choose to use cpu...
NaN or Inf found in input tensor.
NaN or Inf found in input tensor.
NaN or Inf found in input tensor.
NaN or Inf found in input tensor.
NaN or Inf found in input tensor.
NaN or Inf found in input tensor.
NaN or Inf found in input tensor.
NaN or Inf found in input tensor.
NaN or Inf found in input tensor.
NaN or Inf found in input tensor.
NaN or Inf found in input tensor.
NaN or Inf found in input tensor.
NaN or Inf found in input tensor.
NaN or Inf found in input tensor.
NaN or Inf found in input tensor.
NaN or Inf found in input tensor.
NaN or Inf found in input tensor.
NaN or Inf found in input tensor.
NaN or Inf found in input tensor.
NaN or Inf found in input tensor.
NaN or Inf found in input tensor.
NaN or Inf found in input tensor.
NaN or Inf found in input tensor.
NaN or Inf found in input tensor.
NaN or Inf found in input tensor.
NaN or Inf found in input tensor.
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 267.

team_policy eval average step individual rewards of agent0: -0.08613161625310735
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: -0.0830610111457722
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: -0.09690300848308261
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: -0.003796794525585696
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: -0.015856207560691615
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: -0.07011704295121399
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: -0.06393107036666087
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: -0.015416098602623518
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: -0.03423328340247809
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: -0.05533530515254574
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 1/10000 episodes, total num timesteps 400/2000000, FPS 230.


 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 257.


 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 265.


 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 269.


 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 281.


 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 285.


 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 278.


 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 274.


 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 277.


 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 282.


 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 287.


 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 288.


 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 292.


 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 297.


 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 296.


 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 295.


 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 298.


 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 301.


 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 303.


 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 303.


 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 304.


 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 306.


 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 307.


 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 307.


 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 305.

team_policy eval average step individual rewards of agent0: -0.013845944426036483
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: -0.07992539276378609
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: 0.0154801351460714
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: 0.01867770909430182
team_policy eval average team episode rewards of agent3: 0.0
team_policy eval idv catch total num of agent3: 4
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent4: -0.09641137478932828
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: -0.05972876289549121
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: -0.1270192810090618
idv_policy eval average team episode rewards of agent1: 0.0
idv_policy eval idv catch total num of agent1: 0
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent2: -0.1053059327005789
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: -0.13118861333750725
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: -0.10606301132547807
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 26/10000 episodes, total num timesteps 5400/2000000, FPS 302.


 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 303.


 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 306.


 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 306.


 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 307.


 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 308.


 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 310.


 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 310.


 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 311.


 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 313.


 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 314.


 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 313.


 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 312.


 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 311.


 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 310.


 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 311.


 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 312.


 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 312.


 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 313.


 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 312.


 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 313.


 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 312.


 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 312.


 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 311.


 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 310.

team_policy eval average step individual rewards of agent0: 0.048986305529576904
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: 0.022665941605887144
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: 0.09037578570734325
team_policy eval average team episode rewards of agent2: 0.0
team_policy eval idv catch total num of agent2: 8
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent3: -0.07550035861324127
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: -0.05362354857408043
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: -0.03516293094903406
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: -0.1417660932948106
idv_policy eval average team episode rewards of agent1: 0.0
idv_policy eval idv catch total num of agent1: 0
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent2: -0.042869153116261854
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: -0.03037730239574445
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: -0.09514434045307694
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 310.


 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 310.


 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 309.


 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 309.


 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 308.


 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 309.


 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 309.


 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 309.


 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 309.


 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 309.


 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 308.


 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 309.


 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 309.


 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 310.


 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 309.


 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 310.


 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 310.


 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 311.


 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 311.


 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 311.


 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 312.


 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 312.


 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 313.


 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 312.


 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 312.

team_policy eval average step individual rewards of agent0: 0.03654191670079948
team_policy eval average team episode rewards of agent0: 2.5
team_policy eval idv catch total num of agent0: 7
team_policy eval team catch total num: 1
team_policy eval average step individual rewards of agent1: -0.03209681058557796
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: -0.045577953969262314
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: -0.13391620696363288
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: -0.09904674485689144
team_policy eval average team episode rewards of agent4: 2.5
team_policy eval idv catch total num of agent4: 1
team_policy eval team catch total num: 1
idv_policy eval average step individual rewards of agent0: -0.13676988689791478
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: -0.09854651610500856
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: -0.09766370407016517
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: -0.05190287875688953
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: -0.1435798538958162
idv_policy eval average team episode rewards of agent4: 0.0
idv_policy eval idv catch total num of agent4: 0
idv_policy eval team catch total num: 0

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 76/10000 episodes, total num timesteps 15400/2000000, FPS 312.


 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 312.


 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 312.


 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 313.


 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 313.


 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 314.


 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 313.


 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 314.


 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 314.


 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 315.


 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 315.


 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 315.


 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 315.


 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 316.


 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 315.


 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 316.


 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 316.


 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 316.


 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 317.


 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 316.


 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 317.


 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 317.


 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 317.


 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 317.


 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 317.

team_policy eval average step individual rewards of agent0: -0.0776125752535363
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: -0.12566176209064633
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: -0.09226538610176481
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: -0.08621834261608269
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: -0.08626748290345908
team_policy eval average team episode rewards of agent4: 0.0
team_policy eval idv catch total num of agent4: 1
team_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent0: -0.06502632375317476
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: -0.07882545304541891
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: -0.1030235253374676
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: -0.005295548196343427
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: 0.017628575490267
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 101/10000 episodes, total num timesteps 20400/2000000, FPS 317.


 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 317.


 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 317.


 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 317.


 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 318.


 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 318.


 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 318.


 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 318.


 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 318.


 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 318.


 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 319.


 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 319.


 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 319.


 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 319.


 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 319.


 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 319.


 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 319.


 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 320.


 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 320.


 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 320.


 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 320.


 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 320.


 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 320.


 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 320.


 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 320.

team_policy eval average step individual rewards of agent0: -0.05037775402598018
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: -0.14285487983529546
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: -0.036984222280741254
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: -0.09435575631687884
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: -0.12681693765640956
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: -0.09946337984451364
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: -0.14769289063072114
idv_policy eval average team episode rewards of agent1: 0.0
idv_policy eval idv catch total num of agent1: 0
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent2: -0.0066009017216314535
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: -0.0012805028275292506
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: -0.1326116282591857
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 126/10000 episodes, total num timesteps 25400/2000000, FPS 320.


 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 320.


 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 320.


 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 320.


 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 320.


 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 320.


 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 320.


 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 320.


 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 320.


 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 320.


 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 320.


 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 321.


 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 321.


 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 321.


 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 321.


 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 321.


 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 320.


 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 319.


 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 318.


 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 318.


 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 318.


 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 317.


 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 317.


 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 317.


 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 317.

team_policy eval average step individual rewards of agent0: 0.008460860252321485
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: -0.12287934527806683
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: -0.11479700811707592
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: -0.0764412456172266
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: -0.05631231430634301
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: -0.08223148842160866
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: -0.043531055060435433
idv_policy eval average team episode rewards of agent1: 0.0
idv_policy eval idv catch total num of agent1: 3
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent2: -0.13194136807275053
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: -0.08608871173308291
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: -0.07042823044873017
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 151/10000 episodes, total num timesteps 30400/2000000, FPS 316.


 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 316.


 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 316.


 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 315.


 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 315.


 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 315.


 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 314.


 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 313.


 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 313.


 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 312.


 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 312.


 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 311.


 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 310.


 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 310.


 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 310.


 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 310.


 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 310.


 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 310.


 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 310.


 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 309.


 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 307.


 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 307.


 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 307.


 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 307.


 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 307.

team_policy eval average step individual rewards of agent0: -0.10244089507916457
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: -0.12570807800486702
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: -0.1286946822792824
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: -0.005070879508557793
team_policy eval average team episode rewards of agent3: 0.0
team_policy eval idv catch total num of agent3: 4
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent4: -0.10618595795462905
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: -0.05943745366690668
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: -0.09986028397054826
idv_policy eval average team episode rewards of agent1: 2.5
idv_policy eval idv catch total num of agent1: 0
idv_policy eval team catch total num: 1
idv_policy eval average step individual rewards of agent2: 0.017797009170504835
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: 0.10790274673913036
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: 0.19553895262736773
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 176/10000 episodes, total num timesteps 35400/2000000, FPS 306.


 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 306.


 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 306.


 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 306.


 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 306.


 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 305.


 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 304.


 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 304.


 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 303.


 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 303.


 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 303.


 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 303.


 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 303.


 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 303.


 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 303.


 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 302.


 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 302.


 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 302.


 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 302.


 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 302.


 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 301.


 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 301.


 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 301.


 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 301.


 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 300.

team_policy eval average step individual rewards of agent0: -0.1028623957839897
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: -0.0979858123348408
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: -0.14065737826553618
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: -0.08778796122280426
team_policy eval average team episode rewards of agent3: 2.5
team_policy eval idv catch total num of agent3: 1
team_policy eval team catch total num: 1
team_policy eval average step individual rewards of agent4: -0.07512722675293353
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: -0.0845185849609094
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: -0.1406371208688268
idv_policy eval average team episode rewards of agent1: 0.0
idv_policy eval idv catch total num of agent1: 0
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent2: -0.11571137301080998
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: -0.0918186152895926
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: -0.003834014871705762
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 201/10000 episodes, total num timesteps 40400/2000000, FPS 299.


 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 299.


 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 299.


 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 299.


 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 299.


 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 299.


 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 299.


 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 298.


 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 298.


 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 298.


 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 298.


 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 298.


 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 298.


 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 298.


 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 298.


 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 298.


 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 298.


 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 298.


 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 298.


 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 297.


 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 297.


 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 296.


 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 296.


 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 296.


 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 296.

team_policy eval average step individual rewards of agent0: -0.020542486787985235
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: -0.10736436831536939
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: -0.16768906502180456
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: -0.16173138769286666
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: -0.16141630654038017
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: -0.05568531858928336
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: -0.10983122565289988
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: -0.10601557555031206
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: -0.11856155709063454
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: -0.07144843439339604
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 226/10000 episodes, total num timesteps 45400/2000000, FPS 295.


 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 295.


 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 295.


 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 295.


 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 294.


 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 294.


 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 294.


 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 294.


 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 294.


 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 294.


 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 293.


 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 293.


 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 293.


 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 293.


 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 293.


 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 292.


 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 292.


 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 292.


 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 292.


 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 292.


 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 292.


 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 292.


 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 292.


 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 292.


 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 292.

team_policy eval average step individual rewards of agent0: -0.11040359456131053
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: -0.08535103355409172
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: -0.08371345213373996
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: 0.09472791694373434
team_policy eval average team episode rewards of agent3: 0.0
team_policy eval idv catch total num of agent3: 8
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent4: -0.11165220445300043
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: -0.06418975536051769
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: -0.13450174046530206
idv_policy eval average team episode rewards of agent1: 0.0
idv_policy eval idv catch total num of agent1: 0
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent2: -0.14877153975002327
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: -0.0203261393293536
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: -0.10828643353487703
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 251/10000 episodes, total num timesteps 50400/2000000, FPS 292.


 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 292.


 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 291.


 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 291.


 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 291.


 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 291.


 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 291.


 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 291.


 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 291.


 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 291.


 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 290.


 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 290.


 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 290.


 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 290.


 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 290.


 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 290.


 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 290.


 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 290.


 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 290.


 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 290.


 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 290.


 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 290.


 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 290.


 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 290.


 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 290.

team_policy eval average step individual rewards of agent0: -0.07744740611268482
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: -0.04165041336402637
team_policy eval average team episode rewards of agent1: 0.0
team_policy eval idv catch total num of agent1: 4
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent2: -0.06541986632186923
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: -0.0254623924393759
team_policy eval average team episode rewards of agent3: 0.0
team_policy eval idv catch total num of agent3: 4
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent4: -0.14948604434768348
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: -0.030509530253479723
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: -0.14078597467988682
idv_policy eval average team episode rewards of agent1: 2.5
idv_policy eval idv catch total num of agent1: 0
idv_policy eval team catch total num: 1
idv_policy eval average step individual rewards of agent2: -0.04732516508083545
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: -0.15016190687763414
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: -0.0823422689419047
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 276/10000 episodes, total num timesteps 55400/2000000, FPS 290.


 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 289.


 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 289.


 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 289.


 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 289.


 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 289.


 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 289.


 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 289.


 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 289.


 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 289.


 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 288.


 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 288.


 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 288.


 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 288.


 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 289.


 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 288.


 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 288.


 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 288.


 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 288.


 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 288.


 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 288.


 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 288.


 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 288.


 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 288.


 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 288.

team_policy eval average step individual rewards of agent0: -0.11176785916151008
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: 0.01665363474376007
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: -0.06088723139770976
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: 0.015063386300969235
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: -0.0688082969283373
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: -0.05926613692780665
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: 0.03502012237543184
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: -0.026244907462254295
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: -0.05564315000234545
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: -0.03590043664271998
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 301/10000 episodes, total num timesteps 60400/2000000, FPS 287.


 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 287.


 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 287.


 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 287.


 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 287.


 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 287.


 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 287.


 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 287.


 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 287.


 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 287.


 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 287.


 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 286.


 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 286.


 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 286.


 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 286.


 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 286.


 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 286.


 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 286.


 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 286.


 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 286.


 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 286.


 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 286.


 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 285.


 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 285.


 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 285.

team_policy eval average step individual rewards of agent0: 0.0030899671163207108
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: -0.033545693595676225
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: -0.12508350819721717
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: -0.05644674213496357
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: -0.11923906108624482
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: -0.07041328811105597
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: -0.13256625799225577
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: -0.05751393216020196
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: 0.019655653443496738
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: -0.14610260374957942
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 326/10000 episodes, total num timesteps 65400/2000000, FPS 285.


 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 285.


 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 285.


 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 285.


 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 285.


 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 285.


 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 285.


 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 285.


 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 285.


 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 285.


 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 285.


 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 285.


 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 285.


 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 285.


 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 284.


 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 284.


 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 284.


 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 284.


 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 284.


 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 284.


 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 284.


 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 284.


 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 284.


 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 284.


 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 284.

team_policy eval average step individual rewards of agent0: -0.09906778627454774
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: -0.07041005173455842
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: -0.05584250331768529
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: 0.026092758445662022
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: -0.08728587052677803
team_policy eval average team episode rewards of agent4: 0.0
team_policy eval idv catch total num of agent4: 1
team_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent0: 0.03818523487410444
idv_policy eval average team episode rewards of agent0: 0.0
idv_policy eval idv catch total num of agent0: 6
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent1: -0.08021969935081576
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: -0.02932753600394766
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: -0.11256149366377886
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: -0.1063960015938709
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 351/10000 episodes, total num timesteps 70400/2000000, FPS 284.


 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 284.


 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 284.


 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 284.


 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 284.


 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 284.


 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 284.


 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 284.


 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 284.


 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 284.


 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 283.


 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 283.


 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 283.


 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 283.


 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 283.


 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 283.


 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 283.


 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 283.


 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 283.


 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 283.


 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 283.


 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 283.


 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 283.


 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 283.


 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 283.

team_policy eval average step individual rewards of agent0: -0.01711968493870348
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: -0.03310179247308773
team_policy eval average team episode rewards of agent1: 0.0
team_policy eval idv catch total num of agent1: 4
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent2: -0.12651116057811687
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: -0.08941572318805434
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: -0.1209796370561917
team_policy eval average team episode rewards of agent4: 0.0
team_policy eval idv catch total num of agent4: 1
team_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent0: 0.09108859575707381
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: -0.07971316151697726
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: -0.12002403280173478
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: -0.09375575860489999
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: 0.007752820619007501
idv_policy eval average team episode rewards of agent4: 0.0
idv_policy eval idv catch total num of agent4: 5
idv_policy eval team catch total num: 0

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 376/10000 episodes, total num timesteps 75400/2000000, FPS 283.


 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 283.


 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 283.


 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 283.


 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 283.


 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 283.


 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 282.


 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 283.


 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 283.


 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 283.


 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 282.


 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 282.


 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 283.


 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 283.


 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 282.


 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 282.


 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 282.


 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 282.


 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 282.


 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 282.


 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 282.


 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 282.


 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 282.


 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 282.


 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 282.

team_policy eval average step individual rewards of agent0: -0.09417697729384099
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: -0.08370475638772838
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: -0.037700688253468354
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: -0.12135128901543894
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: 0.010103720353500868
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: 0.010557913146199352
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: -0.10050642846584312
idv_policy eval average team episode rewards of agent1: 0.0
idv_policy eval idv catch total num of agent1: 0
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent2: -0.09951845275241755
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: -0.06262396136528528
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: -0.0399868528196513
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 401/10000 episodes, total num timesteps 80400/2000000, FPS 281.


 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 281.


 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 281.


 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 281.


 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 281.


 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 281.


 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 281.


 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 281.


 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 281.


 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 281.


 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 281.


 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 281.


 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 281.


 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 281.


 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 281.


 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 281.


 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 281.


 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 281.


 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 281.


 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 281.


 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 281.


 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 281.


 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 280.


 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 280.


 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 280.

team_policy eval average step individual rewards of agent0: -0.04105268415137155
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: -0.13005490280447812
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: -0.08710660039437652
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: -0.10034162016469997
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: -0.06353162485820937
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: 0.006617303138210113
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: -0.09439365067983019
idv_policy eval average team episode rewards of agent1: 0.0
idv_policy eval idv catch total num of agent1: 0
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent2: -0.04912095073103125
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: -0.06686769646226381
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: -0.02952822170566655
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 280.


 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 280.


 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 280.


 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 280.


 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 280.


 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 280.


 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 280.


 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 280.


 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 280.


 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 280.


 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 280.


 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 280.


 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 280.


 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 280.


 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 280.


 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 280.


 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 280.


 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 280.


 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 280.


 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 280.


 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 280.


 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 280.


 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 280.


 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 280.


 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 280.

team_policy eval average step individual rewards of agent0: -0.1498277699395877
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: -0.014392396038905941
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: -0.09715479645250381
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: -0.06152357708591612
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: -0.06997693924478723
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: -0.003205082547991367
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: -0.11910175217794443
idv_policy eval average team episode rewards of agent1: 2.5
idv_policy eval idv catch total num of agent1: 0
idv_policy eval team catch total num: 1
idv_policy eval average step individual rewards of agent2: -0.07401313993002014
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: 0.01850677668948013
idv_policy eval average team episode rewards of agent3: 2.5
idv_policy eval idv catch total num of agent3: 5
idv_policy eval team catch total num: 1
idv_policy eval average step individual rewards of agent4: -0.06424043448735561
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 451/10000 episodes, total num timesteps 90400/2000000, FPS 280.


 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 280.


 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 280.


 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 280.


 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 280.


 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 280.


 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 279.


 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 279.


 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 279.


 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 279.


 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 279.


 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 279.


 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 279.


 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 279.


 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 279.


 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 279.


 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 279.


 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 279.


 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 279.


 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 278.


 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 278.


 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 278.


 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 278.


 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 278.


 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 278.

team_policy eval average step individual rewards of agent0: -0.08752946844277862
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: -0.08195752989476651
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: -0.046377006451036006
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: 0.04204657074495324
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: 0.027629676552789775
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: -0.052856737963292016
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: -0.06520901429199716
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: 0.007302732047662186
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: -0.01974295660840838
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: -0.09905886082826684
idv_policy eval average team episode rewards of agent4: 0.0
idv_policy eval idv catch total num of agent4: 0
idv_policy eval team catch total num: 0

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 476/10000 episodes, total num timesteps 95400/2000000, FPS 278.


 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 278.


 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 278.


 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 278.


 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 278.


 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 278.


 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 278.


 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 278.


 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 278.


 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 278.


 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 278.


 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 278.


 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 278.


 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 278.


 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 278.


 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 278.


 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 278.


 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 278.


 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 278.


 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 278.


 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 277.


 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 277.


 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 277.


 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 277.


 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 277.

team_policy eval average step individual rewards of agent0: -0.14308077375273898
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: -0.10954157338503041
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: -0.13999037793565938
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: -0.06664542256483524
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: -0.02819979286254668
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: -0.07517327491469547
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: -0.1410922648156253
idv_policy eval average team episode rewards of agent1: 0.0
idv_policy eval idv catch total num of agent1: 0
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent2: -0.04469097804073142
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: -0.053194480993886545
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: -0.11172414239102511
idv_policy eval average team episode rewards of agent4: 0.0
idv_policy eval idv catch total num of agent4: 1
idv_policy eval team catch total num: 0

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 501/10000 episodes, total num timesteps 100400/2000000, FPS 277.


 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 276.


 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 276.


 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 276.


 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 276.


 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 276.


 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 276.


 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 276.


 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 276.


 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 276.


 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 276.


 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 276.


 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 276.


 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 276.


 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 276.


 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 276.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.

team_policy eval average step individual rewards of agent0: -0.03785535291475803
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: -0.04503016449830895
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: 0.039065290597916066
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: 0.030031965357179516
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: 0.0035812933894196017
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: -0.015567501531923647
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: 0.02980397189493862
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: -0.0710975865897918
idv_policy eval average team episode rewards of agent2: 7.5
idv_policy eval idv catch total num of agent2: 2
idv_policy eval team catch total num: 3
idv_policy eval average step individual rewards of agent3: -0.032493930528050836
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: 0.036274428481158284
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 526/10000 episodes, total num timesteps 105400/2000000, FPS 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.

team_policy eval average step individual rewards of agent0: 0.055491072502290954
team_policy eval average team episode rewards of agent0: 2.5
team_policy eval idv catch total num of agent0: 6
team_policy eval team catch total num: 1
team_policy eval average step individual rewards of agent1: 0.04160379792079033
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: -0.09259877841844698
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: -0.0028937695945406806
team_policy eval average team episode rewards of agent3: 2.5
team_policy eval idv catch total num of agent3: 4
team_policy eval team catch total num: 1
team_policy eval average step individual rewards of agent4: 0.0027894764458594513
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: -0.06449131290498808
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: -0.1127672374845751
idv_policy eval average team episode rewards of agent1: 0.0
idv_policy eval idv catch total num of agent1: 0
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent2: -0.0912385741257778
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: -0.1160887524474078
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: -0.04905657691886043
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 551/10000 episodes, total num timesteps 110400/2000000, FPS 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.

team_policy eval average step individual rewards of agent0: -0.09615698745946881
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: -0.10759844326697024
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: -0.13290382720674918
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: -0.12518916484316292
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: -0.019331956953352377
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: -0.1272638529504843
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: -0.056796563723383466
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: -0.07268701667801776
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: -0.03265169920870692
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: -0.042498445893642184
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 576/10000 episodes, total num timesteps 115400/2000000, FPS 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.

team_policy eval average step individual rewards of agent0: 0.054425572655102325
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: -0.11124667047608999
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: -0.06908888766321758
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: -0.05725423216566568
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: -0.023444579418733853
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: -0.07734561743210949
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: 0.10215146340656453
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: -0.06539059236906969
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: -0.09246814825235652
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: -0.011623792073429486
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 601/10000 episodes, total num timesteps 120400/2000000, FPS 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.

team_policy eval average step individual rewards of agent0: -0.06596676086185077
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: -0.06535587557375092
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: 0.03763239620611623
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: -0.06564081169479756
team_policy eval average team episode rewards of agent3: 2.5
team_policy eval idv catch total num of agent3: 1
team_policy eval team catch total num: 1
team_policy eval average step individual rewards of agent4: 0.05058909854471736
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: 0.012106233254230441
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: 0.10164120777482825
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: -0.02405716843100745
idv_policy eval average team episode rewards of agent2: 7.5
idv_policy eval idv catch total num of agent2: 2
idv_policy eval team catch total num: 3
idv_policy eval average step individual rewards of agent3: -0.007553772243838859
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: 0.02588778986638768
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 626/10000 episodes, total num timesteps 125400/2000000, FPS 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.

team_policy eval average step individual rewards of agent0: -0.10554173166095879
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: -0.051571991157036694
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: -0.10520272891160516
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: 0.026415156759751944
team_policy eval average team episode rewards of agent3: 2.5
team_policy eval idv catch total num of agent3: 5
team_policy eval team catch total num: 1
team_policy eval average step individual rewards of agent4: -0.056598152618463814
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: 0.017560712711074795
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: -0.012577056076067485
idv_policy eval average team episode rewards of agent1: 12.5
idv_policy eval idv catch total num of agent1: 3
idv_policy eval team catch total num: 5
idv_policy eval average step individual rewards of agent2: -0.008268911472819399
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: 0.0019602294925707307
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: -0.013214000124957463
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 651/10000 episodes, total num timesteps 130400/2000000, FPS 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.

team_policy eval average step individual rewards of agent0: 0.029973438970380878
team_policy eval average team episode rewards of agent0: 2.5
team_policy eval idv catch total num of agent0: 5
team_policy eval team catch total num: 1
team_policy eval average step individual rewards of agent1: 0.04911790748733564
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: 0.1129904272968615
team_policy eval average team episode rewards of agent2: 2.5
team_policy eval idv catch total num of agent2: 9
team_policy eval team catch total num: 1
team_policy eval average step individual rewards of agent3: -0.09773234303151934
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: -0.06969648031619151
team_policy eval average team episode rewards of agent4: 2.5
team_policy eval idv catch total num of agent4: 1
team_policy eval team catch total num: 1
idv_policy eval average step individual rewards of agent0: -0.09951849344692479
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: -0.07041548132416249
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: -0.0473833283661922
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: -0.048365791026995314
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: -0.0906829374867674
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 676/10000 episodes, total num timesteps 135400/2000000, FPS 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.

