rewards of agent0: 140.0
team_policy eval idv catch total num of agent0: 34
team_policy eval team catch total num: 56
team_policy eval average step individual rewards of agent1: 1.401403825515206
team_policy eval average team episode rewards of agent1: 140.0
team_policy eval idv catch total num of agent1: 57
team_policy eval team catch total num: 56
team_policy eval average step individual rewards of agent2: 0.9981750541243113
team_policy eval average team episode rewards of agent2: 140.0
team_policy eval idv catch total num of agent2: 41
team_policy eval team catch total num: 56
team_policy eval average step individual rewards of agent3: 1.064638751420039
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: 0.5543842228449258
team_policy eval average team episode rewards of agent4: 140.0
team_policy eval idv catch total num of agent4: 24
team_policy eval team catch total num: 56
idv_policy eval average step individual rewards of agent0: 0.7400223258019953
idv_policy eval average team episode rewards of agent0: 95.0
idv_policy eval idv catch total num of agent0: 31
idv_policy eval team catch total num: 38
idv_policy eval average step individual rewards of agent1: 1.0158936665480602
idv_policy eval average team episode rewards of agent1: 95.0
idv_policy eval idv catch total num of agent1: 42
idv_policy eval team catch total num: 38
idv_policy eval average step individual rewards of agent2: 0.9604573591354847
idv_policy eval average team episode rewards of agent2: 95.0
idv_policy eval idv catch total num of agent2: 40
idv_policy eval team catch total num: 38
idv_policy eval average step individual rewards of agent3: 0.700888708575417
idv_policy eval average team episode rewards of agent3: 95.0
idv_policy eval idv catch total num of agent3: 30
idv_policy eval team catch total num: 38
idv_policy eval average step individual rewards of agent4: 0.7544402920209162
idv_policy eval average team episode rewards of agent4: 95.0
idv_policy eval idv catch total num of agent4: 32
idv_policy eval team catch total num: 38

 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.

team_policy eval average step individual rewards of agent0: 0.6878814378677189
team_policy eval average team episode rewards of agent0: 140.0
team_policy eval idv catch total num of agent0: 29
team_policy eval team catch total num: 56
team_policy eval average step individual rewards of agent1: 0.9234499707779571
team_policy eval average team episode rewards of agent1: 140.0
team_policy eval idv catch total num of agent1: 38
team_policy eval team catch total num: 56
team_policy eval average step individual rewards of agent2: 1.2712203342495274
team_policy eval average team episode rewards of agent2: 140.0
team_policy eval idv catch total num of agent2: 52
team_policy eval team catch total num: 56
team_policy eval average step individual rewards of agent3: 1.1709029293905397
team_policy eval average team episode rewards of agent3: 140.0
team_policy eval idv catch total num of agent3: 48
team_policy eval team catch total num: 56
team_policy eval average step individual rewards of agent4: 1.173758358136822
team_policy eval average team episode rewards of agent4: 140.0
team_policy eval idv catch total num of agent4: 48
team_policy eval team catch total num: 56
idv_policy eval average step individual rewards of agent0: 0.8646534129533765
idv_policy eval average team episode rewards of agent0: 115.0
idv_policy eval idv catch total num of agent0: 36
idv_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent1: 0.9697201797946436
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: 0.6089750736140292
idv_policy eval average team episode rewards of agent2: 115.0
idv_policy eval idv catch total num of agent2: 26
idv_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent3: 0.6540248691575763
idv_policy eval average team episode rewards of agent3: 115.0
idv_policy eval idv catch total num of agent3: 28
idv_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent4: 1.1211121453972754
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 7801/10000 episodes, total num timesteps 1560400/2000000, FPS 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.

team_policy eval average step individual rewards of agent0: 0.48717196822692677
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: 0.9056181326471576
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: 0.7561158085230284
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: 0.6326032890978428
team_policy eval average team episode rewards of agent3: 110.0
team_policy eval idv catch total num of agent3: 27
team_policy eval team catch total num: 44
team_policy eval average step individual rewards of agent4: 1.022150273958343
team_policy eval average team episode rewards of agent4: 110.0
team_policy eval idv catch total num of agent4: 42
team_policy eval team catch total num: 44
idv_policy eval average step individual rewards of agent0: 0.7688893208885635
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: 0.8180365629487796
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: 0.4310682078488892
idv_policy eval average team episode rewards of agent2: 92.5
idv_policy eval idv catch total num of agent2: 19
idv_policy eval team catch total num: 37
idv_policy eval average step individual rewards of agent3: 0.41263010037170855
idv_policy eval average team episode rewards of agent3: 92.5
idv_policy eval idv catch total num of agent3: 18
idv_policy eval team catch total num: 37
idv_policy eval average step individual rewards of agent4: 0.5072758594734408
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 7826/10000 episodes, total num timesteps 1565400/2000000, FPS 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.

team_policy eval average step individual rewards of agent0: 0.5807362134432532
team_policy eval average team episode rewards of agent0: 102.5
team_policy eval idv catch total num of agent0: 25
team_policy eval team catch total num: 41
team_policy eval average step individual rewards of agent1: 0.7698706431787014
team_policy eval average team episode rewards of agent1: 102.5
team_policy eval idv catch total num of agent1: 32
team_policy eval team catch total num: 41
team_policy eval average step individual rewards of agent2: 0.9374279342566516
team_policy eval average team episode rewards of agent2: 102.5
team_policy eval idv catch total num of agent2: 39
team_policy eval team catch total num: 41
team_policy eval average step individual rewards of agent3: 0.331867359190818
team_policy eval average team episode rewards of agent3: 102.5
team_policy eval idv catch total num of agent3: 15
team_policy eval team catch total num: 41
team_policy eval average step individual rewards of agent4: 1.2435088859752028
team_policy eval average team episode rewards of agent4: 102.5
team_policy eval idv catch total num of agent4: 51
team_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent0: 0.728036412902775
idv_policy eval average team episode rewards of agent0: 87.5
idv_policy eval idv catch total num of agent0: 31
idv_policy eval team catch total num: 35
idv_policy eval average step individual rewards of agent1: 0.34869271910718225
idv_policy eval average team episode rewards of agent1: 87.5
idv_policy eval idv catch total num of agent1: 16
idv_policy eval team catch total num: 35
idv_policy eval average step individual rewards of agent2: 1.345414223673241
idv_policy eval average team episode rewards of agent2: 87.5
idv_policy eval idv catch total num of agent2: 55
idv_policy eval team catch total num: 35
idv_policy eval average step individual rewards of agent3: 0.477427362074505
idv_policy eval average team episode rewards of agent3: 87.5
idv_policy eval idv catch total num of agent3: 21
idv_policy eval team catch total num: 35
idv_policy eval average step individual rewards of agent4: 0.5583714772862249
idv_policy eval average team episode rewards of agent4: 87.5
idv_policy eval idv catch total num of agent4: 24
idv_policy eval team catch total num: 35

 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.

team_policy eval average step individual rewards of agent0: 0.6967021195833142
team_policy eval average team episode rewards of agent0: 100.0
team_policy eval idv catch total num of agent0: 30
team_policy eval team catch total num: 40
team_policy eval average step individual rewards of agent1: 0.8076713255452862
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: 0.8083954366443674
team_policy eval average team episode rewards of agent2: 100.0
team_policy eval idv catch total num of agent2: 34
team_policy eval team catch total num: 40
team_policy eval average step individual rewards of agent3: 0.421152552601152
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: 1.2980065988093485
team_policy eval average team episode rewards of agent4: 100.0
team_policy eval idv catch total num of agent4: 53
team_policy eval team catch total num: 40
idv_policy eval average step individual rewards of agent0: 0.8821426627990954
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: 0.8497272162581075
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: 1.1247106942796619
idv_policy eval average team episode rewards of agent2: 117.5
idv_policy eval idv catch total num of agent2: 46
idv_policy eval team catch total num: 47
idv_policy eval average step individual rewards of agent3: 0.6402234071834525
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: 0.7409568859439003
idv_policy eval average team episode rewards of agent4: 117.5
idv_policy eval idv catch total num of agent4: 31
idv_policy eval team catch total num: 47

 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.

team_policy eval average step individual rewards of agent0: 0.6873009474004244
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: 0.6354500701907179
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: 0.6076855938997504
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: 0.6567351804169086
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: 0.49881745341483297
team_policy eval average team episode rewards of agent4: 87.5
team_policy eval idv catch total num of agent4: 22
team_policy eval team catch total num: 35
idv_policy eval average step individual rewards of agent0: 0.35455846202600205
idv_policy eval average team episode rewards of agent0: 75.0
idv_policy eval idv catch total num of agent0: 16
idv_policy eval team catch total num: 30
idv_policy eval average step individual rewards of agent1: 0.6576131087498581
idv_policy eval average team episode rewards of agent1: 75.0
idv_policy eval idv catch total num of agent1: 28
idv_policy eval team catch total num: 30
idv_policy eval average step individual rewards of agent2: 0.4831770133461031
idv_policy eval average team episode rewards of agent2: 75.0
idv_policy eval idv catch total num of agent2: 21
idv_policy eval team catch total num: 30
idv_policy eval average step individual rewards of agent3: 0.7649806752286418
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: 0.40533252154656396
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 7901/10000 episodes, total num timesteps 1580400/2000000, FPS 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.

team_policy eval average step individual rewards of agent0: 0.8610400802671814
team_policy eval average team episode rewards of agent0: 150.0
team_policy eval idv catch total num of agent0: 36
team_policy eval team catch total num: 60
team_policy eval average step individual rewards of agent1: 0.7830972761339313
team_policy eval average team episode rewards of agent1: 150.0
team_policy eval idv catch total num of agent1: 33
team_policy eval team catch total num: 60
team_policy eval average step individual rewards of agent2: 1.143991644512681
team_policy eval average team episode rewards of agent2: 150.0
team_policy eval idv catch total num of agent2: 47
team_policy eval team catch total num: 60
team_policy eval average step individual rewards of agent3: 0.8085718968232275
team_policy eval average team episode rewards of agent3: 150.0
team_policy eval idv catch total num of agent3: 34
team_policy eval team catch total num: 60
team_policy eval average step individual rewards of agent4: 1.4437047491890171
team_policy eval average team episode rewards of agent4: 150.0
team_policy eval idv catch total num of agent4: 59
team_policy eval team catch total num: 60
idv_policy eval average step individual rewards of agent0: 0.24006157773439762
idv_policy eval average team episode rewards of agent0: 57.5
idv_policy eval idv catch total num of agent0: 12
idv_policy eval team catch total num: 23
idv_policy eval average step individual rewards of agent1: 0.3468049978862248
idv_policy eval average team episode rewards of agent1: 57.5
idv_policy eval idv catch total num of agent1: 16
idv_policy eval team catch total num: 23
idv_policy eval average step individual rewards of agent2: 0.5322764312309975
idv_policy eval average team episode rewards of agent2: 57.5
idv_policy eval idv catch total num of agent2: 23
idv_policy eval team catch total num: 23
idv_policy eval average step individual rewards of agent3: 0.5844498502532044
idv_policy eval average team episode rewards of agent3: 57.5
idv_policy eval idv catch total num of agent3: 25
idv_policy eval team catch total num: 23
idv_policy eval average step individual rewards of agent4: 0.5532032074242621
idv_policy eval average team episode rewards of agent4: 57.5
idv_policy eval idv catch total num of agent4: 24
idv_policy eval team catch total num: 23

 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.

team_policy eval average step individual rewards of agent0: 1.0145527883052945
team_policy eval average team episode rewards of agent0: 115.0
team_policy eval idv catch total num of agent0: 42
team_policy eval team catch total num: 46
team_policy eval average step individual rewards of agent1: 0.8646803923561212
team_policy eval average team episode rewards of agent1: 115.0
team_policy eval idv catch total num of agent1: 36
team_policy eval team catch total num: 46
team_policy eval average step individual rewards of agent2: 0.8843234932077965
team_policy eval average team episode rewards of agent2: 115.0
team_policy eval idv catch total num of agent2: 37
team_policy eval team catch total num: 46
team_policy eval average step individual rewards of agent3: 0.8166514584222678
team_policy eval average team episode rewards of agent3: 115.0
team_policy eval idv catch total num of agent3: 34
team_policy eval team catch total num: 46
team_policy eval average step individual rewards of agent4: 0.6697387554310749
team_policy eval average team episode rewards of agent4: 115.0
team_policy eval idv catch total num of agent4: 29
team_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent0: 0.8155233145716931
idv_policy eval average team episode rewards of agent0: 85.0
idv_policy eval idv catch total num of agent0: 34
idv_policy eval team catch total num: 34
idv_policy eval average step individual rewards of agent1: 0.2976964677960603
idv_policy eval average team episode rewards of agent1: 85.0
idv_policy eval idv catch total num of agent1: 14
idv_policy eval team catch total num: 34
idv_policy eval average step individual rewards of agent2: 0.7086425470594171
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: 0.43216687808987264
idv_policy eval average team episode rewards of agent3: 85.0
idv_policy eval idv catch total num of agent3: 19
idv_policy eval team catch total num: 34
idv_policy eval average step individual rewards of agent4: 0.6571741943362608
idv_policy eval average team episode rewards of agent4: 85.0
idv_policy eval idv catch total num of agent4: 28
idv_policy eval team catch total num: 34

 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.

team_policy eval average step individual rewards of agent0: 0.78457788485075
team_policy eval average team episode rewards of agent0: 62.5
team_policy eval idv catch total num of agent0: 33
team_policy eval team catch total num: 25
team_policy eval average step individual rewards of agent1: 0.9450536967806283
team_policy eval average team episode rewards of agent1: 62.5
team_policy eval idv catch total num of agent1: 39
team_policy eval team catch total num: 25
team_policy eval average step individual rewards of agent2: 0.4643837190462087
team_policy eval average team episode rewards of agent2: 62.5
team_policy eval idv catch total num of agent2: 20
team_policy eval team catch total num: 25
team_policy eval average step individual rewards of agent3: 0.2827453604491736
team_policy eval average team episode rewards of agent3: 62.5
team_policy eval idv catch total num of agent3: 13
team_policy eval team catch total num: 25
team_policy eval average step individual rewards of agent4: 0.8225606701300382
team_policy eval average team episode rewards of agent4: 62.5
team_policy eval idv catch total num of agent4: 34
team_policy eval team catch total num: 25
idv_policy eval average step individual rewards of agent0: 0.5060632691828745
idv_policy eval average team episode rewards of agent0: 80.0
idv_policy eval idv catch total num of agent0: 22
idv_policy eval team catch total num: 32
idv_policy eval average step individual rewards of agent1: 0.5740330590130327
idv_policy eval average team episode rewards of agent1: 80.0
idv_policy eval idv catch total num of agent1: 25
idv_policy eval team catch total num: 32
idv_policy eval average step individual rewards of agent2: 0.4069807673844112
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: 0.7338367925587704
idv_policy eval average team episode rewards of agent3: 80.0
idv_policy eval idv catch total num of agent3: 31
idv_policy eval team catch total num: 32
idv_policy eval average step individual rewards of agent4: 1.0129020040528027
idv_policy eval average team episode rewards of agent4: 80.0
idv_policy eval idv catch total num of agent4: 42
idv_policy eval team catch total num: 32

 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.

team_policy eval average step individual rewards of agent0: 1.1409832117919647
team_policy eval average team episode rewards of agent0: 140.0
team_policy eval idv catch total num of agent0: 47
team_policy eval team catch total num: 56
team_policy eval average step individual rewards of agent1: 0.7916703602275044
team_policy eval average team episode rewards of agent1: 140.0
team_policy eval idv catch total num of agent1: 33
team_policy eval team catch total num: 56
team_policy eval average step individual rewards of agent2: 1.2185195234483788
team_policy eval average team episode rewards of agent2: 140.0
team_policy eval idv catch total num of agent2: 50
team_policy eval team catch total num: 56
team_policy eval average step individual rewards of agent3: 0.9191305966817103
team_policy eval average team episode rewards of agent3: 140.0
team_policy eval idv catch total num of agent3: 38
team_policy eval team catch total num: 56
team_policy eval average step individual rewards of agent4: 0.9135975281577937
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: 0.3583912385414753
idv_policy eval average team episode rewards of agent0: 85.0
idv_policy eval idv catch total num of agent0: 16
idv_policy eval team catch total num: 34
idv_policy eval average step individual rewards of agent1: 1.0969954660760939
idv_policy eval average team episode rewards of agent1: 85.0
idv_policy eval idv catch total num of agent1: 45
idv_policy eval team catch total num: 34
idv_policy eval average step individual rewards of agent2: 0.4837674524318691
idv_policy eval average team episode rewards of agent2: 85.0
idv_policy eval idv catch total num of agent2: 21
idv_policy eval team catch total num: 34
idv_policy eval average step individual rewards of agent3: 0.8612606692913835
idv_policy eval average team episode rewards of agent3: 85.0
idv_policy eval idv catch total num of agent3: 36
idv_policy eval team catch total num: 34
idv_policy eval average step individual rewards of agent4: 0.9453650695572526
idv_policy eval average team episode rewards of agent4: 85.0
idv_policy eval idv catch total num of agent4: 39
idv_policy eval team catch total num: 34

 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.


 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 238.

team_policy eval average step individual rewards of agent0: 1.0396477012914778
team_policy eval average team episode rewards of agent0: 115.0
team_policy eval idv catch total num of agent0: 43
team_policy eval team catch total num: 46
team_policy eval average step individual rewards of agent1: 0.8199051612695287
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: 0.43103666381680333
team_policy eval average team episode rewards of agent2: 115.0
team_policy eval idv catch total num of agent2: 19
team_policy eval team catch total num: 46
team_policy eval average step individual rewards of agent3: 0.9164978963351768
team_policy eval average team episode rewards of agent3: 115.0
team_policy eval idv catch total num of agent3: 38
team_policy eval team catch total num: 46
team_policy eval average step individual rewards of agent4: 0.5846488475175982
team_policy eval average team episode rewards of agent4: 115.0
team_policy eval idv catch total num of agent4: 25
team_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent0: 0.5289604242906555
idv_policy eval average team episode rewards of agent0: 82.5
idv_policy eval idv catch total num of agent0: 23
idv_policy eval team catch total num: 33
idv_policy eval average step individual rewards of agent1: 0.34524572817344856
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: 0.6593266401626174
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: 0.735689146093844
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: 0.893795379886767
idv_policy eval average team episode rewards of agent4: 82.5
idv_policy eval idv catch total num of agent4: 37
idv_policy eval team catch total num: 33

 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 238.


 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 238.


 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 238.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.

team_policy eval average step individual rewards of agent0: 0.8407262516799573
team_policy eval average team episode rewards of agent0: 115.0
team_policy eval idv catch total num of agent0: 35
team_policy eval team catch total num: 46
team_policy eval average step individual rewards of agent1: 0.6112891926648607
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: 0.710726742654015
team_policy eval average team episode rewards of agent2: 115.0
team_policy eval idv catch total num of agent2: 30
team_policy eval team catch total num: 46
team_policy eval average step individual rewards of agent3: 1.143901011498686
team_policy eval average team episode rewards of agent3: 115.0
team_policy eval idv catch total num of agent3: 47
team_policy eval team catch total num: 46
team_policy eval average step individual rewards of agent4: 0.3101347884611896
team_policy eval average team episode rewards of agent4: 115.0
team_policy eval idv catch total num of agent4: 14
team_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent0: 0.7113312835131719
idv_policy eval average team episode rewards of agent0: 75.0
idv_policy eval idv catch total num of agent0: 30
idv_policy eval team catch total num: 30
idv_policy eval average step individual rewards of agent1: 0.13563955859097768
idv_policy eval average team episode rewards of agent1: 75.0
idv_policy eval idv catch total num of agent1: 8
idv_policy eval team catch total num: 30
idv_policy eval average step individual rewards of agent2: 0.7349081846282346
idv_policy eval average team episode rewards of agent2: 75.0
idv_policy eval idv catch total num of agent2: 31
idv_policy eval team catch total num: 30
idv_policy eval average step individual rewards of agent3: 0.7034697303238935
idv_policy eval average team episode rewards of agent3: 75.0
idv_policy eval idv catch total num of agent3: 30
idv_policy eval team catch total num: 30
idv_policy eval average step individual rewards of agent4: 0.5544778274302495
idv_policy eval average team episode rewards of agent4: 75.0
idv_policy eval idv catch total num of agent4: 24
idv_policy eval team catch total num: 30

 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 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 237.


 Scenario simple_tag_tr Algo rmappotrsyn 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.

o rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8068/10000 episodes, total num timesteps 1613800/2000000, FPS 239.


 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 239.


 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 239.


 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 239.


 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 239.


 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 239.


 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 239.


 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 239.

team_policy eval average step individual rewards of agent0: 1.139242326113758
team_policy eval average team episode rewards of agent0: 137.5
team_policy eval idv catch total num of agent0: 47
team_policy eval team catch total num: 55
team_policy eval average step individual rewards of agent1: 0.8159241091188241
team_policy eval average team episode rewards of agent1: 137.5
team_policy eval idv catch total num of agent1: 34
team_policy eval team catch total num: 55
team_policy eval average step individual rewards of agent2: 0.817480504208473
team_policy eval average team episode rewards of agent2: 137.5
team_policy eval idv catch total num of agent2: 34
team_policy eval team catch total num: 55
team_policy eval average step individual rewards of agent3: 1.1447514787168995
team_policy eval average team episode rewards of agent3: 137.5
team_policy eval idv catch total num of agent3: 47
team_policy eval team catch total num: 55
team_policy eval average step individual rewards of agent4: 0.7672207617918673
team_policy eval average team episode rewards of agent4: 137.5
team_policy eval idv catch total num of agent4: 32
team_policy eval team catch total num: 55
idv_policy eval average step individual rewards of agent0: 1.0126303993376709
idv_policy eval average team episode rewards of agent0: 75.0
idv_policy eval idv catch total num of agent0: 42
idv_policy eval team catch total num: 30
idv_policy eval average step individual rewards of agent1: 0.14787029400515406
idv_policy eval average team episode rewards of agent1: 75.0
idv_policy eval idv catch total num of agent1: 8
idv_policy eval team catch total num: 30
idv_policy eval average step individual rewards of agent2: 0.8225810217193701
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: 0.35402093944276475
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: 0.7939881698606124
idv_policy eval average team episode rewards of agent4: 75.0
idv_policy eval idv catch total num of agent4: 33
idv_policy eval team catch total num: 30

 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 239.


 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 239.


 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 239.


 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 239.


 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 239.


 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 239.


 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 239.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.

team_policy eval average step individual rewards of agent0: 0.9195063892954218
team_policy eval average team episode rewards of agent0: 175.0
team_policy eval idv catch total num of agent0: 38
team_policy eval team catch total num: 70
team_policy eval average step individual rewards of agent1: 0.9860926754890781
team_policy eval average team episode rewards of agent1: 175.0
team_policy eval idv catch total num of agent1: 41
team_policy eval team catch total num: 70
team_policy eval average step individual rewards of agent2: 0.8623216049951102
team_policy eval average team episode rewards of agent2: 175.0
team_policy eval idv catch total num of agent2: 36
team_policy eval team catch total num: 70
team_policy eval average step individual rewards of agent3: 1.2207760994856571
team_policy eval average team episode rewards of agent3: 175.0
team_policy eval idv catch total num of agent3: 50
team_policy eval team catch total num: 70
team_policy eval average step individual rewards of agent4: 1.4492965828531026
team_policy eval average team episode rewards of agent4: 175.0
team_policy eval idv catch total num of agent4: 59
team_policy eval team catch total num: 70
idv_policy eval average step individual rewards of agent0: 0.9977966135794912
idv_policy eval average team episode rewards of agent0: 110.0
idv_policy eval idv catch total num of agent0: 41
idv_policy eval team catch total num: 44
idv_policy eval average step individual rewards of agent1: 1.0223046686207962
idv_policy eval average team episode rewards of agent1: 110.0
idv_policy eval idv catch total num of agent1: 42
idv_policy eval team catch total num: 44
idv_policy eval average step individual rewards of agent2: 0.6842510411440107
idv_policy eval average team episode rewards of agent2: 110.0
idv_policy eval idv catch total num of agent2: 29
idv_policy eval team catch total num: 44
idv_policy eval average step individual rewards of agent3: 0.6610586721687602
idv_policy eval average team episode rewards of agent3: 110.0
idv_policy eval idv catch total num of agent3: 28
idv_policy eval team catch total num: 44
idv_policy eval average step individual rewards of agent4: 0.532113195477578
idv_policy eval average team episode rewards of agent4: 110.0
idv_policy eval idv catch total num of agent4: 23
idv_policy eval team catch total num: 44

 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.

team_policy eval average step individual rewards of agent0: 0.8097390162255977
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: 1.0136007274547147
team_policy eval average team episode rewards of agent1: 92.5
team_policy eval idv catch total num of agent1: 42
team_policy eval team catch total num: 37
team_policy eval average step individual rewards of agent2: 0.32885412014023324
team_policy eval average team episode rewards of agent2: 92.5
team_policy eval idv catch total num of agent2: 15
team_policy eval team catch total num: 37
team_policy eval average step individual rewards of agent3: 0.807359796519148
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: 0.38124029175796836
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: 0.9678968448463465
idv_policy eval average team episode rewards of agent0: 92.5
idv_policy eval idv catch total num of agent0: 40
idv_policy eval team catch total num: 37
idv_policy eval average step individual rewards of agent1: 0.8156959502081648
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: 0.19180200479189857
idv_policy eval average team episode rewards of agent2: 92.5
idv_policy eval idv catch total num of agent2: 10
idv_policy eval team catch total num: 37
idv_policy eval average step individual rewards of agent3: 0.7046111598042504
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: 0.7659259465659559
idv_policy eval average team episode rewards of agent4: 92.5
idv_policy eval idv catch total num of agent4: 32
idv_policy eval team catch total num: 37

 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.

team_policy eval average step individual rewards of agent0: 1.3429761003990053
team_policy eval average team episode rewards of agent0: 127.5
team_policy eval idv catch total num of agent0: 55
team_policy eval team catch total num: 51
team_policy eval average step individual rewards of agent1: 0.6600174169497839
team_policy eval average team episode rewards of agent1: 127.5
team_policy eval idv catch total num of agent1: 28
team_policy eval team catch total num: 51
team_policy eval average step individual rewards of agent2: 0.5323553860697738
team_policy eval average team episode rewards of agent2: 127.5
team_policy eval idv catch total num of agent2: 23
team_policy eval team catch total num: 51
team_policy eval average step individual rewards of agent3: 0.759741827504616
team_policy eval average team episode rewards of agent3: 127.5
team_policy eval idv catch total num of agent3: 32
team_policy eval team catch total num: 51
team_policy eval average step individual rewards of agent4: 1.0179212203295822
team_policy eval average team episode rewards of agent4: 127.5
team_policy eval idv catch total num of agent4: 42
team_policy eval team catch total num: 51
idv_policy eval average step individual rewards of agent0: 0.7335063722773802
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: 0.8357202417621333
idv_policy eval average team episode rewards of agent1: 105.0
idv_policy eval idv catch total num of agent1: 35
idv_policy eval team catch total num: 42
idv_policy eval average step individual rewards of agent2: 0.8387007590876254
idv_policy eval average team episode rewards of agent2: 105.0
idv_policy eval idv catch total num of agent2: 35
idv_policy eval team catch total num: 42
idv_policy eval average step individual rewards of agent3: 0.789332793741916
idv_policy eval average team episode rewards of agent3: 105.0
idv_policy eval idv catch total num of agent3: 33
idv_policy eval team catch total num: 42
idv_policy eval average step individual rewards of agent4: 0.35192970024313314
idv_policy eval average team episode rewards of agent4: 105.0
idv_policy eval idv catch total num of agent4: 16
idv_policy eval team catch total num: 42

 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.

team_policy eval average step individual rewards of agent0: 0.46255563759936225
team_policy eval average team episode rewards of agent0: 87.5
team_policy eval idv catch total num of agent0: 20
team_policy eval team catch total num: 35
team_policy eval average step individual rewards of agent1: 0.6617079197859905
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: 0.5568482604367665
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: 0.6929766361312285
team_policy eval average team episode rewards of agent3: 87.5
team_policy eval idv catch total num of agent3: 29
team_policy eval team catch total num: 35
team_policy eval average step individual rewards of agent4: 0.9559117159809513
team_policy eval average team episode rewards of agent4: 87.5
team_policy eval idv catch total num of agent4: 40
team_policy eval team catch total num: 35
idv_policy eval average step individual rewards of agent0: 1.0179286192090762
idv_policy eval average team episode rewards of agent0: 110.0
idv_policy eval idv catch total num of agent0: 42
idv_policy eval team catch total num: 44
idv_policy eval average step individual rewards of agent1: 0.9162553841732634
idv_policy eval average team episode rewards of agent1: 110.0
idv_policy eval idv catch total num of agent1: 38
idv_policy eval team catch total num: 44
idv_policy eval average step individual rewards of agent2: 0.9116207102233735
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: 1.0674458573830585
idv_policy eval average team episode rewards of agent3: 110.0
idv_policy eval idv catch total num of agent3: 44
idv_policy eval team catch total num: 44
idv_policy eval average step individual rewards of agent4: 0.5252318582540254
idv_policy eval average team episode rewards of agent4: 110.0
idv_policy eval idv catch total num of agent4: 23
idv_policy eval team catch total num: 44

 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8198/10000 episodes, total num timesteps 1639800/2000000, FPS 240.


 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 240.


 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 240.

team_policy eval average step individual rewards of agent0: 0.5933920101627207
team_policy eval average team episode rewards of agent0: 42.5
team_policy eval idv catch total num of agent0: 25
team_policy eval team catch total num: 17
team_policy eval average step individual rewards of agent1: 0.704966011957954
team_policy eval average team episode rewards of agent1: 42.5
team_policy eval idv catch total num of agent1: 30
team_policy eval team catch total num: 17
team_policy eval average step individual rewards of agent2: 0.32082705688152546
team_policy eval average team episode rewards of agent2: 42.5
team_policy eval idv catch total num of agent2: 15
team_policy eval team catch total num: 17
team_policy eval average step individual rewards of agent3: 0.23753218006498628
team_policy eval average team episode rewards of agent3: 42.5
team_policy eval idv catch total num of agent3: 12
team_policy eval team catch total num: 17
team_policy eval average step individual rewards of agent4: 0.353996990915398
team_policy eval average team episode rewards of agent4: 42.5
team_policy eval idv catch total num of agent4: 16
team_policy eval team catch total num: 17
idv_policy eval average step individual rewards of agent0: 0.6107731045893576
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: 0.6864933340764833
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: 0.8099782312865824
idv_policy eval average team episode rewards of agent2: 97.5
idv_policy eval idv catch total num of agent2: 34
idv_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent3: 1.0046605249090534
idv_policy eval average team episode rewards of agent3: 97.5
idv_policy eval idv catch total num of agent3: 42
idv_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent4: 0.5331368943879311
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 8201/10000 episodes, total num timesteps 1640400/2000000, FPS 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.

team_policy eval average step individual rewards of agent0: 1.0120412013852709
team_policy eval average team episode rewards of agent0: 142.5
team_policy eval idv catch total num of agent0: 42
team_policy eval team catch total num: 57
team_policy eval average step individual rewards of agent1: 1.2407695489120139
team_policy eval average team episode rewards of agent1: 142.5
team_policy eval idv catch total num of agent1: 51
team_policy eval team catch total num: 57
team_policy eval average step individual rewards of agent2: 0.9896888843288614
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: 1.0958019700734325
team_policy eval average team episode rewards of agent3: 142.5
team_policy eval idv catch total num of agent3: 45
team_policy eval team catch total num: 57
team_policy eval average step individual rewards of agent4: 0.7536575879665304
team_policy eval average team episode rewards of agent4: 142.5
team_policy eval idv catch total num of agent4: 32
team_policy eval team catch total num: 57
idv_policy eval average step individual rewards of agent0: 1.0684230627314133
idv_policy eval average team episode rewards of agent0: 132.5
idv_policy eval idv catch total num of agent0: 44
idv_policy eval team catch total num: 53
idv_policy eval average step individual rewards of agent1: 1.0601770727760003
idv_policy eval average team episode rewards of agent1: 132.5
idv_policy eval idv catch total num of agent1: 44
idv_policy eval team catch total num: 53
idv_policy eval average step individual rewards of agent2: 1.2227846335863979
idv_policy eval average team episode rewards of agent2: 132.5
idv_policy eval idv catch total num of agent2: 50
idv_policy eval team catch total num: 53
idv_policy eval average step individual rewards of agent3: 0.9303815697256327
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: 0.536315842889507
idv_policy eval average team episode rewards of agent4: 132.5
idv_policy eval idv catch total num of agent4: 23
idv_policy eval team catch total num: 53

 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.

team_policy eval average step individual rewards of agent0: 0.9324756294401204
team_policy eval average team episode rewards of agent0: 110.0
team_policy eval idv catch total num of agent0: 39
team_policy eval team catch total num: 44
team_policy eval average step individual rewards of agent1: 0.6104297328755325
team_policy eval average team episode rewards of agent1: 110.0
team_policy eval idv catch total num of agent1: 26
team_policy eval team catch total num: 44
team_policy eval average step individual rewards of agent2: 0.9306026526380768
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: 0.37123335213534014
team_policy eval average team episode rewards of agent3: 110.0
team_policy eval idv catch total num of agent3: 17
team_policy eval team catch total num: 44
team_policy eval average step individual rewards of agent4: 0.7833242169930233
team_policy eval average team episode rewards of agent4: 110.0
team_policy eval idv catch total num of agent4: 33
team_policy eval team catch total num: 44
idv_policy eval average step individual rewards of agent0: 0.7403884630849233
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: 0.7313205004306598
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: 0.8122346473292374
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: 1.390467181759752
idv_policy eval average team episode rewards of agent3: 125.0
idv_policy eval idv catch total num of agent3: 57
idv_policy eval team catch total num: 50
idv_policy eval average step individual rewards of agent4: 1.1958193254410532
idv_policy eval average team episode rewards of agent4: 125.0
idv_policy eval idv catch total num of agent4: 49
idv_policy eval team catch total num: 50

 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.

team_policy eval average step individual rewards of agent0: 0.7632592994784492
team_policy eval average team episode rewards of agent0: 105.0
team_policy eval idv catch total num of agent0: 32
team_policy eval team catch total num: 42
team_policy eval average step individual rewards of agent1: 0.8657327442392527
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: 0.7888167448529622
team_policy eval average team episode rewards of agent2: 105.0
team_policy eval idv catch total num of agent2: 33
team_policy eval team catch total num: 42
team_policy eval average step individual rewards of agent3: 0.6128344224720229
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: 0.78939049210969
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: 0.8676769694295544
idv_policy eval average team episode rewards of agent0: 120.0
idv_policy eval idv catch total num of agent0: 36
idv_policy eval team catch total num: 48
idv_policy eval average step individual rewards of agent1: 1.0194266845198932
idv_policy eval average team episode rewards of agent1: 120.0
idv_policy eval idv catch total num of agent1: 42
idv_policy eval team catch total num: 48
idv_policy eval average step individual rewards of agent2: 0.5644784406049226
idv_policy eval average team episode rewards of agent2: 120.0
idv_policy eval idv catch total num of agent2: 24
idv_policy eval team catch total num: 48
idv_policy eval average step individual rewards of agent3: 0.7365514415333791
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: 0.9958387082063714
idv_policy eval average team episode rewards of agent4: 120.0
idv_policy eval idv catch total num of agent4: 41
idv_policy eval team catch total num: 48

 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.


 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 240.

team_policy eval average step individual rewards of agent0: 1.4668540004247075
team_policy eval average team episode rewards of agent0: 162.5
team_policy eval idv catch total num of agent0: 60
team_policy eval team catch total num: 65
team_policy eval average step individual rewards of agent1: 1.4279602893249776
team_policy eval average team episode rewards of agent1: 162.5
team_policy eval idv catch total num of agent1: 58
team_policy eval team catch total num: 65
team_policy eval average step individual rewards of agent2: 1.396594893424401
team_policy eval average team episode rewards of agent2: 162.5
team_policy eval idv catch total num of agent2: 57
team_policy eval team catch total num: 65
team_policy eval average step individual rewards of agent3: 1.1146604327652505
team_policy eval average team episode rewards of agent3: 162.5
team_policy eval idv catch total num of agent3: 46
team_policy eval team catch total num: 65
team_policy eval average step individual rewards of agent4: 1.059458051033487
team_policy eval average team episode rewards of agent4: 162.5
team_policy eval idv catch total num of agent4: 44
team_policy eval team catch total num: 65
idv_policy eval average step individual rewards of agent0: 0.6748208627987917
idv_policy eval average team episode rewards of agent0: 97.5
idv_policy eval idv catch total num of agent0: 29
idv_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent1: 0.8389170398898033
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: 0.7953628262388
idv_policy eval average team episode rewards of agent2: 97.5
idv_policy eval idv catch total num of agent2: 33
idv_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent3: 0.5860901254374294
idv_policy eval average team episode rewards of agent3: 97.5
idv_policy eval idv catch total num of agent3: 25
idv_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent4: 0.47950661828008356
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 8301/10000 episodes, total num timesteps 1660400/2000000, FPS 240.


 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 240.


 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 240.


 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 239.


 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 239.


 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 239.


 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 239.


 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 239.


 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 239.


 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 239.


 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 239.


 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 239.


 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 239.


 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 239.


 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 239.


 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 239.


 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 239.


 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 239.


 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 239.


 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 239.


 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 239.


 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 239.


 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 239.


 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 239.


 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 239.

team_policy eval average step individual rewards of agent0: 0.8986692776783746
team_policy eval average team episode rewards of agent0: 80.0
team_policy eval idv catch total num of agent0: 37
team_policy eval team catch total num: 32
team_policy eval average step individual rewards of agent1: 0.6050963672448562
team_policy eval average team episode rewards of agent1: 80.0
team_policy eval idv catch total num of agent1: 26
team_policy eval team catch total num: 32
team_policy eval average step individual rewards of agent2: 0.5854205477948246
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: 0.43456687357035884
team_policy eval average team episode rewards of agent3: 80.0
team_policy eval idv catch total num of agent3: 19
team_policy eval team catch total num: 32
team_policy eval average step individual rewards of agent4: 0.5081229715340432
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: 0.6512272224102952
idv_policy eval average team episode rewards of agent0: 112.5
idv_policy eval idv catch total num of agent0: 28
idv_policy eval team catch total num: 45
idv_policy eval average step individual rewards of agent1: 1.1106618657730178
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: 0.8395546724009961
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: 0.741729839121463
idv_policy eval average team episode rewards of agent3: 112.5
idv_policy eval idv catch total num of agent3: 31
idv_policy eval team catch total num: 45
idv_policy eval average step individual rewards of agent4: 0.44612417403436494
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 8326/10000 episodes, total num timesteps 1665400/2000000, FPS 239.


 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 239.


 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 239.


 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 239.


 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 239.


 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 239.


 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 239.


 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 239.


 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 239.


 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 239.


 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 239.


 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 239.


 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 239.


 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 239.


 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 239.


 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 239.


 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 239.


 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 239.


 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 239.


 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 239.


 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 239.


 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 239.


 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 239.