team_policy eval average step individual rewards of agent0: 0.0790719798883201
team_policy eval average team episode rewards of agent0: 10.0
team_policy eval idv catch total num of agent0: 6
team_policy eval team catch total num: 4
team_policy eval average step individual rewards of agent1: 0.16072293871620202
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: 0.05523563747181945
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: 0.08647797172611633
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: -0.037569687910200156
team_policy eval average team episode rewards of agent4: 10.0
team_policy eval idv catch total num of agent4: 1
team_policy eval team catch total num: 4
idv_policy eval average step individual rewards of agent0: 0.03708463491831842
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: 0.03174316593686524
idv_policy eval average team episode rewards of agent1: 12.5
idv_policy eval idv catch total num of agent1: 4
idv_policy eval team catch total num: 5
idv_policy eval average step individual rewards of agent2: -0.04152115808115744
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: -0.01695096053052671
idv_policy eval average team episode rewards of agent3: 12.5
idv_policy eval idv catch total num of agent3: 2
idv_policy eval team catch total num: 5
idv_policy eval average step individual rewards of agent4: 0.1670861072152632
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 701/10000 episodes, total num timesteps 140400/2000000, FPS 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.

team_policy eval average step individual rewards of agent0: 0.13978166915438447
team_policy eval average team episode rewards of agent0: 2.5
team_policy eval idv catch total num of agent0: 8
team_policy eval team catch total num: 1
team_policy eval average step individual rewards of agent1: -0.04017782184868276
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: 0.058125693790542145
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: 0.034655316491917096
team_policy eval average team episode rewards of agent3: 2.5
team_policy eval idv catch total num of agent3: 4
team_policy eval team catch total num: 1
team_policy eval average step individual rewards of agent4: -0.013794720307498036
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: -0.044127549606326345
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: -0.06989397278410964
idv_policy eval average team episode rewards of agent1: 0.0
idv_policy eval idv catch total num of agent1: 0
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent2: 0.027854791739589838
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: -0.07579023911390097
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: 0.013009399996034595
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 726/10000 episodes, total num timesteps 145400/2000000, FPS 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.

team_policy eval average step individual rewards of agent0: 0.050706930488663156
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: 0.03214926886980983
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: -0.011003185526799247
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: 0.030662534215084813
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: 0.07612421353936795
team_policy eval average team episode rewards of agent4: 10.0
team_policy eval idv catch total num of agent4: 6
team_policy eval team catch total num: 4
idv_policy eval average step individual rewards of agent0: -0.05465139743003415
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: -0.07892323648786666
idv_policy eval average team episode rewards of agent1: 2.5
idv_policy eval idv catch total num of agent1: 0
idv_policy eval team catch total num: 1
idv_policy eval average step individual rewards of agent2: -0.004372387149265852
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: -0.05669940137891805
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: -0.050956835528661586
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 751/10000 episodes, total num timesteps 150400/2000000, FPS 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.

team_policy eval average step individual rewards of agent0: 0.27554681669885783
team_policy eval average team episode rewards of agent0: 20.0
team_policy eval idv catch total num of agent0: 13
team_policy eval team catch total num: 8
team_policy eval average step individual rewards of agent1: 0.17325655500582215
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: 0.16636457875695762
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: 0.14772663516078657
team_policy eval average team episode rewards of agent3: 20.0
team_policy eval idv catch total num of agent3: 8
team_policy eval team catch total num: 8
team_policy eval average step individual rewards of agent4: 0.04466120215976945
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: 0.011799872744476242
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: 0.13643947831002476
idv_policy eval average team episode rewards of agent1: 2.5
idv_policy eval idv catch total num of agent1: 8
idv_policy eval team catch total num: 1
idv_policy eval average step individual rewards of agent2: -0.041848632479452384
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: 0.0060458288930629635
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: 0.11606546767759657
idv_policy eval average team episode rewards of agent4: 2.5
idv_policy eval idv catch total num of agent4: 7
idv_policy eval team catch total num: 1

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 776/10000 episodes, total num timesteps 155400/2000000, FPS 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.

team_policy eval average step individual rewards of agent0: 0.17198766292059284
team_policy eval average team episode rewards of agent0: 20.0
team_policy eval idv catch total num of agent0: 9
team_policy eval team catch total num: 8
team_policy eval average step individual rewards of agent1: 0.27443671106084466
team_policy eval average team episode rewards of agent1: 20.0
team_policy eval idv catch total num of agent1: 13
team_policy eval team catch total num: 8
team_policy eval average step individual rewards of agent2: 0.19596886368704966
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: 0.1736229332694014
team_policy eval average team episode rewards of agent3: 20.0
team_policy eval idv catch total num of agent3: 9
team_policy eval team catch total num: 8
team_policy eval average step individual rewards of agent4: 0.15252611345267866
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: 0.039948732519103414
idv_policy eval average team episode rewards of agent0: 2.5
idv_policy eval idv catch total num of agent0: 5
idv_policy eval team catch total num: 1
idv_policy eval average step individual rewards of agent1: -0.03461516257033588
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: -0.0832827446989731
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: -0.06578862810054363
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: -0.006779991137715356
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 801/10000 episodes, total num timesteps 160400/2000000, FPS 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.

team_policy eval average step individual rewards of agent0: 0.1309894018482776
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: -0.03486056756657163
team_policy eval average team episode rewards of agent1: 25.0
team_policy eval idv catch total num of agent1: 1
team_policy eval team catch total num: 10
team_policy eval average step individual rewards of agent2: 0.21232265158464897
team_policy eval average team episode rewards of agent2: 25.0
team_policy eval idv catch total num of agent2: 11
team_policy eval team catch total num: 10
team_policy eval average step individual rewards of agent3: 0.3374792804709599
team_policy eval average team episode rewards of agent3: 25.0
team_policy eval idv catch total num of agent3: 16
team_policy eval team catch total num: 10
team_policy eval average step individual rewards of agent4: 0.09036367528707298
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: -0.04990851594570638
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: 0.0996933652942185
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: -0.05657753532419611
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: -0.02928816215628424
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: 0.07440382491412437
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 826/10000 episodes, total num timesteps 165400/2000000, FPS 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.

team_policy eval average step individual rewards of agent0: 0.006243194819383917
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: -0.042040933954467175
team_policy eval average team episode rewards of agent1: 5.0
team_policy eval idv catch total num of agent1: 1
team_policy eval team catch total num: 2
team_policy eval average step individual rewards of agent2: 0.031314263800849444
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: 0.08686711644242237
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: 0.03158345664056524
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: -0.008614061798761848
idv_policy eval average team episode rewards of agent0: 7.5
idv_policy eval idv catch total num of agent0: 2
idv_policy eval team catch total num: 3
idv_policy eval average step individual rewards of agent1: 0.12002867860735629
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: 0.16963744008962534
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: 0.013109662798469876
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: 0.020627417474860035
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 851/10000 episodes, total num timesteps 170400/2000000, FPS 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.

team_policy eval average step individual rewards of agent0: 0.2218158224969756
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: 0.11878676883514958
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: 0.1927230479135611
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: -0.030228407668423656
team_policy eval average team episode rewards of agent3: 20.0
team_policy eval idv catch total num of agent3: 1
team_policy eval team catch total num: 8
team_policy eval average step individual rewards of agent4: 0.06452642676735856
team_policy eval average team episode rewards of agent4: 20.0
team_policy eval idv catch total num of agent4: 5
team_policy eval team catch total num: 8
idv_policy eval average step individual rewards of agent0: 0.03054499314087198
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: -0.02290354065411072
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: 0.005967676353735541
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: 0.004355784315825404
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: -0.026318910786057918
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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 275.


 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 274.


 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 274.


 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 274.


 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 274.


 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 274.


 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 275.


 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 275.


 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 275.


 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 274.


 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 274.


 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 274.


 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 274.


 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 275.


 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 274.


 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 274.


 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 274.


 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 274.

team_policy eval average step individual rewards of agent0: 0.12485357938469166
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: 0.20027043873235853
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: 0.3023074261626352
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: 0.19635918221321697
team_policy eval average team episode rewards of agent3: 15.0
team_policy eval idv catch total num of agent3: 10
team_policy eval team catch total num: 6
team_policy eval average step individual rewards of agent4: 0.14673346174087645
team_policy eval average team episode rewards of agent4: 15.0
team_policy eval idv catch total num of agent4: 8
team_policy eval team catch total num: 6
idv_policy eval average step individual rewards of agent0: -0.017516460734332176
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: 0.056893512950104155
idv_policy eval average team episode rewards of agent1: 0.0
idv_policy eval idv catch total num of agent1: 5
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent2: -0.07515678212870103
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: 0.014567639272766709
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: -0.08170522403078458
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 901/10000 episodes, total num timesteps 180400/2000000, FPS 274.


 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 274.


 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 274.


 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 274.


 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 274.


 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 274.


 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 274.


 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 274.


 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 274.


 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 274.


 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 274.


 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 274.


 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 274.


 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 274.


 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 274.


 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 274.


 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 274.


 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 274.


 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 274.


 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 274.


 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 274.


 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 274.


 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 274.


 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 274.


 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 274.

team_policy eval average step individual rewards of agent0: 0.0650527022254706
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: 0.08701915384007641
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: -0.014441452486980167
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: -0.06237146457173579
team_policy eval average team episode rewards of agent3: 7.5
team_policy eval idv catch total num of agent3: 0
team_policy eval team catch total num: 3
team_policy eval average step individual rewards of agent4: 0.0685788683517731
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: 0.06239662125622233
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: -0.01085699245516246
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: -0.012065711527475112
idv_policy eval average team episode rewards of agent2: 7.5
idv_policy eval idv catch total num of agent2: 2
idv_policy eval team catch total num: 3
idv_policy eval average step individual rewards of agent3: -0.012058345280145306
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: 0.03248943126496454
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 926/10000 episodes, total num timesteps 185400/2000000, FPS 274.


 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 274.


 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 274.


 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 274.


 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 274.


 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 274.


 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 274.


 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 274.


 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 274.


 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 274.


 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 274.


 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 274.


 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 274.


 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 274.


 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 273.


 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 273.


 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 273.


 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 273.


 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 273.


 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 273.


 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 273.


 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 273.


 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 273.


 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 273.


 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 273.

team_policy eval average step individual rewards of agent0: 0.048383848033369994
team_policy eval average team episode rewards of agent0: 20.0
team_policy eval idv catch total num of agent0: 4
team_policy eval team catch total num: 8
team_policy eval average step individual rewards of agent1: 0.04012467597456485
team_policy eval average team episode rewards of agent1: 20.0
team_policy eval idv catch total num of agent1: 4
team_policy eval team catch total num: 8
team_policy eval average step individual rewards of agent2: 0.3302280843866538
team_policy eval average team episode rewards of agent2: 20.0
team_policy eval idv catch total num of agent2: 15
team_policy eval team catch total num: 8
team_policy eval average step individual rewards of agent3: 0.17713442447728578
team_policy eval average team episode rewards of agent3: 20.0
team_policy eval idv catch total num of agent3: 9
team_policy eval team catch total num: 8
team_policy eval average step individual rewards of agent4: 0.1479550448448722
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: 0.12388655893649397
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: 0.014232889422050955
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: 0.0929496539796148
idv_policy eval average team episode rewards of agent2: 2.5
idv_policy eval idv catch total num of agent2: 6
idv_policy eval team catch total num: 1
idv_policy eval average step individual rewards of agent3: 0.01805180840170449
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: 0.09371427131560423
idv_policy eval average team episode rewards of agent4: 2.5
idv_policy eval idv catch total num of agent4: 6
idv_policy eval team catch total num: 1

 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 273.


 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 273.


 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 273.


 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 273.


 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 273.


 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 273.


 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 273.


 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 273.


 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 273.


 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 273.


 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 273.


 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 273.


 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 273.


 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 273.


 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 273.


 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 273.


 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 273.


 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 273.


 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 273.


 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 273.


 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 273.


 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 273.


 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 273.


 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 273.


 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 273.

team_policy eval average step individual rewards of agent0: 0.3535618256627505
team_policy eval average team episode rewards of agent0: 27.5
team_policy eval idv catch total num of agent0: 16
team_policy eval team catch total num: 11
team_policy eval average step individual rewards of agent1: 0.361175823592254
team_policy eval average team episode rewards of agent1: 27.5
team_policy eval idv catch total num of agent1: 16
team_policy eval team catch total num: 11
team_policy eval average step individual rewards of agent2: 0.23154442564323408
team_policy eval average team episode rewards of agent2: 27.5
team_policy eval idv catch total num of agent2: 11
team_policy eval team catch total num: 11
team_policy eval average step individual rewards of agent3: 0.1786787940234457
team_policy eval average team episode rewards of agent3: 27.5
team_policy eval idv catch total num of agent3: 9
team_policy eval team catch total num: 11
team_policy eval average step individual rewards of agent4: 0.23718447512255625
team_policy eval average team episode rewards of agent4: 27.5
team_policy eval idv catch total num of agent4: 11
team_policy eval team catch total num: 11
idv_policy eval average step individual rewards of agent0: 0.15987223685677113
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: 0.03538639710987854
idv_policy eval average team episode rewards of agent1: 12.5
idv_policy eval idv catch total num of agent1: 4
idv_policy eval team catch total num: 5
idv_policy eval average step individual rewards of agent2: 0.13605364375767817
idv_policy eval average team episode rewards of agent2: 12.5
idv_policy eval idv catch total num of agent2: 8
idv_policy eval team catch total num: 5
idv_policy eval average step individual rewards of agent3: 0.13833795413049446
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: 0.21114519072407475
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 976/10000 episodes, total num timesteps 195400/2000000, FPS 273.


 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 273.


 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 273.


 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 273.


 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 273.


 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 273.


 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 273.


 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 273.


 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 273.


 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 273.


 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 273.


 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 273.


 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 273.


 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 273.


 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 273.


 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 273.


 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 273.


 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 273.


 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 273.


 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 273.


 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 273.


 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 273.


 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 273.


 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 273.


 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 273.

team_policy eval average step individual rewards of agent0: 0.09780770847485024
team_policy eval average team episode rewards of agent0: 10.0
team_policy eval idv catch total num of agent0: 6
team_policy eval team catch total num: 4
team_policy eval average step individual rewards of agent1: 0.06667216663793071
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: -0.058014730080805064
team_policy eval average team episode rewards of agent2: 10.0
team_policy eval idv catch total num of agent2: 0
team_policy eval team catch total num: 4
team_policy eval average step individual rewards of agent3: 0.0947068434496896
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: 0.04509853811524831
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: -0.04644425591036871
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: -0.04912984600097467
idv_policy eval average team episode rewards of agent1: 5.0
idv_policy eval idv catch total num of agent1: 1
idv_policy eval team catch total num: 2
idv_policy eval average step individual rewards of agent2: -0.0482163796974629
idv_policy eval average team episode rewards of agent2: 5.0
idv_policy eval idv catch total num of agent2: 1
idv_policy eval team catch total num: 2
idv_policy eval average step individual rewards of agent3: 0.07935056553588629
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: 0.0026674219713206516
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 1001/10000 episodes, total num timesteps 200400/2000000, FPS 273.


 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 273.


 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 273.


 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 273.


 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 273.


 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 273.


 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 273.


 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 274.


 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 273.


 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 274.


 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 274.


 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 274.


 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 274.


 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 274.


 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 274.


 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 274.


 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 274.


 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 274.


 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 274.


 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 274.


 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 274.


 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 274.


 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 274.


 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 274.


 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 274.

team_policy eval average step individual rewards of agent0: 0.011666291807653549
team_policy eval average team episode rewards of agent0: 12.5
team_policy eval idv catch total num of agent0: 3
team_policy eval team catch total num: 5
team_policy eval average step individual rewards of agent1: 0.03852842803161972
team_policy eval average team episode rewards of agent1: 12.5
team_policy eval idv catch total num of agent1: 4
team_policy eval team catch total num: 5
team_policy eval average step individual rewards of agent2: 0.0056308547914377845
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: 0.1424147173222733
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: 0.012485576463583914
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: -0.011816956676856047
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: 0.06408243583354549
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: -0.02390419879529921
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: 0.03486543661072192
idv_policy eval average team episode rewards of agent3: 5.0
idv_policy eval idv catch total num of agent3: 4
idv_policy eval team catch total num: 2
idv_policy eval average step individual rewards of agent4: 0.030383030750822392
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 1026/10000 episodes, total num timesteps 205400/2000000, FPS 274.


 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 274.


 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 274.


 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 274.


 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 274.


 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 274.


 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 274.


 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 274.


 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 274.


 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 274.


 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 274.


 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 273.


 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 273.


 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 273.


 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 273.


 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 273.


 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 273.


 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 273.


 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 273.


 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 273.


 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 273.


 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 273.


 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 273.


 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 273.


 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 272.

team_policy eval average step individual rewards of agent0: -0.05077338645364224
team_policy eval average team episode rewards of agent0: 7.5
team_policy eval idv catch total num of agent0: 1
team_policy eval team catch total num: 3
team_policy eval average step individual rewards of agent1: 1.0830095330869315e-05
team_policy eval average team episode rewards of agent1: 7.5
team_policy eval idv catch total num of agent1: 3
team_policy eval team catch total num: 3
team_policy eval average step individual rewards of agent2: -0.042222523345098385
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: 0.030161005730162883
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: 0.029584245108116476
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: 0.05934071782468973
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: 0.0863174240479852
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: 0.08339626290629543
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: 0.03702219907772066
idv_policy eval average team episode rewards of agent3: 15.0
idv_policy eval idv catch total num of agent3: 4
idv_policy eval team catch total num: 6
idv_policy eval average step individual rewards of agent4: 0.02871771218232663
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 1051/10000 episodes, total num timesteps 210400/2000000, FPS 272.


 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 272.


 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 272.


 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 272.


 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 272.


 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 272.


 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 272.


 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 272.


 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 272.


 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 272.


 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 272.


 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 272.


 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 271.


 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 271.


 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 271.


 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 271.


 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 271.


 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 271.


 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 271.


 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 271.


 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 271.


 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 271.


 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 271.


 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 271.


 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 271.

team_policy eval average step individual rewards of agent0: 0.25421167137890605
team_policy eval average team episode rewards of agent0: 17.5
team_policy eval idv catch total num of agent0: 12
team_policy eval team catch total num: 7
team_policy eval average step individual rewards of agent1: 0.24380227653726202
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: 0.2015342500783526
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: 0.17474690029705314
team_policy eval average team episode rewards of agent3: 17.5
team_policy eval idv catch total num of agent3: 9
team_policy eval team catch total num: 7
team_policy eval average step individual rewards of agent4: 0.09994883350538573
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: 0.05783482460250292
idv_policy eval average team episode rewards of agent0: 22.5
idv_policy eval idv catch total num of agent0: 5
idv_policy eval team catch total num: 9
idv_policy eval average step individual rewards of agent1: 0.10629215010611194
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: 0.08519751032625919
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: 0.08317077540062937
idv_policy eval average team episode rewards of agent3: 22.5
idv_policy eval idv catch total num of agent3: 6
idv_policy eval team catch total num: 9
idv_policy eval average step individual rewards of agent4: 0.16071421120243953
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 1076/10000 episodes, total num timesteps 215400/2000000, FPS 270.


 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 270.


 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 270.


 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 270.


 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 270.


 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 270.


 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 270.


 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 270.


 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 270.


 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 270.


 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 270.


 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 270.


 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 270.


 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 270.


 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 270.


 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 270.


 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 269.


 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 269.


 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 269.


 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 269.


 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 269.


 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 269.


 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 269.


 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 269.


 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 269.

team_policy eval average step individual rewards of agent0: 0.08409640794422853
team_policy eval average team episode rewards of agent0: 2.5
team_policy eval idv catch total num of agent0: 6
team_policy eval team catch total num: 1
team_policy eval average step individual rewards of agent1: -0.04058863885967064
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: 0.03280667462338985
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: 0.1349414755774046
team_policy eval average team episode rewards of agent3: 2.5
team_policy eval idv catch total num of agent3: 8
team_policy eval team catch total num: 1
team_policy eval average step individual rewards of agent4: 0.008048892293015078
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: 0.09597915474357968
idv_policy eval average team episode rewards of agent0: 32.5
idv_policy eval idv catch total num of agent0: 6
idv_policy eval team catch total num: 13
idv_policy eval average step individual rewards of agent1: 0.20587085900425897
idv_policy eval average team episode rewards of agent1: 32.5
idv_policy eval idv catch total num of agent1: 10
idv_policy eval team catch total num: 13
idv_policy eval average step individual rewards of agent2: 0.19550414436757904
idv_policy eval average team episode rewards of agent2: 32.5
idv_policy eval idv catch total num of agent2: 10
idv_policy eval team catch total num: 13
idv_policy eval average step individual rewards of agent3: 0.02750753846366731
idv_policy eval average team episode rewards of agent3: 32.5
idv_policy eval idv catch total num of agent3: 3
idv_policy eval team catch total num: 13
idv_policy eval average step individual rewards of agent4: 0.27116031309323796
idv_policy eval average team episode rewards of agent4: 32.5
idv_policy eval idv catch total num of agent4: 13
idv_policy eval team catch total num: 13

 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 269.


 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 269.


 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 269.


 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 269.


 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 268.


 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 268.


 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 268.


 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 268.


 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 268.


 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 268.


 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 268.


 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 268.


 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 268.


 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 268.


 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 268.


 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 268.


 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 268.


 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 268.


 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 267.


 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 267.


 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 267.


 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 267.


 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 267.


 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 267.


 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 267.

team_policy eval average step individual rewards of agent0: 0.06055946002251222
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: -0.034070950132703864
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: 0.11452553412344335
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: -0.016486532237284447
team_policy eval average team episode rewards of agent3: 10.0
team_policy eval idv catch total num of agent3: 2
team_policy eval team catch total num: 4
team_policy eval average step individual rewards of agent4: 0.013750554169926116
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: 0.050934835228752584
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: 0.17717927598790376
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: -0.06862998682922763
idv_policy eval average team episode rewards of agent2: 7.5
idv_policy eval idv catch total num of agent2: 0
idv_policy eval team catch total num: 3
idv_policy eval average step individual rewards of agent3: -0.05009573528855097
idv_policy eval average team episode rewards of agent3: 7.5
idv_policy eval idv catch total num of agent3: 1
idv_policy eval team catch total num: 3
idv_policy eval average step individual rewards of agent4: 0.024294619790179838
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 1126/10000 episodes, total num timesteps 225400/2000000, FPS 267.


 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 267.


 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 267.


 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 267.


 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 267.


 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 267.


 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 267.


 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 267.


 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 267.


 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 267.


 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 267.


 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 266.


 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 266.


 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 266.


 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 266.


 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 266.


 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 266.


 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 266.


 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 266.


 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 266.


 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 266.


 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 266.


 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 266.


 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 266.


 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 266.

team_policy eval average step individual rewards of agent0: 0.06270177164211642
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: -0.014995722724216641
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: 0.08382213867459268
team_policy eval average team episode rewards of agent2: 10.0
team_policy eval idv catch total num of agent2: 6
team_policy eval team catch total num: 4
team_policy eval average step individual rewards of agent3: 0.08763072995963611
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: 0.03302918341937637
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: 0.22610867338860552
idv_policy eval average team episode rewards of agent0: 17.5
idv_policy eval idv catch total num of agent0: 11
idv_policy eval team catch total num: 7
idv_policy eval average step individual rewards of agent1: 0.240635486043539
idv_policy eval average team episode rewards of agent1: 17.5
idv_policy eval idv catch total num of agent1: 12
idv_policy eval team catch total num: 7
idv_policy eval average step individual rewards of agent2: -0.03419964131445937
idv_policy eval average team episode rewards of agent2: 17.5
idv_policy eval idv catch total num of agent2: 1
idv_policy eval team catch total num: 7
idv_policy eval average step individual rewards of agent3: -0.011618569741668474
idv_policy eval average team episode rewards of agent3: 17.5
idv_policy eval idv catch total num of agent3: 2
idv_policy eval team catch total num: 7
idv_policy eval average step individual rewards of agent4: 0.16290107325201583
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 1151/10000 episodes, total num timesteps 230400/2000000, FPS 266.


 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 266.


 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 266.


 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 265.


 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 265.


 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 265.


 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 265.


 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 265.


 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 265.


 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 265.


 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 265.


 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 265.


 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 265.


 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 265.


 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 265.


 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 265.


 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 265.


 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 265.


 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 265.


 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 265.


 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 264.


 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 264.


 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 264.


 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 264.


 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 264.

team_policy eval average step individual rewards of agent0: 0.09712985288537819
team_policy eval average team episode rewards of agent0: 12.5
team_policy eval idv catch total num of agent0: 6
team_policy eval team catch total num: 5
team_policy eval average step individual rewards of agent1: 0.09121836623895944
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: 0.09559531055890076
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: 0.09624232752999436
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: 0.17029066133976278
team_policy eval average team episode rewards of agent4: 12.5
team_policy eval idv catch total num of agent4: 9
team_policy eval team catch total num: 5
idv_policy eval average step individual rewards of agent0: 0.02808260034365263
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: -0.04802423675997071
idv_policy eval average team episode rewards of agent1: 5.0
idv_policy eval idv catch total num of agent1: 1
idv_policy eval team catch total num: 2
idv_policy eval average step individual rewards of agent2: -0.05309045149693839
idv_policy eval average team episode rewards of agent2: 5.0
idv_policy eval idv catch total num of agent2: 1
idv_policy eval team catch total num: 2
idv_policy eval average step individual rewards of agent3: -0.07046459965794685
idv_policy eval average team episode rewards of agent3: 5.0
idv_policy eval idv catch total num of agent3: 0
idv_policy eval team catch total num: 2
idv_policy eval average step individual rewards of agent4: 0.033890155533465444
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 1176/10000 episodes, total num timesteps 235400/2000000, FPS 264.


 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 264.


 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 264.


 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 264.


 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 264.


 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 264.


 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 264.


 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 264.


 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 264.


 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 264.


 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 264.


 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 263.


 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 263.


 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 263.


 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 263.


 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 263.


 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 263.


 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 263.


 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 263.


 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 263.


 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 263.


 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 263.


 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 263.


 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 263.


 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 263.

team_policy eval average step individual rewards of agent0: 0.06870229657232402
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: -0.030479487880914317
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: 0.1958787621473749
team_policy eval average team episode rewards of agent2: 10.0
team_policy eval idv catch total num of agent2: 10
team_policy eval team catch total num: 4
team_policy eval average step individual rewards of agent3: 0.06735955976567404
team_policy eval average team episode rewards of agent3: 10.0
team_policy eval idv catch total num of agent3: 5
team_policy eval team catch total num: 4
team_policy eval average step individual rewards of agent4: 0.06738779003972217
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: 0.16760159380665293
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: 0.01965449567276547
idv_policy eval average team episode rewards of agent1: 12.5
idv_policy eval idv catch total num of agent1: 3
idv_policy eval team catch total num: 5
idv_policy eval average step individual rewards of agent2: -0.005184603713621936
idv_policy eval average team episode rewards of agent2: 12.5
idv_policy eval idv catch total num of agent2: 2
idv_policy eval team catch total num: 5
idv_policy eval average step individual rewards of agent3: 0.08812592623596378
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: 0.01864443509334041
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 1201/10000 episodes, total num timesteps 240400/2000000, FPS 263.


 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 263.


 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 263.


 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 262.


 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 262.


 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 262.


 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 262.


 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 262.


 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 262.


 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 262.


 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 262.


 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 262.


 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 262.


 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 262.


 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 262.


 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 262.


 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 262.


 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 262.


 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 262.


 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 262.


 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 261.


 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 261.


 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 261.


 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 261.


 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 261.

team_policy eval average step individual rewards of agent0: 0.05850313172384494
team_policy eval average team episode rewards of agent0: 27.5
team_policy eval idv catch total num of agent0: 5
team_policy eval team catch total num: 11
team_policy eval average step individual rewards of agent1: 0.1575060950831049
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: 0.05964947533214009
team_policy eval average team episode rewards of agent2: 27.5
team_policy eval idv catch total num of agent2: 5
team_policy eval team catch total num: 11
team_policy eval average step individual rewards of agent3: 0.08308896388282466
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: 0.26430830977964065
team_policy eval average team episode rewards of agent4: 27.5
team_policy eval idv catch total num of agent4: 13
team_policy eval team catch total num: 11
idv_policy eval average step individual rewards of agent0: -0.010927482309079037
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: 0.04331943939638979
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: -0.012215605278679873
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: 0.0962082481742955
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: -0.06266057353985233
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 1226/10000 episodes, total num timesteps 245400/2000000, FPS 261.


 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 261.


 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 261.


 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 261.


 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 261.


 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 261.


 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 261.


 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 261.


 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 261.


 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 261.


 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 261.


 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 261.


 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 261.


 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 261.


 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 261.


 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 260.


 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 260.


 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 260.


 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 260.


 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 260.


 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 260.


 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 260.


 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 260.


 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 260.


 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 260.

team_policy eval average step individual rewards of agent0: 0.1387294665124569
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: 0.015431609561528585
team_policy eval average team episode rewards of agent1: 10.0
team_policy eval idv catch total num of agent1: 3
team_policy eval team catch total num: 4
team_policy eval average step individual rewards of agent2: 0.014394566976889712
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: -0.010385226637700109
team_policy eval average team episode rewards of agent3: 10.0
team_policy eval idv catch total num of agent3: 2
team_policy eval team catch total num: 4
team_policy eval average step individual rewards of agent4: 0.0338892391457447
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: 0.27110528710896864
idv_policy eval average team episode rewards of agent0: 22.5
idv_policy eval idv catch total num of agent0: 13
idv_policy eval team catch total num: 9
idv_policy eval average step individual rewards of agent1: 0.11754027804112832
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: 0.09323371816006518
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: 0.11751330450578765
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: 0.16853503711551024
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 1251/10000 episodes, total num timesteps 250400/2000000, FPS 260.


 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 260.


 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 260.


 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 260.


 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 260.


 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 260.


 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 260.


 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 260.


 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 260.


 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 260.


 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 260.


 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 260.


 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 260.


 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 260.


 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 260.


 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 260.


 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 259.


 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 259.


 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 259.


 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 259.


 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 259.


 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 259.


 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 259.


 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 259.


 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 259.

team_policy eval average step individual rewards of agent0: 0.04213187795574259
team_policy eval average team episode rewards of agent0: 2.5
team_policy eval idv catch total num of agent0: 5
team_policy eval team catch total num: 1
team_policy eval average step individual rewards of agent1: 0.00048677701270011476
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: -0.0019501067467786416
team_policy eval average team episode rewards of agent2: 2.5
team_policy eval idv catch total num of agent2: 3
team_policy eval team catch total num: 1
team_policy eval average step individual rewards of agent3: 0.02671086974614483
team_policy eval average team episode rewards of agent3: 2.5
team_policy eval idv catch total num of agent3: 4
team_policy eval team catch total num: 1
team_policy eval average step individual rewards of agent4: 0.09803594864566213
team_policy eval average team episode rewards of agent4: 2.5
team_policy eval idv catch total num of agent4: 7
team_policy eval team catch total num: 1
idv_policy eval average step individual rewards of agent0: -0.06865481335825267
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: -0.06451408536838125
idv_policy eval average team episode rewards of agent1: 0.0
idv_policy eval idv catch total num of agent1: 0
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent2: -0.018739039758462743
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: -0.06593875297876978
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: -0.045600043622170655
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 1276/10000 episodes, total num timesteps 255400/2000000, FPS 259.


 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 259.


 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 259.


 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 259.


 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 259.


 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 259.


 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 259.


 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 259.


 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 259.


 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 259.


 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 259.


 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 259.


 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 258.


 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 258.


 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 258.


 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 258.


 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 258.


 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 258.


 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 258.


 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 258.


 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 258.


 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 258.


 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 258.


 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 258.


 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 258.

team_policy eval average step individual rewards of agent0: -0.016076736707188625
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: 0.0135668852701979
team_policy eval average team episode rewards of agent1: 7.5
team_policy eval idv catch total num of agent1: 3
team_policy eval team catch total num: 3
team_policy eval average step individual rewards of agent2: 0.10627746667571608
team_policy eval average team episode rewards of agent2: 7.5
team_policy eval idv catch total num of agent2: 7
team_policy eval team catch total num: 3
team_policy eval average step individual rewards of agent3: -0.021795621008373508
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: -0.03831140072187768
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: 0.0908949897542839
idv_policy eval average team episode rewards of agent0: 20.0
idv_policy eval idv catch total num of agent0: 6
idv_policy eval team catch total num: 8
idv_policy eval average step individual rewards of agent1: 0.043178943659955255
idv_policy eval average team episode rewards of agent1: 20.0
idv_policy eval idv catch total num of agent1: 4
idv_policy eval team catch total num: 8
idv_policy eval average step individual rewards of agent2: 0.2194391977597993
idv_policy eval average team episode rewards of agent2: 20.0
idv_policy eval idv catch total num of agent2: 11
idv_policy eval team catch total num: 8
idv_policy eval average step individual rewards of agent3: 0.04212317969235709
idv_policy eval average team episode rewards of agent3: 20.0
idv_policy eval idv catch total num of agent3: 4
idv_policy eval team catch total num: 8
idv_policy eval average step individual rewards of agent4: 0.09058993485023281
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 1301/10000 episodes, total num timesteps 260400/2000000, FPS 258.


 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 258.


 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 258.


 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 258.


 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 258.


 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 258.


 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 258.


 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 258.


 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 258.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.

team_policy eval average step individual rewards of agent0: -0.05699545494444727
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: -0.07934717112915693
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: -0.029833382721166323
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: -0.03912508774228378
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: -0.035430782709247925
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: 0.19129764604647007
idv_policy eval average team episode rewards of agent0: 17.5
idv_policy eval idv catch total num of agent0: 10
idv_policy eval team catch total num: 7
idv_policy eval average step individual rewards of agent1: 0.05939282229313166
idv_policy eval average team episode rewards of agent1: 17.5
idv_policy eval idv catch total num of agent1: 5
idv_policy eval team catch total num: 7
idv_policy eval average step individual rewards of agent2: 0.09145812777274383
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: 0.18890487416214646
idv_policy eval average team episode rewards of agent3: 17.5
idv_policy eval idv catch total num of agent3: 10
idv_policy eval team catch total num: 7
idv_policy eval average step individual rewards of agent4: 0.11280739581880196
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 1326/10000 episodes, total num timesteps 265400/2000000, FPS 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.


 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 257.

team_policy eval average step individual rewards of agent0: -0.04067224296575392
team_policy eval average team episode rewards of agent0: 12.5
team_policy eval idv catch total num of agent0: 1
team_policy eval team catch total num: 5
team_policy eval average step individual rewards of agent1: 0.0425451740911552
team_policy eval average team episode rewards of agent1: 12.5
team_policy eval idv catch total num of agent1: 4
team_policy eval team catch total num: 5
team_policy eval average step individual rewards of agent2: 0.08240356724829515
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: 0.0035574348190778073
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: 0.06487264797257045
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: 0.09326564693536411
idv_policy eval average team episode rewards of agent0: 22.5
idv_policy eval idv catch total num of agent0: 6
idv_policy eval team catch total num: 9
idv_policy eval average step individual rewards of agent1: 0.22030854274461142
idv_policy eval average team episode rewards of agent1: 22.5
idv_policy eval idv catch total num of agent1: 11
idv_policy eval team catch total num: 9
idv_policy eval average step individual rewards of agent2: 0.1231723248918329
idv_policy eval average team episode rewards of agent2: 22.5
idv_policy eval idv catch total num of agent2: 7
idv_policy eval team catch total num: 9
idv_policy eval average step individual rewards of agent3: 0.17112705523448216
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: 0.1172097881928785
idv_policy eval average team episode rewards of agent4: 22.5
idv_policy eval idv catch total num of agent4: 7
idv_policy eval team catch total num: 9

 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 257.


 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 257.


 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 257.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.


 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 256.

team_policy eval average step individual rewards of agent0: -0.07166757644374393
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: -0.021347614186014097
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: 0.025544254850292297
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: -0.01998013879278912
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: 0.027392823345625414
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: -0.020743017489642447
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: 0.007565637520165538
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: -0.01531150875477387
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: -0.04536234068500215
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: 0.036341462304487315
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 1376/10000 episodes, total num timesteps 275400/2000000, FPS 256.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.


 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 255.

team_policy eval average step individual rewards of agent0: 0.12820866336655545
team_policy eval average team episode rewards of agent0: 35.0
team_policy eval idv catch total num of agent0: 7
team_policy eval team catch total num: 14
team_policy eval average step individual rewards of agent1: 0.24807728089371017
team_policy eval average team episode rewards of agent1: 35.0
team_policy eval idv catch total num of agent1: 12
team_policy eval team catch total num: 14
team_policy eval average step individual rewards of agent2: 0.22367676735784947
team_policy eval average team episode rewards of agent2: 35.0
team_policy eval idv catch total num of agent2: 11
team_policy eval team catch total num: 14
team_policy eval average step individual rewards of agent3: 0.29893253483646137
team_policy eval average team episode rewards of agent3: 35.0
team_policy eval idv catch total num of agent3: 14
team_policy eval team catch total num: 14
team_policy eval average step individual rewards of agent4: 0.27242766360126636
team_policy eval average team episode rewards of agent4: 35.0
team_policy eval idv catch total num of agent4: 13
team_policy eval team catch total num: 14
idv_policy eval average step individual rewards of agent0: 0.04280528872205568
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: 0.013124296949614603
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: -0.005801486956412161
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: 0.017500547515771105
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: -0.03292835192553994
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 1401/10000 episodes, total num timesteps 280400/2000000, FPS 255.


 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 255.


 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 255.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.

team_policy eval average step individual rewards of agent0: -0.029467601195237173
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: -0.02476268724971015
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: -0.05305575503625372
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: -0.04860019496858553
team_policy eval average team episode rewards of agent3: 2.5
team_policy eval idv catch total num of agent3: 1
team_policy eval team catch total num: 1
team_policy eval average step individual rewards of agent4: -0.07645903846768336
team_policy eval average team episode rewards of agent4: 2.5
team_policy eval idv catch total num of agent4: 0
team_policy eval team catch total num: 1
idv_policy eval average step individual rewards of agent0: -0.0325240079925912
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: 0.020465280042626183
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: 0.04721075510926262
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: -0.012473845711970737
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: 0.05091577315614194
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 1426/10000 episodes, total num timesteps 285400/2000000, FPS 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 254.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.

team_policy eval average step individual rewards of agent0: 0.13493281679414423
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: 0.08587139818635567
team_policy eval average team episode rewards of agent1: 22.5
team_policy eval idv catch total num of agent1: 6
team_policy eval team catch total num: 9
team_policy eval average step individual rewards of agent2: 0.15948343043563667
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: 0.054899284055074916
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: 0.10734617617582763
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: 0.09505762902969722
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: 0.04209279954400515
idv_policy eval average team episode rewards of agent1: 12.5
idv_policy eval idv catch total num of agent1: 4
idv_policy eval team catch total num: 5
idv_policy eval average step individual rewards of agent2: 0.14571080461868205
idv_policy eval average team episode rewards of agent2: 12.5
idv_policy eval idv catch total num of agent2: 8
idv_policy eval team catch total num: 5
idv_policy eval average step individual rewards of agent3: 0.09674469345363733
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: 0.03924402090901267
idv_policy eval average team episode rewards of agent4: 12.5
idv_policy eval idv catch total num of agent4: 4
idv_policy eval team catch total num: 5

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1451/10000 episodes, total num timesteps 290400/2000000, FPS 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 253.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.

team_policy eval average step individual rewards of agent0: 0.12018463572766937
team_policy eval average team episode rewards of agent0: 27.5
team_policy eval idv catch total num of agent0: 7
team_policy eval team catch total num: 11
team_policy eval average step individual rewards of agent1: 0.33146615091521553
team_policy eval average team episode rewards of agent1: 27.5
team_policy eval idv catch total num of agent1: 15
team_policy eval team catch total num: 11
team_policy eval average step individual rewards of agent2: 0.20098400186261156
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: 0.16805404794415843
team_policy eval average team episode rewards of agent3: 27.5
team_policy eval idv catch total num of agent3: 9
team_policy eval team catch total num: 11
team_policy eval average step individual rewards of agent4: 0.06726794873147274
team_policy eval average team episode rewards of agent4: 27.5
team_policy eval idv catch total num of agent4: 5
team_policy eval team catch total num: 11
idv_policy eval average step individual rewards of agent0: 0.19213041328098307
idv_policy eval average team episode rewards of agent0: 20.0
idv_policy eval idv catch total num of agent0: 10
idv_policy eval team catch total num: 8
idv_policy eval average step individual rewards of agent1: 0.13938319139645336
idv_policy eval average team episode rewards of agent1: 20.0
idv_policy eval idv catch total num of agent1: 8
idv_policy eval team catch total num: 8
idv_policy eval average step individual rewards of agent2: 0.11766425873830375
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: -0.00992097893983253
idv_policy eval average team episode rewards of agent3: 20.0
idv_policy eval idv catch total num of agent3: 2
idv_policy eval team catch total num: 8
idv_policy eval average step individual rewards of agent4: 0.0883716802245936
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 1476/10000 episodes, total num timesteps 295400/2000000, FPS 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 252.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.

team_policy eval average step individual rewards of agent0: 0.11532821032913887
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: 0.17188229121456672
team_policy eval average team episode rewards of agent1: 30.0
team_policy eval idv catch total num of agent1: 9
team_policy eval team catch total num: 12
team_policy eval average step individual rewards of agent2: 0.17237123769923163
team_policy eval average team episode rewards of agent2: 30.0
team_policy eval idv catch total num of agent2: 9
team_policy eval team catch total num: 12
team_policy eval average step individual rewards of agent3: 0.09882053168864995
team_policy eval average team episode rewards of agent3: 30.0
team_policy eval idv catch total num of agent3: 6
team_policy eval team catch total num: 12
team_policy eval average step individual rewards of agent4: 0.1260576087194636
team_policy eval average team episode rewards of agent4: 30.0
team_policy eval idv catch total num of agent4: 7
team_policy eval team catch total num: 12
idv_policy eval average step individual rewards of agent0: 0.033578761453357656
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: 0.08604728084807993
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: -0.04267663210678962
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: -0.01813994263596846
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: -0.04218075475586969
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 1501/10000 episodes, total num timesteps 300400/2000000, FPS 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.

team_policy eval average step individual rewards of agent0: 0.04716362531276391
team_policy eval average team episode rewards of agent0: 15.0
team_policy eval idv catch total num of agent0: 4
team_policy eval team catch total num: 6
team_policy eval average step individual rewards of agent1: 0.019844315642342555
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: 0.09414429572273632
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: 0.11696620026184512
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: 0.08955923598252476
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: -0.03119419012803383
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: -0.0662315825708966
idv_policy eval average team episode rewards of agent1: 0.0
idv_policy eval idv catch total num of agent1: 0
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent2: -0.05949926446932631
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: 0.06115199399540982
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: 0.035947622841111165
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 1526/10000 episodes, total num timesteps 305400/2000000, FPS 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.

team_policy eval average step individual rewards of agent0: 0.08646078428723024
team_policy eval average team episode rewards of agent0: 12.5
team_policy eval idv catch total num of agent0: 6
team_policy eval team catch total num: 5
team_policy eval average step individual rewards of agent1: -0.016664267013279374
team_policy eval average team episode rewards of agent1: 12.5
team_policy eval idv catch total num of agent1: 2
team_policy eval team catch total num: 5
team_policy eval average step individual rewards of agent2: 0.001372605237118245
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: 0.00752330893603754
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: 0.1397036761980208
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: 0.10294610455193627
idv_policy eval average team episode rewards of agent0: 22.5
idv_policy eval idv catch total num of agent0: 7
idv_policy eval team catch total num: 9
idv_policy eval average step individual rewards of agent1: 0.08115678968100969
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: 0.11075337004886425
idv_policy eval average team episode rewards of agent2: 22.5
idv_policy eval idv catch total num of agent2: 7
idv_policy eval team catch total num: 9
idv_policy eval average step individual rewards of agent3: 0.12377446286966821
idv_policy eval average team episode rewards of agent3: 22.5
idv_policy eval idv catch total num of agent3: 8
idv_policy eval team catch total num: 9
idv_policy eval average step individual rewards of agent4: 0.14597992854414601
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 1551/10000 episodes, total num timesteps 310400/2000000, FPS 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.

team_policy eval average step individual rewards of agent0: 0.16373000200609972
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: -0.04583607043022389
team_policy eval average team episode rewards of agent1: 15.0
team_policy eval idv catch total num of agent1: 1
team_policy eval team catch total num: 6
team_policy eval average step individual rewards of agent2: 0.037900468402308735
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: 0.08537150340575726
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: 0.13604290724868295
team_policy eval average team episode rewards of agent4: 15.0
team_policy eval idv catch total num of agent4: 8
team_policy eval team catch total num: 6
idv_policy eval average step individual rewards of agent0: -0.04688617250985506
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: 0.034824513690695336
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: 0.0868442467220697
idv_policy eval average team episode rewards of agent2: 7.5
idv_policy eval idv catch total num of agent2: 6
idv_policy eval team catch total num: 3
idv_policy eval average step individual rewards of agent3: -0.015271114317858294
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: 0.008171302032073401
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 1576/10000 episodes, total num timesteps 315400/2000000, FPS 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.

team_policy eval average step individual rewards of agent0: -0.034334081330892136
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: 0.09996208371616368
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: -0.0816962170462499
team_policy eval average team episode rewards of agent2: 7.5
team_policy eval idv catch total num of agent2: 0
team_policy eval team catch total num: 3
team_policy eval average step individual rewards of agent3: -0.02579737076404869
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: -0.07648966044209243
team_policy eval average team episode rewards of agent4: 7.5
team_policy eval idv catch total num of agent4: 0
team_policy eval team catch total num: 3
idv_policy eval average step individual rewards of agent0: 0.22211263501566478
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: -0.03555755061830824
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: 0.07218531903154937
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: 0.037896237233005045
idv_policy eval average team episode rewards of agent3: 15.0
idv_policy eval idv catch total num of agent3: 4
idv_policy eval team catch total num: 6
idv_policy eval average step individual rewards of agent4: 0.1946866393586609
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 1601/10000 episodes, total num timesteps 320400/2000000, FPS 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.

team_policy eval average step individual rewards of agent0: 0.05151364861096692
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: 0.07211982990072313
team_policy eval average team episode rewards of agent1: 20.0
team_policy eval idv catch total num of agent1: 6
team_policy eval team catch total num: 8
team_policy eval average step individual rewards of agent2: 0.10737586337713045
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: 0.181736560837196
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: 0.08247191070346728
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: 0.06642809994214485
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: 0.0872817962395293
idv_policy eval average team episode rewards of agent1: 7.5
idv_policy eval idv catch total num of agent1: 6
idv_policy eval team catch total num: 3
idv_policy eval average step individual rewards of agent2: 0.06807322620572157
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: 0.08662596577742197
idv_policy eval average team episode rewards of agent3: 7.5
idv_policy eval idv catch total num of agent3: 6
idv_policy eval team catch total num: 3
idv_policy eval average step individual rewards of agent4: 0.06342329959878096
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 1626/10000 episodes, total num timesteps 325400/2000000, FPS 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 247.


 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 247.

team_policy eval average step individual rewards of agent0: 0.013599747347827967
team_policy eval average team episode rewards of agent0: 20.0
team_policy eval idv catch total num of agent0: 4
team_policy eval team catch total num: 8
team_policy eval average step individual rewards of agent1: 0.04712901509962809
team_policy eval average team episode rewards of agent1: 20.0
team_policy eval idv catch total num of agent1: 5
team_policy eval team catch total num: 8
team_policy eval average step individual rewards of agent2: 0.023049523369898087
team_policy eval average team episode rewards of agent2: 20.0
team_policy eval idv catch total num of agent2: 4
team_policy eval team catch total num: 8
team_policy eval average step individual rewards of agent3: 0.012051534170111053
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: 0.039635620167324935
team_policy eval average team episode rewards of agent4: 20.0
team_policy eval idv catch total num of agent4: 5
team_policy eval team catch total num: 8
idv_policy eval average step individual rewards of agent0: 0.1678935812090881
idv_policy eval average team episode rewards of agent0: 20.0
idv_policy eval idv catch total num of agent0: 9
idv_policy eval team catch total num: 8
idv_policy eval average step individual rewards of agent1: 0.06861807823174132
idv_policy eval average team episode rewards of agent1: 20.0
idv_policy eval idv catch total num of agent1: 5
idv_policy eval team catch total num: 8
idv_policy eval average step individual rewards of agent2: 0.0581412488712853
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: 0.142187771836079
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: 0.08965266978497695
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 1651/10000 episodes, total num timesteps 330400/2000000, FPS 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.

team_policy eval average step individual rewards of agent0: 0.2346796944880426
team_policy eval average team episode rewards of agent0: 32.5
team_policy eval idv catch total num of agent0: 11
team_policy eval team catch total num: 13
team_policy eval average step individual rewards of agent1: 0.23379793393681708
team_policy eval average team episode rewards of agent1: 32.5
team_policy eval idv catch total num of agent1: 11
team_policy eval team catch total num: 13
team_policy eval average step individual rewards of agent2: 0.1837265132596804
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: 0.23298154708728333
team_policy eval average team episode rewards of agent3: 32.5
team_policy eval idv catch total num of agent3: 11
team_policy eval team catch total num: 13
team_policy eval average step individual rewards of agent4: 0.20965683005874594
team_policy eval average team episode rewards of agent4: 32.5
team_policy eval idv catch total num of agent4: 10
team_policy eval team catch total num: 13
idv_policy eval average step individual rewards of agent0: 0.09020282265693724
idv_policy eval average team episode rewards of agent0: 17.5
idv_policy eval idv catch total num of agent0: 6
idv_policy eval team catch total num: 7
idv_policy eval average step individual rewards of agent1: 0.08231287812450136
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: 0.06850025846400881
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: 0.09071497045898547
idv_policy eval average team episode rewards of agent3: 17.5
idv_policy eval idv catch total num of agent3: 6
idv_policy eval team catch total num: 7
idv_policy eval average step individual rewards of agent4: 0.12150554542670779
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 1676/10000 episodes, total num timesteps 335400/2000000, FPS 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.

team_policy eval average step individual rewards of agent0: 0.08380222080361825
team_policy eval average team episode rewards of agent0: 10.0
team_policy eval idv catch total num of agent0: 6
team_policy eval team catch total num: 4
team_policy eval average step individual rewards of agent1: 0.007756394492042324
team_policy eval average team episode rewards of agent1: 10.0
team_policy eval idv catch total num of agent1: 3
team_policy eval team catch total num: 4
team_policy eval average step individual rewards of agent2: 0.00961031585836224
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: 0.0044645730193138755
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: 0.08455147449331049
team_policy eval average team episode rewards of agent4: 10.0
team_policy eval idv catch total num of agent4: 6
team_policy eval team catch total num: 4
idv_policy eval average step individual rewards of agent0: -0.046790326870126873
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: -0.04904006698174128
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: 0.010016316987754665
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: -0.018944957809221154
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: -0.06936770821468558
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 1701/10000 episodes, total num timesteps 340400/2000000, FPS 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.

team_policy eval average step individual rewards of agent0: -0.040130016522842586
team_policy eval average team episode rewards of agent0: 7.5
team_policy eval idv catch total num of agent0: 1
team_policy eval team catch total num: 3
team_policy eval average step individual rewards of agent1: 0.0568772211166497
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: 0.11311527741388161
team_policy eval average team episode rewards of agent2: 7.5
team_policy eval idv catch total num of agent2: 7
team_policy eval team catch total num: 3
team_policy eval average step individual rewards of agent3: 0.015849351971367505
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: 0.011999643952175343
team_policy eval average team episode rewards of agent4: 7.5
team_policy eval idv catch total num of agent4: 3
team_policy eval team catch total num: 3
idv_policy eval average step individual rewards of agent0: 0.06953002254134004
idv_policy eval average team episode rewards of agent0: 12.5
idv_policy eval idv catch total num of agent0: 5
idv_policy eval team catch total num: 5
idv_policy eval average step individual rewards of agent1: 0.12002376623399325
idv_policy eval average team episode rewards of agent1: 12.5
idv_policy eval idv catch total num of agent1: 7
idv_policy eval team catch total num: 5
idv_policy eval average step individual rewards of agent2: -0.009343580145353214
idv_policy eval average team episode rewards of agent2: 12.5
idv_policy eval idv catch total num of agent2: 2
idv_policy eval team catch total num: 5
idv_policy eval average step individual rewards of agent3: 0.04550935458510375
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: 0.25067930429403107
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 1726/10000 episodes, total num timesteps 345400/2000000, FPS 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.