 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 239.


 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 239.

team_policy eval average step individual rewards of agent0: 0.48822044096587874
team_policy eval average team episode rewards of agent0: 135.0
team_policy eval idv catch total num of agent0: 21
team_policy eval team catch total num: 54
team_policy eval average step individual rewards of agent1: 1.1192784896337438
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: 0.5803828763812977
team_policy eval average team episode rewards of agent2: 135.0
team_policy eval idv catch total num of agent2: 25
team_policy eval team catch total num: 54
team_policy eval average step individual rewards of agent3: 1.452317248573934
team_policy eval average team episode rewards of agent3: 135.0
team_policy eval idv catch total num of agent3: 59
team_policy eval team catch total num: 54
team_policy eval average step individual rewards of agent4: 1.048346875709457
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: 0.9184330425688763
idv_policy eval average team episode rewards of agent0: 92.5
idv_policy eval idv catch total num of agent0: 38
idv_policy eval team catch total num: 37
idv_policy eval average step individual rewards of agent1: 0.5290226991284811
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: 0.836228633158882
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: 0.5084163043875574
idv_policy eval average team episode rewards of agent3: 92.5
idv_policy eval idv catch total num of agent3: 22
idv_policy eval team catch total num: 37
idv_policy eval average step individual rewards of agent4: 0.9405150525731029
idv_policy eval average team episode rewards of agent4: 92.5
idv_policy eval idv catch total num of agent4: 39
idv_policy eval team catch total num: 37

 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 239.


 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 239.


 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 239.


 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 239.


 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 239.


 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 239.


 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 239.


 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 239.


 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 239.


 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 239.


 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 239.

wandb: - 0.007 MB of 0.007 MB uploaded
wandb: \ 0.007 MB of 0.007 MB uploaded
wandb: | 0.007 MB of 2.656 MB uploaded
wandb: / 1.784 MB of 2.656 MB uploaded
wandb: - 2.656 MB of 2.656 MB uploaded
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 -0.03504
wandb:                                          Ab_policy_loss -0.00028
wandb:                                     Ac_idv_ppo_loss_abs 0.80492
wandb:                                         Ad_idv_ppo_prop 0.92852
wandb:                                                  Ae_eta 0.99899
wandb:                                    Af_noclip_proportion 0.9952
wandb:                                    Ag_update_proportion 0.5105
wandb:                                          Ah_update_loss 0.09308
wandb:                                         Ai_idv_epsilon' 0.41805
wandb:                                            Aj_idv_sigma 1.0137
wandb:              Ak_idv_clip(sigma, 1-epislon', 1+epislon') 1.01143
wandb:                                Al_idv_noclip_proportion 0.9897
wandb:                       Am_idv_(sigma*A)update_proportion 0.4818
wandb:                             An_idv_(sigma*A)update_loss -0.11609
wandb:                                     Ao_idv_entropy_prop 0.05581
wandb:                                         Ap_dist_entropy 4.83763
wandb:                                          Aq_idv_kl_prop 0.01567
wandb:                                          Ar_idv_kl_coef 6.0166
wandb:                                          As_idv_kl_loss 0.00226
wandb:                                    At_idv_cross_entropy 0.0
wandb:                                           Au_value_loss 0.21074
wandb:                                           Av_advantages 0.0
wandb:                                       Aw_idv_actor_norm 0.61981
wandb:                                      Ax_idv_critic_norm 0.12639
wandb:                                     Ba_idv_org_min_prop 0.4194
wandb:                                     Bb_idv_org_max_prop 0.0911
wandb:                                     Bc_idv_org_org_prop 0.0
wandb:                                     Bd_idv_new_min_prop 0.0919
wandb:                                     Be_idv_new_max_prop 0.3899
wandb:                                      Ta_team_actor_loss -0.04374
wandb:                                     Tb_team_policy_loss 0.00202
wandb:                                    Tc_team_ppo_loss_abs 0.79964
wandb:                                        Td_team_ppo_prop 0.94005
wandb:                                        Te_team_epsilon^ 0.2
wandb:                                          Tf_team_sigma^ 0.99701
wandb:          Tg_team_clip(sigma^, 1-epislon^', 1+epislon^') 0.9964
wandb:                               Th_team_noclip_proportion 0.9484
wandb:                     Ti_team_(sigma^*A)update_proportion 0.9737
wandb:                           Tj_team_(sigma^*A)update_loss 0.00161
wandb:                                    Tk_team_entropy_prop 0.05687
wandb:                                    Tl_team_dist_entropy 4.83763
wandb:                                         Tm_team_kl_prop 0.00308
wandb:                                         Tn_team_kl_coef 1.03278
wandb:                                         To_team_kl_loss 0.00254
wandb:                                   Tp_team_cross_entropy 0.0
wandb:                                      Tq_team_value_loss 0.18788
wandb:                                      Tr_team_advantages 0.0
wandb:                                      Ts_team_actor_norm 0.36818
wandb:                                     Tt_team_critic_norm 0.14423
wandb:                     agent0/average_episode_team_rewards 62.5
wandb:                  agent0/average_step_individual_rewards 0.44839
wandb:     agent0/idv_policy_eval_average_episode_team_rewards 92.5
wandb:  agent0/idv_policy_eval_average_step_individual_rewards 0.91843
wandb:              agent0/idv_policy_eval_idv_catch_total_num 38
wandb:             agent0/idv_policy_eval_team_catch_total_num 37
wandb:    agent0/team_policy_eval_average_episode_team_rewards 135.0
wandb: agent0/team_policy_eval_average_step_individual_rewards 0.48822
wandb:             agent0/team_policy_eval_idv_catch_total_num 21
wandb:            agent0/team_policy_eval_team_catch_total_num 54
wandb:                     agent1/average_episode_team_rewards 62.5
wandb:                  agent1/average_step_individual_rewards 0.36418
wandb:     agent1/idv_policy_eval_average_episode_team_rewards 92.5
wandb:  agent1/idv_policy_eval_average_step_individual_rewards 0.52902
wandb:              agent1/idv_policy_eval_idv_catch_total_num 23
wandb:             agent1/idv_policy_eval_team_catch_total_num 37
wandb:    agent1/team_policy_eval_average_episode_team_rewards 135.0
wandb: agent1/team_policy_eval_average_step_individual_rewards 1.11928
wandb:             agent1/team_policy_eval_idv_catch_total_num 46
wandb:            agent1/team_policy_eval_team_catch_total_num 54
wandb:                     agent2/average_episode_team_rewards 62.5
wandb:                  agent2/average_step_individual_rewards 0.71518
wandb:     agent2/idv_policy_eval_average_episode_team_rewards 92.5
wandb:  agent2/idv_policy_eval_average_step_individual_rewards 0.83623
wandb:              agent2/idv_policy_eval_idv_catch_total_num 35
wandb:             agent2/idv_policy_eval_team_catch_total_num 37
wandb:    agent2/team_policy_eval_average_episode_team_rewards 135.0
wandb: agent2/team_policy_eval_average_step_individual_rewards 0.58038
wandb:             agent2/team_policy_eval_idv_catch_total_num 25
wandb:            agent2/team_policy_eval_team_catch_total_num 54
wandb:                     agent3/average_episode_team_rewards 62.5
wandb:                  agent3/average_step_individual_rewards 0.51156
wandb:     agent3/idv_policy_eval_average_episode_team_rewards 92.5
wandb:  agent3/idv_policy_eval_average_step_individual_rewards 0.50842
wandb:              agent3/idv_policy_eval_idv_catch_total_num 22
wandb:             agent3/idv_policy_eval_team_catch_total_num 37
wandb:    agent3/team_policy_eval_average_episode_team_rewards 135.0
wandb: agent3/team_policy_eval_average_step_individual_rewards 1.45232
wandb:             agent3/team_policy_eval_idv_catch_total_num 59
wandb:            agent3/team_policy_eval_team_catch_total_num 54
wandb:                     agent4/average_episode_team_rewards 62.5
wandb:                  agent4/average_step_individual_rewards 0.65344
wandb:     agent4/idv_policy_eval_average_episode_team_rewards 92.5
wandb:  agent4/idv_policy_eval_average_step_individual_rewards 0.94052
wandb:              agent4/idv_policy_eval_idv_catch_total_num 39
wandb:             agent4/idv_policy_eval_team_catch_total_num 37
wandb:    agent4/team_policy_eval_average_episode_team_rewards 135.0
wandb: agent4/team_policy_eval_average_step_individual_rewards 1.04835
wandb:             agent4/team_policy_eval_idv_catch_total_num 43
wandb:            agent4/team_policy_eval_team_catch_total_num 54
wandb: 
wandb: 🚀 View run MPE_1 at: https://wandb.ai/804703098/Continue_Tag_Base_v1/runs/hzpgffhz
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_170625-hzpgffhz/logs
Traceback (most recent call last):
  File "train/train_mpe_trsyn.py", line 244, in <module>
    main(sys.argv[1:])
  File "train/train_mpe_trsyn.py", line 229, in main
    runner.run()
  File "/home/user/zhangyang/PycharmProjects/Nips2024-ITPC-v2/Nips2024-ITPC-v2/onpolicy/runner/shared/mpe_runner_trsyn.py", line 64, in run
    obs, rewards, dones, infos = self.envs.step(actions_env)
  File "/home/user/zhangyang/PycharmProjects/Nips2024-ITPC-v2/Nips2024-ITPC-v2/onpolicy/envs/env_wrappers.py", line 107, in step
    return self.step_wait()
  File "/home/user/zhangyang/PycharmProjects/Nips2024-ITPC-v2/Nips2024-ITPC-v2/onpolicy/envs/env_wrappers.py", line 265, in step_wait
    results = [remote.recv() for remote in self.remotes]
  File "/home/user/zhangyang/PycharmProjects/Nips2024-ITPC-v2/Nips2024-ITPC-v2/onpolicy/envs/env_wrappers.py", line 265, in <listcomp>
    results = [remote.recv() for remote in self.remotes]
  File "/home/user/anaconda3/envs/zypy38/lib/python3.8/multiprocessing/connection.py", line 250, in recv
    buf = self._recv_bytes()
  File "/home/user/anaconda3/envs/zypy38/lib/python3.8/multiprocessing/connection.py", line 414, in _recv_bytes
    buf = self._recv(4)
  File "/home/user/anaconda3/envs/zypy38/lib/python3.8/multiprocessing/connection.py", line 379, in _recv
    chunk = read(handle, remaining)
ConnectionResetError: [Errno 104] Connection reset by peer
mple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 8474/10000 episodes, total num timesteps 1695000/2000000, FPS 258.


 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 258.

team_policy eval average step individual rewards of agent0: 0.5130762321637217
team_policy eval average team episode rewards of agent0: 20.0
team_policy eval idv catch total num of agent0: 23
team_policy eval team catch total num: 8
team_policy eval average step individual rewards of agent1: 0.17353963128401326
team_policy eval average team episode rewards of agent1: 20.0
team_policy eval idv catch total num of agent1: 10
team_policy eval team catch total num: 8
team_policy eval average step individual rewards of agent2: 0.2620051834257847
team_policy eval average team episode rewards of agent2: 20.0
team_policy eval idv catch total num of agent2: 13
team_policy eval team catch total num: 8
team_policy eval average step individual rewards of agent3: 0.03315372887647497
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: 0.2310104991309142
team_policy eval average team episode rewards of agent4: 20.0
team_policy eval idv catch total num of agent4: 12
team_policy eval team catch total num: 8
idv_policy eval average step individual rewards of agent0: 0.021043283324440134
idv_policy eval average team episode rewards of agent0: 7.5
idv_policy eval idv catch total num of agent0: 4
idv_policy eval team catch total num: 3
idv_policy eval average step individual rewards of agent1: 0.10903429617114418
idv_policy eval average team episode rewards of agent1: 7.5
idv_policy eval idv catch total num of agent1: 7
idv_policy eval team catch total num: 3
idv_policy eval average step individual rewards of agent2: 0.12310547368177102
idv_policy eval average team episode rewards of agent2: 7.5
idv_policy eval idv catch total num of agent2: 8
idv_policy eval team catch total num: 3
idv_policy eval average step individual rewards of agent3: -0.028828457565230173
idv_policy eval average team episode rewards of agent3: 7.5
idv_policy eval idv catch total num of agent3: 2
idv_policy eval team catch total num: 3
idv_policy eval average step individual rewards of agent4: 0.05327783058847438
idv_policy eval average team episode rewards of agent4: 7.5
idv_policy eval idv catch total num of agent4: 6
idv_policy eval team catch total num: 3

 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 258.


 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 258.


 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 258.


 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 258.


 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 258.


 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 258.


 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 258.


 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 258.


 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 258.


 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 258.


 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 258.


 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 258.


 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 258.


 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 258.


 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 258.


 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 258.


 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 258.


 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 258.


 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 258.


 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 258.


 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 258.


 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 258.


 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 258.


 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 258.


 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 258.

team_policy eval average step individual rewards of agent0: 0.23696297069163072
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: 0.19270771681441687
team_policy eval average team episode rewards of agent1: 12.5
team_policy eval idv catch total num of agent1: 11
team_policy eval team catch total num: 5
team_policy eval average step individual rewards of agent2: 0.13012225578791697
team_policy eval average team episode rewards of agent2: 12.5
team_policy eval idv catch total num of agent2: 9
team_policy eval team catch total num: 5
team_policy eval average step individual rewards of agent3: 0.33024799384091297
team_policy eval average team episode rewards of agent3: 12.5
team_policy eval idv catch total num of agent3: 16
team_policy eval team catch total num: 5
team_policy eval average step individual rewards of agent4: 0.04808008365337596
team_policy eval average team episode rewards of agent4: 12.5
team_policy eval idv catch total num of agent4: 5
team_policy eval team catch total num: 5
idv_policy eval average step individual rewards of agent0: -0.03139363172252429
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: 0.07728612932357219
idv_policy eval average team episode rewards of agent1: 12.5
idv_policy eval idv catch total num of agent1: 6
idv_policy eval team catch total num: 5
idv_policy eval average step individual rewards of agent2: 0.16132309817725254
idv_policy eval average team episode rewards of agent2: 12.5
idv_policy eval idv catch total num of agent2: 9
idv_policy eval team catch total num: 5
idv_policy eval average step individual rewards of agent3: 0.20895792714979763
idv_policy eval average team episode rewards of agent3: 12.5
idv_policy eval idv catch total num of agent3: 11
idv_policy eval team catch total num: 5
idv_policy eval average step individual rewards of agent4: 0.0736407111763197
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 8501/10000 episodes, total num timesteps 1700400/2000000, FPS 258.


 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 258.


 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 258.


 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 258.


 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 258.


 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 258.


 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 258.


 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 258.


 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 258.


 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 258.


 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 258.


 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 258.


 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 258.


 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 258.


 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 258.


 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 258.


 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 258.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.

team_policy eval average step individual rewards of agent0: 0.11076455581205556
team_policy eval average team episode rewards of agent0: 5.0
team_policy eval idv catch total num of agent0: 7
team_policy eval team catch total num: 2
team_policy eval average step individual rewards of agent1: 0.16235689249968704
team_policy eval average team episode rewards of agent1: 5.0
team_policy eval idv catch total num of agent1: 9
team_policy eval team catch total num: 2
team_policy eval average step individual rewards of agent2: 0.44498253031650664
team_policy eval average team episode rewards of agent2: 5.0
team_policy eval idv catch total num of agent2: 20
team_policy eval team catch total num: 2
team_policy eval average step individual rewards of agent3: 0.23199760895123261
team_policy eval average team episode rewards of agent3: 5.0
team_policy eval idv catch total num of agent3: 12
team_policy eval team catch total num: 2
team_policy eval average step individual rewards of agent4: 0.05532689847600647
team_policy eval average team episode rewards of agent4: 5.0
team_policy eval idv catch total num of agent4: 5
team_policy eval team catch total num: 2
idv_policy eval average step individual rewards of agent0: 0.14000599787562318
idv_policy eval average team episode rewards of agent0: 12.5
idv_policy eval idv catch total num of agent0: 10
idv_policy eval team catch total num: 5
idv_policy eval average step individual rewards of agent1: 0.29517475285582945
idv_policy eval average team episode rewards of agent1: 12.5
idv_policy eval idv catch total num of agent1: 14
idv_policy eval team catch total num: 5
idv_policy eval average step individual rewards of agent2: -0.0811407654097282
idv_policy eval average team episode rewards of agent2: 12.5
idv_policy eval idv catch total num of agent2: 0
idv_policy eval team catch total num: 5
idv_policy eval average step individual rewards of agent3: 0.3107037992667889
idv_policy eval average team episode rewards of agent3: 12.5
idv_policy eval idv catch total num of agent3: 15
idv_policy eval team catch total num: 5
idv_policy eval average step individual rewards of agent4: 0.10313495031526573
idv_policy eval average team episode rewards of agent4: 12.5
idv_policy eval idv catch total num of agent4: 9
idv_policy eval team catch total num: 5

 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.

team_policy eval average step individual rewards of agent0: -0.06816949507316619
team_policy eval average team episode rewards of agent0: 2.5
team_policy eval idv catch total num of agent0: 1
team_policy eval team catch total num: 1
team_policy eval average step individual rewards of agent1: 0.04921513930719145
team_policy eval average team episode rewards of agent1: 2.5
team_policy eval idv catch total num of agent1: 5
team_policy eval team catch total num: 1
team_policy eval average step individual rewards of agent2: 0.3579605399380705
team_policy eval average team episode rewards of agent2: 2.5
team_policy eval idv catch total num of agent2: 17
team_policy eval team catch total num: 1
team_policy eval average step individual rewards of agent3: 0.21383387550356864
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: 0.1824592671708919
team_policy eval average team episode rewards of agent4: 2.5
team_policy eval idv catch total num of agent4: 10
team_policy eval team catch total num: 1
idv_policy eval average step individual rewards of agent0: -0.0028911250818727963
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: 0.21304500836469817
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: 0.31937252912220826
idv_policy eval average team episode rewards of agent2: 2.5
idv_policy eval idv catch total num of agent2: 15
idv_policy eval team catch total num: 1
idv_policy eval average step individual rewards of agent3: -0.021842952962286014
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: 0.024307365296757713
idv_policy eval average team episode rewards of agent4: 2.5
idv_policy eval idv catch total num of agent4: 4
idv_policy eval team catch total num: 1

 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.

team_policy eval average step individual rewards of agent0: -0.0024658463466100677
team_policy eval average team episode rewards of agent0: 10.0
team_policy eval idv catch total num of agent0: 3
team_policy eval team catch total num: 4
team_policy eval average step individual rewards of agent1: 0.21036829455413442
team_policy eval average team episode rewards of agent1: 10.0
team_policy eval idv catch total num of agent1: 11
team_policy eval team catch total num: 4
team_policy eval average step individual rewards of agent2: 0.2536671924252543
team_policy eval average team episode rewards of agent2: 10.0
team_policy eval idv catch total num of agent2: 13
team_policy eval team catch total num: 4
team_policy eval average step individual rewards of agent3: 0.08945993893321035
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: 0.22725075394902192
team_policy eval average team episode rewards of agent4: 10.0
team_policy eval idv catch total num of agent4: 12
team_policy eval team catch total num: 4
idv_policy eval average step individual rewards of agent0: 0.21660755164522655
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: 0.20513563430340184
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: 0.03509872064336356
idv_policy eval average team episode rewards of agent2: 12.5
idv_policy eval idv catch total num of agent2: 4
idv_policy eval team catch total num: 5
idv_policy eval average step individual rewards of agent3: 0.40250509030454423
idv_policy eval average team episode rewards of agent3: 12.5
idv_policy eval idv catch total num of agent3: 19
idv_policy eval team catch total num: 5
idv_policy eval average step individual rewards of agent4: 0.2956865100657685
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 8576/10000 episodes, total num timesteps 1715400/2000000, FPS 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.

team_policy eval average step individual rewards of agent0: -0.07261581037209756
team_policy eval average team episode rewards of agent0: 5.0
team_policy eval idv catch total num of agent0: 1
team_policy eval team catch total num: 2
team_policy eval average step individual rewards of agent1: 0.25360229719563643
team_policy eval average team episode rewards of agent1: 5.0
team_policy eval idv catch total num of agent1: 14
team_policy eval team catch total num: 2
team_policy eval average step individual rewards of agent2: 0.003257490865326469
team_policy eval average team episode rewards of agent2: 5.0
team_policy eval idv catch total num of agent2: 5
team_policy eval team catch total num: 2
team_policy eval average step individual rewards of agent3: 0.050497436408919925
team_policy eval average team episode rewards of agent3: 5.0
team_policy eval idv catch total num of agent3: 6
team_policy eval team catch total num: 2
team_policy eval average step individual rewards of agent4: 0.06732126797772038
team_policy eval average team episode rewards of agent4: 5.0
team_policy eval idv catch total num of agent4: 6
team_policy eval team catch total num: 2
idv_policy eval average step individual rewards of agent0: 0.038487304381644744
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: 0.15436621527535213
idv_policy eval average team episode rewards of agent1: 0.0
idv_policy eval idv catch total num of agent1: 9
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent2: -0.01717962828489131
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: -0.05259744796241769
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: -0.0007198517806067795
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 8601/10000 episodes, total num timesteps 1720400/2000000, FPS 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.

team_policy eval average step individual rewards of agent0: 0.09898595691042153
team_policy eval average team episode rewards of agent0: 22.5
team_policy eval idv catch total num of agent0: 7
team_policy eval team catch total num: 9
team_policy eval average step individual rewards of agent1: 0.5279525021235485
team_policy eval average team episode rewards of agent1: 22.5
team_policy eval idv catch total num of agent1: 23
team_policy eval team catch total num: 9
team_policy eval average step individual rewards of agent2: 0.1056598148599294
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: 0.243690306009225
team_policy eval average team episode rewards of agent3: 22.5
team_policy eval idv catch total num of agent3: 12
team_policy eval team catch total num: 9
team_policy eval average step individual rewards of agent4: 0.25417271036089323
team_policy eval average team episode rewards of agent4: 22.5
team_policy eval idv catch total num of agent4: 13
team_policy eval team catch total num: 9
idv_policy eval average step individual rewards of agent0: 0.3592615556199721
idv_policy eval average team episode rewards of agent0: 20.0
idv_policy eval idv catch total num of agent0: 16
idv_policy eval team catch total num: 8
idv_policy eval average step individual rewards of agent1: 0.2564878650313245
idv_policy eval average team episode rewards of agent1: 20.0
idv_policy eval idv catch total num of agent1: 12
idv_policy eval team catch total num: 8
idv_policy eval average step individual rewards of agent2: 0.2856844413253392
idv_policy eval average team episode rewards of agent2: 20.0
idv_policy eval idv catch total num of agent2: 14
idv_policy eval team catch total num: 8
idv_policy eval average step individual rewards of agent3: 0.27229963000567886
idv_policy eval average team episode rewards of agent3: 20.0
idv_policy eval idv catch total num of agent3: 13
idv_policy eval team catch total num: 8
idv_policy eval average step individual rewards of agent4: 0.0903012981441151
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 8626/10000 episodes, total num timesteps 1725400/2000000, FPS 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.

team_policy eval average step individual rewards of agent0: 0.17589129148213797
team_policy eval average team episode rewards of agent0: 17.5
team_policy eval idv catch total num of agent0: 10
team_policy eval team catch total num: 7
team_policy eval average step individual rewards of agent1: 0.06411682874644621
team_policy eval average team episode rewards of agent1: 17.5
team_policy eval idv catch total num of agent1: 6
team_policy eval team catch total num: 7
team_policy eval average step individual rewards of agent2: 0.17006762309625123
team_policy eval average team episode rewards of agent2: 17.5
team_policy eval idv catch total num of agent2: 10
team_policy eval team catch total num: 7
team_policy eval average step individual rewards of agent3: 0.28145674093200035
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: 0.21109928317817775
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: 0.19125818973991748
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: 0.21523648848555715
idv_policy eval average team episode rewards of agent1: 5.0
idv_policy eval idv catch total num of agent1: 11
idv_policy eval team catch total num: 2
idv_policy eval average step individual rewards of agent2: 0.18149967841673753
idv_policy eval average team episode rewards of agent2: 5.0
idv_policy eval idv catch total num of agent2: 10
idv_policy eval team catch total num: 2
idv_policy eval average step individual rewards of agent3: 0.17724019961612897
idv_policy eval average team episode rewards of agent3: 5.0
idv_policy eval idv catch total num of agent3: 10
idv_policy eval team catch total num: 2
idv_policy eval average step individual rewards of agent4: 0.21912605141426192
idv_policy eval average team episode rewards of agent4: 5.0
idv_policy eval idv catch total num of agent4: 11
idv_policy eval team catch total num: 2

 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.

team_policy eval average step individual rewards of agent0: 0.27863184480087455
team_policy eval average team episode rewards of agent0: 17.5
team_policy eval idv catch total num of agent0: 14
team_policy eval team catch total num: 7
team_policy eval average step individual rewards of agent1: 0.16773448195282942
team_policy eval average team episode rewards of agent1: 17.5
team_policy eval idv catch total num of agent1: 10
team_policy eval team catch total num: 7
team_policy eval average step individual rewards of agent2: 0.06687246553892899
team_policy eval average team episode rewards of agent2: 17.5
team_policy eval idv catch total num of agent2: 6
team_policy eval team catch total num: 7
team_policy eval average step individual rewards of agent3: 0.31425940391767776
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: 0.25448499239792
team_policy eval average team episode rewards of agent4: 17.5
team_policy eval idv catch total num of agent4: 13
team_policy eval team catch total num: 7
idv_policy eval average step individual rewards of agent0: 0.12313896528693952
idv_policy eval average team episode rewards of agent0: 12.5
idv_policy eval idv catch total num of agent0: 7
idv_policy eval team catch total num: 5
idv_policy eval average step individual rewards of agent1: 0.29714303323295205
idv_policy eval average team episode rewards of agent1: 12.5
idv_policy eval idv catch total num of agent1: 14
idv_policy eval team catch total num: 5
idv_policy eval average step individual rewards of agent2: 0.8830776547483771
idv_policy eval average team episode rewards of agent2: 12.5
idv_policy eval idv catch total num of agent2: 37
idv_policy eval team catch total num: 5
idv_policy eval average step individual rewards of agent3: 0.08340901847352981
idv_policy eval average team episode rewards of agent3: 12.5
idv_policy eval idv catch total num of agent3: 6
idv_policy eval team catch total num: 5
idv_policy eval average step individual rewards of agent4: 0.29362691492827986
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 8676/10000 episodes, total num timesteps 1735400/2000000, FPS 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.

team_policy eval average step individual rewards of agent0: 0.3220718357096328
team_policy eval average team episode rewards of agent0: 20.0
team_policy eval idv catch total num of agent0: 15
team_policy eval team catch total num: 8
team_policy eval average step individual rewards of agent1: 0.16631439856985986
team_policy eval average team episode rewards of agent1: 20.0
team_policy eval idv catch total num of agent1: 9
team_policy eval team catch total num: 8
team_policy eval average step individual rewards of agent2: 0.5390122848908699
team_policy eval average team episode rewards of agent2: 20.0
team_policy eval idv catch total num of agent2: 24
team_policy eval team catch total num: 8
team_policy eval average step individual rewards of agent3: 0.1845873042179397
team_policy eval average team episode rewards of agent3: 20.0
team_policy eval idv catch total num of agent3: 10
team_policy eval team catch total num: 8
team_policy eval average step individual rewards of agent4: 0.2863232165728099
team_policy eval average team episode rewards of agent4: 20.0
team_policy eval idv catch total num of agent4: 14
team_policy eval team catch total num: 8
idv_policy eval average step individual rewards of agent0: 0.2936191769143274
idv_policy eval average team episode rewards of agent0: 22.5
idv_policy eval idv catch total num of agent0: 14
idv_policy eval team catch total num: 9
idv_policy eval average step individual rewards of agent1: 0.169297023053418
idv_policy eval average team episode rewards of agent1: 22.5
idv_policy eval idv catch total num of agent1: 9
idv_policy eval team catch total num: 9
idv_policy eval average step individual rewards of agent2: 0.14600957300685266
idv_policy eval average team episode rewards of agent2: 22.5
idv_policy eval idv catch total num of agent2: 8
idv_policy eval team catch total num: 9
idv_policy eval average step individual rewards of agent3: 0.42742826360722086
idv_policy eval average team episode rewards of agent3: 22.5
idv_policy eval idv catch total num of agent3: 19
idv_policy eval team catch total num: 9
idv_policy eval average step individual rewards of agent4: 0.34765816211529826
idv_policy eval average team episode rewards of agent4: 22.5
idv_policy eval idv catch total num of agent4: 16
idv_policy eval team catch total num: 9

 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.

team_policy eval average step individual rewards of agent0: 0.23353332774095037
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: 0.17957438181275254
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: 0.39625608434029813
team_policy eval average team episode rewards of agent2: 25.0
team_policy eval idv catch total num of agent2: 19
team_policy eval team catch total num: 10
team_policy eval average step individual rewards of agent3: 0.29328015595191337
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: 0.11640507344643783
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: 0.05406893549688917
idv_policy eval average team episode rewards of agent0: 12.5
idv_policy eval idv catch total num of agent0: 6
idv_policy eval team catch total num: 5
idv_policy eval average step individual rewards of agent1: 0.09667001484004192
idv_policy eval average team episode rewards of agent1: 12.5
idv_policy eval idv catch total num of agent1: 8
idv_policy eval team catch total num: 5
idv_policy eval average step individual rewards of agent2: 0.17246179378805115
idv_policy eval average team episode rewards of agent2: 12.5
idv_policy eval idv catch total num of agent2: 10
idv_policy eval team catch total num: 5
idv_policy eval average step individual rewards of agent3: 0.0486917393285113
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: -0.05553052882872136
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 8726/10000 episodes, total num timesteps 1745400/2000000, FPS 257.


 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 257.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.

team_policy eval average step individual rewards of agent0: 0.3088627207828825
team_policy eval average team episode rewards of agent0: 10.0
team_policy eval idv catch total num of agent0: 15
team_policy eval team catch total num: 4
team_policy eval average step individual rewards of agent1: 0.053380684468079344
team_policy eval average team episode rewards of agent1: 10.0
team_policy eval idv catch total num of agent1: 5
team_policy eval team catch total num: 4
team_policy eval average step individual rewards of agent2: 0.0032633413053538797
team_policy eval average team episode rewards of agent2: 10.0
team_policy eval idv catch total num of agent2: 3
team_policy eval team catch total num: 4
team_policy eval average step individual rewards of agent3: 0.33004329482629513
team_policy eval average team episode rewards of agent3: 10.0
team_policy eval idv catch total num of agent3: 16
team_policy eval team catch total num: 4
team_policy eval average step individual rewards of agent4: 0.014201448618451549
team_policy eval average team episode rewards of agent4: 10.0
team_policy eval idv catch total num of agent4: 4
team_policy eval team catch total num: 4
idv_policy eval average step individual rewards of agent0: 0.3506644767231624
idv_policy eval average team episode rewards of agent0: 22.5
idv_policy eval idv catch total num of agent0: 16
idv_policy eval team catch total num: 9
idv_policy eval average step individual rewards of agent1: 0.24405494801841665
idv_policy eval average team episode rewards of agent1: 22.5
idv_policy eval idv catch total num of agent1: 12
idv_policy eval team catch total num: 9
idv_policy eval average step individual rewards of agent2: 0.4635677204303446
idv_policy eval average team episode rewards of agent2: 22.5
idv_policy eval idv catch total num of agent2: 21
idv_policy eval team catch total num: 9
idv_policy eval average step individual rewards of agent3: 0.09668676812228508
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: 0.15686091899036023
idv_policy eval average team episode rewards of agent4: 22.5
idv_policy eval idv catch total num of agent4: 9
idv_policy eval team catch total num: 9

 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.

team_policy eval average step individual rewards of agent0: 0.29660073329610653
team_policy eval average team episode rewards of agent0: 30.0
team_policy eval idv catch total num of agent0: 15
team_policy eval team catch total num: 12
team_policy eval average step individual rewards of agent1: 0.028970148977683494
team_policy eval average team episode rewards of agent1: 30.0
team_policy eval idv catch total num of agent1: 4
team_policy eval team catch total num: 12
team_policy eval average step individual rewards of agent2: 0.1893340843588884
team_policy eval average team episode rewards of agent2: 30.0
team_policy eval idv catch total num of agent2: 11
team_policy eval team catch total num: 12
team_policy eval average step individual rewards of agent3: 0.31277824663184306
team_policy eval average team episode rewards of agent3: 30.0
team_policy eval idv catch total num of agent3: 15
team_policy eval team catch total num: 12
team_policy eval average step individual rewards of agent4: 0.34602691759279663
team_policy eval average team episode rewards of agent4: 30.0
team_policy eval idv catch total num of agent4: 18
team_policy eval team catch total num: 12
idv_policy eval average step individual rewards of agent0: 0.16257839990959005
idv_policy eval average team episode rewards of agent0: 17.5
idv_policy eval idv catch total num of agent0: 9
idv_policy eval team catch total num: 7
idv_policy eval average step individual rewards of agent1: 0.005813798210148225
idv_policy eval average team episode rewards of agent1: 17.5
idv_policy eval idv catch total num of agent1: 3
idv_policy eval team catch total num: 7
idv_policy eval average step individual rewards of agent2: 0.7550691536399364
idv_policy eval average team episode rewards of agent2: 17.5
idv_policy eval idv catch total num of agent2: 32
idv_policy eval team catch total num: 7
idv_policy eval average step individual rewards of agent3: 0.2827585226440112
idv_policy eval average team episode rewards of agent3: 17.5
idv_policy eval idv catch total num of agent3: 13
idv_policy eval team catch total num: 7
idv_policy eval average step individual rewards of agent4: 0.28318724247143723
idv_policy eval average team episode rewards of agent4: 17.5
idv_policy eval idv catch total num of agent4: 13
idv_policy eval team catch total num: 7

 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.

team_policy eval average step individual rewards of agent0: 0.5148834094567106
team_policy eval average team episode rewards of agent0: 27.5
team_policy eval idv catch total num of agent0: 23
team_policy eval team catch total num: 11
team_policy eval average step individual rewards of agent1: 0.14213216664267803
team_policy eval average team episode rewards of agent1: 27.5
team_policy eval idv catch total num of agent1: 8
team_policy eval team catch total num: 11
team_policy eval average step individual rewards of agent2: 0.23408617626758832
team_policy eval average team episode rewards of agent2: 27.5
team_policy eval idv catch total num of agent2: 12
team_policy eval team catch total num: 11
team_policy eval average step individual rewards of agent3: 0.2937605515496136
team_policy eval average team episode rewards of agent3: 27.5
team_policy eval idv catch total num of agent3: 14
team_policy eval team catch total num: 11
team_policy eval average step individual rewards of agent4: 0.15474908654644892
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: -0.049937244221481346
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: 0.4608265745764589
idv_policy eval average team episode rewards of agent1: 15.0
idv_policy eval idv catch total num of agent1: 21
idv_policy eval team catch total num: 6
idv_policy eval average step individual rewards of agent2: 0.05502370465741691
idv_policy eval average team episode rewards of agent2: 15.0
idv_policy eval idv catch total num of agent2: 5
idv_policy eval team catch total num: 6
idv_policy eval average step individual rewards of agent3: 0.01170407623641428
idv_policy eval average team episode rewards of agent3: 15.0
idv_policy eval idv catch total num of agent3: 3
idv_policy eval team catch total num: 6
idv_policy eval average step individual rewards of agent4: 0.8700442181740903
idv_policy eval average team episode rewards of agent4: 15.0
idv_policy eval idv catch total num of agent4: 37
idv_policy eval team catch total num: 6

 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.

team_policy eval average step individual rewards of agent0: 0.10689734076019786
team_policy eval average team episode rewards of agent0: 10.0
team_policy eval idv catch total num of agent0: 7
team_policy eval team catch total num: 4
team_policy eval average step individual rewards of agent1: 0.19726390928963025
team_policy eval average team episode rewards of agent1: 10.0
team_policy eval idv catch total num of agent1: 10
team_policy eval team catch total num: 4
team_policy eval average step individual rewards of agent2: 0.15233792001690402
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: -0.009606903163105467
team_policy eval average team episode rewards of agent3: 10.0
team_policy eval idv catch total num of agent3: 3
team_policy eval team catch total num: 4
team_policy eval average step individual rewards of agent4: 0.28240340414287746
team_policy eval average team episode rewards of agent4: 10.0
team_policy eval idv catch total num of agent4: 14
team_policy eval team catch total num: 4
idv_policy eval average step individual rewards of agent0: 0.14125725372248343
idv_policy eval average team episode rewards of agent0: 17.5
idv_policy eval idv catch total num of agent0: 9
idv_policy eval team catch total num: 7
idv_policy eval average step individual rewards of agent1: 0.423840627791391
idv_policy eval average team episode rewards of agent1: 17.5
idv_policy eval idv catch total num of agent1: 20
idv_policy eval team catch total num: 7
idv_policy eval average step individual rewards of agent2: 0.05054652667911986
idv_policy eval average team episode rewards of agent2: 17.5
idv_policy eval idv catch total num of agent2: 5
idv_policy eval team catch total num: 7
idv_policy eval average step individual rewards of agent3: 0.43399825589563235
idv_policy eval average team episode rewards of agent3: 17.5
idv_policy eval idv catch total num of agent3: 20
idv_policy eval team catch total num: 7
idv_policy eval average step individual rewards of agent4: 0.4264219466326687
idv_policy eval average team episode rewards of agent4: 17.5
idv_policy eval idv catch total num of agent4: 20
idv_policy eval team catch total num: 7

 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.

team_policy eval average step individual rewards of agent0: 0.01788563426243522
team_policy eval average team episode rewards of agent0: 5.0
team_policy eval idv catch total num of agent0: 4
team_policy eval team catch total num: 2
team_policy eval average step individual rewards of agent1: 0.005786706034933333
team_policy eval average team episode rewards of agent1: 5.0
team_policy eval idv catch total num of agent1: 4
team_policy eval team catch total num: 2
team_policy eval average step individual rewards of agent2: 0.08084372355829046
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: 0.04250301961141734
team_policy eval average team episode rewards of agent3: 5.0
team_policy eval idv catch total num of agent3: 5
team_policy eval team catch total num: 2
team_policy eval average step individual rewards of agent4: 0.43355541064717557
team_policy eval average team episode rewards of agent4: 5.0
team_policy eval idv catch total num of agent4: 19
team_policy eval team catch total num: 2
idv_policy eval average step individual rewards of agent0: 0.17798521593945027
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: 0.08791706253284329
idv_policy eval average team episode rewards of agent1: 10.0
idv_policy eval idv catch total num of agent1: 7
idv_policy eval team catch total num: 4
idv_policy eval average step individual rewards of agent2: 0.11147205855744151
idv_policy eval average team episode rewards of agent2: 10.0
idv_policy eval idv catch total num of agent2: 7
idv_policy eval team catch total num: 4
idv_policy eval average step individual rewards of agent3: 0.12049226491336955
idv_policy eval average team episode rewards of agent3: 10.0
idv_policy eval idv catch total num of agent3: 9
idv_policy eval team catch total num: 4
idv_policy eval average step individual rewards of agent4: 0.3671012648784941
idv_policy eval average team episode rewards of agent4: 10.0
idv_policy eval idv catch total num of agent4: 18
idv_policy eval team catch total num: 4