team_policy eval average step individual rewards of agent0: 0.06496833623027473
team_policy eval average team episode rewards of agent0: 12.5
team_policy eval idv catch total num of agent0: 5
team_policy eval team catch total num: 5
team_policy eval average step individual rewards of agent1: -0.03648142364808278
team_policy eval average team episode rewards of agent1: 12.5
team_policy eval idv catch total num of agent1: 1
team_policy eval team catch total num: 5
team_policy eval average step individual rewards of agent2: 0.09148238894925839
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: 0.08761212199516275
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: 0.06831603704816945
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: 0.0637996931566873
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: 0.03384230989913424
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: 0.036949284942241735
idv_policy eval average team episode rewards of agent2: 5.0
idv_policy eval idv catch total num of agent2: 4
idv_policy eval team catch total num: 2
idv_policy eval average step individual rewards of agent3: 0.08641140779315455
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: 0.03612335257350023
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 1751/10000 episodes, total num timesteps 350400/2000000, FPS 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.

team_policy eval average step individual rewards of agent0: 0.19636428054092
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: 0.1436871821138397
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: 0.14526929092181212
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: 0.24784498666917965
team_policy eval average team episode rewards of agent3: 12.5
team_policy eval idv catch total num of agent3: 12
team_policy eval team catch total num: 5
team_policy eval average step individual rewards of agent4: 0.06857546608665327
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: 0.24406195828581004
idv_policy eval average team episode rewards of agent0: 30.0
idv_policy eval idv catch total num of agent0: 12
idv_policy eval team catch total num: 12
idv_policy eval average step individual rewards of agent1: 0.22105297148278225
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: -0.008456410219770782
idv_policy eval average team episode rewards of agent2: 30.0
idv_policy eval idv catch total num of agent2: 2
idv_policy eval team catch total num: 12
idv_policy eval average step individual rewards of agent3: 0.1429871109841269
idv_policy eval average team episode rewards of agent3: 30.0
idv_policy eval idv catch total num of agent3: 8
idv_policy eval team catch total num: 12
idv_policy eval average step individual rewards of agent4: 0.12036143310683495
idv_policy eval average team episode rewards of agent4: 30.0
idv_policy eval idv catch total num of agent4: 7
idv_policy eval team catch total num: 12

 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.

team_policy eval average step individual rewards of agent0: 0.0438051366172911
team_policy eval average team episode rewards of agent0: 22.5
team_policy eval idv catch total num of agent0: 4
team_policy eval team catch total num: 9
team_policy eval average step individual rewards of agent1: 0.06870553782865933
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: 0.1755454146698842
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: 0.09882321822939102
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: 0.1716972497857004
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: -0.059218900006840484
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: -0.013434174906363472
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: -0.009832109073465704
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: -0.061155227191735044
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: -0.016025773654995957
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 1801/10000 episodes, total num timesteps 360400/2000000, FPS 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.

team_policy eval average step individual rewards of agent0: 0.005755342660890887
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: -0.04472783002660973
team_policy eval average team episode rewards of agent1: 15.0
team_policy eval idv catch total num of agent1: 1
team_policy eval team catch total num: 6
team_policy eval average step individual rewards of agent2: 0.05208324288611767
team_policy eval average team episode rewards of agent2: 15.0
team_policy eval idv catch total num of agent2: 5
team_policy eval team catch total num: 6
team_policy eval average step individual rewards of agent3: 0.1128065071238045
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: 0.06192052670773011
team_policy eval average team episode rewards of agent4: 15.0
team_policy eval idv catch total num of agent4: 5
team_policy eval team catch total num: 6
idv_policy eval average step individual rewards of agent0: 0.030437847565455353
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: 0.059377462964252975
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: 0.08055780493332822
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: 0.034376738865801885
idv_policy eval average team episode rewards of agent3: 5.0
idv_policy eval idv catch total num of agent3: 4
idv_policy eval team catch total num: 2
idv_policy eval average step individual rewards of agent4: -0.07748108467840015
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 1826/10000 episodes, total num timesteps 365400/2000000, FPS 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 244.


 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 244.


 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 244.


 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 244.

team_policy eval average step individual rewards of agent0: 0.08412677126562441
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: 0.11278473571747437
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: 0.08453595708439718
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: 0.1382401595942816
team_policy eval average team episode rewards of agent3: 15.0
team_policy eval idv catch total num of agent3: 8
team_policy eval team catch total num: 6
team_policy eval average step individual rewards of agent4: 0.02680199216099813
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: 0.11022600237750767
idv_policy eval average team episode rewards of agent0: 7.5
idv_policy eval idv catch total num of agent0: 7
idv_policy eval team catch total num: 3
idv_policy eval average step individual rewards of agent1: -0.02025608962801291
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: 0.049568938991721774
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: 0.002250005271670725
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: 0.1580572525416183
idv_policy eval average team episode rewards of agent4: 7.5
idv_policy eval idv catch total num of agent4: 9
idv_policy eval team catch total num: 3

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1851/10000 episodes, total num timesteps 370400/2000000, FPS 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.

team_policy eval average step individual rewards of agent0: 0.09170830266420829
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: 0.08636654968105337
team_policy eval average team episode rewards of agent1: 15.0
team_policy eval idv catch total num of agent1: 6
team_policy eval team catch total num: 6
team_policy eval average step individual rewards of agent2: -0.04324326097604704
team_policy eval average team episode rewards of agent2: 15.0
team_policy eval idv catch total num of agent2: 1
team_policy eval team catch total num: 6
team_policy eval average step individual rewards of agent3: 0.1815869708014889
team_policy eval average team episode rewards of agent3: 15.0
team_policy eval idv catch total num of agent3: 10
team_policy eval team catch total num: 6
team_policy eval average step individual rewards of agent4: 0.05688864603775901
team_policy eval average team episode rewards of agent4: 15.0
team_policy eval idv catch total num of agent4: 5
team_policy eval team catch total num: 6
idv_policy eval average step individual rewards of agent0: 0.09282517967363417
idv_policy eval average team episode rewards of agent0: 17.5
idv_policy eval idv catch total num of agent0: 6
idv_policy eval team catch total num: 7
idv_policy eval average step individual rewards of agent1: 0.15533861122512926
idv_policy eval average team episode rewards of agent1: 17.5
idv_policy eval idv catch total num of agent1: 8
idv_policy eval team catch total num: 7
idv_policy eval average step individual rewards of agent2: 0.07053570857322404
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: 0.11613561071894042
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: 0.10487086162120633
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 1876/10000 episodes, total num timesteps 375400/2000000, FPS 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.

team_policy eval average step individual rewards of agent0: -0.03998134414081571
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: 0.039949073598318015
team_policy eval average team episode rewards of agent1: 0.0
team_policy eval idv catch total num of agent1: 4
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent2: 0.08779018239767691
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: -0.03956302777871415
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: -0.039425773750753
team_policy eval average team episode rewards of agent4: 0.0
team_policy eval idv catch total num of agent4: 1
team_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent0: 0.1188620229918996
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: 0.40476474332187096
idv_policy eval average team episode rewards of agent1: 25.0
idv_policy eval idv catch total num of agent1: 18
idv_policy eval team catch total num: 10
idv_policy eval average step individual rewards of agent2: 0.12127050203186669
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: 0.1228693741722386
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: 0.19426143436255966
idv_policy eval average team episode rewards of agent4: 25.0
idv_policy eval idv catch total num of agent4: 10
idv_policy eval team catch total num: 10

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 1901/10000 episodes, total num timesteps 380400/2000000, FPS 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.

team_policy eval average step individual rewards of agent0: 0.008874985309723353
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: -0.03542873818246381
team_policy eval average team episode rewards of agent1: 15.0
team_policy eval idv catch total num of agent1: 1
team_policy eval team catch total num: 6
team_policy eval average step individual rewards of agent2: 0.11312304889277541
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: 0.0646630964272226
team_policy eval average team episode rewards of agent3: 15.0
team_policy eval idv catch total num of agent3: 5
team_policy eval team catch total num: 6
team_policy eval average step individual rewards of agent4: 0.11023769097834521
team_policy eval average team episode rewards of agent4: 15.0
team_policy eval idv catch total num of agent4: 7
team_policy eval team catch total num: 6
idv_policy eval average step individual rewards of agent0: 0.08057500367239868
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: 0.060371215397873816
idv_policy eval average team episode rewards of agent1: 15.0
idv_policy eval idv catch total num of agent1: 5
idv_policy eval team catch total num: 6
idv_policy eval average step individual rewards of agent2: 0.035053585364560914
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: -8.64947666765481e-06
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: 0.0819222612293426
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 1926/10000 episodes, total num timesteps 385400/2000000, FPS 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.

team_policy eval average step individual rewards of agent0: -0.0028334113555485273
team_policy eval average team episode rewards of agent0: 12.5
team_policy eval idv catch total num of agent0: 3
team_policy eval team catch total num: 5
team_policy eval average step individual rewards of agent1: -0.003171399185320718
team_policy eval average team episode rewards of agent1: 12.5
team_policy eval idv catch total num of agent1: 3
team_policy eval team catch total num: 5
team_policy eval average step individual rewards of agent2: 0.023721607712131783
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: 0.12359314651865193
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: 0.10007024539364542
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: 0.029464504064662105
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: 0.10269836355014483
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: -0.01950432681291827
idv_policy eval average team episode rewards of agent2: 10.0
idv_policy eval idv catch total num of agent2: 2
idv_policy eval team catch total num: 4
idv_policy eval average step individual rewards of agent3: 0.08457711011034927
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: 0.14102351226829846
idv_policy eval average team episode rewards of agent4: 10.0
idv_policy eval idv catch total num of agent4: 8
idv_policy eval team catch total num: 4

 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.

team_policy eval average step individual rewards of agent0: 0.11659480596524567
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: 0.21552025745166464
team_policy eval average team episode rewards of agent1: 22.5
team_policy eval idv catch total num of agent1: 11
team_policy eval team catch total num: 9
team_policy eval average step individual rewards of agent2: 0.16911934748005994
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: 0.10627535204354432
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: 0.11141238113808229
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: 0.010476410139484381
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: 0.015669573834303754
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: 0.06463957628334634
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: 0.04397239782998721
idv_policy eval average team episode rewards of agent3: 5.0
idv_policy eval idv catch total num of agent3: 4
idv_policy eval team catch total num: 2
idv_policy eval average step individual rewards of agent4: 0.039804916547486247
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 1976/10000 episodes, total num timesteps 395400/2000000, FPS 244.


 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 244.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.

team_policy eval average step individual rewards of agent0: 0.12269057393784598
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: 0.024837503029142706
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: 0.14494411083479966
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: 0.09985771625005939
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: 0.22591460402411762
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: 0.07766113658906307
idv_policy eval average team episode rewards of agent0: 20.0
idv_policy eval idv catch total num of agent0: 6
idv_policy eval team catch total num: 8
idv_policy eval average step individual rewards of agent1: 0.002161945871015396
idv_policy eval average team episode rewards of agent1: 20.0
idv_policy eval idv catch total num of agent1: 3
idv_policy eval team catch total num: 8
idv_policy eval average step individual rewards of agent2: 0.10056277005709244
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: 0.04980044211443617
idv_policy eval average team episode rewards of agent3: 20.0
idv_policy eval idv catch total num of agent3: 5
idv_policy eval team catch total num: 8
idv_policy eval average step individual rewards of agent4: 0.09973574346520919
idv_policy eval average team episode rewards of agent4: 20.0
idv_policy eval idv catch total num of agent4: 7
idv_policy eval team catch total num: 8

 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.

team_policy eval average step individual rewards of agent0: -0.07332852149557736
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: -0.0706527815994885
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: 0.013295506218549998
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: 0.01478542026264846
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: 0.009200003216740119
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: 0.05201231528929107
idv_policy eval average team episode rewards of agent0: 12.5
idv_policy eval idv catch total num of agent0: 5
idv_policy eval team catch total num: 5
idv_policy eval average step individual rewards of agent1: -0.0413458420195639
idv_policy eval average team episode rewards of agent1: 12.5
idv_policy eval idv catch total num of agent1: 1
idv_policy eval team catch total num: 5
idv_policy eval average step individual rewards of agent2: 0.012091476672206891
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: 0.10004707707311108
idv_policy eval average team episode rewards of agent3: 12.5
idv_policy eval idv catch total num of agent3: 7
idv_policy eval team catch total num: 5
idv_policy eval average step individual rewards of agent4: 0.07728033427880955
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 2026/10000 episodes, total num timesteps 405400/2000000, FPS 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.

team_policy eval average step individual rewards of agent0: -0.024915048518803093
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: 0.03142196980449791
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: -0.04764600883499277
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: 0.03248617627479684
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: -0.044410135351166675
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: 0.05439567552573979
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: -0.0005250253523471038
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: -0.05631763667558988
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: -0.023571369370159766
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: -0.032899571362339126
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 2051/10000 episodes, total num timesteps 410400/2000000, FPS 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.

team_policy eval average step individual rewards of agent0: 0.29351921373186785
team_policy eval average team episode rewards of agent0: 15.0
team_policy eval idv catch total num of agent0: 14
team_policy eval team catch total num: 6
team_policy eval average step individual rewards of agent1: 0.04228485987095376
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: -0.03280141028114254
team_policy eval average team episode rewards of agent2: 15.0
team_policy eval idv catch total num of agent2: 1
team_policy eval team catch total num: 6
team_policy eval average step individual rewards of agent3: 0.07013040354997263
team_policy eval average team episode rewards of agent3: 15.0
team_policy eval idv catch total num of agent3: 5
team_policy eval team catch total num: 6
team_policy eval average step individual rewards of agent4: 0.14015842180543256
team_policy eval average team episode rewards of agent4: 15.0
team_policy eval idv catch total num of agent4: 8
team_policy eval team catch total num: 6
idv_policy eval average step individual rewards of agent0: 0.13948012461494216
idv_policy eval average team episode rewards of agent0: 40.0
idv_policy eval idv catch total num of agent0: 8
idv_policy eval team catch total num: 16
idv_policy eval average step individual rewards of agent1: 0.2651722230931968
idv_policy eval average team episode rewards of agent1: 40.0
idv_policy eval idv catch total num of agent1: 13
idv_policy eval team catch total num: 16
idv_policy eval average step individual rewards of agent2: 0.36898508278106634
idv_policy eval average team episode rewards of agent2: 40.0
idv_policy eval idv catch total num of agent2: 17
idv_policy eval team catch total num: 16
idv_policy eval average step individual rewards of agent3: 0.21296560020819594
idv_policy eval average team episode rewards of agent3: 40.0
idv_policy eval idv catch total num of agent3: 11
idv_policy eval team catch total num: 16
idv_policy eval average step individual rewards of agent4: 0.3202488945348212
idv_policy eval average team episode rewards of agent4: 40.0
idv_policy eval idv catch total num of agent4: 15
idv_policy eval team catch total num: 16

 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.

team_policy eval average step individual rewards of agent0: 0.07236845874402519
team_policy eval average team episode rewards of agent0: 15.0
team_policy eval idv catch total num of agent0: 5
team_policy eval team catch total num: 6
team_policy eval average step individual rewards of agent1: 0.14105279193234596
team_policy eval average team episode rewards of agent1: 15.0
team_policy eval idv catch total num of agent1: 8
team_policy eval team catch total num: 6
team_policy eval average step individual rewards of agent2: 0.09585622418758306
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: 0.01893192591423992
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: 0.12266708515284887
team_policy eval average team episode rewards of agent4: 15.0
team_policy eval idv catch total num of agent4: 7
team_policy eval team catch total num: 6
idv_policy eval average step individual rewards of agent0: 0.37603072120799735
idv_policy eval average team episode rewards of agent0: 35.0
idv_policy eval idv catch total num of agent0: 17
idv_policy eval team catch total num: 14
idv_policy eval average step individual rewards of agent1: 0.11712369323817078
idv_policy eval average team episode rewards of agent1: 35.0
idv_policy eval idv catch total num of agent1: 7
idv_policy eval team catch total num: 14
idv_policy eval average step individual rewards of agent2: 0.09473432219709282
idv_policy eval average team episode rewards of agent2: 35.0
idv_policy eval idv catch total num of agent2: 6
idv_policy eval team catch total num: 14
idv_policy eval average step individual rewards of agent3: 0.11574015092781341
idv_policy eval average team episode rewards of agent3: 35.0
idv_policy eval idv catch total num of agent3: 7
idv_policy eval team catch total num: 14
idv_policy eval average step individual rewards of agent4: 0.21615347928950904
idv_policy eval average team episode rewards of agent4: 35.0
idv_policy eval idv catch total num of agent4: 11
idv_policy eval team catch total num: 14

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2101/10000 episodes, total num timesteps 420400/2000000, FPS 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 243.


 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 244.


 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 244.

team_policy eval average step individual rewards of agent0: 0.0072087512445710815
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: 0.05915001390368802
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: -0.07139703639819242
team_policy eval average team episode rewards of agent2: 7.5
team_policy eval idv catch total num of agent2: 0
team_policy eval team catch total num: 3
team_policy eval average step individual rewards of agent3: 0.08687934881955925
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: 0.015116848801276546
team_policy eval average team episode rewards of agent4: 7.5
team_policy eval idv catch total num of agent4: 3
team_policy eval team catch total num: 3
idv_policy eval average step individual rewards of agent0: 0.030452933956754098
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: -0.017240822253409272
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: 0.08658552251737495
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: 0.0065335201098550314
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: 0.08342389776662239
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 2126/10000 episodes, total num timesteps 425400/2000000, FPS 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.

team_policy eval average step individual rewards of agent0: 0.16831529114992816
team_policy eval average team episode rewards of agent0: 12.5
team_policy eval idv catch total num of agent0: 9
team_policy eval team catch total num: 5
team_policy eval average step individual rewards of agent1: 0.1408743755041565
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: 0.0568133947260101
team_policy eval average team episode rewards of agent2: 12.5
team_policy eval idv catch total num of agent2: 5
team_policy eval team catch total num: 5
team_policy eval average step individual rewards of agent3: 0.06458835301777978
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: -0.014351469706341937
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: 0.020480071613084253
idv_policy eval average team episode rewards of agent0: 22.5
idv_policy eval idv catch total num of agent0: 3
idv_policy eval team catch total num: 9
idv_policy eval average step individual rewards of agent1: 0.1738383608234276
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: 0.2474418733552615
idv_policy eval average team episode rewards of agent2: 22.5
idv_policy eval idv catch total num of agent2: 12
idv_policy eval team catch total num: 9
idv_policy eval average step individual rewards of agent3: 0.17235421690774388
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: -0.013721145098740123
idv_policy eval average team episode rewards of agent4: 22.5
idv_policy eval idv catch total num of agent4: 2
idv_policy eval team catch total num: 9

 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.

team_policy eval average step individual rewards of agent0: 0.27290284873864307
team_policy eval average team episode rewards of agent0: 20.0
team_policy eval idv catch total num of agent0: 13
team_policy eval team catch total num: 8
team_policy eval average step individual rewards of agent1: 0.09424767114880658
team_policy eval average team episode rewards of agent1: 20.0
team_policy eval idv catch total num of agent1: 6
team_policy eval team catch total num: 8
team_policy eval average step individual rewards of agent2: 0.06636429937288803
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: 0.04139608241889353
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: 0.06174150131482875
team_policy eval average team episode rewards of agent4: 20.0
team_policy eval idv catch total num of agent4: 5
team_policy eval team catch total num: 8
idv_policy eval average step individual rewards of agent0: 0.1472311389537189
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: 0.1699781377250414
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: 0.0381146762525639
idv_policy eval average team episode rewards of agent2: 20.0
idv_policy eval idv catch total num of agent2: 4
idv_policy eval team catch total num: 8
idv_policy eval average step individual rewards of agent3: 0.14810634550581955
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: 0.013470539782503845
idv_policy eval average team episode rewards of agent4: 20.0
idv_policy eval idv catch total num of agent4: 3
idv_policy eval team catch total num: 8

 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.

team_policy eval average step individual rewards of agent0: 0.02905300853615556
team_policy eval average team episode rewards of agent0: 20.0
team_policy eval idv catch total num of agent0: 4
team_policy eval team catch total num: 8
team_policy eval average step individual rewards of agent1: 0.054811145532008124
team_policy eval average team episode rewards of agent1: 20.0
team_policy eval idv catch total num of agent1: 5
team_policy eval team catch total num: 8
team_policy eval average step individual rewards of agent2: 0.15461209892469016
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: 0.3087462144661836
team_policy eval average team episode rewards of agent3: 20.0
team_policy eval idv catch total num of agent3: 15
team_policy eval team catch total num: 8
team_policy eval average step individual rewards of agent4: 0.13702040078742259
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: 0.016563498738641463
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: 0.1388293586157653
idv_policy eval average team episode rewards of agent1: 5.0
idv_policy eval idv catch total num of agent1: 8
idv_policy eval team catch total num: 2
idv_policy eval average step individual rewards of agent2: 0.014347711629690906
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: 0.015078625745637564
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: -0.0627987392643695
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 2201/10000 episodes, total num timesteps 440400/2000000, FPS 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.

team_policy eval average step individual rewards of agent0: 0.09932336112574376
team_policy eval average team episode rewards of agent0: 17.5
team_policy eval idv catch total num of agent0: 6
team_policy eval team catch total num: 7
team_policy eval average step individual rewards of agent1: 0.12681738772614853
team_policy eval average team episode rewards of agent1: 17.5
team_policy eval idv catch total num of agent1: 7
team_policy eval team catch total num: 7
team_policy eval average step individual rewards of agent2: 0.051998274898015645
team_policy eval average team episode rewards of agent2: 17.5
team_policy eval idv catch total num of agent2: 4
team_policy eval team catch total num: 7
team_policy eval average step individual rewards of agent3: 0.1288297465558766
team_policy eval average team episode rewards of agent3: 17.5
team_policy eval idv catch total num of agent3: 7
team_policy eval team catch total num: 7
team_policy eval average step individual rewards of agent4: 0.09751710649943236
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: -0.00986099435124445
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: 0.0653514889581076
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: -0.015469921642958849
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: -0.06201302159796377
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: -0.06867179332128023
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 2226/10000 episodes, total num timesteps 445400/2000000, FPS 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 244.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.

team_policy eval average step individual rewards of agent0: 0.011867509575848582
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: -0.005142756255183325
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: -0.0425842860403474
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: 0.04331482683452277
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: 0.039615972439979164
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: 0.30009183658657074
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: 0.1514135160228224
idv_policy eval average team episode rewards of agent1: 30.0
idv_policy eval idv catch total num of agent1: 8
idv_policy eval team catch total num: 12
idv_policy eval average step individual rewards of agent2: 0.12613417902071702
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: 0.15419106811504185
idv_policy eval average team episode rewards of agent3: 30.0
idv_policy eval idv catch total num of agent3: 8
idv_policy eval team catch total num: 12
idv_policy eval average step individual rewards of agent4: 0.25406406332737863
idv_policy eval average team episode rewards of agent4: 30.0
idv_policy eval idv catch total num of agent4: 12
idv_policy eval team catch total num: 12

 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.

team_policy eval average step individual rewards of agent0: 0.13667379369933472
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: -0.006425476488486324
team_policy eval average team episode rewards of agent1: 17.5
team_policy eval idv catch total num of agent1: 2
team_policy eval team catch total num: 7
team_policy eval average step individual rewards of agent2: 0.11444429273674604
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: 0.14240339654597212
team_policy eval average team episode rewards of agent3: 17.5
team_policy eval idv catch total num of agent3: 8
team_policy eval team catch total num: 7
team_policy eval average step individual rewards of agent4: 0.144626574009245
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: -0.017945829975622715
idv_policy eval average team episode rewards of agent0: 20.0
idv_policy eval idv catch total num of agent0: 2
idv_policy eval team catch total num: 8
idv_policy eval average step individual rewards of agent1: 0.05724958424712453
idv_policy eval average team episode rewards of agent1: 20.0
idv_policy eval idv catch total num of agent1: 5
idv_policy eval team catch total num: 8
idv_policy eval average step individual rewards of agent2: 0.13472499734103022
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: 0.09256035482482186
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: 0.1349041688845233
idv_policy eval average team episode rewards of agent4: 20.0
idv_policy eval idv catch total num of agent4: 8
idv_policy eval team catch total num: 8

 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.

team_policy eval average step individual rewards of agent0: 0.0031047168680237492
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: 0.1494104274198869
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: 0.0765825275393382
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: 0.12023684136984736
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: 0.028580654973300482
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: 0.01772027942411671
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: 0.08424743868197511
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: 0.09286769845236752
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: 0.05826852851793665
idv_policy eval average team episode rewards of agent3: 10.0
idv_policy eval idv catch total num of agent3: 5
idv_policy eval team catch total num: 4
idv_policy eval average step individual rewards of agent4: -0.009649221673451946
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 2301/10000 episodes, total num timesteps 460400/2000000, FPS 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.

team_policy eval average step individual rewards of agent0: 0.14631380522447007
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: 0.12318606071756907
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: 0.07181724503192154
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: 0.1709767285129724
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: 0.06011121453758934
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: -0.049423778451812256
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: 0.18933461719591077
idv_policy eval average team episode rewards of agent1: 15.0
idv_policy eval idv catch total num of agent1: 10
idv_policy eval team catch total num: 6
idv_policy eval average step individual rewards of agent2: 0.21193499704590293
idv_policy eval average team episode rewards of agent2: 15.0
idv_policy eval idv catch total num of agent2: 11
idv_policy eval team catch total num: 6
idv_policy eval average step individual rewards of agent3: 0.13602242722170832
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: 0.062181210757551994
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 2326/10000 episodes, total num timesteps 465400/2000000, FPS 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.

team_policy eval average step individual rewards of agent0: 0.09002595073907407
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: 0.02117454394282367
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: 0.06255924353592976
team_policy eval average team episode rewards of agent2: 15.0
team_policy eval idv catch total num of agent2: 5
team_policy eval team catch total num: 6
team_policy eval average step individual rewards of agent3: 0.08932857560997977
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: 0.04089430989168303
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: 0.006633975287197329
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: 0.0036535983237432836
idv_policy eval average team episode rewards of agent1: 12.5
idv_policy eval idv catch total num of agent1: 3
idv_policy eval team catch total num: 5
idv_policy eval average step individual rewards of agent2: 0.08590564880734707
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: 0.06056984239409937
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: 0.13465636066673972
idv_policy eval average team episode rewards of agent4: 12.5
idv_policy eval idv catch total num of agent4: 8
idv_policy eval team catch total num: 5

 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.

team_policy eval average step individual rewards of agent0: 0.16539381173965023
team_policy eval average team episode rewards of agent0: 20.0
team_policy eval idv catch total num of agent0: 9
team_policy eval team catch total num: 8
team_policy eval average step individual rewards of agent1: -0.01510832010092761
team_policy eval average team episode rewards of agent1: 20.0
team_policy eval idv catch total num of agent1: 2
team_policy eval team catch total num: 8
team_policy eval average step individual rewards of agent2: 0.09236131406540492
team_policy eval average team episode rewards of agent2: 20.0
team_policy eval idv catch total num of agent2: 6
team_policy eval team catch total num: 8
team_policy eval average step individual rewards of agent3: 0.32185222551186365
team_policy eval average team episode rewards of agent3: 20.0
team_policy eval idv catch total num of agent3: 15
team_policy eval team catch total num: 8
team_policy eval average step individual rewards of agent4: 0.06801554998967244
team_policy eval average team episode rewards of agent4: 20.0
team_policy eval idv catch total num of agent4: 5
team_policy eval team catch total num: 8
idv_policy eval average step individual rewards of agent0: 0.23224479407108617
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: 0.192851970483665
idv_policy eval average team episode rewards of agent1: 32.5
idv_policy eval idv catch total num of agent1: 10
idv_policy eval team catch total num: 13
idv_policy eval average step individual rewards of agent2: 0.14413452579726768
idv_policy eval average team episode rewards of agent2: 32.5
idv_policy eval idv catch total num of agent2: 8
idv_policy eval team catch total num: 13
idv_policy eval average step individual rewards of agent3: 0.11785857170052584
idv_policy eval average team episode rewards of agent3: 32.5
idv_policy eval idv catch total num of agent3: 7
idv_policy eval team catch total num: 13
idv_policy eval average step individual rewards of agent4: 0.3293392243531387
idv_policy eval average team episode rewards of agent4: 32.5
idv_policy eval idv catch total num of agent4: 15
idv_policy eval team catch total num: 13

 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.

team_policy eval average step individual rewards of agent0: 0.10861492010854767
team_policy eval average team episode rewards of agent0: 30.0
team_policy eval idv catch total num of agent0: 6
team_policy eval team catch total num: 12
team_policy eval average step individual rewards of agent1: 0.20904150925728132
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: 0.15494493463823886
team_policy eval average team episode rewards of agent2: 30.0
team_policy eval idv catch total num of agent2: 8
team_policy eval team catch total num: 12
team_policy eval average step individual rewards of agent3: 0.20105449367266395
team_policy eval average team episode rewards of agent3: 30.0
team_policy eval idv catch total num of agent3: 10
team_policy eval team catch total num: 12
team_policy eval average step individual rewards of agent4: 0.10279043795086373
team_policy eval average team episode rewards of agent4: 30.0
team_policy eval idv catch total num of agent4: 6
team_policy eval team catch total num: 12
idv_policy eval average step individual rewards of agent0: 0.014943020559365219
idv_policy eval average team episode rewards of agent0: 22.5
idv_policy eval idv catch total num of agent0: 3
idv_policy eval team catch total num: 9
idv_policy eval average step individual rewards of agent1: 0.09874206418167994
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: 0.24795132749395257
idv_policy eval average team episode rewards of agent2: 22.5
idv_policy eval idv catch total num of agent2: 12
idv_policy eval team catch total num: 9
idv_policy eval average step individual rewards of agent3: 0.0973791033844435
idv_policy eval average team episode rewards of agent3: 22.5
idv_policy eval idv catch total num of agent3: 6
idv_policy eval team catch total num: 9
idv_policy eval average step individual rewards of agent4: 0.22676427026927923
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 2401/10000 episodes, total num timesteps 480400/2000000, FPS 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.

team_policy eval average step individual rewards of agent0: 0.16944702640927878
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: 0.23597282588860438
team_policy eval average team episode rewards of agent1: 27.5
team_policy eval idv catch total num of agent1: 12
team_policy eval team catch total num: 11
team_policy eval average step individual rewards of agent2: 0.21999961569116425
team_policy eval average team episode rewards of agent2: 27.5
team_policy eval idv catch total num of agent2: 11
team_policy eval team catch total num: 11
team_policy eval average step individual rewards of agent3: 0.1956120055567471
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: 0.22339185781839518
team_policy eval average team episode rewards of agent4: 27.5
team_policy eval idv catch total num of agent4: 11
team_policy eval team catch total num: 11
idv_policy eval average step individual rewards of agent0: 0.1001201881466009
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: 0.1237379432908659
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: 0.04533612228281785
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: 0.06752511073958839
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: 0.06294896817849345
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 2426/10000 episodes, total num timesteps 485400/2000000, FPS 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.

team_policy eval average step individual rewards of agent0: 0.08271150995926353
team_policy eval average team episode rewards of agent0: 10.0
team_policy eval idv catch total num of agent0: 6
team_policy eval team catch total num: 4
team_policy eval average step individual rewards of agent1: 0.0029567944933233557
team_policy eval average team episode rewards of agent1: 10.0
team_policy eval idv catch total num of agent1: 3
team_policy eval team catch total num: 4
team_policy eval average step individual rewards of agent2: 0.048426040820173634
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: 0.07731585910539301
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: -0.07561124162824175
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: 0.19592881168519066
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: 0.0655944399127884
idv_policy eval average team episode rewards of agent1: 10.0
idv_policy eval idv catch total num of agent1: 5
idv_policy eval team catch total num: 4
idv_policy eval average step individual rewards of agent2: 0.06911767455082603
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: -0.009299100009975221
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: 0.19678266577185377
idv_policy eval average team episode rewards of agent4: 10.0
idv_policy eval idv catch total num of agent4: 10
idv_policy eval team catch total num: 4

 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.

team_policy eval average step individual rewards of agent0: 0.15803830172447259
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: 0.06379965777728845
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: 0.16075953360071518
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: 0.13928011956384265
team_policy eval average team episode rewards of agent3: 22.5
team_policy eval idv catch total num of agent3: 8
team_policy eval team catch total num: 9
team_policy eval average step individual rewards of agent4: 0.135239133046521
team_policy eval average team episode rewards of agent4: 22.5
team_policy eval idv catch total num of agent4: 8
team_policy eval team catch total num: 9
idv_policy eval average step individual rewards of agent0: 0.24915486862925776
idv_policy eval average team episode rewards of agent0: 40.0
idv_policy eval idv catch total num of agent0: 12
idv_policy eval team catch total num: 16
idv_policy eval average step individual rewards of agent1: 0.24257014706808264
idv_policy eval average team episode rewards of agent1: 40.0
idv_policy eval idv catch total num of agent1: 12
idv_policy eval team catch total num: 16
idv_policy eval average step individual rewards of agent2: 0.11990882710577068
idv_policy eval average team episode rewards of agent2: 40.0
idv_policy eval idv catch total num of agent2: 7
idv_policy eval team catch total num: 16
idv_policy eval average step individual rewards of agent3: 0.40313922926789386
idv_policy eval average team episode rewards of agent3: 40.0
idv_policy eval idv catch total num of agent3: 18
idv_policy eval team catch total num: 16
idv_policy eval average step individual rewards of agent4: 0.22702304130167625
idv_policy eval average team episode rewards of agent4: 40.0
idv_policy eval idv catch total num of agent4: 11
idv_policy eval team catch total num: 16

 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.

team_policy eval average step individual rewards of agent0: 0.35703224631161573
team_policy eval average team episode rewards of agent0: 27.5
team_policy eval idv catch total num of agent0: 16
team_policy eval team catch total num: 11
team_policy eval average step individual rewards of agent1: 0.07129304346441188
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: 0.22353006744137346
team_policy eval average team episode rewards of agent2: 27.5
team_policy eval idv catch total num of agent2: 11
team_policy eval team catch total num: 11
team_policy eval average step individual rewards of agent3: 0.2504483451934127
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: 0.14932233021795852
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: 0.1712443036190887
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: 0.14195345526295353
idv_policy eval average team episode rewards of agent1: 22.5
idv_policy eval idv catch total num of agent1: 8
idv_policy eval team catch total num: 9
idv_policy eval average step individual rewards of agent2: 0.16877428963512586
idv_policy eval average team episode rewards of agent2: 22.5
idv_policy eval idv catch total num of agent2: 9
idv_policy eval team catch total num: 9
idv_policy eval average step individual rewards of agent3: 0.05039494789872153
idv_policy eval average team episode rewards of agent3: 22.5
idv_policy eval idv catch total num of agent3: 4
idv_policy eval team catch total num: 9
idv_policy eval average step individual rewards of agent4: 0.12634339047068466
idv_policy eval average team episode rewards of agent4: 22.5
idv_policy eval idv catch total num of agent4: 7
idv_policy eval team catch total num: 9

 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 247.


 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 246.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.

team_policy eval average step individual rewards of agent0: 0.14207801005291568
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: -0.04007215160719309
team_policy eval average team episode rewards of agent1: 25.0
team_policy eval idv catch total num of agent1: 1
team_policy eval team catch total num: 10
team_policy eval average step individual rewards of agent2: 0.2454008317815586
team_policy eval average team episode rewards of agent2: 25.0
team_policy eval idv catch total num of agent2: 12
team_policy eval team catch total num: 10
team_policy eval average step individual rewards of agent3: 0.03620944248294935
team_policy eval average team episode rewards of agent3: 25.0
team_policy eval idv catch total num of agent3: 4
team_policy eval team catch total num: 10
team_policy eval average step individual rewards of agent4: 0.19163211587844395
team_policy eval average team episode rewards of agent4: 25.0
team_policy eval idv catch total num of agent4: 10
team_policy eval team catch total num: 10
idv_policy eval average step individual rewards of agent0: 0.07088361855130045
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: 0.11959146424115465
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: 0.12447246298776936
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: 0.09256771388781697
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: 0.0936869844947869
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 2526/10000 episodes, total num timesteps 505400/2000000, FPS 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.

team_policy eval average step individual rewards of agent0: 0.3431393540194938
team_policy eval average team episode rewards of agent0: 42.5
team_policy eval idv catch total num of agent0: 16
team_policy eval team catch total num: 17
team_policy eval average step individual rewards of agent1: 0.16205370905032318
team_policy eval average team episode rewards of agent1: 42.5
team_policy eval idv catch total num of agent1: 9
team_policy eval team catch total num: 17
team_policy eval average step individual rewards of agent2: 0.19851917547841144
team_policy eval average team episode rewards of agent2: 42.5
team_policy eval idv catch total num of agent2: 10
team_policy eval team catch total num: 17
team_policy eval average step individual rewards of agent3: 0.2947707290938515
team_policy eval average team episode rewards of agent3: 42.5
team_policy eval idv catch total num of agent3: 14
team_policy eval team catch total num: 17
team_policy eval average step individual rewards of agent4: 0.26903266257959174
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: 0.35421406301877484
idv_policy eval average team episode rewards of agent0: 27.5
idv_policy eval idv catch total num of agent0: 16
idv_policy eval team catch total num: 11
idv_policy eval average step individual rewards of agent1: 0.10885757240392063
idv_policy eval average team episode rewards of agent1: 27.5
idv_policy eval idv catch total num of agent1: 6
idv_policy eval team catch total num: 11
idv_policy eval average step individual rewards of agent2: 0.17807897838405784
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: 0.12267796459913204
idv_policy eval average team episode rewards of agent3: 27.5
idv_policy eval idv catch total num of agent3: 7
idv_policy eval team catch total num: 11
idv_policy eval average step individual rewards of agent4: 0.43575607955883927
idv_policy eval average team episode rewards of agent4: 27.5
idv_policy eval idv catch total num of agent4: 19
idv_policy eval team catch total num: 11

 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.

team_policy eval average step individual rewards of agent0: 0.5334067818435634
team_policy eval average team episode rewards of agent0: 72.5
team_policy eval idv catch total num of agent0: 23
team_policy eval team catch total num: 29
team_policy eval average step individual rewards of agent1: 0.5597455516437397
team_policy eval average team episode rewards of agent1: 72.5
team_policy eval idv catch total num of agent1: 24
team_policy eval team catch total num: 29
team_policy eval average step individual rewards of agent2: 0.5869626063169346
team_policy eval average team episode rewards of agent2: 72.5
team_policy eval idv catch total num of agent2: 25
team_policy eval team catch total num: 29
team_policy eval average step individual rewards of agent3: 0.31099150515614804
team_policy eval average team episode rewards of agent3: 72.5
team_policy eval idv catch total num of agent3: 14
team_policy eval team catch total num: 29
team_policy eval average step individual rewards of agent4: 0.5076770346899755
team_policy eval average team episode rewards of agent4: 72.5
team_policy eval idv catch total num of agent4: 22
team_policy eval team catch total num: 29
idv_policy eval average step individual rewards of agent0: -0.04075001437736881
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: -0.012823473564288106
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: -0.036080623712689885
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: -0.0697205393045744
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: -0.04077604744844518
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 2576/10000 episodes, total num timesteps 515400/2000000, FPS 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.

team_policy eval average step individual rewards of agent0: 0.07092983873084219
team_policy eval average team episode rewards of agent0: 30.0
team_policy eval idv catch total num of agent0: 5
team_policy eval team catch total num: 12
team_policy eval average step individual rewards of agent1: 0.24844048854119521
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: 0.34463203745675236
team_policy eval average team episode rewards of agent2: 30.0
team_policy eval idv catch total num of agent2: 16
team_policy eval team catch total num: 12
team_policy eval average step individual rewards of agent3: 0.1617959901683654
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: 0.11827723560834062
team_policy eval average team episode rewards of agent4: 30.0
team_policy eval idv catch total num of agent4: 7
team_policy eval team catch total num: 12
idv_policy eval average step individual rewards of agent0: 0.007074327762555184
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: -0.07875134374015899
idv_policy eval average team episode rewards of agent1: 2.5
idv_policy eval idv catch total num of agent1: 0
idv_policy eval team catch total num: 1
idv_policy eval average step individual rewards of agent2: -0.012588588889786558
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: 0.004543365814875313
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: 0.07965907147669178
idv_policy eval average team episode rewards of agent4: 2.5
idv_policy eval idv catch total num of agent4: 6
idv_policy eval team catch total num: 1

 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.

team_policy eval average step individual rewards of agent0: 0.07221335939820367
team_policy eval average team episode rewards of agent0: 25.0
team_policy eval idv catch total num of agent0: 5
team_policy eval team catch total num: 10
team_policy eval average step individual rewards of agent1: 0.06887054015223991
team_policy eval average team episode rewards of agent1: 25.0
team_policy eval idv catch total num of agent1: 5
team_policy eval team catch total num: 10
team_policy eval average step individual rewards of agent2: 0.3306499225534511
team_policy eval average team episode rewards of agent2: 25.0
team_policy eval idv catch total num of agent2: 15
team_policy eval team catch total num: 10
team_policy eval average step individual rewards of agent3: 0.22681524459756897
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: 0.2513016805995998
team_policy eval average team episode rewards of agent4: 25.0
team_policy eval idv catch total num of agent4: 12
team_policy eval team catch total num: 10
idv_policy eval average step individual rewards of agent0: 0.2890026644978348
idv_policy eval average team episode rewards of agent0: 32.5
idv_policy eval idv catch total num of agent0: 14
idv_policy eval team catch total num: 13
idv_policy eval average step individual rewards of agent1: 0.19276453852390538
idv_policy eval average team episode rewards of agent1: 32.5
idv_policy eval idv catch total num of agent1: 10
idv_policy eval team catch total num: 13
idv_policy eval average step individual rewards of agent2: 0.24463516190928403
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: 0.1892311875948251
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: 0.017016268587686553
idv_policy eval average team episode rewards of agent4: 32.5
idv_policy eval idv catch total num of agent4: 3
idv_policy eval team catch total num: 13

 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.

team_policy eval average step individual rewards of agent0: 0.24647607647963476
team_policy eval average team episode rewards of agent0: 20.0
team_policy eval idv catch total num of agent0: 12
team_policy eval team catch total num: 8
team_policy eval average step individual rewards of agent1: 0.01937022758477541
team_policy eval average team episode rewards of agent1: 20.0
team_policy eval idv catch total num of agent1: 3
team_policy eval team catch total num: 8
team_policy eval average step individual rewards of agent2: 0.11758682377583923
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: 0.044485745830578835
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: 0.09160064295308479
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: 0.09613427547585213
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: 0.249141031894276
idv_policy eval average team episode rewards of agent1: 27.5
idv_policy eval idv catch total num of agent1: 12
idv_policy eval team catch total num: 11
idv_policy eval average step individual rewards of agent2: 0.04772289526861315
idv_policy eval average team episode rewards of agent2: 27.5
idv_policy eval idv catch total num of agent2: 4
idv_policy eval team catch total num: 11
idv_policy eval average step individual rewards of agent3: 0.04483479143871382
idv_policy eval average team episode rewards of agent3: 27.5
idv_policy eval idv catch total num of agent3: 4
idv_policy eval team catch total num: 11
idv_policy eval average step individual rewards of agent4: 0.27261774264187716
idv_policy eval average team episode rewards of agent4: 27.5
idv_policy eval idv catch total num of agent4: 13
idv_policy eval team catch total num: 11

 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.

team_policy eval average step individual rewards of agent0: -0.015231891636291486
team_policy eval average team episode rewards of agent0: 15.0
team_policy eval idv catch total num of agent0: 2
team_policy eval team catch total num: 6
team_policy eval average step individual rewards of agent1: 0.11919000470067585
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: -0.028327317582057948
team_policy eval average team episode rewards of agent2: 15.0
team_policy eval idv catch total num of agent2: 1
team_policy eval team catch total num: 6
team_policy eval average step individual rewards of agent3: 0.09249373836558172
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: 0.07016805088705094
team_policy eval average team episode rewards of agent4: 15.0
team_policy eval idv catch total num of agent4: 5
team_policy eval team catch total num: 6
idv_policy eval average step individual rewards of agent0: 0.4488751984670045
idv_policy eval average team episode rewards of agent0: 47.5
idv_policy eval idv catch total num of agent0: 20
idv_policy eval team catch total num: 19
idv_policy eval average step individual rewards of agent1: 0.4379849180480296
idv_policy eval average team episode rewards of agent1: 47.5
idv_policy eval idv catch total num of agent1: 20
idv_policy eval team catch total num: 19
idv_policy eval average step individual rewards of agent2: 0.21851231832351792
idv_policy eval average team episode rewards of agent2: 47.5
idv_policy eval idv catch total num of agent2: 11
idv_policy eval team catch total num: 19
idv_policy eval average step individual rewards of agent3: 0.3457656721067781
idv_policy eval average team episode rewards of agent3: 47.5
idv_policy eval idv catch total num of agent3: 16
idv_policy eval team catch total num: 19
idv_policy eval average step individual rewards of agent4: 0.16625882876160175
idv_policy eval average team episode rewards of agent4: 47.5
idv_policy eval idv catch total num of agent4: 9
idv_policy eval team catch total num: 19

 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.

team_policy eval average step individual rewards of agent0: 0.059170607726817634
team_policy eval average team episode rewards of agent0: 15.0
team_policy eval idv catch total num of agent0: 5
team_policy eval team catch total num: 6
team_policy eval average step individual rewards of agent1: 0.08441654760552066
team_policy eval average team episode rewards of agent1: 15.0
team_policy eval idv catch total num of agent1: 6
team_policy eval team catch total num: 6
team_policy eval average step individual rewards of agent2: 0.011710689296709938
team_policy eval average team episode rewards of agent2: 15.0
team_policy eval idv catch total num of agent2: 3
team_policy eval team catch total num: 6
team_policy eval average step individual rewards of agent3: 0.009275419623339642
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: 0.11478635612491833
team_policy eval average team episode rewards of agent4: 15.0
team_policy eval idv catch total num of agent4: 7
team_policy eval team catch total num: 6
idv_policy eval average step individual rewards of agent0: 0.027080259876713652
idv_policy eval average team episode rewards of agent0: 27.5
idv_policy eval idv catch total num of agent0: 3
idv_policy eval team catch total num: 11
idv_policy eval average step individual rewards of agent1: 0.2011988285208834
idv_policy eval average team episode rewards of agent1: 27.5
idv_policy eval idv catch total num of agent1: 10
idv_policy eval team catch total num: 11
idv_policy eval average step individual rewards of agent2: 0.10551357959275784
idv_policy eval average team episode rewards of agent2: 27.5
idv_policy eval idv catch total num of agent2: 6
idv_policy eval team catch total num: 11
idv_policy eval average step individual rewards of agent3: 0.28003695929402433
idv_policy eval average team episode rewards of agent3: 27.5
idv_policy eval idv catch total num of agent3: 13
idv_policy eval team catch total num: 11
idv_policy eval average step individual rewards of agent4: 0.30633397527976997
idv_policy eval average team episode rewards of agent4: 27.5
idv_policy eval idv catch total num of agent4: 14
idv_policy eval team catch total num: 11

 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.

team_policy eval average step individual rewards of agent0: -0.046318110468310074
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: -0.0016893721211472324
team_policy eval average team episode rewards of agent1: 5.0
team_policy eval idv catch total num of agent1: 3
team_policy eval team catch total num: 2
team_policy eval average step individual rewards of agent2: 0.10367186125679463
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: -0.007323810286149058
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: -0.05009743319248796
team_policy eval average team episode rewards of agent4: 5.0
team_policy eval idv catch total num of agent4: 1
team_policy eval team catch total num: 2
idv_policy eval average step individual rewards of agent0: 0.276144139491883
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: 0.4328269768419463
idv_policy eval average team episode rewards of agent1: 67.5
idv_policy eval idv catch total num of agent1: 19
idv_policy eval team catch total num: 27
idv_policy eval average step individual rewards of agent2: 0.6804933042145174
idv_policy eval average team episode rewards of agent2: 67.5
idv_policy eval idv catch total num of agent2: 29
idv_policy eval team catch total num: 27
idv_policy eval average step individual rewards of agent3: 0.5571589469615535
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: 0.5793536662299072
idv_policy eval average team episode rewards of agent4: 67.5
idv_policy eval idv catch total num of agent4: 25
idv_policy eval team catch total num: 27

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2726/10000 episodes, total num timesteps 545400/2000000, FPS 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.

team_policy eval average step individual rewards of agent0: 0.20522677345542628
team_policy eval average team episode rewards of agent0: 27.5
team_policy eval idv catch total num of agent0: 10
team_policy eval team catch total num: 11
team_policy eval average step individual rewards of agent1: 0.2001369429475944
team_policy eval average team episode rewards of agent1: 27.5
team_policy eval idv catch total num of agent1: 10
team_policy eval team catch total num: 11
team_policy eval average step individual rewards of agent2: 0.06783242440640672
team_policy eval average team episode rewards of agent2: 27.5
team_policy eval idv catch total num of agent2: 5
team_policy eval team catch total num: 11
team_policy eval average step individual rewards of agent3: 0.14933114857427085
team_policy eval average team episode rewards of agent3: 27.5
team_policy eval idv catch total num of agent3: 8
team_policy eval team catch total num: 11
team_policy eval average step individual rewards of agent4: 0.3277198839211555
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: 0.49908897762120946
idv_policy eval average team episode rewards of agent0: 35.0
idv_policy eval idv catch total num of agent0: 22
idv_policy eval team catch total num: 14
idv_policy eval average step individual rewards of agent1: 0.08794259297729587
idv_policy eval average team episode rewards of agent1: 35.0
idv_policy eval idv catch total num of agent1: 6
idv_policy eval team catch total num: 14
idv_policy eval average step individual rewards of agent2: 0.16948419487613858
idv_policy eval average team episode rewards of agent2: 35.0
idv_policy eval idv catch total num of agent2: 9
idv_policy eval team catch total num: 14
idv_policy eval average step individual rewards of agent3: 0.19222635598263918
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: 0.26888703713770645
idv_policy eval average team episode rewards of agent4: 35.0
idv_policy eval idv catch total num of agent4: 13
idv_policy eval team catch total num: 14

 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.

team_policy eval average step individual rewards of agent0: 0.12810113795972733
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: 0.07205652644474589
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: 0.051402797256713734
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: 0.07199513425056712
team_policy eval average team episode rewards of agent3: 10.0
team_policy eval idv catch total num of agent3: 5
team_policy eval team catch total num: 4
team_policy eval average step individual rewards of agent4: 0.07233744654489944
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: 0.15441394840789438
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: 0.22546599462690323
idv_policy eval average team episode rewards of agent1: 25.0
idv_policy eval idv catch total num of agent1: 11
idv_policy eval team catch total num: 10
idv_policy eval average step individual rewards of agent2: 0.25019092065161325
idv_policy eval average team episode rewards of agent2: 25.0
idv_policy eval idv catch total num of agent2: 12
idv_policy eval team catch total num: 10
idv_policy eval average step individual rewards of agent3: 0.3821246416170889
idv_policy eval average team episode rewards of agent3: 25.0
idv_policy eval idv catch total num of agent3: 17
idv_policy eval team catch total num: 10
idv_policy eval average step individual rewards of agent4: 0.17551848477291926
idv_policy eval average team episode rewards of agent4: 25.0
idv_policy eval idv catch total num of agent4: 9
idv_policy eval team catch total num: 10

 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.

team_policy eval average step individual rewards of agent0: 0.2272258410312303
team_policy eval average team episode rewards of agent0: 40.0
team_policy eval idv catch total num of agent0: 11
team_policy eval team catch total num: 16
team_policy eval average step individual rewards of agent1: 0.33181748067552064
team_policy eval average team episode rewards of agent1: 40.0
team_policy eval idv catch total num of agent1: 15
team_policy eval team catch total num: 16
team_policy eval average step individual rewards of agent2: 0.399585071328673
team_policy eval average team episode rewards of agent2: 40.0
team_policy eval idv catch total num of agent2: 18
team_policy eval team catch total num: 16
team_policy eval average step individual rewards of agent3: 0.14802489191642335
team_policy eval average team episode rewards of agent3: 40.0
team_policy eval idv catch total num of agent3: 8
team_policy eval team catch total num: 16
team_policy eval average step individual rewards of agent4: 0.19450594770072832
team_policy eval average team episode rewards of agent4: 40.0
team_policy eval idv catch total num of agent4: 10
team_policy eval team catch total num: 16
idv_policy eval average step individual rewards of agent0: 0.12703217914238266
idv_policy eval average team episode rewards of agent0: 40.0
idv_policy eval idv catch total num of agent0: 7
idv_policy eval team catch total num: 16
idv_policy eval average step individual rewards of agent1: 0.3330651911913044
idv_policy eval average team episode rewards of agent1: 40.0
idv_policy eval idv catch total num of agent1: 15
idv_policy eval team catch total num: 16
idv_policy eval average step individual rewards of agent2: 0.3549277250120388
idv_policy eval average team episode rewards of agent2: 40.0
idv_policy eval idv catch total num of agent2: 16
idv_policy eval team catch total num: 16
idv_policy eval average step individual rewards of agent3: 0.17087938756247076
idv_policy eval average team episode rewards of agent3: 40.0
idv_policy eval idv catch total num of agent3: 9
idv_policy eval team catch total num: 16
idv_policy eval average step individual rewards of agent4: 0.28098795287202577
idv_policy eval average team episode rewards of agent4: 40.0
idv_policy eval idv catch total num of agent4: 13
idv_policy eval team catch total num: 16

 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.

team_policy eval average step individual rewards of agent0: 0.43077670192649903
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: 0.17580522457246342
team_policy eval average team episode rewards of agent1: 45.0
team_policy eval idv catch total num of agent1: 9
team_policy eval team catch total num: 18
team_policy eval average step individual rewards of agent2: 0.3025369999939616
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: 0.357991641005805
team_policy eval average team episode rewards of agent3: 45.0
team_policy eval idv catch total num of agent3: 16
team_policy eval team catch total num: 18
team_policy eval average step individual rewards of agent4: 0.36166888801482844
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: 0.14681635033985263
idv_policy eval average team episode rewards of agent0: 45.0
idv_policy eval idv catch total num of agent0: 8
idv_policy eval team catch total num: 18
idv_policy eval average step individual rewards of agent1: 0.27725769422296415
idv_policy eval average team episode rewards of agent1: 45.0
idv_policy eval idv catch total num of agent1: 13
idv_policy eval team catch total num: 18
idv_policy eval average step individual rewards of agent2: 0.16310844723172915
idv_policy eval average team episode rewards of agent2: 45.0
idv_policy eval idv catch total num of agent2: 9
idv_policy eval team catch total num: 18
idv_policy eval average step individual rewards of agent3: 0.35418120991071406
idv_policy eval average team episode rewards of agent3: 45.0
idv_policy eval idv catch total num of agent3: 16
idv_policy eval team catch total num: 18
idv_policy eval average step individual rewards of agent4: 0.2778228918198166
idv_policy eval average team episode rewards of agent4: 45.0
idv_policy eval idv catch total num of agent4: 13
idv_policy eval team catch total num: 18

 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 249.