 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.

team_policy eval average step individual rewards of agent0: 0.10439585839915588
team_policy eval average team episode rewards of agent0: 7.5
team_policy eval idv catch total num of agent0: 8
team_policy eval team catch total num: 3
team_policy eval average step individual rewards of agent1: 0.15394606234088687
team_policy eval average team episode rewards of agent1: 7.5
team_policy eval idv catch total num of agent1: 10
team_policy eval team catch total num: 3
team_policy eval average step individual rewards of agent2: 0.4400826316704146
team_policy eval average team episode rewards of agent2: 7.5
team_policy eval idv catch total num of agent2: 21
team_policy eval team catch total num: 3
team_policy eval average step individual rewards of agent3: -0.025040783350420762
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: -0.09027553517901438
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: 0.3497672428506883
idv_policy eval average team episode rewards of agent0: 30.0
idv_policy eval idv catch total num of agent0: 17
idv_policy eval team catch total num: 12
idv_policy eval average step individual rewards of agent1: 0.3049346960408554
idv_policy eval average team episode rewards of agent1: 30.0
idv_policy eval idv catch total num of agent1: 16
idv_policy eval team catch total num: 12
idv_policy eval average step individual rewards of agent2: 0.2796380534628766
idv_policy eval average team episode rewards of agent2: 30.0
idv_policy eval idv catch total num of agent2: 15
idv_policy eval team catch total num: 12
idv_policy eval average step individual rewards of agent3: 0.44079801164598637
idv_policy eval average team episode rewards of agent3: 30.0
idv_policy eval idv catch total num of agent3: 20
idv_policy eval team catch total num: 12
idv_policy eval average step individual rewards of agent4: 0.5117316495078884
idv_policy eval average team episode rewards of agent4: 30.0
idv_policy eval idv catch total num of agent4: 23
idv_policy eval team catch total num: 12

 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.

team_policy eval average step individual rewards of agent0: 0.11884383977762561
team_policy eval average team episode rewards of agent0: 17.5
team_policy eval idv catch total num of agent0: 8
team_policy eval team catch total num: 7
team_policy eval average step individual rewards of agent1: 0.23225185932801765
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: 0.004258150462669907
team_policy eval average team episode rewards of agent2: 17.5
team_policy eval idv catch total num of agent2: 3
team_policy eval team catch total num: 7
team_policy eval average step individual rewards of agent3: 0.3817114989610059
team_policy eval average team episode rewards of agent3: 17.5
team_policy eval idv catch total num of agent3: 18
team_policy eval team catch total num: 7
team_policy eval average step individual rewards of agent4: 0.11690445575004987
team_policy eval average team episode rewards of agent4: 17.5
team_policy eval idv catch total num of agent4: 8
team_policy eval team catch total num: 7
idv_policy eval average step individual rewards of agent0: 0.011369285600397428
idv_policy eval average team episode rewards of agent0: 12.5
idv_policy eval idv catch total num of agent0: 4
idv_policy eval team catch total num: 5
idv_policy eval average step individual rewards of agent1: 0.4202020436287215
idv_policy eval average team episode rewards of agent1: 12.5
idv_policy eval idv catch total num of agent1: 19
idv_policy eval team catch total num: 5
idv_policy eval average step individual rewards of agent2: 0.1296199410090905
idv_policy eval average team episode rewards of agent2: 12.5
idv_policy eval idv catch total num of agent2: 9
idv_policy eval team catch total num: 5
idv_policy eval average step individual rewards of agent3: 0.20555667909159137
idv_policy eval average team episode rewards of agent3: 12.5
idv_policy eval idv catch total num of agent3: 12
idv_policy eval team catch total num: 5
idv_policy eval average step individual rewards of agent4: 0.18431292312791445
idv_policy eval average team episode rewards of agent4: 12.5
idv_policy eval idv catch total num of agent4: 11
idv_policy eval team catch total num: 5

 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.

team_policy eval average step individual rewards of agent0: 0.06967975888128124
team_policy eval average team episode rewards of agent0: 25.0
team_policy eval idv catch total num of agent0: 6
team_policy eval team catch total num: 10
team_policy eval average step individual rewards of agent1: 0.27629639768352865
team_policy eval average team episode rewards of agent1: 25.0
team_policy eval idv catch total num of agent1: 14
team_policy eval team catch total num: 10
team_policy eval average step individual rewards of agent2: 0.4443875856784689
team_policy eval average team episode rewards of agent2: 25.0
team_policy eval idv catch total num of agent2: 21
team_policy eval team catch total num: 10
team_policy eval average step individual rewards of agent3: 0.12616101554406947
team_policy eval average team episode rewards of agent3: 25.0
team_policy eval idv catch total num of agent3: 8
team_policy eval team catch total num: 10
team_policy eval average step individual rewards of agent4: 0.28258227098449107
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: 0.20913638911420762
idv_policy eval average team episode rewards of agent0: 10.0
idv_policy eval idv catch total num of agent0: 12
idv_policy eval team catch total num: 4
idv_policy eval average step individual rewards of agent1: 0.07894061648873346
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: 0.28820605125530674
idv_policy eval average team episode rewards of agent2: 10.0
idv_policy eval idv catch total num of agent2: 15
idv_policy eval team catch total num: 4
idv_policy eval average step individual rewards of agent3: 0.19910176758715842
idv_policy eval average team episode rewards of agent3: 10.0
idv_policy eval idv catch total num of agent3: 10
idv_policy eval team catch total num: 4
idv_policy eval average step individual rewards of agent4: 0.09413417983828853
idv_policy eval average team episode rewards of agent4: 10.0
idv_policy eval idv catch total num of agent4: 7
idv_policy eval team catch total num: 4

 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.

team_policy eval average step individual rewards of agent0: 0.15468303910505152
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: -0.034010443168232755
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: -0.08308799772482389
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: -0.01504095717792242
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: 0.16470691358868475
team_policy eval average team episode rewards of agent4: 0.0
team_policy eval idv catch total num of agent4: 9
team_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent0: 0.09406368095634864
idv_policy eval average team episode rewards of agent0: 15.0
idv_policy eval idv catch total num of agent0: 7
idv_policy eval team catch total num: 6
idv_policy eval average step individual rewards of agent1: 0.08829437102391095
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: 0.14055365471332418
idv_policy eval average team episode rewards of agent2: 15.0
idv_policy eval idv catch total num of agent2: 9
idv_policy eval team catch total num: 6
idv_policy eval average step individual rewards of agent3: 0.1254284335755827
idv_policy eval average team episode rewards of agent3: 15.0
idv_policy eval idv catch total num of agent3: 8
idv_policy eval team catch total num: 6
idv_policy eval average step individual rewards of agent4: 0.0827062332081882
idv_policy eval average team episode rewards of agent4: 15.0
idv_policy eval idv catch total num of agent4: 8
idv_policy eval team catch total num: 6

 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.

team_policy eval average step individual rewards of agent0: -0.020641371056432056
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: 0.09727852053596306
team_policy eval average team episode rewards of agent1: 7.5
team_policy eval idv catch total num of agent1: 7
team_policy eval team catch total num: 3
team_policy eval average step individual rewards of agent2: 0.2680923509079942
team_policy eval average team episode rewards of agent2: 7.5
team_policy eval idv catch total num of agent2: 13
team_policy eval team catch total num: 3
team_policy eval average step individual rewards of agent3: 0.21322660562480675
team_policy eval average team episode rewards of agent3: 7.5
team_policy eval idv catch total num of agent3: 12
team_policy eval team catch total num: 3
team_policy eval average step individual rewards of agent4: 0.18763369970419752
team_policy eval average team episode rewards of agent4: 7.5
team_policy eval idv catch total num of agent4: 10
team_policy eval team catch total num: 3
idv_policy eval average step individual rewards of agent0: 0.032190363537159754
idv_policy eval average team episode rewards of agent0: 7.5
idv_policy eval idv catch total num of agent0: 5
idv_policy eval team catch total num: 3
idv_policy eval average step individual rewards of agent1: 0.2308794944504808
idv_policy eval average team episode rewards of agent1: 7.5
idv_policy eval idv catch total num of agent1: 12
idv_policy eval team catch total num: 3
idv_policy eval average step individual rewards of agent2: 0.2351665998910183
idv_policy eval average team episode rewards of agent2: 7.5
idv_policy eval idv catch total num of agent2: 13
idv_policy eval team catch total num: 3
idv_policy eval average step individual rewards of agent3: 0.3128874666134485
idv_policy eval average team episode rewards of agent3: 7.5
idv_policy eval idv catch total num of agent3: 16
idv_policy eval team catch total num: 3
idv_policy eval average step individual rewards of agent4: 0.013380005670646976
idv_policy eval average team episode rewards of agent4: 7.5
idv_policy eval idv catch total num of agent4: 5
idv_policy eval team catch total num: 3

 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.

team_policy eval average step individual rewards of agent0: 0.11379502614523823
team_policy eval average team episode rewards of agent0: 10.0
team_policy eval idv catch total num of agent0: 7
team_policy eval team catch total num: 4
team_policy eval average step individual rewards of agent1: 0.4408583961792618
team_policy eval average team episode rewards of agent1: 10.0
team_policy eval idv catch total num of agent1: 20
team_policy eval team catch total num: 4
team_policy eval average step individual rewards of agent2: 0.19809066753445329
team_policy eval average team episode rewards of agent2: 10.0
team_policy eval idv catch total num of agent2: 11
team_policy eval team catch total num: 4
team_policy eval average step individual rewards of agent3: 0.1402492636515189
team_policy eval average team episode rewards of agent3: 10.0
team_policy eval idv catch total num of agent3: 8
team_policy eval team catch total num: 4
team_policy eval average step individual rewards of agent4: 0.19626355037057683
team_policy eval average team episode rewards of agent4: 10.0
team_policy eval idv catch total num of agent4: 11
team_policy eval team catch total num: 4
idv_policy eval average step individual rewards of agent0: 0.16129352234325134
idv_policy eval average team episode rewards of agent0: 12.5
idv_policy eval idv catch total num of agent0: 10
idv_policy eval team catch total num: 5
idv_policy eval average step individual rewards of agent1: 0.49251337477979357
idv_policy eval average team episode rewards of agent1: 12.5
idv_policy eval idv catch total num of agent1: 22
idv_policy eval team catch total num: 5
idv_policy eval average step individual rewards of agent2: 0.23996378130971485
idv_policy eval average team episode rewards of agent2: 12.5
idv_policy eval idv catch total num of agent2: 12
idv_policy eval team catch total num: 5
idv_policy eval average step individual rewards of agent3: 0.019666488784805144
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: 0.21299013945598763
idv_policy eval average team episode rewards of agent4: 12.5
idv_policy eval idv catch total num of agent4: 11
idv_policy eval team catch total num: 5

 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.

team_policy eval average step individual rewards of agent0: 0.28746566855562966
team_policy eval average team episode rewards of agent0: 5.0
team_policy eval idv catch total num of agent0: 15
team_policy eval team catch total num: 2
team_policy eval average step individual rewards of agent1: 0.10361367408510681
team_policy eval average team episode rewards of agent1: 5.0
team_policy eval idv catch total num of agent1: 7
team_policy eval team catch total num: 2
team_policy eval average step individual rewards of agent2: 0.02564716278208791
team_policy eval average team episode rewards of agent2: 5.0
team_policy eval idv catch total num of agent2: 5
team_policy eval team catch total num: 2
team_policy eval average step individual rewards of agent3: -0.035859190199945795
team_policy eval average team episode rewards of agent3: 5.0
team_policy eval idv catch total num of agent3: 3
team_policy eval team catch total num: 2
team_policy eval average step individual rewards of agent4: 0.04870190508309258
team_policy eval average team episode rewards of agent4: 5.0
team_policy eval idv catch total num of agent4: 5
team_policy eval team catch total num: 2
idv_policy eval average step individual rewards of agent0: -0.08356833444902463
idv_policy eval average team episode rewards of agent0: 7.5
idv_policy eval idv catch total num of agent0: 0
idv_policy eval team catch total num: 3
idv_policy eval average step individual rewards of agent1: 0.12293002146075065
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: 0.15026851331301505
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: 0.1723752110849641
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: 0.5427907693487741
idv_policy eval average team episode rewards of agent4: 7.5
idv_policy eval idv catch total num of agent4: 24
idv_policy eval team catch total num: 3

 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.

team_policy eval average step individual rewards of agent0: 0.4222673928440663
team_policy eval average team episode rewards of agent0: 22.5
team_policy eval idv catch total num of agent0: 20
team_policy eval team catch total num: 9
team_policy eval average step individual rewards of agent1: 0.09706425789264449
team_policy eval average team episode rewards of agent1: 22.5
team_policy eval idv catch total num of agent1: 7
team_policy eval team catch total num: 9
team_policy eval average step individual rewards of agent2: 0.19458352725031466
team_policy eval average team episode rewards of agent2: 22.5
team_policy eval idv catch total num of agent2: 10
team_policy eval team catch total num: 9
team_policy eval average step individual rewards of agent3: 0.010611415249527613
team_policy eval average team episode rewards of agent3: 22.5
team_policy eval idv catch total num of agent3: 4
team_policy eval team catch total num: 9
team_policy eval average step individual rewards of agent4: 0.3624208846357284
team_policy eval average team episode rewards of agent4: 22.5
team_policy eval idv catch total num of agent4: 17
team_policy eval team catch total num: 9
idv_policy eval average step individual rewards of agent0: 0.08329811178090604
idv_policy eval average team episode rewards of agent0: 15.0
idv_policy eval idv catch total num of agent0: 7
idv_policy eval team catch total num: 6
idv_policy eval average step individual rewards of agent1: -0.08129730234221182
idv_policy eval average team episode rewards of agent1: 15.0
idv_policy eval idv catch total num of agent1: 1
idv_policy eval team catch total num: 6
idv_policy eval average step individual rewards of agent2: -0.1310920173768723
idv_policy eval average team episode rewards of agent2: 15.0
idv_policy eval idv catch total num of agent2: 0
idv_policy eval team catch total num: 6
idv_policy eval average step individual rewards of agent3: 0.23487179360226537
idv_policy eval average team episode rewards of agent3: 15.0
idv_policy eval idv catch total num of agent3: 13
idv_policy eval team catch total num: 6
idv_policy eval average step individual rewards of agent4: 0.459853834359937
idv_policy eval average team episode rewards of agent4: 15.0
idv_policy eval idv catch total num of agent4: 21
idv_policy eval team catch total num: 6

 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.

team_policy eval average step individual rewards of agent0: 0.10885017290218127
team_policy eval average team episode rewards of agent0: 12.5
team_policy eval idv catch total num of agent0: 7
team_policy eval team catch total num: 5
team_policy eval average step individual rewards of agent1: 0.2670704356187169
team_policy eval average team episode rewards of agent1: 12.5
team_policy eval idv catch total num of agent1: 13
team_policy eval team catch total num: 5
team_policy eval average step individual rewards of agent2: 0.1939227078720109
team_policy eval average team episode rewards of agent2: 12.5
team_policy eval idv catch total num of agent2: 11
team_policy eval team catch total num: 5
team_policy eval average step individual rewards of agent3: 0.17538619848805287
team_policy eval average team episode rewards of agent3: 12.5
team_policy eval idv catch total num of agent3: 10
team_policy eval team catch total num: 5
team_policy eval average step individual rewards of agent4: 0.1253551282938422
team_policy eval average team episode rewards of agent4: 12.5
team_policy eval idv catch total num of agent4: 8
team_policy eval team catch total num: 5
idv_policy eval average step individual rewards of agent0: 0.07986572793761905
idv_policy eval average team episode rewards of agent0: 15.0
idv_policy eval idv catch total num of agent0: 7
idv_policy eval team catch total num: 6
idv_policy eval average step individual rewards of agent1: 0.04939283428235361
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: 0.21753861017914772
idv_policy eval average team episode rewards of agent2: 15.0
idv_policy eval idv catch total num of agent2: 12
idv_policy eval team catch total num: 6
idv_policy eval average step individual rewards of agent3: 0.11415457138685525
idv_policy eval average team episode rewards of agent3: 15.0
idv_policy eval idv catch total num of agent3: 8
idv_policy eval team catch total num: 6
idv_policy eval average step individual rewards of agent4: -0.026291051569410265
idv_policy eval average team episode rewards of agent4: 15.0
idv_policy eval idv catch total num of agent4: 4
idv_policy eval team catch total num: 6

 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.

team_policy eval average step individual rewards of agent0: -0.08678132796907288
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: 0.17119064333384368
team_policy eval average team episode rewards of agent1: 0.0
team_policy eval idv catch total num of agent1: 10
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent2: 0.012327523561922132
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: -0.07766771985469058
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: -0.009321313982631528
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: 0.14537476191104162
idv_policy eval average team episode rewards of agent0: 2.5
idv_policy eval idv catch total num of agent0: 9
idv_policy eval team catch total num: 1
idv_policy eval average step individual rewards of agent1: 0.21227213838473266
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: -0.10157544672015505
idv_policy eval average team episode rewards of agent2: 2.5
idv_policy eval idv catch total num of agent2: 0
idv_policy eval team catch total num: 1
idv_policy eval average step individual rewards of agent3: 0.2710045152665716
idv_policy eval average team episode rewards of agent3: 2.5
idv_policy eval idv catch total num of agent3: 14
idv_policy eval team catch total num: 1
idv_policy eval average step individual rewards of agent4: -0.018081709736171143
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 9101/10000 episodes, total num timesteps 1820400/2000000, FPS 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.

team_policy eval average step individual rewards of agent0: 0.08924693840600384
team_policy eval average team episode rewards of agent0: 12.5
team_policy eval idv catch total num of agent0: 8
team_policy eval team catch total num: 5
team_policy eval average step individual rewards of agent1: 0.11634223930748398
team_policy eval average team episode rewards of agent1: 12.5
team_policy eval idv catch total num of agent1: 10
team_policy eval team catch total num: 5
team_policy eval average step individual rewards of agent2: 0.06886780968759618
team_policy eval average team episode rewards of agent2: 12.5
team_policy eval idv catch total num of agent2: 6
team_policy eval team catch total num: 5
team_policy eval average step individual rewards of agent3: -0.0189773380453102
team_policy eval average team episode rewards of agent3: 12.5
team_policy eval idv catch total num of agent3: 3
team_policy eval team catch total num: 5
team_policy eval average step individual rewards of agent4: -0.042859953052618166
team_policy eval average team episode rewards of agent4: 12.5
team_policy eval idv catch total num of agent4: 3
team_policy eval team catch total num: 5
idv_policy eval average step individual rewards of agent0: 0.1198175384684007
idv_policy eval average team episode rewards of agent0: 5.0
idv_policy eval idv catch total num of agent0: 8
idv_policy eval team catch total num: 2
idv_policy eval average step individual rewards of agent1: 0.06584845208124199
idv_policy eval average team episode rewards of agent1: 5.0
idv_policy eval idv catch total num of agent1: 6
idv_policy eval team catch total num: 2
idv_policy eval average step individual rewards of agent2: 0.01824932025757003
idv_policy eval average team episode rewards of agent2: 5.0
idv_policy eval idv catch total num of agent2: 5
idv_policy eval team catch total num: 2
idv_policy eval average step individual rewards of agent3: -0.06826168667533049
idv_policy eval average team episode rewards of agent3: 5.0
idv_policy eval idv catch total num of agent3: 1
idv_policy eval team catch total num: 2
idv_policy eval average step individual rewards of agent4: -0.007346239639274899
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 9126/10000 episodes, total num timesteps 1825400/2000000, FPS 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.