 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 249.


 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 248.

team_policy eval average step individual rewards of agent0: 0.004091486947295628
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: 0.07881741289376774
team_policy eval average team episode rewards of agent1: 15.0
team_policy eval idv catch total num of agent1: 6
team_policy eval team catch total num: 6
team_policy eval average step individual rewards of agent2: 0.1071923700703057
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: -0.044106719196456506
team_policy eval average team episode rewards of agent3: 15.0
team_policy eval idv catch total num of agent3: 1
team_policy eval team catch total num: 6
team_policy eval average step individual rewards of agent4: 0.032160167923729326
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: 0.049745068321285224
idv_policy eval average team episode rewards of agent0: 25.0
idv_policy eval idv catch total num of agent0: 4
idv_policy eval team catch total num: 10
idv_policy eval average step individual rewards of agent1: 0.15094412206033028
idv_policy eval average team episode rewards of agent1: 25.0
idv_policy eval idv catch total num of agent1: 8
idv_policy eval team catch total num: 10
idv_policy eval average step individual rewards of agent2: 0.1541682041086875
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: 0.2253157416531871
idv_policy eval average team episode rewards of agent3: 25.0
idv_policy eval idv catch total num of agent3: 11
idv_policy eval team catch total num: 10
idv_policy eval average step individual rewards of agent4: 0.3288024740988834
idv_policy eval average team episode rewards of agent4: 25.0
idv_policy eval idv catch total num of agent4: 15
idv_policy eval team catch total num: 10

 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 248.


 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 248.


 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 248.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.

team_policy eval average step individual rewards of agent0: 0.512416707790199
team_policy eval average team episode rewards of agent0: 57.5
team_policy eval idv catch total num of agent0: 22
team_policy eval team catch total num: 23
team_policy eval average step individual rewards of agent1: 0.20032443927232838
team_policy eval average team episode rewards of agent1: 57.5
team_policy eval idv catch total num of agent1: 10
team_policy eval team catch total num: 23
team_policy eval average step individual rewards of agent2: 0.3580911698315806
team_policy eval average team episode rewards of agent2: 57.5
team_policy eval idv catch total num of agent2: 16
team_policy eval team catch total num: 23
team_policy eval average step individual rewards of agent3: 0.3060929022017532
team_policy eval average team episode rewards of agent3: 57.5
team_policy eval idv catch total num of agent3: 14
team_policy eval team catch total num: 23
team_policy eval average step individual rewards of agent4: 0.43567029890175957
team_policy eval average team episode rewards of agent4: 57.5
team_policy eval idv catch total num of agent4: 19
team_policy eval team catch total num: 23
idv_policy eval average step individual rewards of agent0: 0.13902018705669353
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: 0.23897411165845167
idv_policy eval average team episode rewards of agent1: 25.0
idv_policy eval idv catch total num of agent1: 12
idv_policy eval team catch total num: 10
idv_policy eval average step individual rewards of agent2: 0.08203454985579255
idv_policy eval average team episode rewards of agent2: 25.0
idv_policy eval idv catch total num of agent2: 6
idv_policy eval team catch total num: 10
idv_policy eval average step individual rewards of agent3: 0.27070590592737936
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: 0.1917866015107547
idv_policy eval average team episode rewards of agent4: 25.0
idv_policy eval idv catch total num of agent4: 10
idv_policy eval team catch total num: 10

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2876/10000 episodes, total num timesteps 575400/2000000, FPS 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.

team_policy eval average step individual rewards of agent0: 0.059094451581245676
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: 0.20750766983164318
team_policy eval average team episode rewards of agent1: 20.0
team_policy eval idv catch total num of agent1: 11
team_policy eval team catch total num: 8
team_policy eval average step individual rewards of agent2: 0.08870397843571023
team_policy eval average team episode rewards of agent2: 20.0
team_policy eval idv catch total num of agent2: 6
team_policy eval team catch total num: 8
team_policy eval average step individual rewards of agent3: 0.08471731987480492
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: 0.11106258375456576
team_policy eval average team episode rewards of agent4: 20.0
team_policy eval idv catch total num of agent4: 7
team_policy eval team catch total num: 8
idv_policy eval average step individual rewards of agent0: 0.0009766527270275117
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: 0.06463446759967839
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: -0.006774968632526131
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: 0.11832439275994716
idv_policy eval average team episode rewards of agent3: 5.0
idv_policy eval idv catch total num of agent3: 7
idv_policy eval team catch total num: 2
idv_policy eval average step individual rewards of agent4: 0.04434898873610499
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 2901/10000 episodes, total num timesteps 580400/2000000, FPS 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.

team_policy eval average step individual rewards of agent0: 0.2095354776380907
team_policy eval average team episode rewards of agent0: 42.5
team_policy eval idv catch total num of agent0: 10
team_policy eval team catch total num: 17
team_policy eval average step individual rewards of agent1: 0.27917934619972573
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: 0.48857313496777044
team_policy eval average team episode rewards of agent2: 42.5
team_policy eval idv catch total num of agent2: 21
team_policy eval team catch total num: 17
team_policy eval average step individual rewards of agent3: 0.15572828926835316
team_policy eval average team episode rewards of agent3: 42.5
team_policy eval idv catch total num of agent3: 8
team_policy eval team catch total num: 17
team_policy eval average step individual rewards of agent4: 0.41341766385196066
team_policy eval average team episode rewards of agent4: 42.5
team_policy eval idv catch total num of agent4: 18
team_policy eval team catch total num: 17
idv_policy eval average step individual rewards of agent0: 0.507647883628092
idv_policy eval average team episode rewards of agent0: 55.0
idv_policy eval idv catch total num of agent0: 22
idv_policy eval team catch total num: 22
idv_policy eval average step individual rewards of agent1: 0.17289628554917916
idv_policy eval average team episode rewards of agent1: 55.0
idv_policy eval idv catch total num of agent1: 9
idv_policy eval team catch total num: 22
idv_policy eval average step individual rewards of agent2: 0.37476481421667635
idv_policy eval average team episode rewards of agent2: 55.0
idv_policy eval idv catch total num of agent2: 17
idv_policy eval team catch total num: 22
idv_policy eval average step individual rewards of agent3: 0.4273440395387745
idv_policy eval average team episode rewards of agent3: 55.0
idv_policy eval idv catch total num of agent3: 19
idv_policy eval team catch total num: 22
idv_policy eval average step individual rewards of agent4: 0.27667782954781844
idv_policy eval average team episode rewards of agent4: 55.0
idv_policy eval idv catch total num of agent4: 13
idv_policy eval team catch total num: 22

 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.

team_policy eval average step individual rewards of agent0: 0.05065826191536047
team_policy eval average team episode rewards of agent0: 35.0
team_policy eval idv catch total num of agent0: 5
team_policy eval team catch total num: 14
team_policy eval average step individual rewards of agent1: 0.15966542135256565
team_policy eval average team episode rewards of agent1: 35.0
team_policy eval idv catch total num of agent1: 9
team_policy eval team catch total num: 14
team_policy eval average step individual rewards of agent2: 0.40963882612545194
team_policy eval average team episode rewards of agent2: 35.0
team_policy eval idv catch total num of agent2: 19
team_policy eval team catch total num: 14
team_policy eval average step individual rewards of agent3: 0.11129734705025393
team_policy eval average team episode rewards of agent3: 35.0
team_policy eval idv catch total num of agent3: 7
team_policy eval team catch total num: 14
team_policy eval average step individual rewards of agent4: 0.1295629739424997
team_policy eval average team episode rewards of agent4: 35.0
team_policy eval idv catch total num of agent4: 8
team_policy eval team catch total num: 14
idv_policy eval average step individual rewards of agent0: 0.21581009316216465
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: 0.046976328811781286
idv_policy eval average team episode rewards of agent1: 35.0
idv_policy eval idv catch total num of agent1: 4
idv_policy eval team catch total num: 14
idv_policy eval average step individual rewards of agent2: 0.3462660040922171
idv_policy eval average team episode rewards of agent2: 35.0
idv_policy eval idv catch total num of agent2: 16
idv_policy eval team catch total num: 14
idv_policy eval average step individual rewards of agent3: 0.2699360818415792
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: 0.12157943197335053
idv_policy eval average team episode rewards of agent4: 35.0
idv_policy eval idv catch total num of agent4: 7
idv_policy eval team catch total num: 14

 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.

team_policy eval average step individual rewards of agent0: 0.2990029835553891
team_policy eval average team episode rewards of agent0: 40.0
team_policy eval idv catch total num of agent0: 14
team_policy eval team catch total num: 16
team_policy eval average step individual rewards of agent1: 0.10156183946575278
team_policy eval average team episode rewards of agent1: 40.0
team_policy eval idv catch total num of agent1: 6
team_policy eval team catch total num: 16
team_policy eval average step individual rewards of agent2: 0.36978811137863443
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: 0.2682637394169667
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: 0.3514992460814766
team_policy eval average team episode rewards of agent4: 40.0
team_policy eval idv catch total num of agent4: 16
team_policy eval team catch total num: 16
idv_policy eval average step individual rewards of agent0: 0.28349944916236036
idv_policy eval average team episode rewards of agent0: 50.0
idv_policy eval idv catch total num of agent0: 13
idv_policy eval team catch total num: 20
idv_policy eval average step individual rewards of agent1: 0.6154009371777359
idv_policy eval average team episode rewards of agent1: 50.0
idv_policy eval idv catch total num of agent1: 26
idv_policy eval team catch total num: 20
idv_policy eval average step individual rewards of agent2: 0.35957739467830374
idv_policy eval average team episode rewards of agent2: 50.0
idv_policy eval idv catch total num of agent2: 16
idv_policy eval team catch total num: 20
idv_policy eval average step individual rewards of agent3: 0.41296536921478144
idv_policy eval average team episode rewards of agent3: 50.0
idv_policy eval idv catch total num of agent3: 18
idv_policy eval team catch total num: 20
idv_policy eval average step individual rewards of agent4: 0.3857679137742929
idv_policy eval average team episode rewards of agent4: 50.0
idv_policy eval idv catch total num of agent4: 17
idv_policy eval team catch total num: 20

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 2976/10000 episodes, total num timesteps 595400/2000000, FPS 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.

team_policy eval average step individual rewards of agent0: 0.38965825887958333
team_policy eval average team episode rewards of agent0: 42.5
team_policy eval idv catch total num of agent0: 17
team_policy eval team catch total num: 17
team_policy eval average step individual rewards of agent1: 0.29979429103228133
team_policy eval average team episode rewards of agent1: 42.5
team_policy eval idv catch total num of agent1: 14
team_policy eval team catch total num: 17
team_policy eval average step individual rewards of agent2: 0.23287865863750434
team_policy eval average team episode rewards of agent2: 42.5
team_policy eval idv catch total num of agent2: 11
team_policy eval team catch total num: 17
team_policy eval average step individual rewards of agent3: 0.278799614266942
team_policy eval average team episode rewards of agent3: 42.5
team_policy eval idv catch total num of agent3: 13
team_policy eval team catch total num: 17
team_policy eval average step individual rewards of agent4: 0.48611437061995366
team_policy eval average team episode rewards of agent4: 42.5
team_policy eval idv catch total num of agent4: 21
team_policy eval team catch total num: 17
idv_policy eval average step individual rewards of agent0: 0.18720085761265787
idv_policy eval average team episode rewards of agent0: 37.5
idv_policy eval idv catch total num of agent0: 10
idv_policy eval team catch total num: 15
idv_policy eval average step individual rewards of agent1: 0.27179203000350416
idv_policy eval average team episode rewards of agent1: 37.5
idv_policy eval idv catch total num of agent1: 13
idv_policy eval team catch total num: 15
idv_policy eval average step individual rewards of agent2: 0.3401123674561832
idv_policy eval average team episode rewards of agent2: 37.5
idv_policy eval idv catch total num of agent2: 16
idv_policy eval team catch total num: 15
idv_policy eval average step individual rewards of agent3: 0.24366783122178334
idv_policy eval average team episode rewards of agent3: 37.5
idv_policy eval idv catch total num of agent3: 12
idv_policy eval team catch total num: 15
idv_policy eval average step individual rewards of agent4: 0.1358768478144225
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 3001/10000 episodes, total num timesteps 600400/2000000, FPS 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.

team_policy eval average step individual rewards of agent0: 0.5335969113074858
team_policy eval average team episode rewards of agent0: 70.0
team_policy eval idv catch total num of agent0: 23
team_policy eval team catch total num: 28
team_policy eval average step individual rewards of agent1: 0.7892721559025182
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: 0.4300492493527442
team_policy eval average team episode rewards of agent2: 70.0
team_policy eval idv catch total num of agent2: 19
team_policy eval team catch total num: 28
team_policy eval average step individual rewards of agent3: 0.3037236681747646
team_policy eval average team episode rewards of agent3: 70.0
team_policy eval idv catch total num of agent3: 14
team_policy eval team catch total num: 28
team_policy eval average step individual rewards of agent4: 0.45726167855660177
team_policy eval average team episode rewards of agent4: 70.0
team_policy eval idv catch total num of agent4: 20
team_policy eval team catch total num: 28
idv_policy eval average step individual rewards of agent0: 0.32918424664981105
idv_policy eval average team episode rewards of agent0: 30.0
idv_policy eval idv catch total num of agent0: 15
idv_policy eval team catch total num: 12
idv_policy eval average step individual rewards of agent1: 0.13687803675209279
idv_policy eval average team episode rewards of agent1: 30.0
idv_policy eval idv catch total num of agent1: 8
idv_policy eval team catch total num: 12
idv_policy eval average step individual rewards of agent2: 0.2967976985282012
idv_policy eval average team episode rewards of agent2: 30.0
idv_policy eval idv catch total num of agent2: 14
idv_policy eval team catch total num: 12
idv_policy eval average step individual rewards of agent3: 0.266657638018259
idv_policy eval average team episode rewards of agent3: 30.0
idv_policy eval idv catch total num of agent3: 13
idv_policy eval team catch total num: 12
idv_policy eval average step individual rewards of agent4: 0.17345733921991782
idv_policy eval average team episode rewards of agent4: 30.0
idv_policy eval idv catch total num of agent4: 9
idv_policy eval team catch total num: 12

 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.

team_policy eval average step individual rewards of agent0: 0.045903382287542184
team_policy eval average team episode rewards of agent0: 20.0
team_policy eval idv catch total num of agent0: 4
team_policy eval team catch total num: 8
team_policy eval average step individual rewards of agent1: 0.15167587944876038
team_policy eval average team episode rewards of agent1: 20.0
team_policy eval idv catch total num of agent1: 8
team_policy eval team catch total num: 8
team_policy eval average step individual rewards of agent2: 0.22148961160090827
team_policy eval average team episode rewards of agent2: 20.0
team_policy eval idv catch total num of agent2: 11
team_policy eval team catch total num: 8
team_policy eval average step individual rewards of agent3: 0.0932711596894909
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: 0.19632933664838284
team_policy eval average team episode rewards of agent4: 20.0
team_policy eval idv catch total num of agent4: 10
team_policy eval team catch total num: 8
idv_policy eval average step individual rewards of agent0: 0.32573739298501025
idv_policy eval average team episode rewards of agent0: 40.0
idv_policy eval idv catch total num of agent0: 15
idv_policy eval team catch total num: 16
idv_policy eval average step individual rewards of agent1: 0.5172846139342272
idv_policy eval average team episode rewards of agent1: 40.0
idv_policy eval idv catch total num of agent1: 23
idv_policy eval team catch total num: 16
idv_policy eval average step individual rewards of agent2: 0.24719465155932316
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: 0.19942768906306627
idv_policy eval average team episode rewards of agent3: 40.0
idv_policy eval idv catch total num of agent3: 10
idv_policy eval team catch total num: 16
idv_policy eval average step individual rewards of agent4: 0.35449438647692605
idv_policy eval average team episode rewards of agent4: 40.0
idv_policy eval idv catch total num of agent4: 16
idv_policy eval team catch total num: 16

 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.

team_policy eval average step individual rewards of agent0: 0.24769060622498817
team_policy eval average team episode rewards of agent0: 32.5
team_policy eval idv catch total num of agent0: 12
team_policy eval team catch total num: 13
team_policy eval average step individual rewards of agent1: 0.37766788948787355
team_policy eval average team episode rewards of agent1: 32.5
team_policy eval idv catch total num of agent1: 17
team_policy eval team catch total num: 13
team_policy eval average step individual rewards of agent2: 0.12751556306116227
team_policy eval average team episode rewards of agent2: 32.5
team_policy eval idv catch total num of agent2: 7
team_policy eval team catch total num: 13
team_policy eval average step individual rewards of agent3: 0.12611476684927658
team_policy eval average team episode rewards of agent3: 32.5
team_policy eval idv catch total num of agent3: 7
team_policy eval team catch total num: 13
team_policy eval average step individual rewards of agent4: 0.3553468448392125
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: 0.26320976345164854
idv_policy eval average team episode rewards of agent0: 35.0
idv_policy eval idv catch total num of agent0: 13
idv_policy eval team catch total num: 14
idv_policy eval average step individual rewards of agent1: 0.37343237400529405
idv_policy eval average team episode rewards of agent1: 35.0
idv_policy eval idv catch total num of agent1: 17
idv_policy eval team catch total num: 14
idv_policy eval average step individual rewards of agent2: 0.3200755129561142
idv_policy eval average team episode rewards of agent2: 35.0
idv_policy eval idv catch total num of agent2: 15
idv_policy eval team catch total num: 14
idv_policy eval average step individual rewards of agent3: 0.11923885233138186
idv_policy eval average team episode rewards of agent3: 35.0
idv_policy eval idv catch total num of agent3: 7
idv_policy eval team catch total num: 14
idv_policy eval average step individual rewards of agent4: 0.13531549626227726
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 3076/10000 episodes, total num timesteps 615400/2000000, FPS 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.

team_policy eval average step individual rewards of agent0: 0.06441924907456306
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: 0.00981200259743932
team_policy eval average team episode rewards of agent1: 7.5
team_policy eval idv catch total num of agent1: 3
team_policy eval team catch total num: 3
team_policy eval average step individual rewards of agent2: -0.013537175647567906
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: 0.015275559728405854
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: 0.11755363920193819
team_policy eval average team episode rewards of agent4: 7.5
team_policy eval idv catch total num of agent4: 7
team_policy eval team catch total num: 3
idv_policy eval average step individual rewards of agent0: 0.29154866675123087
idv_policy eval average team episode rewards of agent0: 40.0
idv_policy eval idv catch total num of agent0: 14
idv_policy eval team catch total num: 16
idv_policy eval average step individual rewards of agent1: 0.21721445216133448
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: 0.28520912801504134
idv_policy eval average team episode rewards of agent2: 40.0
idv_policy eval idv catch total num of agent2: 14
idv_policy eval team catch total num: 16
idv_policy eval average step individual rewards of agent3: 0.47195471400052424
idv_policy eval average team episode rewards of agent3: 40.0
idv_policy eval idv catch total num of agent3: 21
idv_policy eval team catch total num: 16
idv_policy eval average step individual rewards of agent4: 0.21843108238148748
idv_policy eval average team episode rewards of agent4: 40.0
idv_policy eval idv catch total num of agent4: 11
idv_policy eval team catch total num: 16

 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.

team_policy eval average step individual rewards of agent0: 0.2868396191679596
team_policy eval average team episode rewards of agent0: 42.5
team_policy eval idv catch total num of agent0: 13
team_policy eval team catch total num: 17
team_policy eval average step individual rewards of agent1: 0.5044510733386284
team_policy eval average team episode rewards of agent1: 42.5
team_policy eval idv catch total num of agent1: 22
team_policy eval team catch total num: 17
team_policy eval average step individual rewards of agent2: 0.3232278817178502
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: 0.2556857776208606
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: 0.22623243605416474
team_policy eval average team episode rewards of agent4: 42.5
team_policy eval idv catch total num of agent4: 11
team_policy eval team catch total num: 17
idv_policy eval average step individual rewards of agent0: 0.3586791055372439
idv_policy eval average team episode rewards of agent0: 55.0
idv_policy eval idv catch total num of agent0: 16
idv_policy eval team catch total num: 22
idv_policy eval average step individual rewards of agent1: 0.5347551669903706
idv_policy eval average team episode rewards of agent1: 55.0
idv_policy eval idv catch total num of agent1: 23
idv_policy eval team catch total num: 22
idv_policy eval average step individual rewards of agent2: 0.21982913712832086
idv_policy eval average team episode rewards of agent2: 55.0
idv_policy eval idv catch total num of agent2: 11
idv_policy eval team catch total num: 22
idv_policy eval average step individual rewards of agent3: 0.23033029970928745
idv_policy eval average team episode rewards of agent3: 55.0
idv_policy eval idv catch total num of agent3: 11
idv_policy eval team catch total num: 22
idv_policy eval average step individual rewards of agent4: 0.5817060441826256
idv_policy eval average team episode rewards of agent4: 55.0
idv_policy eval idv catch total num of agent4: 25
idv_policy eval team catch total num: 22

 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.

team_policy eval average step individual rewards of agent0: 0.35339564034423865
team_policy eval average team episode rewards of agent0: 42.5
team_policy eval idv catch total num of agent0: 17
team_policy eval team catch total num: 17
team_policy eval average step individual rewards of agent1: 0.15811706103654258
team_policy eval average team episode rewards of agent1: 42.5
team_policy eval idv catch total num of agent1: 9
team_policy eval team catch total num: 17
team_policy eval average step individual rewards of agent2: 0.2390768924160273
team_policy eval average team episode rewards of agent2: 42.5
team_policy eval idv catch total num of agent2: 12
team_policy eval team catch total num: 17
team_policy eval average step individual rewards of agent3: 0.23328682345698304
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: 0.3134151283190088
team_policy eval average team episode rewards of agent4: 42.5
team_policy eval idv catch total num of agent4: 15
team_policy eval team catch total num: 17
idv_policy eval average step individual rewards of agent0: 0.3071497353350694
idv_policy eval average team episode rewards of agent0: 60.0
idv_policy eval idv catch total num of agent0: 14
idv_policy eval team catch total num: 24
idv_policy eval average step individual rewards of agent1: 0.6535967458369555
idv_policy eval average team episode rewards of agent1: 60.0
idv_policy eval idv catch total num of agent1: 28
idv_policy eval team catch total num: 24
idv_policy eval average step individual rewards of agent2: 0.49437377575709307
idv_policy eval average team episode rewards of agent2: 60.0
idv_policy eval idv catch total num of agent2: 22
idv_policy eval team catch total num: 24
idv_policy eval average step individual rewards of agent3: 0.22430672040588892
idv_policy eval average team episode rewards of agent3: 60.0
idv_policy eval idv catch total num of agent3: 11
idv_policy eval team catch total num: 24
idv_policy eval average step individual rewards of agent4: 0.6765149493378607
idv_policy eval average team episode rewards of agent4: 60.0
idv_policy eval idv catch total num of agent4: 29
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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.