team_policy eval average step individual rewards of agent0: 0.13652459509668838
team_policy eval average team episode rewards of agent0: 0.0
team_policy eval idv catch total num of agent0: 10
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent1: -0.061773189061498396
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: -0.08722070118359863
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: -0.02315092015320583
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: 0.25029814619532387
team_policy eval average team episode rewards of agent4: 0.0
team_policy eval idv catch total num of agent4: 14
team_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent0: 0.2595875306064731
idv_policy eval average team episode rewards of agent0: 15.0
idv_policy eval idv catch total num of agent0: 14
idv_policy eval team catch total num: 6
idv_policy eval average step individual rewards of agent1: 0.1308302387558144
idv_policy eval average team episode rewards of agent1: 15.0
idv_policy eval idv catch total num of agent1: 8
idv_policy eval team catch total num: 6
idv_policy eval average step individual rewards of agent2: 0.4111954006276406
idv_policy eval average team episode rewards of agent2: 15.0
idv_policy eval idv catch total num of agent2: 19
idv_policy eval team catch total num: 6
idv_policy eval average step individual rewards of agent3: 0.0775237509049433
idv_policy eval average team episode rewards of agent3: 15.0
idv_policy eval idv catch total num of agent3: 7
idv_policy eval team catch total num: 6
idv_policy eval average step individual rewards of agent4: 0.12645908955740548
idv_policy eval average team episode rewards of agent4: 15.0
idv_policy eval idv catch total num of agent4: 8
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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.

team_policy eval average step individual rewards of agent0: -0.04504445767881753
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: 0.036815137990663646
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: 0.04192016559045192
team_policy eval average team episode rewards of agent2: 0.0
team_policy eval idv catch total num of agent2: 5
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent3: -0.0870830341490043
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: 0.2634933396226803
team_policy eval average team episode rewards of agent4: 0.0
team_policy eval idv catch total num of agent4: 13
team_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent0: 0.12942802286047017
idv_policy eval average team episode rewards of agent0: 0.0
idv_policy eval idv catch total num of agent0: 8
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent1: 0.15304603792079974
idv_policy eval average team episode rewards of agent1: 0.0
idv_policy eval idv catch total num of agent1: 10
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent2: 0.019180859320284217
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: -0.013010105507203207
idv_policy eval average team episode rewards of agent3: 0.0
idv_policy eval idv catch total num of agent3: 3
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent4: 0.1375035010205927
idv_policy eval average team episode rewards of agent4: 0.0
idv_policy eval idv catch total num of agent4: 9
idv_policy eval team catch total num: 0

 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.

team_policy eval average step individual rewards of agent0: 0.017998105873454847
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: 0.37205056944629306
team_policy eval average team episode rewards of agent1: 10.0
team_policy eval idv catch total num of agent1: 18
team_policy eval team catch total num: 4
team_policy eval average step individual rewards of agent2: 0.07780767428431513
team_policy eval average team episode rewards of agent2: 10.0
team_policy eval idv catch total num of agent2: 7
team_policy eval team catch total num: 4
team_policy eval average step individual rewards of agent3: 0.36058133251549307
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: 0.05613310331491105
team_policy eval average team episode rewards of agent4: 10.0
team_policy eval idv catch total num of agent4: 5
team_policy eval team catch total num: 4
idv_policy eval average step individual rewards of agent0: 0.198105063965831
idv_policy eval average team episode rewards of agent0: 5.0
idv_policy eval idv catch total num of agent0: 11
idv_policy eval team catch total num: 2
idv_policy eval average step individual rewards of agent1: 0.0380460761781638
idv_policy eval average team episode rewards of agent1: 5.0
idv_policy eval idv catch total num of agent1: 6
idv_policy eval team catch total num: 2
idv_policy eval average step individual rewards of agent2: 0.17958673608112655
idv_policy eval average team episode rewards of agent2: 5.0
idv_policy eval idv catch total num of agent2: 11
idv_policy eval team catch total num: 2
idv_policy eval average step individual rewards of agent3: -0.01867712342885519
idv_policy eval average team episode rewards of agent3: 5.0
idv_policy eval idv catch total num of agent3: 3
idv_policy eval team catch total num: 2
idv_policy eval average step individual rewards of agent4: 0.22070774165272083
idv_policy eval average team episode rewards of agent4: 5.0
idv_policy eval idv catch total num of agent4: 12
idv_policy eval team catch total num: 2

 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.

team_policy eval average step individual rewards of agent0: 0.2087769975424114
team_policy eval average team episode rewards of agent0: 15.0
team_policy eval idv catch total num of agent0: 11
team_policy eval team catch total num: 6
team_policy eval average step individual rewards of agent1: 0.18673259717795787
team_policy eval average team episode rewards of agent1: 15.0
team_policy eval idv catch total num of agent1: 10
team_policy eval team catch total num: 6
team_policy eval average step individual rewards of agent2: 0.274896054694759
team_policy eval average team episode rewards of agent2: 15.0
team_policy eval idv catch total num of agent2: 14
team_policy eval team catch total num: 6
team_policy eval average step individual rewards of agent3: 0.3370156890446861
team_policy eval average team episode rewards of agent3: 15.0
team_policy eval idv catch total num of agent3: 16
team_policy eval team catch total num: 6
team_policy eval average step individual rewards of agent4: 0.20536293496646735
team_policy eval average team episode rewards of agent4: 15.0
team_policy eval idv catch total num of agent4: 11
team_policy eval team catch total num: 6
idv_policy eval average step individual rewards of agent0: -0.016086396115683715
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: 0.2089723525959018
idv_policy eval average team episode rewards of agent1: 17.5
idv_policy eval idv catch total num of agent1: 11
idv_policy eval team catch total num: 7
idv_policy eval average step individual rewards of agent2: 0.17419377659674926
idv_policy eval average team episode rewards of agent2: 17.5
idv_policy eval idv catch total num of agent2: 9
idv_policy eval team catch total num: 7
idv_policy eval average step individual rewards of agent3: 0.25875245347099163
idv_policy eval average team episode rewards of agent3: 17.5
idv_policy eval idv catch total num of agent3: 13
idv_policy eval team catch total num: 7
idv_policy eval average step individual rewards of agent4: 0.20118503655302006
idv_policy eval average team episode rewards of agent4: 17.5
idv_policy eval idv catch total num of agent4: 11
idv_policy eval team catch total num: 7

 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.

team_policy eval average step individual rewards of agent0: 0.20661923099189028
team_policy eval average team episode rewards of agent0: 7.5
team_policy eval idv catch total num of agent0: 11
team_policy eval team catch total num: 3
team_policy eval average step individual rewards of agent1: 0.23583168377769398
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: 0.22870645707006676
team_policy eval average team episode rewards of agent2: 7.5
team_policy eval idv catch total num of agent2: 12
team_policy eval team catch total num: 3
team_policy eval average step individual rewards of agent3: -0.0063318854359446465
team_policy eval average team episode rewards of agent3: 7.5
team_policy eval idv catch total num of agent3: 3
team_policy eval team catch total num: 3
team_policy eval average step individual rewards of agent4: 0.30432875004874377
team_policy eval average team episode rewards of agent4: 7.5
team_policy eval idv catch total num of agent4: 15
team_policy eval team catch total num: 3
idv_policy eval average step individual rewards of agent0: 0.29424136203376233
idv_policy eval average team episode rewards of agent0: 27.5
idv_policy eval idv catch total num of agent0: 14
idv_policy eval team catch total num: 11
idv_policy eval average step individual rewards of agent1: 0.16547105584660543
idv_policy eval average team episode rewards of agent1: 27.5
idv_policy eval idv catch total num of agent1: 9
idv_policy eval team catch total num: 11
idv_policy eval average step individual rewards of agent2: 0.39892444301070595
idv_policy eval average team episode rewards of agent2: 27.5
idv_policy eval idv catch total num of agent2: 18
idv_policy eval team catch total num: 11
idv_policy eval average step individual rewards of agent3: 0.22172169943063388
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: 0.3099174404477131
idv_policy eval average team episode rewards of agent4: 27.5
idv_policy eval idv catch total num of agent4: 15
idv_policy eval team catch total num: 11

 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.

team_policy eval average step individual rewards of agent0: 0.03176013723364545
team_policy eval average team episode rewards of agent0: 5.0
team_policy eval idv catch total num of agent0: 5
team_policy eval team catch total num: 2
team_policy eval average step individual rewards of agent1: 0.2877781240101735
team_policy eval average team episode rewards of agent1: 5.0
team_policy eval idv catch total num of agent1: 14
team_policy eval team catch total num: 2
team_policy eval average step individual rewards of agent2: 0.0874078195224205
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: 0.1625970206519255
team_policy eval average team episode rewards of agent3: 5.0
team_policy eval idv catch total num of agent3: 10
team_policy eval team catch total num: 2
team_policy eval average step individual rewards of agent4: -0.09958627394761427
team_policy eval average team episode rewards of agent4: 5.0
team_policy eval idv catch total num of agent4: 0
team_policy eval team catch total num: 2
idv_policy eval average step individual rewards of agent0: 0.40181495438658515
idv_policy eval average team episode rewards of agent0: 7.5
idv_policy eval idv catch total num of agent0: 18
idv_policy eval team catch total num: 3
idv_policy eval average step individual rewards of agent1: 0.16552679262611883
idv_policy eval average team episode rewards of agent1: 7.5
idv_policy eval idv catch total num of agent1: 10
idv_policy eval team catch total num: 3
idv_policy eval average step individual rewards of agent2: 0.35959000094710647
idv_policy eval average team episode rewards of agent2: 7.5
idv_policy eval idv catch total num of agent2: 17
idv_policy eval team catch total num: 3
idv_policy eval average step individual rewards of agent3: 0.02955605547006148
idv_policy eval average team episode rewards of agent3: 7.5
idv_policy eval idv catch total num of agent3: 4
idv_policy eval team catch total num: 3
idv_policy eval average step individual rewards of agent4: -0.05984715560795517
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 9276/10000 episodes, total num timesteps 1855400/2000000, FPS 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.

team_policy eval average step individual rewards of agent0: 0.2687668770320565
team_policy eval average team episode rewards of agent0: 12.5
team_policy eval idv catch total num of agent0: 13
team_policy eval team catch total num: 5
team_policy eval average step individual rewards of agent1: 0.29483398872861094
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: 0.19509981801629955
team_policy eval average team episode rewards of agent2: 12.5
team_policy eval idv catch total num of agent2: 11
team_policy eval team catch total num: 5
team_policy eval average step individual rewards of agent3: 0.1326521306593407
team_policy eval average team episode rewards of agent3: 12.5
team_policy eval idv catch total num of agent3: 8
team_policy eval team catch total num: 5
team_policy eval average step individual rewards of agent4: 0.2021156136748899
team_policy eval average team episode rewards of agent4: 12.5
team_policy eval idv catch total num of agent4: 11
team_policy eval team catch total num: 5
idv_policy eval average step individual rewards of agent0: 0.11772501181062289
idv_policy eval average team episode rewards of agent0: 10.0
idv_policy eval idv catch total num of agent0: 7
idv_policy eval team catch total num: 4
idv_policy eval average step individual rewards of agent1: 0.2963437852259747
idv_policy eval average team episode rewards of agent1: 10.0
idv_policy eval idv catch total num of agent1: 15
idv_policy eval team catch total num: 4
idv_policy eval average step individual rewards of agent2: 0.1978467031504093
idv_policy eval average team episode rewards of agent2: 10.0
idv_policy eval idv catch total num of agent2: 11
idv_policy eval team catch total num: 4
idv_policy eval average step individual rewards of agent3: -0.02289939600555175
idv_policy eval average team episode rewards of agent3: 10.0
idv_policy eval idv catch total num of agent3: 2
idv_policy eval team catch total num: 4
idv_policy eval average step individual rewards of agent4: -0.07574054933079837
idv_policy eval average team episode rewards of agent4: 10.0
idv_policy eval idv catch total num of agent4: 1
idv_policy eval team catch total num: 4

 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.

team_policy eval average step individual rewards of agent0: 0.36759324367798046
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: 0.17802513645179224
team_policy eval average team episode rewards of agent1: 20.0
team_policy eval idv catch total num of agent1: 10
team_policy eval team catch total num: 8
team_policy eval average step individual rewards of agent2: 0.12827010121631044
team_policy eval average team episode rewards of agent2: 20.0
team_policy eval idv catch total num of agent2: 10
team_policy eval team catch total num: 8
team_policy eval average step individual rewards of agent3: 0.03479820426607052
team_policy eval average team episode rewards of agent3: 20.0
team_policy eval idv catch total num of agent3: 6
team_policy eval team catch total num: 8
team_policy eval average step individual rewards of agent4: 0.14479613834103872
team_policy eval average team episode rewards of agent4: 20.0
team_policy eval idv catch total num of agent4: 9
team_policy eval team catch total num: 8
idv_policy eval average step individual rewards of agent0: 0.2854971766023321
idv_policy eval average team episode rewards of agent0: 45.0
idv_policy eval idv catch total num of agent0: 14
idv_policy eval team catch total num: 18
idv_policy eval average step individual rewards of agent1: 0.4244763471561441
idv_policy eval average team episode rewards of agent1: 45.0
idv_policy eval idv catch total num of agent1: 19
idv_policy eval team catch total num: 18
idv_policy eval average step individual rewards of agent2: 0.35421704661839565
idv_policy eval average team episode rewards of agent2: 45.0
idv_policy eval idv catch total num of agent2: 16
idv_policy eval team catch total num: 18
idv_policy eval average step individual rewards of agent3: 0.6524728890253271
idv_policy eval average team episode rewards of agent3: 45.0
idv_policy eval idv catch total num of agent3: 28
idv_policy eval team catch total num: 18
idv_policy eval average step individual rewards of agent4: 0.4063834703760778
idv_policy eval average team episode rewards of agent4: 45.0
idv_policy eval idv catch total num of agent4: 18
idv_policy eval team catch total num: 18

 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.

team_policy eval average step individual rewards of agent0: 0.19075023994507012
team_policy eval average team episode rewards of agent0: 12.5
team_policy eval idv catch total num of agent0: 10
team_policy eval team catch total num: 5
team_policy eval average step individual rewards of agent1: 0.2631996670050211
team_policy eval average team episode rewards of agent1: 12.5
team_policy eval idv catch total num of agent1: 13
team_policy eval team catch total num: 5
team_policy eval average step individual rewards of agent2: 0.11312843141089958
team_policy eval average team episode rewards of agent2: 12.5
team_policy eval idv catch total num of agent2: 7
team_policy eval team catch total num: 5
team_policy eval average step individual rewards of agent3: 0.27157353498178133
team_policy eval average team episode rewards of agent3: 12.5
team_policy eval idv catch total num of agent3: 13
team_policy eval team catch total num: 5
team_policy eval average step individual rewards of agent4: 0.45269915200200717
team_policy eval average team episode rewards of agent4: 12.5
team_policy eval idv catch total num of agent4: 20
team_policy eval team catch total num: 5
idv_policy eval average step individual rewards of agent0: 0.40586034920014297
idv_policy eval average team episode rewards of agent0: 15.0
idv_policy eval idv catch total num of agent0: 19
idv_policy eval team catch total num: 6
idv_policy eval average step individual rewards of agent1: 0.2597229449441646
idv_policy eval average team episode rewards of agent1: 15.0
idv_policy eval idv catch total num of agent1: 14
idv_policy eval team catch total num: 6
idv_policy eval average step individual rewards of agent2: 0.0896344345236267
idv_policy eval average team episode rewards of agent2: 15.0
idv_policy eval idv catch total num of agent2: 7
idv_policy eval team catch total num: 6
idv_policy eval average step individual rewards of agent3: 0.3333403170161466
idv_policy eval average team episode rewards of agent3: 15.0
idv_policy eval idv catch total num of agent3: 17
idv_policy eval team catch total num: 6
idv_policy eval average step individual rewards of agent4: 0.05137189135699069
idv_policy eval average team episode rewards of agent4: 15.0
idv_policy eval idv catch total num of agent4: 5
idv_policy eval team catch total num: 6

 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.

team_policy eval average step individual rewards of agent0: 0.05936733385457977
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: 0.19541500555261887
team_policy eval average team episode rewards of agent1: 10.0
team_policy eval idv catch total num of agent1: 10
team_policy eval team catch total num: 4
team_policy eval average step individual rewards of agent2: 0.2714676129187896
team_policy eval average team episode rewards of agent2: 10.0
team_policy eval idv catch total num of agent2: 13
team_policy eval team catch total num: 4
team_policy eval average step individual rewards of agent3: 0.09968304931026062
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: 0.23461282389248328
team_policy eval average team episode rewards of agent4: 10.0
team_policy eval idv catch total num of agent4: 12
team_policy eval team catch total num: 4
idv_policy eval average step individual rewards of agent0: 0.2829895506493024
idv_policy eval average team episode rewards of agent0: 17.5
idv_policy eval idv catch total num of agent0: 14
idv_policy eval team catch total num: 7
idv_policy eval average step individual rewards of agent1: 0.32025739536110737
idv_policy eval average team episode rewards of agent1: 17.5
idv_policy eval idv catch total num of agent1: 16
idv_policy eval team catch total num: 7
idv_policy eval average step individual rewards of agent2: 0.2595821566990671
idv_policy eval average team episode rewards of agent2: 17.5
idv_policy eval idv catch total num of agent2: 14
idv_policy eval team catch total num: 7
idv_policy eval average step individual rewards of agent3: 0.09511263003024814
idv_policy eval average team episode rewards of agent3: 17.5
idv_policy eval idv catch total num of agent3: 7
idv_policy eval team catch total num: 7
idv_policy eval average step individual rewards of agent4: 0.05705431423490797
idv_policy eval average team episode rewards of agent4: 17.5
idv_policy eval idv catch total num of agent4: 6
idv_policy eval team catch total num: 7

 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 254.


 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 254.


 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 254.


 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 254.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.

team_policy eval average step individual rewards of agent0: 0.2945331650777748
team_policy eval average team episode rewards of agent0: 7.5
team_policy eval idv catch total num of agent0: 14
team_policy eval team catch total num: 3
team_policy eval average step individual rewards of agent1: 0.3012088279909354
team_policy eval average team episode rewards of agent1: 7.5
team_policy eval idv catch total num of agent1: 15
team_policy eval team catch total num: 3
team_policy eval average step individual rewards of agent2: 0.2358727908548629
team_policy eval average team episode rewards of agent2: 7.5
team_policy eval idv catch total num of agent2: 13
team_policy eval team catch total num: 3
team_policy eval average step individual rewards of agent3: 0.26733925826846533
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: 0.23744222269950693
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: 0.07946385351958739
idv_policy eval average team episode rewards of agent0: 2.5
idv_policy eval idv catch total num of agent0: 7
idv_policy eval team catch total num: 1
idv_policy eval average step individual rewards of agent1: 0.046206143540367596
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: -0.10138944258622057
idv_policy eval average team episode rewards of agent2: 2.5
idv_policy eval idv catch total num of agent2: 0
idv_policy eval team catch total num: 1
idv_policy eval average step individual rewards of agent3: 0.23237379260364144
idv_policy eval average team episode rewards of agent3: 2.5
idv_policy eval idv catch total num of agent3: 13
idv_policy eval team catch total num: 1
idv_policy eval average step individual rewards of agent4: 0.189176130308347
idv_policy eval average team episode rewards of agent4: 2.5
idv_policy eval idv catch total num of agent4: 11
idv_policy eval team catch total num: 1

 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.

team_policy eval average step individual rewards of agent0: 0.24678096147261272
team_policy eval average team episode rewards of agent0: 10.0
team_policy eval idv catch total num of agent0: 13
team_policy eval team catch total num: 4
team_policy eval average step individual rewards of agent1: 0.09715078487047149
team_policy eval average team episode rewards of agent1: 10.0
team_policy eval idv catch total num of agent1: 7
team_policy eval team catch total num: 4
team_policy eval average step individual rewards of agent2: 0.2767478321276339
team_policy eval average team episode rewards of agent2: 10.0
team_policy eval idv catch total num of agent2: 14
team_policy eval team catch total num: 4
team_policy eval average step individual rewards of agent3: 0.23171910919871394
team_policy eval average team episode rewards of agent3: 10.0
team_policy eval idv catch total num of agent3: 12
team_policy eval team catch total num: 4
team_policy eval average step individual rewards of agent4: -0.025832094860445437
team_policy eval average team episode rewards of agent4: 10.0
team_policy eval idv catch total num of agent4: 3
team_policy eval team catch total num: 4
idv_policy eval average step individual rewards of agent0: 0.3818875238402111
idv_policy eval average team episode rewards of agent0: 10.0
idv_policy eval idv catch total num of agent0: 18
idv_policy eval team catch total num: 4
idv_policy eval average step individual rewards of agent1: 0.2258807563545053
idv_policy eval average team episode rewards of agent1: 10.0
idv_policy eval idv catch total num of agent1: 12
idv_policy eval team catch total num: 4
idv_policy eval average step individual rewards of agent2: 0.26195291924013486
idv_policy eval average team episode rewards of agent2: 10.0
idv_policy eval idv catch total num of agent2: 13
idv_policy eval team catch total num: 4
idv_policy eval average step individual rewards of agent3: 0.012379162709100777
idv_policy eval average team episode rewards of agent3: 10.0
idv_policy eval idv catch total num of agent3: 4
idv_policy eval team catch total num: 4
idv_policy eval average step individual rewards of agent4: 0.11001504118476316
idv_policy eval average team episode rewards of agent4: 10.0
idv_policy eval idv catch total num of agent4: 7
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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.

team_policy eval average step individual rewards of agent0: -0.0314874910684875
team_policy eval average team episode rewards of agent0: 2.5
team_policy eval idv catch total num of agent0: 3
team_policy eval team catch total num: 1
team_policy eval average step individual rewards of agent1: 0.07000876465259523
team_policy eval average team episode rewards of agent1: 2.5
team_policy eval idv catch total num of agent1: 6
team_policy eval team catch total num: 1
team_policy eval average step individual rewards of agent2: -0.04561140256748802
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: -0.009565404602507438
team_policy eval average team episode rewards of agent3: 2.5
team_policy eval idv catch total num of agent3: 3
team_policy eval team catch total num: 1
team_policy eval average step individual rewards of agent4: 0.010016134070146893
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: -0.01926645712716115
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: 0.0910823853691716
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: 0.35273200633349205
idv_policy eval average team episode rewards of agent2: 2.5
idv_policy eval idv catch total num of agent2: 16
idv_policy eval team catch total num: 1
idv_policy eval average step individual rewards of agent3: -0.040896767305054114
idv_policy eval average team episode rewards of agent3: 2.5
idv_policy eval idv catch total num of agent3: 3
idv_policy eval team catch total num: 1
idv_policy eval average step individual rewards of agent4: 0.22303929664279504
idv_policy eval average team episode rewards of agent4: 2.5
idv_policy eval idv catch total num of agent4: 11
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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.

team_policy eval average step individual rewards of agent0: 0.3896723288982139
team_policy eval average team episode rewards of agent0: 12.5
team_policy eval idv catch total num of agent0: 18
team_policy eval team catch total num: 5
team_policy eval average step individual rewards of agent1: 0.3824858117932218
team_policy eval average team episode rewards of agent1: 12.5
team_policy eval idv catch total num of agent1: 18
team_policy eval team catch total num: 5
team_policy eval average step individual rewards of agent2: 0.0830432272210341
team_policy eval average team episode rewards of agent2: 12.5
team_policy eval idv catch total num of agent2: 7
team_policy eval team catch total num: 5
team_policy eval average step individual rewards of agent3: 0.3503653812314213
team_policy eval average team episode rewards of agent3: 12.5
team_policy eval idv catch total num of agent3: 17
team_policy eval team catch total num: 5
team_policy eval average step individual rewards of agent4: 0.09509844881233309
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: -0.08418642222725295
idv_policy eval average team episode rewards of agent0: 17.5
idv_policy eval idv catch total num of agent0: 1
idv_policy eval team catch total num: 7
idv_policy eval average step individual rewards of agent1: 0.38256209753745773
idv_policy eval average team episode rewards of agent1: 17.5
idv_policy eval idv catch total num of agent1: 18
idv_policy eval team catch total num: 7
idv_policy eval average step individual rewards of agent2: 0.40047175156341935
idv_policy eval average team episode rewards of agent2: 17.5
idv_policy eval idv catch total num of agent2: 18
idv_policy eval team catch total num: 7
idv_policy eval average step individual rewards of agent3: 0.07477489639127118
idv_policy eval average team episode rewards of agent3: 17.5
idv_policy eval idv catch total num of agent3: 7
idv_policy eval team catch total num: 7
idv_policy eval average step individual rewards of agent4: 0.305154793333781
idv_policy eval average team episode rewards of agent4: 17.5
idv_policy eval idv catch total num of agent4: 15
idv_policy eval team catch total num: 7

 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.

team_policy eval average step individual rewards of agent0: -0.003879817483248691
team_policy eval average team episode rewards of agent0: 0.0
team_policy eval idv catch total num of agent0: 4
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent1: -0.09816117703669969
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: -0.019375757464742887
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: -0.10460975167335927
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: 0.04926746448473104
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: 0.4370335220867741
idv_policy eval average team episode rewards of agent0: 5.0
idv_policy eval idv catch total num of agent0: 20
idv_policy eval team catch total num: 2
idv_policy eval average step individual rewards of agent1: 0.011814821015963766
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: -0.026987205391191534
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: 0.06825923322623237
idv_policy eval average team episode rewards of agent3: 5.0
idv_policy eval idv catch total num of agent3: 6
idv_policy eval team catch total num: 2
idv_policy eval average step individual rewards of agent4: -0.027082150281351337
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 9501/10000 episodes, total num timesteps 1900400/2000000, FPS 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.

team_policy eval average step individual rewards of agent0: 0.028930113047713455
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: 0.17537072154087802
team_policy eval average team episode rewards of agent1: 2.5
team_policy eval idv catch total num of agent1: 11
team_policy eval team catch total num: 1
team_policy eval average step individual rewards of agent2: -0.08364291223505553
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: -0.027455435748702062
team_policy eval average team episode rewards of agent3: 2.5
team_policy eval idv catch total num of agent3: 3
team_policy eval team catch total num: 1
team_policy eval average step individual rewards of agent4: 0.05575989233695402
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: 0.028252668171418357
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: -0.0721917739921881
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: 0.04234349110784415
idv_policy eval average team episode rewards of agent2: 2.5
idv_policy eval idv catch total num of agent2: 7
idv_policy eval team catch total num: 1
idv_policy eval average step individual rewards of agent3: -0.06120835510947879
idv_policy eval average team episode rewards of agent3: 2.5
idv_policy eval idv catch total num of agent3: 3
idv_policy eval team catch total num: 1
idv_policy eval average step individual rewards of agent4: 0.09269825503046597
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 9526/10000 episodes, total num timesteps 1905400/2000000, FPS 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.

team_policy eval average step individual rewards of agent0: 0.02622421979204173
team_policy eval average team episode rewards of agent0: 0.0
team_policy eval idv catch total num of agent0: 4
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent1: 0.08771694973030536
team_policy eval average team episode rewards of agent1: 0.0
team_policy eval idv catch total num of agent1: 7
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent2: 0.07989100285931222
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: 0.4229002960032393
team_policy eval average team episode rewards of agent3: 0.0
team_policy eval idv catch total num of agent3: 20
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent4: 0.16828380306116741
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: -0.05867008908523122
idv_policy eval average team episode rewards of agent0: 2.5
idv_policy eval idv catch total num of agent0: 1
idv_policy eval team catch total num: 1
idv_policy eval average step individual rewards of agent1: 0.05260396420208174
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: -0.05726826390954474
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: 0.17844089372927144
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: -0.013491668459483847
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 9551/10000 episodes, total num timesteps 1910400/2000000, FPS 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.

team_policy eval average step individual rewards of agent0: -0.0011631396910586388
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: 0.07924130426705572
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: 0.06137701280838517
team_policy eval average team episode rewards of agent2: 2.5
team_policy eval idv catch total num of agent2: 5
team_policy eval team catch total num: 1
team_policy eval average step individual rewards of agent3: 0.23924533984782356
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: 0.010486097087324993
team_policy eval average team episode rewards of agent4: 2.5
team_policy eval idv catch total num of agent4: 5
team_policy eval team catch total num: 1
idv_policy eval average step individual rewards of agent0: 0.31194725186773664
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: 0.16599563319379496
idv_policy eval average team episode rewards of agent1: 12.5
idv_policy eval idv catch total num of agent1: 10
idv_policy eval team catch total num: 5
idv_policy eval average step individual rewards of agent2: 0.18478906622171387
idv_policy eval average team episode rewards of agent2: 12.5
idv_policy eval idv catch total num of agent2: 10
idv_policy eval team catch total num: 5
idv_policy eval average step individual rewards of agent3: 0.12173995963335932
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: 0.5713805783999765
idv_policy eval average team episode rewards of agent4: 12.5
idv_policy eval idv catch total num of agent4: 25
idv_policy eval team catch total num: 5

 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.

team_policy eval average step individual rewards of agent0: 0.12155725259795004
team_policy eval average team episode rewards of agent0: 17.5
team_policy eval idv catch total num of agent0: 8
team_policy eval team catch total num: 7
team_policy eval average step individual rewards of agent1: 0.0514296500839475
team_policy eval average team episode rewards of agent1: 17.5
team_policy eval idv catch total num of agent1: 6
team_policy eval team catch total num: 7
team_policy eval average step individual rewards of agent2: 0.05301759890550932
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: -0.036205009890041176
team_policy eval average team episode rewards of agent3: 17.5
team_policy eval idv catch total num of agent3: 2
team_policy eval team catch total num: 7
team_policy eval average step individual rewards of agent4: 0.20085813167447245
team_policy eval average team episode rewards of agent4: 17.5
team_policy eval idv catch total num of agent4: 10
team_policy eval team catch total num: 7
idv_policy eval average step individual rewards of agent0: 0.09991027761614577
idv_policy eval average team episode rewards of agent0: 10.0
idv_policy eval idv catch total num of agent0: 7
idv_policy eval team catch total num: 4
idv_policy eval average step individual rewards of agent1: 0.28348046482376144
idv_policy eval average team episode rewards of agent1: 10.0
idv_policy eval idv catch total num of agent1: 14
idv_policy eval team catch total num: 4
idv_policy eval average step individual rewards of agent2: 0.2470195445419958
idv_policy eval average team episode rewards of agent2: 10.0
idv_policy eval idv catch total num of agent2: 12
idv_policy eval team catch total num: 4
idv_policy eval average step individual rewards of agent3: -0.03616085653235701
idv_policy eval average team episode rewards of agent3: 10.0
idv_policy eval idv catch total num of agent3: 2
idv_policy eval team catch total num: 4
idv_policy eval average step individual rewards of agent4: 0.0739379014566497
idv_policy eval average team episode rewards of agent4: 10.0
idv_policy eval idv catch total num of agent4: 6
idv_policy eval team catch total num: 4

 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 253.


 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 253.


 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 253.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.

team_policy eval average step individual rewards of agent0: -0.043040984122019905
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: 0.06028136571490373
team_policy eval average team episode rewards of agent1: 0.0
team_policy eval idv catch total num of agent1: 7
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent2: 0.019532059231130657
team_policy eval average team episode rewards of agent2: 0.0
team_policy eval idv catch total num of agent2: 5
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent3: -0.006467075367007746
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: -0.11901773796877459
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: 0.10537320630495475
idv_policy eval average team episode rewards of agent0: 2.5
idv_policy eval idv catch total num of agent0: 9
idv_policy eval team catch total num: 1
idv_policy eval average step individual rewards of agent1: -0.0913339705971563
idv_policy eval average team episode rewards of agent1: 2.5
idv_policy eval idv catch total num of agent1: 1
idv_policy eval team catch total num: 1
idv_policy eval average step individual rewards of agent2: -0.05467573515029878
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: -0.12509615787797085
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: 0.16891268369953624
idv_policy eval average team episode rewards of agent4: 2.5
idv_policy eval idv catch total num of agent4: 10
idv_policy eval team catch total num: 1

 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.

team_policy eval average step individual rewards of agent0: 0.19732633830664847
team_policy eval average team episode rewards of agent0: 0.0
team_policy eval idv catch total num of agent0: 11
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent1: 0.11374126275650223
team_policy eval average team episode rewards of agent1: 0.0
team_policy eval idv catch total num of agent1: 7
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent2: -0.041587819013658614
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: 0.23687458298762432
team_policy eval average team episode rewards of agent3: 0.0
team_policy eval idv catch total num of agent3: 12
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent4: -0.006469056386678043
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: 0.013498848860428705
idv_policy eval average team episode rewards of agent0: 7.5
idv_policy eval idv catch total num of agent0: 4
idv_policy eval team catch total num: 3
idv_policy eval average step individual rewards of agent1: 0.02732771557792656
idv_policy eval average team episode rewards of agent1: 7.5
idv_policy eval idv catch total num of agent1: 4
idv_policy eval team catch total num: 3
idv_policy eval average step individual rewards of agent2: 0.10244765590120075
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: 0.16664455882266652
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: 0.09526242633978761
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 9651/10000 episodes, total num timesteps 1930400/2000000, FPS 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.

team_policy eval average step individual rewards of agent0: 0.1488917585401368
team_policy eval average team episode rewards of agent0: 17.5
team_policy eval idv catch total num of agent0: 8
team_policy eval team catch total num: 7
team_policy eval average step individual rewards of agent1: 0.2680120651826821
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: 0.2966283900086968
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: 0.1017097050497189
team_policy eval average team episode rewards of agent3: 17.5
team_policy eval idv catch total num of agent3: 6
team_policy eval team catch total num: 7
team_policy eval average step individual rewards of agent4: 0.48584979907373205
team_policy eval average team episode rewards of agent4: 17.5
team_policy eval idv catch total num of agent4: 21
team_policy eval team catch total num: 7
idv_policy eval average step individual rewards of agent0: 0.1661960263382415
idv_policy eval average team episode rewards of agent0: 22.5
idv_policy eval idv catch total num of agent0: 9
idv_policy eval team catch total num: 9
idv_policy eval average step individual rewards of agent1: 0.42104622001996517
idv_policy eval average team episode rewards of agent1: 22.5
idv_policy eval idv catch total num of agent1: 19
idv_policy eval team catch total num: 9
idv_policy eval average step individual rewards of agent2: 0.1770707102454378
idv_policy eval average team episode rewards of agent2: 22.5
idv_policy eval idv catch total num of agent2: 10
idv_policy eval team catch total num: 9
idv_policy eval average step individual rewards of agent3: 0.15743486450254826
idv_policy eval average team episode rewards of agent3: 22.5
idv_policy eval idv catch total num of agent3: 9
idv_policy eval team catch total num: 9
idv_policy eval average step individual rewards of agent4: 0.4107020529133457
idv_policy eval average team episode rewards of agent4: 22.5
idv_policy eval idv catch total num of agent4: 19
idv_policy eval team catch total num: 9

 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.

team_policy eval average step individual rewards of agent0: 0.13810281747763423
team_policy eval average team episode rewards of agent0: 7.5
team_policy eval idv catch total num of agent0: 8
team_policy eval team catch total num: 3
team_policy eval average step individual rewards of agent1: 0.19988022089201013
team_policy eval average team episode rewards of agent1: 7.5
team_policy eval idv catch total num of agent1: 10
team_policy eval team catch total num: 3
team_policy eval average step individual rewards of agent2: -0.032331066730739044
team_policy eval average team episode rewards of agent2: 7.5
team_policy eval idv catch total num of agent2: 2
team_policy eval team catch total num: 3
team_policy eval average step individual rewards of agent3: 0.11347523851072903
team_policy eval average team episode rewards of agent3: 7.5
team_policy eval idv catch total num of agent3: 7
team_policy eval team catch total num: 3
team_policy eval average step individual rewards of agent4: 0.25023050749020487
team_policy eval average team episode rewards of agent4: 7.5
team_policy eval idv catch total num of agent4: 13
team_policy eval team catch total num: 3
idv_policy eval average step individual rewards of agent0: 0.15726989505659886
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: 0.16264353404457704
idv_policy eval average team episode rewards of agent1: 5.0
idv_policy eval idv catch total num of agent1: 9
idv_policy eval team catch total num: 2
idv_policy eval average step individual rewards of agent2: 0.5674029251758425
idv_policy eval average team episode rewards of agent2: 5.0
idv_policy eval idv catch total num of agent2: 25
idv_policy eval team catch total num: 2
idv_policy eval average step individual rewards of agent3: 0.045489100935411884
idv_policy eval average team episode rewards of agent3: 5.0
idv_policy eval idv catch total num of agent3: 5
idv_policy eval team catch total num: 2
idv_policy eval average step individual rewards of agent4: -0.09585166071572473
idv_policy eval average team episode rewards of agent4: 5.0
idv_policy eval idv catch total num of agent4: 0
idv_policy eval team catch total num: 2

 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.

team_policy eval average step individual rewards of agent0: 0.038458105697678706
team_policy eval average team episode rewards of agent0: 0.0
team_policy eval idv catch total num of agent0: 5
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent1: -0.028988382598356236
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: -0.0487462014240101
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: -0.029546759002562425
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: -0.0020886756668108395
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: -0.12377150716776816
idv_policy eval average team episode rewards of agent0: 2.5
idv_policy eval idv catch total num of agent0: 0
idv_policy eval team catch total num: 1
idv_policy eval average step individual rewards of agent1: -0.07535927351420511
idv_policy eval average team episode rewards of agent1: 2.5
idv_policy eval idv catch total num of agent1: 1
idv_policy eval team catch total num: 1
idv_policy eval average step individual rewards of agent2: -0.09498407432863251
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: 0.018730840954754687
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: -0.0776372997613033
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 9726/10000 episodes, total num timesteps 1945400/2000000, FPS 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.

team_policy eval average step individual rewards of agent0: 0.0910096017146374
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: 0.12988624750966024
team_policy eval average team episode rewards of agent1: 10.0
team_policy eval idv catch total num of agent1: 9
team_policy eval team catch total num: 4
team_policy eval average step individual rewards of agent2: 0.02910331286119634
team_policy eval average team episode rewards of agent2: 10.0
team_policy eval idv catch total num of agent2: 5
team_policy eval team catch total num: 4
team_policy eval average step individual rewards of agent3: 0.14664949914244865
team_policy eval average team episode rewards of agent3: 10.0
team_policy eval idv catch total num of agent3: 10
team_policy eval team catch total num: 4
team_policy eval average step individual rewards of agent4: 0.023751284241166463
team_policy eval average team episode rewards of agent4: 10.0
team_policy eval idv catch total num of agent4: 4
team_policy eval team catch total num: 4
idv_policy eval average step individual rewards of agent0: 0.06705825388525194
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: 0.07758344141035764
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: 0.17124692589773086
idv_policy eval average team episode rewards of agent2: 10.0
idv_policy eval idv catch total num of agent2: 10
idv_policy eval team catch total num: 4
idv_policy eval average step individual rewards of agent3: 0.19022015589619282
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: -0.01073848457969775
idv_policy eval average team episode rewards of agent4: 10.0
idv_policy eval idv catch total num of agent4: 3
idv_policy eval team catch total num: 4

 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.