team_policy eval average step individual rewards of agent0: 0.020410977863296088
team_policy eval average team episode rewards of agent0: 12.5
team_policy eval idv catch total num of agent0: 3
team_policy eval team catch total num: 5
team_policy eval average step individual rewards of agent1: 0.20180252424878353
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: 0.02623056259711295
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: 0.0914661002019298
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: 0.1562394216722759
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: 0.4849175197700334
idv_policy eval average team episode rewards of agent0: 60.0
idv_policy eval idv catch total num of agent0: 21
idv_policy eval team catch total num: 24
idv_policy eval average step individual rewards of agent1: 0.3007864532695305
idv_policy eval average team episode rewards of agent1: 60.0
idv_policy eval idv catch total num of agent1: 14
idv_policy eval team catch total num: 24
idv_policy eval average step individual rewards of agent2: 0.4884855474968597
idv_policy eval average team episode rewards of agent2: 60.0
idv_policy eval idv catch total num of agent2: 21
idv_policy eval team catch total num: 24
idv_policy eval average step individual rewards of agent3: 0.5375694735935341
idv_policy eval average team episode rewards of agent3: 60.0
idv_policy eval idv catch total num of agent3: 23
idv_policy eval team catch total num: 24
idv_policy eval average step individual rewards of agent4: 0.07965708252871187
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 3176/10000 episodes, total num timesteps 635400/2000000, FPS 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.

team_policy eval average step individual rewards of agent0: 0.3804575844486183
team_policy eval average team episode rewards of agent0: 65.0
team_policy eval idv catch total num of agent0: 17
team_policy eval team catch total num: 26
team_policy eval average step individual rewards of agent1: 0.37437283499054985
team_policy eval average team episode rewards of agent1: 65.0
team_policy eval idv catch total num of agent1: 17
team_policy eval team catch total num: 26
team_policy eval average step individual rewards of agent2: 0.34690758654388015
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: 0.4034585669443945
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: 0.528006126400241
team_policy eval average team episode rewards of agent4: 65.0
team_policy eval idv catch total num of agent4: 23
team_policy eval team catch total num: 26
idv_policy eval average step individual rewards of agent0: 0.3834956077544006
idv_policy eval average team episode rewards of agent0: 47.5
idv_policy eval idv catch total num of agent0: 17
idv_policy eval team catch total num: 19
idv_policy eval average step individual rewards of agent1: 0.35374182868807497
idv_policy eval average team episode rewards of agent1: 47.5
idv_policy eval idv catch total num of agent1: 16
idv_policy eval team catch total num: 19
idv_policy eval average step individual rewards of agent2: 0.35283152879502383
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: 0.42294222556388605
idv_policy eval average team episode rewards of agent3: 47.5
idv_policy eval idv catch total num of agent3: 19
idv_policy eval team catch total num: 19
idv_policy eval average step individual rewards of agent4: 0.3265993793947264
idv_policy eval average team episode rewards of agent4: 47.5
idv_policy eval idv catch total num of agent4: 15
idv_policy eval team catch total num: 19

 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.

team_policy eval average step individual rewards of agent0: 0.22761659736312254
team_policy eval average team episode rewards of agent0: 47.5
team_policy eval idv catch total num of agent0: 11
team_policy eval team catch total num: 19
team_policy eval average step individual rewards of agent1: 0.3987454973449559
team_policy eval average team episode rewards of agent1: 47.5
team_policy eval idv catch total num of agent1: 18
team_policy eval team catch total num: 19
team_policy eval average step individual rewards of agent2: 0.31990397848852736
team_policy eval average team episode rewards of agent2: 47.5
team_policy eval idv catch total num of agent2: 15
team_policy eval team catch total num: 19
team_policy eval average step individual rewards of agent3: 0.3249769387117877
team_policy eval average team episode rewards of agent3: 47.5
team_policy eval idv catch total num of agent3: 15
team_policy eval team catch total num: 19
team_policy eval average step individual rewards of agent4: 0.27705669468654603
team_policy eval average team episode rewards of agent4: 47.5
team_policy eval idv catch total num of agent4: 13
team_policy eval team catch total num: 19
idv_policy eval average step individual rewards of agent0: 0.17234594314404425
idv_policy eval average team episode rewards of agent0: 37.5
idv_policy eval idv catch total num of agent0: 9
idv_policy eval team catch total num: 15
idv_policy eval average step individual rewards of agent1: 0.3582580378351128
idv_policy eval average team episode rewards of agent1: 37.5
idv_policy eval idv catch total num of agent1: 16
idv_policy eval team catch total num: 15
idv_policy eval average step individual rewards of agent2: 0.15416162763821709
idv_policy eval average team episode rewards of agent2: 37.5
idv_policy eval idv catch total num of agent2: 8
idv_policy eval team catch total num: 15
idv_policy eval average step individual rewards of agent3: 0.16751003680956303
idv_policy eval average team episode rewards of agent3: 37.5
idv_policy eval idv catch total num of agent3: 9
idv_policy eval team catch total num: 15
idv_policy eval average step individual rewards of agent4: 0.5074508671412442
idv_policy eval average team episode rewards of agent4: 37.5
idv_policy eval idv catch total num of agent4: 22
idv_policy eval team catch total num: 15

 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.

team_policy eval average step individual rewards of agent0: 0.5583806252489132
team_policy eval average team episode rewards of agent0: 85.0
team_policy eval idv catch total num of agent0: 24
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent1: 0.4049442359994268
team_policy eval average team episode rewards of agent1: 85.0
team_policy eval idv catch total num of agent1: 18
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent2: 0.42750110189784235
team_policy eval average team episode rewards of agent2: 85.0
team_policy eval idv catch total num of agent2: 19
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent3: 0.5890240937666199
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: 0.6512630286002639
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: 0.14054947104039975
idv_policy eval average team episode rewards of agent0: 37.5
idv_policy eval idv catch total num of agent0: 8
idv_policy eval team catch total num: 15
idv_policy eval average step individual rewards of agent1: 0.2624521428386628
idv_policy eval average team episode rewards of agent1: 37.5
idv_policy eval idv catch total num of agent1: 13
idv_policy eval team catch total num: 15
idv_policy eval average step individual rewards of agent2: 0.22661784221391454
idv_policy eval average team episode rewards of agent2: 37.5
idv_policy eval idv catch total num of agent2: 11
idv_policy eval team catch total num: 15
idv_policy eval average step individual rewards of agent3: 0.23835846799448035
idv_policy eval average team episode rewards of agent3: 37.5
idv_policy eval idv catch total num of agent3: 12
idv_policy eval team catch total num: 15
idv_policy eval average step individual rewards of agent4: 0.137195173543315
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 3251/10000 episodes, total num timesteps 650400/2000000, FPS 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.

team_policy eval average step individual rewards of agent0: 0.17119275117475535
team_policy eval average team episode rewards of agent0: 25.0
team_policy eval idv catch total num of agent0: 9
team_policy eval team catch total num: 10
team_policy eval average step individual rewards of agent1: 0.1143279188629592
team_policy eval average team episode rewards of agent1: 25.0
team_policy eval idv catch total num of agent1: 7
team_policy eval team catch total num: 10
team_policy eval average step individual rewards of agent2: -0.007312242456242375
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: 0.24708460622450265
team_policy eval average team episode rewards of agent3: 25.0
team_policy eval idv catch total num of agent3: 12
team_policy eval team catch total num: 10
team_policy eval average step individual rewards of agent4: 0.22324630151275968
team_policy eval average team episode rewards of agent4: 25.0
team_policy eval idv catch total num of agent4: 11
team_policy eval team catch total num: 10
idv_policy eval average step individual rewards of agent0: 0.31335754320150494
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: 0.30676107494025323
idv_policy eval average team episode rewards of agent1: 35.0
idv_policy eval idv catch total num of agent1: 14
idv_policy eval team catch total num: 14
idv_policy eval average step individual rewards of agent2: 0.2320992937604734
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: 0.1321528854471844
idv_policy eval average team episode rewards of agent3: 35.0
idv_policy eval idv catch total num of agent3: 7
idv_policy eval team catch total num: 14
idv_policy eval average step individual rewards of agent4: 0.2354696108772873
idv_policy eval average team episode rewards of agent4: 35.0
idv_policy eval idv catch total num of agent4: 11
idv_policy eval team catch total num: 14

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3276/10000 episodes, total num timesteps 655400/2000000, FPS 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.

team_policy eval average step individual rewards of agent0: 0.6828425226348293
team_policy eval average team episode rewards of agent0: 60.0
team_policy eval idv catch total num of agent0: 29
team_policy eval team catch total num: 24
team_policy eval average step individual rewards of agent1: 0.4051947764998379
team_policy eval average team episode rewards of agent1: 60.0
team_policy eval idv catch total num of agent1: 18
team_policy eval team catch total num: 24
team_policy eval average step individual rewards of agent2: 0.3756635417444998
team_policy eval average team episode rewards of agent2: 60.0
team_policy eval idv catch total num of agent2: 17
team_policy eval team catch total num: 24
team_policy eval average step individual rewards of agent3: 0.38648601228045565
team_policy eval average team episode rewards of agent3: 60.0
team_policy eval idv catch total num of agent3: 17
team_policy eval team catch total num: 24
team_policy eval average step individual rewards of agent4: 0.3834347849823142
team_policy eval average team episode rewards of agent4: 60.0
team_policy eval idv catch total num of agent4: 17
team_policy eval team catch total num: 24
idv_policy eval average step individual rewards of agent0: 0.28603864265426154
idv_policy eval average team episode rewards of agent0: 37.5
idv_policy eval idv catch total num of agent0: 13
idv_policy eval team catch total num: 15
idv_policy eval average step individual rewards of agent1: 0.3306608838243807
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: 0.25545675926838873
idv_policy eval average team episode rewards of agent2: 37.5
idv_policy eval idv catch total num of agent2: 12
idv_policy eval team catch total num: 15
idv_policy eval average step individual rewards of agent3: 0.22731474280043032
idv_policy eval average team episode rewards of agent3: 37.5
idv_policy eval idv catch total num of agent3: 11
idv_policy eval team catch total num: 15
idv_policy eval average step individual rewards of agent4: 0.18082089248993757
idv_policy eval average team episode rewards of agent4: 37.5
idv_policy eval idv catch total num of agent4: 9
idv_policy eval team catch total num: 15

 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.

team_policy eval average step individual rewards of agent0: 0.17863641726406818
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: 0.14748591008043946
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: 0.2252016950633668
team_policy eval average team episode rewards of agent2: 27.5
team_policy eval idv catch total num of agent2: 11
team_policy eval team catch total num: 11
team_policy eval average step individual rewards of agent3: 0.10560006037373784
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: 0.29693272494239464
team_policy eval average team episode rewards of agent4: 27.5
team_policy eval idv catch total num of agent4: 14
team_policy eval team catch total num: 11
idv_policy eval average step individual rewards of agent0: 0.2363645277337834
idv_policy eval average team episode rewards of agent0: 57.5
idv_policy eval idv catch total num of agent0: 11
idv_policy eval team catch total num: 23
idv_policy eval average step individual rewards of agent1: 0.5255589300641376
idv_policy eval average team episode rewards of agent1: 57.5
idv_policy eval idv catch total num of agent1: 23
idv_policy eval team catch total num: 23
idv_policy eval average step individual rewards of agent2: 0.1592227079917593
idv_policy eval average team episode rewards of agent2: 57.5
idv_policy eval idv catch total num of agent2: 8
idv_policy eval team catch total num: 23
idv_policy eval average step individual rewards of agent3: 0.5099821087551265
idv_policy eval average team episode rewards of agent3: 57.5
idv_policy eval idv catch total num of agent3: 22
idv_policy eval team catch total num: 23
idv_policy eval average step individual rewards of agent4: 0.37721026483938513
idv_policy eval average team episode rewards of agent4: 57.5
idv_policy eval idv catch total num of agent4: 17
idv_policy eval team catch total num: 23

 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.

team_policy eval average step individual rewards of agent0: 0.7112907231640898
team_policy eval average team episode rewards of agent0: 95.0
team_policy eval idv catch total num of agent0: 30
team_policy eval team catch total num: 38
team_policy eval average step individual rewards of agent1: 0.8170265424776741
team_policy eval average team episode rewards of agent1: 95.0
team_policy eval idv catch total num of agent1: 34
team_policy eval team catch total num: 38
team_policy eval average step individual rewards of agent2: 0.5657819469434944
team_policy eval average team episode rewards of agent2: 95.0
team_policy eval idv catch total num of agent2: 24
team_policy eval team catch total num: 38
team_policy eval average step individual rewards of agent3: 0.6895367834423735
team_policy eval average team episode rewards of agent3: 95.0
team_policy eval idv catch total num of agent3: 29
team_policy eval team catch total num: 38
team_policy eval average step individual rewards of agent4: 0.6598704266274413
team_policy eval average team episode rewards of agent4: 95.0
team_policy eval idv catch total num of agent4: 28
team_policy eval team catch total num: 38
idv_policy eval average step individual rewards of agent0: 0.3286974526144504
idv_policy eval average team episode rewards of agent0: 47.5
idv_policy eval idv catch total num of agent0: 15
idv_policy eval team catch total num: 19
idv_policy eval average step individual rewards of agent1: 0.2061363611741589
idv_policy eval average team episode rewards of agent1: 47.5
idv_policy eval idv catch total num of agent1: 10
idv_policy eval team catch total num: 19
idv_policy eval average step individual rewards of agent2: 0.4284286915307371
idv_policy eval average team episode rewards of agent2: 47.5
idv_policy eval idv catch total num of agent2: 19
idv_policy eval team catch total num: 19
idv_policy eval average step individual rewards of agent3: 0.32614109811284053
idv_policy eval average team episode rewards of agent3: 47.5
idv_policy eval idv catch total num of agent3: 15
idv_policy eval team catch total num: 19
idv_policy eval average step individual rewards of agent4: 0.48563554949430127
idv_policy eval average team episode rewards of agent4: 47.5
idv_policy eval idv catch total num of agent4: 21
idv_policy eval team catch total num: 19

 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.

team_policy eval average step individual rewards of agent0: 0.2896781486171455
team_policy eval average team episode rewards of agent0: 45.0
team_policy eval idv catch total num of agent0: 14
team_policy eval team catch total num: 18
team_policy eval average step individual rewards of agent1: 0.2277982181602853
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: 0.0626764468943774
team_policy eval average team episode rewards of agent2: 45.0
team_policy eval idv catch total num of agent2: 5
team_policy eval team catch total num: 18
team_policy eval average step individual rewards of agent3: 0.7193128607715805
team_policy eval average team episode rewards of agent3: 45.0
team_policy eval idv catch total num of agent3: 30
team_policy eval team catch total num: 18
team_policy eval average step individual rewards of agent4: 0.2790834956853655
team_policy eval average team episode rewards of agent4: 45.0
team_policy eval idv catch total num of agent4: 13
team_policy eval team catch total num: 18
idv_policy eval average step individual rewards of agent0: 0.2883775323588548
idv_policy eval average team episode rewards of agent0: 30.0
idv_policy eval idv catch total num of agent0: 13
idv_policy eval team catch total num: 12
idv_policy eval average step individual rewards of agent1: 0.1431533650550908
idv_policy eval average team episode rewards of agent1: 30.0
idv_policy eval idv catch total num of agent1: 8
idv_policy eval team catch total num: 12
idv_policy eval average step individual rewards of agent2: 0.22676680246602113
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: 0.16854373815704876
idv_policy eval average team episode rewards of agent3: 30.0
idv_policy eval idv catch total num of agent3: 9
idv_policy eval team catch total num: 12
idv_policy eval average step individual rewards of agent4: 0.2499705743490335
idv_policy eval average team episode rewards of agent4: 30.0
idv_policy eval idv catch total num of agent4: 12
idv_policy eval team catch total num: 12

 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.

team_policy eval average step individual rewards of agent0: 0.4570077825773701
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: 0.43298810234534224
team_policy eval average team episode rewards of agent1: 57.5
team_policy eval idv catch total num of agent1: 19
team_policy eval team catch total num: 23
team_policy eval average step individual rewards of agent2: 0.25314761640337785
team_policy eval average team episode rewards of agent2: 57.5
team_policy eval idv catch total num of agent2: 12
team_policy eval team catch total num: 23
team_policy eval average step individual rewards of agent3: 0.30429581411477097
team_policy eval average team episode rewards of agent3: 57.5
team_policy eval idv catch total num of agent3: 14
team_policy eval team catch total num: 23
team_policy eval average step individual rewards of agent4: 0.7056819211076527
team_policy eval average team episode rewards of agent4: 57.5
team_policy eval idv catch total num of agent4: 30
team_policy eval team catch total num: 23
idv_policy eval average step individual rewards of agent0: 0.1231258784396732
idv_policy eval average team episode rewards of agent0: 17.5
idv_policy eval idv catch total num of agent0: 7
idv_policy eval team catch total num: 7
idv_policy eval average step individual rewards of agent1: 0.29492946439966583
idv_policy eval average team episode rewards of agent1: 17.5
idv_policy eval idv catch total num of agent1: 14
idv_policy eval team catch total num: 7
idv_policy eval average step individual rewards of agent2: -0.009333296122233761
idv_policy eval average team episode rewards of agent2: 17.5
idv_policy eval idv catch total num of agent2: 2
idv_policy eval team catch total num: 7
idv_policy eval average step individual rewards of agent3: 0.21929465503873133
idv_policy eval average team episode rewards of agent3: 17.5
idv_policy eval idv catch total num of agent3: 11
idv_policy eval team catch total num: 7
idv_policy eval average step individual rewards of agent4: 0.08908657739790829
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 3401/10000 episodes, total num timesteps 680400/2000000, FPS 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.

team_policy eval average step individual rewards of agent0: 0.6652108024020873
team_policy eval average team episode rewards of agent0: 72.5
team_policy eval idv catch total num of agent0: 28
team_policy eval team catch total num: 29
team_policy eval average step individual rewards of agent1: 0.22729254077406943
team_policy eval average team episode rewards of agent1: 72.5
team_policy eval idv catch total num of agent1: 11
team_policy eval team catch total num: 29
team_policy eval average step individual rewards of agent2: 0.6175844854039314
team_policy eval average team episode rewards of agent2: 72.5
team_policy eval idv catch total num of agent2: 26
team_policy eval team catch total num: 29
team_policy eval average step individual rewards of agent3: 0.4112273819298667
team_policy eval average team episode rewards of agent3: 72.5
team_policy eval idv catch total num of agent3: 18
team_policy eval team catch total num: 29
team_policy eval average step individual rewards of agent4: 0.3371347263805056
team_policy eval average team episode rewards of agent4: 72.5
team_policy eval idv catch total num of agent4: 15
team_policy eval team catch total num: 29
idv_policy eval average step individual rewards of agent0: 0.524886274447685
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: 0.655432872992871
idv_policy eval average team episode rewards of agent1: 80.0
idv_policy eval idv catch total num of agent1: 28
idv_policy eval team catch total num: 32
idv_policy eval average step individual rewards of agent2: 0.6152757875634661
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: 0.3594192785901454
idv_policy eval average team episode rewards of agent3: 80.0
idv_policy eval idv catch total num of agent3: 17
idv_policy eval team catch total num: 32
idv_policy eval average step individual rewards of agent4: 0.49033632714344216
idv_policy eval average team episode rewards of agent4: 80.0
idv_policy eval idv catch total num of agent4: 22
idv_policy eval team catch total num: 32

 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.

team_policy eval average step individual rewards of agent0: 0.5021942246323683
team_policy eval average team episode rewards of agent0: 75.0
team_policy eval idv catch total num of agent0: 22
team_policy eval team catch total num: 30
team_policy eval average step individual rewards of agent1: 0.788641517334625
team_policy eval average team episode rewards of agent1: 75.0
team_policy eval idv catch total num of agent1: 33
team_policy eval team catch total num: 30
team_policy eval average step individual rewards of agent2: 0.48449697130189195
team_policy eval average team episode rewards of agent2: 75.0
team_policy eval idv catch total num of agent2: 21
team_policy eval team catch total num: 30
team_policy eval average step individual rewards of agent3: 0.43665728322154607
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: 0.5274862460916053
team_policy eval average team episode rewards of agent4: 75.0
team_policy eval idv catch total num of agent4: 23
team_policy eval team catch total num: 30
idv_policy eval average step individual rewards of agent0: 0.7091232870725783
idv_policy eval average team episode rewards of agent0: 87.5
idv_policy eval idv catch total num of agent0: 30
idv_policy eval team catch total num: 35
idv_policy eval average step individual rewards of agent1: 0.7572297080096843
idv_policy eval average team episode rewards of agent1: 87.5
idv_policy eval idv catch total num of agent1: 32
idv_policy eval team catch total num: 35
idv_policy eval average step individual rewards of agent2: 0.6554391737857999
idv_policy eval average team episode rewards of agent2: 87.5
idv_policy eval idv catch total num of agent2: 28
idv_policy eval team catch total num: 35
idv_policy eval average step individual rewards of agent3: 0.6787255757342896
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: 0.502118141459659
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 3451/10000 episodes, total num timesteps 690400/2000000, FPS 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.

team_policy eval average step individual rewards of agent0: 0.1431850914025608
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: 0.09413405381635669
team_policy eval average team episode rewards of agent1: 27.5
team_policy eval idv catch total num of agent1: 6
team_policy eval team catch total num: 11
team_policy eval average step individual rewards of agent2: 0.22220302126511166
team_policy eval average team episode rewards of agent2: 27.5
team_policy eval idv catch total num of agent2: 11
team_policy eval team catch total num: 11
team_policy eval average step individual rewards of agent3: 0.12162722385208415
team_policy eval average team episode rewards of agent3: 27.5
team_policy eval idv catch total num of agent3: 7
team_policy eval team catch total num: 11
team_policy eval average step individual rewards of agent4: 0.40896860059661544
team_policy eval average team episode rewards of agent4: 27.5
team_policy eval idv catch total num of agent4: 18
team_policy eval team catch total num: 11
idv_policy eval average step individual rewards of agent0: 0.5721911053442806
idv_policy eval average team episode rewards of agent0: 62.5
idv_policy eval idv catch total num of agent0: 25
idv_policy eval team catch total num: 25
idv_policy eval average step individual rewards of agent1: 0.37597282124484255
idv_policy eval average team episode rewards of agent1: 62.5
idv_policy eval idv catch total num of agent1: 17
idv_policy eval team catch total num: 25
idv_policy eval average step individual rewards of agent2: 0.41739989938354966
idv_policy eval average team episode rewards of agent2: 62.5
idv_policy eval idv catch total num of agent2: 19
idv_policy eval team catch total num: 25
idv_policy eval average step individual rewards of agent3: 0.4502957785194346
idv_policy eval average team episode rewards of agent3: 62.5
idv_policy eval idv catch total num of agent3: 20
idv_policy eval team catch total num: 25
idv_policy eval average step individual rewards of agent4: 0.3444711081064484
idv_policy eval average team episode rewards of agent4: 62.5
idv_policy eval idv catch total num of agent4: 16
idv_policy eval team catch total num: 25

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3476/10000 episodes, total num timesteps 695400/2000000, FPS 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.

team_policy eval average step individual rewards of agent0: 0.7398229136046368
team_policy eval average team episode rewards of agent0: 62.5
team_policy eval idv catch total num of agent0: 31
team_policy eval team catch total num: 25
team_policy eval average step individual rewards of agent1: 0.47895931787784124
team_policy eval average team episode rewards of agent1: 62.5
team_policy eval idv catch total num of agent1: 21
team_policy eval team catch total num: 25
team_policy eval average step individual rewards of agent2: 0.5389264169941421
team_policy eval average team episode rewards of agent2: 62.5
team_policy eval idv catch total num of agent2: 23
team_policy eval team catch total num: 25
team_policy eval average step individual rewards of agent3: 0.533521798500177
team_policy eval average team episode rewards of agent3: 62.5
team_policy eval idv catch total num of agent3: 23
team_policy eval team catch total num: 25
team_policy eval average step individual rewards of agent4: 0.40731378741935986
team_policy eval average team episode rewards of agent4: 62.5
team_policy eval idv catch total num of agent4: 18
team_policy eval team catch total num: 25
idv_policy eval average step individual rewards of agent0: 0.27896080136369733
idv_policy eval average team episode rewards of agent0: 47.5
idv_policy eval idv catch total num of agent0: 13
idv_policy eval team catch total num: 19
idv_policy eval average step individual rewards of agent1: 0.30510926324860027
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: 0.43581446838007964
idv_policy eval average team episode rewards of agent2: 47.5
idv_policy eval idv catch total num of agent2: 19
idv_policy eval team catch total num: 19
idv_policy eval average step individual rewards of agent3: 0.43181015497193653
idv_policy eval average team episode rewards of agent3: 47.5
idv_policy eval idv catch total num of agent3: 19
idv_policy eval team catch total num: 19
idv_policy eval average step individual rewards of agent4: 0.4625892981136189
idv_policy eval average team episode rewards of agent4: 47.5
idv_policy eval idv catch total num of agent4: 20
idv_policy eval team catch total num: 19

 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.