team_policy eval average step individual rewards of agent0: 0.08030130584018887
team_policy eval average team episode rewards of agent0: 0.0
team_policy eval idv catch total num of agent0: 7
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent1: 0.0858582514221356
team_policy eval average team episode rewards of agent1: 0.0
team_policy eval idv catch total num of agent1: 7
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent2: 0.04981659105286806
team_policy eval average team episode rewards of agent2: 0.0
team_policy eval idv catch total num of agent2: 6
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent3: 0.09458479778179399
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: -0.08766102884638423
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: 0.19401842624657667
idv_policy eval average team episode rewards of agent0: 15.0
idv_policy eval idv catch total num of agent0: 11
idv_policy eval team catch total num: 6
idv_policy eval average step individual rewards of agent1: 0.2269290874661153
idv_policy eval average team episode rewards of agent1: 15.0
idv_policy eval idv catch total num of agent1: 12
idv_policy eval team catch total num: 6
idv_policy eval average step individual rewards of agent2: 0.3071813780013512
idv_policy eval average team episode rewards of agent2: 15.0
idv_policy eval idv catch total num of agent2: 15
idv_policy eval team catch total num: 6
idv_policy eval average step individual rewards of agent3: 0.10464256550233354
idv_policy eval average team episode rewards of agent3: 15.0
idv_policy eval idv catch total num of agent3: 8
idv_policy eval team catch total num: 6
idv_policy eval average step individual rewards of agent4: 0.09211586175385916
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 9776/10000 episodes, total num timesteps 1955400/2000000, FPS 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.

team_policy eval average step individual rewards of agent0: 0.16931298223613397
team_policy eval average team episode rewards of agent0: 15.0
team_policy eval idv catch total num of agent0: 9
team_policy eval team catch total num: 6
team_policy eval average step individual rewards of agent1: 0.2148989907754041
team_policy eval average team episode rewards of agent1: 15.0
team_policy eval idv catch total num of agent1: 11
team_policy eval team catch total num: 6
team_policy eval average step individual rewards of agent2: 0.264601632823393
team_policy eval average team episode rewards of agent2: 15.0
team_policy eval idv catch total num of agent2: 13
team_policy eval team catch total num: 6
team_policy eval average step individual rewards of agent3: 0.23357791624472612
team_policy eval average team episode rewards of agent3: 15.0
team_policy eval idv catch total num of agent3: 12
team_policy eval team catch total num: 6
team_policy eval average step individual rewards of agent4: 0.5309284779703027
team_policy eval average team episode rewards of agent4: 15.0
team_policy eval idv catch total num of agent4: 23
team_policy eval team catch total num: 6
idv_policy eval average step individual rewards of agent0: 0.20863994522058163
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: 0.09169952665500869
idv_policy eval average team episode rewards of agent1: 10.0
idv_policy eval idv catch total num of agent1: 7
idv_policy eval team catch total num: 4
idv_policy eval average step individual rewards of agent2: 0.008671480085778427
idv_policy eval average team episode rewards of agent2: 10.0
idv_policy eval idv catch total num of agent2: 5
idv_policy eval team catch total num: 4
idv_policy eval average step individual rewards of agent3: 0.32227498736821075
idv_policy eval average team episode rewards of agent3: 10.0
idv_policy eval idv catch total num of agent3: 16
idv_policy eval team catch total num: 4
idv_policy eval average step individual rewards of agent4: 0.336938961487974
idv_policy eval average team episode rewards of agent4: 10.0
idv_policy eval idv catch total num of agent4: 17
idv_policy eval team catch total num: 4

 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.

team_policy eval average step individual rewards of agent0: -0.03739848865538466
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: -0.007618596072122401
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: 0.20106106862678516
team_policy eval average team episode rewards of agent2: 2.5
team_policy eval idv catch total num of agent2: 11
team_policy eval team catch total num: 1
team_policy eval average step individual rewards of agent3: -0.005892459883241532
team_policy eval average team episode rewards of agent3: 2.5
team_policy eval idv catch total num of agent3: 3
team_policy eval team catch total num: 1
team_policy eval average step individual rewards of agent4: 0.08365065765305824
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: 0.19326022695899497
idv_policy eval average team episode rewards of agent0: 10.0
idv_policy eval idv catch total num of agent0: 10
idv_policy eval team catch total num: 4
idv_policy eval average step individual rewards of agent1: 0.14346277112170103
idv_policy eval average team episode rewards of agent1: 10.0
idv_policy eval idv catch total num of agent1: 8
idv_policy eval team catch total num: 4
idv_policy eval average step individual rewards of agent2: 0.02160693188386837
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: 0.428620381025977
idv_policy eval average team episode rewards of agent3: 10.0
idv_policy eval idv catch total num of agent3: 20
idv_policy eval team catch total num: 4
idv_policy eval average step individual rewards of agent4: 0.04535262113613911
idv_policy eval average team episode rewards of agent4: 10.0
idv_policy eval idv catch total num of agent4: 5
idv_policy eval team catch total num: 4

 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.

team_policy eval average step individual rewards of agent0: 0.07667871545398533
team_policy eval average team episode rewards of agent0: 10.0
team_policy eval idv catch total num of agent0: 7
team_policy eval team catch total num: 4
team_policy eval average step individual rewards of agent1: 0.3709814635199497
team_policy eval average team episode rewards of agent1: 10.0
team_policy eval idv catch total num of agent1: 18
team_policy eval team catch total num: 4
team_policy eval average step individual rewards of agent2: 0.05300470055064224
team_policy eval average team episode rewards of agent2: 10.0
team_policy eval idv catch total num of agent2: 5
team_policy eval team catch total num: 4
team_policy eval average step individual rewards of agent3: 0.35859086015953145
team_policy eval average team episode rewards of agent3: 10.0
team_policy eval idv catch total num of agent3: 18
team_policy eval team catch total num: 4
team_policy eval average step individual rewards of agent4: 0.0938827327122982
team_policy eval average team episode rewards of agent4: 10.0
team_policy eval idv catch total num of agent4: 7
team_policy eval team catch total num: 4
idv_policy eval average step individual rewards of agent0: 0.5544848994384705
idv_policy eval average team episode rewards of agent0: 5.0
idv_policy eval idv catch total num of agent0: 24
idv_policy eval team catch total num: 2
idv_policy eval average step individual rewards of agent1: 0.07178882779247923
idv_policy eval average team episode rewards of agent1: 5.0
idv_policy eval idv catch total num of agent1: 6
idv_policy eval team catch total num: 2
idv_policy eval average step individual rewards of agent2: 0.18378744884282283
idv_policy eval average team episode rewards of agent2: 5.0
idv_policy eval idv catch total num of agent2: 10
idv_policy eval team catch total num: 2
idv_policy eval average step individual rewards of agent3: -0.00790732828221164
idv_policy eval average team episode rewards of agent3: 5.0
idv_policy eval idv catch total num of agent3: 3
idv_policy eval team catch total num: 2
idv_policy eval average step individual rewards of agent4: 0.012594561898290025
idv_policy eval average team episode rewards of agent4: 5.0
idv_policy eval idv catch total num of agent4: 4
idv_policy eval team catch total num: 2

 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.

team_policy eval average step individual rewards of agent0: -0.13763949543165768
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: -0.08819720307054679
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: -0.029707252004496764
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: 0.02468237698034816
team_policy eval average team episode rewards of agent3: 0.0
team_policy eval idv catch total num of agent3: 6
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent4: -0.12118759445596967
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: 0.1368975886684386
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: -0.0349091022666201
idv_policy eval average team episode rewards of agent1: 12.5
idv_policy eval idv catch total num of agent1: 2
idv_policy eval team catch total num: 5
idv_policy eval average step individual rewards of agent2: -0.06788690658260324
idv_policy eval average team episode rewards of agent2: 12.5
idv_policy eval idv catch total num of agent2: 1
idv_policy eval team catch total num: 5
idv_policy eval average step individual rewards of agent3: 0.06218555835845805
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: 0.20383041590900292
idv_policy eval average team episode rewards of agent4: 12.5
idv_policy eval idv catch total num of agent4: 12
idv_policy eval team catch total num: 5

 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.

team_policy eval average step individual rewards of agent0: 0.08262004250405335
team_policy eval average team episode rewards of agent0: 0.0
team_policy eval idv catch total num of agent0: 7
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent1: 0.28421299127359617
team_policy eval average team episode rewards of agent1: 0.0
team_policy eval idv catch total num of agent1: 14
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent2: 0.06208524794509479
team_policy eval average team episode rewards of agent2: 0.0
team_policy eval idv catch total num of agent2: 6
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent3: -0.07770514373405212
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: 0.014909241649458407
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: 0.020248323578155373
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: 0.09273829464278449
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: -0.07138523546499846
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: 0.5347378499484852
idv_policy eval average team episode rewards of agent3: 0.0
idv_policy eval idv catch total num of agent3: 24
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent4: -0.06964851800985683
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 9901/10000 episodes, total num timesteps 1980400/2000000, FPS 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.

team_policy eval average step individual rewards of agent0: 0.21229545690395374
team_policy eval average team episode rewards of agent0: 2.5
team_policy eval idv catch total num of agent0: 12
team_policy eval team catch total num: 1
team_policy eval average step individual rewards of agent1: -0.14631610855486
team_policy eval average team episode rewards of agent1: 2.5
team_policy eval idv catch total num of agent1: 0
team_policy eval team catch total num: 1
team_policy eval average step individual rewards of agent2: -0.14755336760566545
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: 0.04026472719701414
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: -0.008071436204676897
team_policy eval average team episode rewards of agent4: 2.5
team_policy eval idv catch total num of agent4: 4
team_policy eval team catch total num: 1
idv_policy eval average step individual rewards of agent0: -0.04816679033649437
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: -0.009154436234502787
idv_policy eval average team episode rewards of agent1: 7.5
idv_policy eval idv catch total num of agent1: 4
idv_policy eval team catch total num: 3
idv_policy eval average step individual rewards of agent2: 0.4003720051317535
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: 0.03077717220272664
idv_policy eval average team episode rewards of agent3: 7.5
idv_policy eval idv catch total num of agent3: 4
idv_policy eval team catch total num: 3
idv_policy eval average step individual rewards of agent4: -0.010961163472633415
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 9926/10000 episodes, total num timesteps 1985400/2000000, FPS 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.

team_policy eval average step individual rewards of agent0: 0.24310503101664885
team_policy eval average team episode rewards of agent0: 0.0
team_policy eval idv catch total num of agent0: 14
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent1: 0.09552199132866297
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: 0.14671788714326053
team_policy eval average team episode rewards of agent2: 0.0
team_policy eval idv catch total num of agent2: 10
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent3: 0.13779252048523496
team_policy eval average team episode rewards of agent3: 0.0
team_policy eval idv catch total num of agent3: 10
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent4: -0.011709626996557358
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: -0.0027210892724303416
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: -0.054151031099711464
idv_policy eval average team episode rewards of agent1: 2.5
idv_policy eval idv catch total num of agent1: 1
idv_policy eval team catch total num: 1
idv_policy eval average step individual rewards of agent2: 0.19523064088477388
idv_policy eval average team episode rewards of agent2: 2.5
idv_policy eval idv catch total num of agent2: 11
idv_policy eval team catch total num: 1
idv_policy eval average step individual rewards of agent3: 0.07115674942854422
idv_policy eval average team episode rewards of agent3: 2.5
idv_policy eval idv catch total num of agent3: 6
idv_policy eval team catch total num: 1
idv_policy eval average step individual rewards of agent4: 0.019254220683596116
idv_policy eval average team episode rewards of agent4: 2.5
idv_policy eval idv catch total num of agent4: 4
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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.

team_policy eval average step individual rewards of agent0: 0.13007507477549468
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: 0.12465965212065554
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: 0.19977783342353778
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: -0.08898302190567467
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: -0.010821377011688931
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: 0.25319859424796703
idv_policy eval average team episode rewards of agent0: 17.5
idv_policy eval idv catch total num of agent0: 14
idv_policy eval team catch total num: 7
idv_policy eval average step individual rewards of agent1: 0.04973629923355985
idv_policy eval average team episode rewards of agent1: 17.5
idv_policy eval idv catch total num of agent1: 7
idv_policy eval team catch total num: 7
idv_policy eval average step individual rewards of agent2: 0.07420839067224126
idv_policy eval average team episode rewards of agent2: 17.5
idv_policy eval idv catch total num of agent2: 6
idv_policy eval team catch total num: 7
idv_policy eval average step individual rewards of agent3: -0.02839105807762001
idv_policy eval average team episode rewards of agent3: 17.5
idv_policy eval idv catch total num of agent3: 3
idv_policy eval team catch total num: 7
idv_policy eval average step individual rewards of agent4: 0.030212560733671876
idv_policy eval average team episode rewards of agent4: 17.5
idv_policy eval idv catch total num of agent4: 6
idv_policy eval team catch total num: 7

 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.

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: / 1.846 MB of 3.048 MB uploaded
wandb: - 1.846 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 -0.04509
wandb:                                          Ab_policy_loss -0.0037
wandb:                                     Ac_idv_ppo_loss_abs 0.6015
wandb:                                         Ad_idv_ppo_prop 0.91571
wandb:                                                  Ae_eta 0.99995
wandb:                                    Af_noclip_proportion 0.9997
wandb:                                    Ag_update_proportion 0.451
wandb:                                          Ah_update_loss 0.21145
wandb:                                         Ai_idv_epsilon' 0.49995
wandb:                                            Aj_idv_sigma 1.00379
wandb:              Ak_idv_clip(sigma, 1-epislon', 1+epislon') 1.00347
wandb:                                Al_idv_noclip_proportion 0.999
wandb:                       Am_idv_(sigma*A)update_proportion 0.5477
wandb:                             An_idv_(sigma*A)update_loss -0.19154
wandb:                                     Ao_idv_entropy_prop 0.07365
wandb:                                         Ap_dist_entropy 4.8378
wandb:                                          Aq_idv_kl_prop 0.01064
wandb:                                          Ar_idv_kl_coef 6.9993
wandb:                                          As_idv_kl_loss 0.001
wandb:                                    At_idv_cross_entropy 0.0
wandb:                                           Au_value_loss 0.01082
wandb:                                           Av_advantages -0.0
wandb:                                       Aw_idv_actor_norm 0.16072
wandb:                                      Ax_idv_critic_norm 0.01327
wandb:                                     Ba_idv_org_min_prop 0.421
wandb:                                     Bb_idv_org_max_prop 0.03
wandb:                                     Bc_idv_org_org_prop 0.0
wandb:                                     Bd_idv_new_min_prop 0.082
wandb:                                     Be_idv_new_max_prop 0.4657
wandb:                                      Ta_team_actor_loss -0.05041
wandb:                                     Tb_team_policy_loss -0.00203
wandb:                                    Tc_team_ppo_loss_abs 0.58016
wandb:                                        Td_team_ppo_prop 0.92303
wandb:                                        Te_team_epsilon^ 0.2
wandb:                                          Tf_team_sigma^ 1.00051
wandb:          Tg_team_clip(sigma^, 1-epislon^', 1+epislon^') 1.00023
wandb:                               Th_team_noclip_proportion 0.9795
wandb:                     Ti_team_(sigma^*A)update_proportion 0.9919
wandb:                           Tj_team_(sigma^*A)update_loss 0.00051
wandb:                                    Tk_team_entropy_prop 0.07697
wandb:                                    Tl_team_dist_entropy 4.83781
wandb:                                         Tm_team_kl_prop 0.0
wandb:                                         Tn_team_kl_coef 0.00012
wandb:                                         To_team_kl_loss 0.00122
wandb:                                   Tp_team_cross_entropy 0.0
wandb:                                      Tq_team_value_loss 0.00635
wandb:                                      Tr_team_advantages -0.0
wandb:                                      Ts_team_actor_norm 0.13005
wandb:                                     Tt_team_critic_norm 0.00584
wandb:                     agent0/average_episode_team_rewards 2.5
wandb:                  agent0/average_step_individual_rewards -0.0446
wandb:     agent0/idv_policy_eval_average_episode_team_rewards 17.5
wandb:  agent0/idv_policy_eval_average_step_individual_rewards 0.2532
wandb:              agent0/idv_policy_eval_idv_catch_total_num 14
wandb:             agent0/idv_policy_eval_team_catch_total_num 7
wandb:    agent0/team_policy_eval_average_episode_team_rewards 0.0
wandb: agent0/team_policy_eval_average_step_individual_rewards 0.13008
wandb:             agent0/team_policy_eval_idv_catch_total_num 9
wandb:            agent0/team_policy_eval_team_catch_total_num 0
wandb:                     agent1/average_episode_team_rewards 2.5
wandb:                  agent1/average_step_individual_rewards -0.10457
wandb:     agent1/idv_policy_eval_average_episode_team_rewards 17.5
wandb:  agent1/idv_policy_eval_average_step_individual_rewards 0.04974
wandb:              agent1/idv_policy_eval_idv_catch_total_num 7
wandb:             agent1/idv_policy_eval_team_catch_total_num 7
wandb:    agent1/team_policy_eval_average_episode_team_rewards 0.0
wandb: agent1/team_policy_eval_average_step_individual_rewards 0.12466
wandb:             agent1/team_policy_eval_idv_catch_total_num 9
wandb:            agent1/team_policy_eval_team_catch_total_num 0
wandb:                     agent2/average_episode_team_rewards 2.5
wandb:                  agent2/average_step_individual_rewards 0.07695
wandb:     agent2/idv_policy_eval_average_episode_team_rewards 17.5
wandb:  agent2/idv_policy_eval_average_step_individual_rewards 0.07421
wandb:              agent2/idv_policy_eval_idv_catch_total_num 6
wandb:             agent2/idv_policy_eval_team_catch_total_num 7
wandb:    agent2/team_policy_eval_average_episode_team_rewards 0.0
wandb: agent2/team_policy_eval_average_step_individual_rewards 0.19978
wandb:             agent2/team_policy_eval_idv_catch_total_num 11
wandb:            agent2/team_policy_eval_team_catch_total_num 0
wandb:                     agent3/average_episode_team_rewards 2.5
wandb:                  agent3/average_step_individual_rewards 0.19738
wandb:     agent3/idv_policy_eval_average_episode_team_rewards 17.5
wandb:  agent3/idv_policy_eval_average_step_individual_rewards -0.02839
wandb:              agent3/idv_policy_eval_idv_catch_total_num 3
wandb:             agent3/idv_policy_eval_team_catch_total_num 7
wandb:    agent3/team_policy_eval_average_episode_team_rewards 0.0
wandb: agent3/team_policy_eval_average_step_individual_rewards -0.08898
wandb:             agent3/team_policy_eval_idv_catch_total_num 1
wandb:            agent3/team_policy_eval_team_catch_total_num 0
wandb:                     agent4/average_episode_team_rewards 2.5
wandb:                  agent4/average_step_individual_rewards 0.01808
wandb:     agent4/idv_policy_eval_average_episode_team_rewards 17.5
wandb:  agent4/idv_policy_eval_average_step_individual_rewards 0.03021
wandb:              agent4/idv_policy_eval_idv_catch_total_num 6
wandb:             agent4/idv_policy_eval_team_catch_total_num 7
wandb:    agent4/team_policy_eval_average_episode_team_rewards 0.0
wandb: agent4/team_policy_eval_average_step_individual_rewards -0.01082
wandb:             agent4/team_policy_eval_idv_catch_total_num 4
wandb:            agent4/team_policy_eval_team_catch_total_num 0
wandb: 
wandb: 🚀 View run MPE_1 at: https://wandb.ai/804703098/Continue_Tag_Base_v1/runs/t6240773
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_155442-t6240773/logs

 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.

000 episodes, total num timesteps 1998800/2000000, FPS 293.


 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 293.


 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 293.


 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 293.


 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 293.


 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 293.


 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 293.