team_policy eval average step individual rewards of agent0: 0.1519458555182671
team_policy eval average team episode rewards of agent0: 35.0
team_policy eval idv catch total num of agent0: 8
team_policy eval team catch total num: 14
team_policy eval average step individual rewards of agent1: 0.15738651930447486
team_policy eval average team episode rewards of agent1: 35.0
team_policy eval idv catch total num of agent1: 8
team_policy eval team catch total num: 14
team_policy eval average step individual rewards of agent2: 0.27767494247325397
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: 0.3527197522022314
team_policy eval average team episode rewards of agent3: 35.0
team_policy eval idv catch total num of agent3: 16
team_policy eval team catch total num: 14
team_policy eval average step individual rewards of agent4: 0.2547221864861074
team_policy eval average team episode rewards of agent4: 35.0
team_policy eval idv catch total num of agent4: 12
team_policy eval team catch total num: 14
idv_policy eval average step individual rewards of agent0: 0.43768817584548286
idv_policy eval average team episode rewards of agent0: 37.5
idv_policy eval idv catch total num of agent0: 19
idv_policy eval team catch total num: 15
idv_policy eval average step individual rewards of agent1: 0.19908343561336436
idv_policy eval average team episode rewards of agent1: 37.5
idv_policy eval idv catch total num of agent1: 10
idv_policy eval team catch total num: 15
idv_policy eval average step individual rewards of agent2: 0.32190683072137055
idv_policy eval average team episode rewards of agent2: 37.5
idv_policy eval idv catch total num of agent2: 15
idv_policy eval team catch total num: 15
idv_policy eval average step individual rewards of agent3: 0.25334217131434195
idv_policy eval average team episode rewards of agent3: 37.5
idv_policy eval idv catch total num of agent3: 12
idv_policy eval team catch total num: 15
idv_policy eval average step individual rewards of agent4: 0.3329257937132921
idv_policy eval average team episode rewards of agent4: 37.5
idv_policy eval idv catch total num of agent4: 15
idv_policy eval team catch total num: 15

 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.

team_policy eval average step individual rewards of agent0: 0.6380122340116487
team_policy eval average team episode rewards of agent0: 87.5
team_policy eval idv catch total num of agent0: 27
team_policy eval team catch total num: 35
team_policy eval average step individual rewards of agent1: 0.49178062524979266
team_policy eval average team episode rewards of agent1: 87.5
team_policy eval idv catch total num of agent1: 21
team_policy eval team catch total num: 35
team_policy eval average step individual rewards of agent2: 0.6412173142665768
team_policy eval average team episode rewards of agent2: 87.5
team_policy eval idv catch total num of agent2: 27
team_policy eval team catch total num: 35
team_policy eval average step individual rewards of agent3: 0.44025078797601624
team_policy eval average team episode rewards of agent3: 87.5
team_policy eval idv catch total num of agent3: 19
team_policy eval team catch total num: 35
team_policy eval average step individual rewards of agent4: 0.41514674595176004
team_policy eval average team episode rewards of agent4: 87.5
team_policy eval idv catch total num of agent4: 18
team_policy eval team catch total num: 35
idv_policy eval average step individual rewards of agent0: 0.5319562067713618
idv_policy eval average team episode rewards of agent0: 70.0
idv_policy eval idv catch total num of agent0: 23
idv_policy eval team catch total num: 28
idv_policy eval average step individual rewards of agent1: 0.6400352119368665
idv_policy eval average team episode rewards of agent1: 70.0
idv_policy eval idv catch total num of agent1: 27
idv_policy eval team catch total num: 28
idv_policy eval average step individual rewards of agent2: 0.6973204670825541
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: 0.5362997986640979
idv_policy eval average team episode rewards of agent3: 70.0
idv_policy eval idv catch total num of agent3: 23
idv_policy eval team catch total num: 28
idv_policy eval average step individual rewards of agent4: 0.6713952162751938
idv_policy eval average team episode rewards of agent4: 70.0
idv_policy eval idv catch total num of agent4: 28
idv_policy eval team catch total num: 28

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3551/10000 episodes, total num timesteps 710400/2000000, FPS 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.

team_policy eval average step individual rewards of agent0: 0.8689322544299753
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: 0.7429798407927306
team_policy eval average team episode rewards of agent1: 95.0
team_policy eval idv catch total num of agent1: 31
team_policy eval team catch total num: 38
team_policy eval average step individual rewards of agent2: 0.5382836208603631
team_policy eval average team episode rewards of agent2: 95.0
team_policy eval idv catch total num of agent2: 23
team_policy eval team catch total num: 38
team_policy eval average step individual rewards of agent3: 0.5155681956370417
team_policy eval average team episode rewards of agent3: 95.0
team_policy eval idv catch total num of agent3: 22
team_policy eval team catch total num: 38
team_policy eval average step individual rewards of agent4: 0.7904206536695935
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: 0.4237340931776935
idv_policy eval average team episode rewards of agent0: 55.0
idv_policy eval idv catch total num of agent0: 19
idv_policy eval team catch total num: 22
idv_policy eval average step individual rewards of agent1: 0.43607629067941095
idv_policy eval average team episode rewards of agent1: 55.0
idv_policy eval idv catch total num of agent1: 19
idv_policy eval team catch total num: 22
idv_policy eval average step individual rewards of agent2: 0.2341182587113775
idv_policy eval average team episode rewards of agent2: 55.0
idv_policy eval idv catch total num of agent2: 11
idv_policy eval team catch total num: 22
idv_policy eval average step individual rewards of agent3: 0.459317558376705
idv_policy eval average team episode rewards of agent3: 55.0
idv_policy eval idv catch total num of agent3: 20
idv_policy eval team catch total num: 22
idv_policy eval average step individual rewards of agent4: 0.5048565084886197
idv_policy eval average team episode rewards of agent4: 55.0
idv_policy eval idv catch total num of agent4: 22
idv_policy eval team catch total num: 22

 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.

team_policy eval average step individual rewards of agent0: 0.4545399658677286
team_policy eval average team episode rewards of agent0: 52.5
team_policy eval idv catch total num of agent0: 20
team_policy eval team catch total num: 21
team_policy eval average step individual rewards of agent1: 0.5559083413808912
team_policy eval average team episode rewards of agent1: 52.5
team_policy eval idv catch total num of agent1: 24
team_policy eval team catch total num: 21
team_policy eval average step individual rewards of agent2: 0.5153738844165149
team_policy eval average team episode rewards of agent2: 52.5
team_policy eval idv catch total num of agent2: 22
team_policy eval team catch total num: 21
team_policy eval average step individual rewards of agent3: 0.25849093208811036
team_policy eval average team episode rewards of agent3: 52.5
team_policy eval idv catch total num of agent3: 12
team_policy eval team catch total num: 21
team_policy eval average step individual rewards of agent4: 0.58069940953182
team_policy eval average team episode rewards of agent4: 52.5
team_policy eval idv catch total num of agent4: 25
team_policy eval team catch total num: 21
idv_policy eval average step individual rewards of agent0: 0.6893478038589529
idv_policy eval average team episode rewards of agent0: 70.0
idv_policy eval idv catch total num of agent0: 29
idv_policy eval team catch total num: 28
idv_policy eval average step individual rewards of agent1: 0.5819578150103459
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: 0.5317016482975299
idv_policy eval average team episode rewards of agent2: 70.0
idv_policy eval idv catch total num of agent2: 23
idv_policy eval team catch total num: 28
idv_policy eval average step individual rewards of agent3: 0.8383382188905465
idv_policy eval average team episode rewards of agent3: 70.0
idv_policy eval idv catch total num of agent3: 35
idv_policy eval team catch total num: 28
idv_policy eval average step individual rewards of agent4: 0.5884241242027155
idv_policy eval average team episode rewards of agent4: 70.0
idv_policy eval idv catch total num of agent4: 25
idv_policy eval team catch total num: 28

 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.

team_policy eval average step individual rewards of agent0: 0.9476838844713619
team_policy eval average team episode rewards of agent0: 90.0
team_policy eval idv catch total num of agent0: 39
team_policy eval team catch total num: 36
team_policy eval average step individual rewards of agent1: 0.6573584395790636
team_policy eval average team episode rewards of agent1: 90.0
team_policy eval idv catch total num of agent1: 28
team_policy eval team catch total num: 36
team_policy eval average step individual rewards of agent2: 0.9637611852666146
team_policy eval average team episode rewards of agent2: 90.0
team_policy eval idv catch total num of agent2: 40
team_policy eval team catch total num: 36
team_policy eval average step individual rewards of agent3: 0.7160427417254372
team_policy eval average team episode rewards of agent3: 90.0
team_policy eval idv catch total num of agent3: 30
team_policy eval team catch total num: 36
team_policy eval average step individual rewards of agent4: 0.6365821912020445
team_policy eval average team episode rewards of agent4: 90.0
team_policy eval idv catch total num of agent4: 27
team_policy eval team catch total num: 36
idv_policy eval average step individual rewards of agent0: 0.6619909120635923
idv_policy eval average team episode rewards of agent0: 85.0
idv_policy eval idv catch total num of agent0: 28
idv_policy eval team catch total num: 34
idv_policy eval average step individual rewards of agent1: 0.4272535144196692
idv_policy eval average team episode rewards of agent1: 85.0
idv_policy eval idv catch total num of agent1: 19
idv_policy eval team catch total num: 34
idv_policy eval average step individual rewards of agent2: 0.52153358488861
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: 0.6849849011148763
idv_policy eval average team episode rewards of agent3: 85.0
idv_policy eval idv catch total num of agent3: 29
idv_policy eval team catch total num: 34
idv_policy eval average step individual rewards of agent4: 0.6567308910688763
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 3626/10000 episodes, total num timesteps 725400/2000000, FPS 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.

team_policy eval average step individual rewards of agent0: 0.8065408372687937
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: 0.7158328652310759
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: 0.30356458671796904
team_policy eval average team episode rewards of agent2: 85.0
team_policy eval idv catch total num of agent2: 14
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent3: 0.5635569795061204
team_policy eval average team episode rewards of agent3: 85.0
team_policy eval idv catch total num of agent3: 24
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent4: 0.5534666439900451
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: 0.2718002084188412
idv_policy eval average team episode rewards of agent0: 60.0
idv_policy eval idv catch total num of agent0: 13
idv_policy eval team catch total num: 24
idv_policy eval average step individual rewards of agent1: 0.708864835835808
idv_policy eval average team episode rewards of agent1: 60.0
idv_policy eval idv catch total num of agent1: 30
idv_policy eval team catch total num: 24
idv_policy eval average step individual rewards of agent2: 0.8189526866468338
idv_policy eval average team episode rewards of agent2: 60.0
idv_policy eval idv catch total num of agent2: 34
idv_policy eval team catch total num: 24
idv_policy eval average step individual rewards of agent3: 0.7090737719126077
idv_policy eval average team episode rewards of agent3: 60.0
idv_policy eval idv catch total num of agent3: 30
idv_policy eval team catch total num: 24
idv_policy eval average step individual rewards of agent4: 0.4306457640094852
idv_policy eval average team episode rewards of agent4: 60.0
idv_policy eval idv catch total num of agent4: 19
idv_policy eval team catch total num: 24

 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.

team_policy eval average step individual rewards of agent0: 0.7329827123978702
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: 0.6253833567018926
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: 0.6318525419610895
team_policy eval average team episode rewards of agent2: 87.5
team_policy eval idv catch total num of agent2: 27
team_policy eval team catch total num: 35
team_policy eval average step individual rewards of agent3: 0.4980152549322441
team_policy eval average team episode rewards of agent3: 87.5
team_policy eval idv catch total num of agent3: 22
team_policy eval team catch total num: 35
team_policy eval average step individual rewards of agent4: 0.5075189471750803
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: 0.40318096195253483
idv_policy eval average team episode rewards of agent0: 80.0
idv_policy eval idv catch total num of agent0: 18
idv_policy eval team catch total num: 32
idv_policy eval average step individual rewards of agent1: 0.5895652898397507
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: 0.7150759559824731
idv_policy eval average team episode rewards of agent2: 80.0
idv_policy eval idv catch total num of agent2: 30
idv_policy eval team catch total num: 32
idv_policy eval average step individual rewards of agent3: 0.5897140289521201
idv_policy eval average team episode rewards of agent3: 80.0
idv_policy eval idv catch total num of agent3: 25
idv_policy eval team catch total num: 32
idv_policy eval average step individual rewards of agent4: 0.5136875229885457
idv_policy eval average team episode rewards of agent4: 80.0
idv_policy eval idv catch total num of agent4: 22
idv_policy eval team catch total num: 32

 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.

team_policy eval average step individual rewards of agent0: 0.40723318209385456
team_policy eval average team episode rewards of agent0: 52.5
team_policy eval idv catch total num of agent0: 18
team_policy eval team catch total num: 21
team_policy eval average step individual rewards of agent1: 0.5563217729611614
team_policy eval average team episode rewards of agent1: 52.5
team_policy eval idv catch total num of agent1: 24
team_policy eval team catch total num: 21
team_policy eval average step individual rewards of agent2: 0.1920119472620158
team_policy eval average team episode rewards of agent2: 52.5
team_policy eval idv catch total num of agent2: 10
team_policy eval team catch total num: 21
team_policy eval average step individual rewards of agent3: 0.561239390488578
team_policy eval average team episode rewards of agent3: 52.5
team_policy eval idv catch total num of agent3: 24
team_policy eval team catch total num: 21
team_policy eval average step individual rewards of agent4: 0.8319455182632757
team_policy eval average team episode rewards of agent4: 52.5
team_policy eval idv catch total num of agent4: 35
team_policy eval team catch total num: 21
idv_policy eval average step individual rewards of agent0: 0.5645322866338385
idv_policy eval average team episode rewards of agent0: 55.0
idv_policy eval idv catch total num of agent0: 24
idv_policy eval team catch total num: 22
idv_policy eval average step individual rewards of agent1: 0.2588804851256542
idv_policy eval average team episode rewards of agent1: 55.0
idv_policy eval idv catch total num of agent1: 12
idv_policy eval team catch total num: 22
idv_policy eval average step individual rewards of agent2: 0.5690344175954953
idv_policy eval average team episode rewards of agent2: 55.0
idv_policy eval idv catch total num of agent2: 24
idv_policy eval team catch total num: 22
idv_policy eval average step individual rewards of agent3: 0.25936454226338823
idv_policy eval average team episode rewards of agent3: 55.0
idv_policy eval idv catch total num of agent3: 12
idv_policy eval team catch total num: 22
idv_policy eval average step individual rewards of agent4: 0.3483489486263369
idv_policy eval average team episode rewards of agent4: 55.0
idv_policy eval idv catch total num of agent4: 16
idv_policy eval team catch total num: 22

 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.

team_policy eval average step individual rewards of agent0: 0.7561367114768638
team_policy eval average team episode rewards of agent0: 100.0
team_policy eval idv catch total num of agent0: 32
team_policy eval team catch total num: 40
team_policy eval average step individual rewards of agent1: 0.38286630361787544
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: 0.7896708934459735
team_policy eval average team episode rewards of agent2: 100.0
team_policy eval idv catch total num of agent2: 33
team_policy eval team catch total num: 40
team_policy eval average step individual rewards of agent3: 1.3271784857693172
team_policy eval average team episode rewards of agent3: 100.0
team_policy eval idv catch total num of agent3: 54
team_policy eval team catch total num: 40
team_policy eval average step individual rewards of agent4: 0.5919864076432596
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: 0.6490541077360021
idv_policy eval average team episode rewards of agent0: 47.5
idv_policy eval idv catch total num of agent0: 28
idv_policy eval team catch total num: 19
idv_policy eval average step individual rewards of agent1: 0.2765256320755235
idv_policy eval average team episode rewards of agent1: 47.5
idv_policy eval idv catch total num of agent1: 13
idv_policy eval team catch total num: 19
idv_policy eval average step individual rewards of agent2: 0.2560957374489934
idv_policy eval average team episode rewards of agent2: 47.5
idv_policy eval idv catch total num of agent2: 12
idv_policy eval team catch total num: 19
idv_policy eval average step individual rewards of agent3: 0.4586842870155874
idv_policy eval average team episode rewards of agent3: 47.5
idv_policy eval idv catch total num of agent3: 20
idv_policy eval team catch total num: 19
idv_policy eval average step individual rewards of agent4: 0.3430958206832452
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 3726/10000 episodes, total num timesteps 745400/2000000, FPS 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.

team_policy eval average step individual rewards of agent0: 0.7652309315947516
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: 0.794203813973797
team_policy eval average team episode rewards of agent1: 95.0
team_policy eval idv catch total num of agent1: 33
team_policy eval team catch total num: 38
team_policy eval average step individual rewards of agent2: 0.8422333952077871
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: 0.4934240620584434
team_policy eval average team episode rewards of agent3: 95.0
team_policy eval idv catch total num of agent3: 21
team_policy eval team catch total num: 38
team_policy eval average step individual rewards of agent4: 0.8405814850755178
team_policy eval average team episode rewards of agent4: 95.0
team_policy eval idv catch total num of agent4: 35
team_policy eval team catch total num: 38
idv_policy eval average step individual rewards of agent0: 1.1147655860857029
idv_policy eval average team episode rewards of agent0: 120.0
idv_policy eval idv catch total num of agent0: 46
idv_policy eval team catch total num: 48
idv_policy eval average step individual rewards of agent1: 0.8036990928239669
idv_policy eval average team episode rewards of agent1: 120.0
idv_policy eval idv catch total num of agent1: 34
idv_policy eval team catch total num: 48
idv_policy eval average step individual rewards of agent2: 0.6833492204310729
idv_policy eval average team episode rewards of agent2: 120.0
idv_policy eval idv catch total num of agent2: 29
idv_policy eval team catch total num: 48
idv_policy eval average step individual rewards of agent3: 0.7633835548376209
idv_policy eval average team episode rewards of agent3: 120.0
idv_policy eval idv catch total num of agent3: 32
idv_policy eval team catch total num: 48
idv_policy eval average step individual rewards of agent4: 0.8105984226346419
idv_policy eval average team episode rewards of agent4: 120.0
idv_policy eval idv catch total num of agent4: 34
idv_policy eval team catch total num: 48

 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 251.


 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 250.

team_policy eval average step individual rewards of agent0: 0.286463627104563
team_policy eval average team episode rewards of agent0: 75.0
team_policy eval idv catch total num of agent0: 13
team_policy eval team catch total num: 30
team_policy eval average step individual rewards of agent1: 0.3860559629442474
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: 0.6689715047518459
team_policy eval average team episode rewards of agent2: 75.0
team_policy eval idv catch total num of agent2: 28
team_policy eval team catch total num: 30
team_policy eval average step individual rewards of agent3: 0.6971303454047802
team_policy eval average team episode rewards of agent3: 75.0
team_policy eval idv catch total num of agent3: 29
team_policy eval team catch total num: 30
team_policy eval average step individual rewards of agent4: 0.6413888424069443
team_policy eval average team episode rewards of agent4: 75.0
team_policy eval idv catch total num of agent4: 27
team_policy eval team catch total num: 30
idv_policy eval average step individual rewards of agent0: 0.4854920604495381
idv_policy eval average team episode rewards of agent0: 55.0
idv_policy eval idv catch total num of agent0: 21
idv_policy eval team catch total num: 22
idv_policy eval average step individual rewards of agent1: 0.4241504265312422
idv_policy eval average team episode rewards of agent1: 55.0
idv_policy eval idv catch total num of agent1: 19
idv_policy eval team catch total num: 22
idv_policy eval average step individual rewards of agent2: 0.5109429840010733
idv_policy eval average team episode rewards of agent2: 55.0
idv_policy eval idv catch total num of agent2: 22
idv_policy eval team catch total num: 22
idv_policy eval average step individual rewards of agent3: 0.28631032954214786
idv_policy eval average team episode rewards of agent3: 55.0
idv_policy eval idv catch total num of agent3: 13
idv_policy eval team catch total num: 22
idv_policy eval average step individual rewards of agent4: 0.4804043225912436
idv_policy eval average team episode rewards of agent4: 55.0
idv_policy eval idv catch total num of agent4: 21
idv_policy eval team catch total num: 22

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3776/10000 episodes, total num timesteps 755400/2000000, FPS 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.

team_policy eval average step individual rewards of agent0: 0.6270121562542033
team_policy eval average team episode rewards of agent0: 112.5
team_policy eval idv catch total num of agent0: 27
team_policy eval team catch total num: 45
team_policy eval average step individual rewards of agent1: 0.725026484635504
team_policy eval average team episode rewards of agent1: 112.5
team_policy eval idv catch total num of agent1: 31
team_policy eval team catch total num: 45
team_policy eval average step individual rewards of agent2: 0.7750928314606458
team_policy eval average team episode rewards of agent2: 112.5
team_policy eval idv catch total num of agent2: 33
team_policy eval team catch total num: 45
team_policy eval average step individual rewards of agent3: 0.7347453613056775
team_policy eval average team episode rewards of agent3: 112.5
team_policy eval idv catch total num of agent3: 31
team_policy eval team catch total num: 45
team_policy eval average step individual rewards of agent4: 1.1185069524472784
team_policy eval average team episode rewards of agent4: 112.5
team_policy eval idv catch total num of agent4: 46
team_policy eval team catch total num: 45
idv_policy eval average step individual rewards of agent0: 0.6668253220951824
idv_policy eval average team episode rewards of agent0: 92.5
idv_policy eval idv catch total num of agent0: 28
idv_policy eval team catch total num: 37
idv_policy eval average step individual rewards of agent1: 0.5833689289361716
idv_policy eval average team episode rewards of agent1: 92.5
idv_policy eval idv catch total num of agent1: 25
idv_policy eval team catch total num: 37
idv_policy eval average step individual rewards of agent2: 0.6167319102084688
idv_policy eval average team episode rewards of agent2: 92.5
idv_policy eval idv catch total num of agent2: 26
idv_policy eval team catch total num: 37
idv_policy eval average step individual rewards of agent3: 0.8871842884215815
idv_policy eval average team episode rewards of agent3: 92.5
idv_policy eval idv catch total num of agent3: 37
idv_policy eval team catch total num: 37
idv_policy eval average step individual rewards of agent4: 0.6161443077838009
idv_policy eval average team episode rewards of agent4: 92.5
idv_policy eval idv catch total num of agent4: 26
idv_policy eval team catch total num: 37

 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.

team_policy eval average step individual rewards of agent0: 0.7647620893377101
team_policy eval average team episode rewards of agent0: 100.0
team_policy eval idv catch total num of agent0: 32
team_policy eval team catch total num: 40
team_policy eval average step individual rewards of agent1: 0.8371487707543953
team_policy eval average team episode rewards of agent1: 100.0
team_policy eval idv catch total num of agent1: 35
team_policy eval team catch total num: 40
team_policy eval average step individual rewards of agent2: 0.694527509380232
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: 0.4848079566704234
team_policy eval average team episode rewards of agent3: 100.0
team_policy eval idv catch total num of agent3: 21
team_policy eval team catch total num: 40
team_policy eval average step individual rewards of agent4: 1.0932685019966784
team_policy eval average team episode rewards of agent4: 100.0
team_policy eval idv catch total num of agent4: 45
team_policy eval team catch total num: 40
idv_policy eval average step individual rewards of agent0: 0.40359032896816915
idv_policy eval average team episode rewards of agent0: 77.5
idv_policy eval idv catch total num of agent0: 18
idv_policy eval team catch total num: 31
idv_policy eval average step individual rewards of agent1: 0.4791578122073723
idv_policy eval average team episode rewards of agent1: 77.5
idv_policy eval idv catch total num of agent1: 21
idv_policy eval team catch total num: 31
idv_policy eval average step individual rewards of agent2: 0.5903158707559949
idv_policy eval average team episode rewards of agent2: 77.5
idv_policy eval idv catch total num of agent2: 25
idv_policy eval team catch total num: 31
idv_policy eval average step individual rewards of agent3: 0.9661594292934336
idv_policy eval average team episode rewards of agent3: 77.5
idv_policy eval idv catch total num of agent3: 40
idv_policy eval team catch total num: 31
idv_policy eval average step individual rewards of agent4: 0.5341893538457355
idv_policy eval average team episode rewards of agent4: 77.5
idv_policy eval idv catch total num of agent4: 23
idv_policy eval team catch total num: 31

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3826/10000 episodes, total num timesteps 765400/2000000, FPS 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.

team_policy eval average step individual rewards of agent0: 0.8306315055692977
team_policy eval average team episode rewards of agent0: 110.0
team_policy eval idv catch total num of agent0: 35
team_policy eval team catch total num: 44
team_policy eval average step individual rewards of agent1: 0.4959659285907756
team_policy eval average team episode rewards of agent1: 110.0
team_policy eval idv catch total num of agent1: 22
team_policy eval team catch total num: 44
team_policy eval average step individual rewards of agent2: 0.6878694235829554
team_policy eval average team episode rewards of agent2: 110.0
team_policy eval idv catch total num of agent2: 29
team_policy eval team catch total num: 44
team_policy eval average step individual rewards of agent3: 1.0357058440629245
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: 0.7642075134181096
team_policy eval average team episode rewards of agent4: 110.0
team_policy eval idv catch total num of agent4: 32
team_policy eval team catch total num: 44
idv_policy eval average step individual rewards of agent0: 0.527733482579948
idv_policy eval average team episode rewards of agent0: 60.0
idv_policy eval idv catch total num of agent0: 23
idv_policy eval team catch total num: 24
idv_policy eval average step individual rewards of agent1: 0.3350108212533211
idv_policy eval average team episode rewards of agent1: 60.0
idv_policy eval idv catch total num of agent1: 15
idv_policy eval team catch total num: 24
idv_policy eval average step individual rewards of agent2: 0.6379327245156915
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: 0.661166003636848
idv_policy eval average team episode rewards of agent3: 60.0
idv_policy eval idv catch total num of agent3: 28
idv_policy eval team catch total num: 24
idv_policy eval average step individual rewards of agent4: 0.07520397901535371
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 3851/10000 episodes, total num timesteps 770400/2000000, FPS 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.

team_policy eval average step individual rewards of agent0: 0.5959806300002627
team_policy eval average team episode rewards of agent0: 85.0
team_policy eval idv catch total num of agent0: 26
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent1: 0.6155522726114374
team_policy eval average team episode rewards of agent1: 85.0
team_policy eval idv catch total num of agent1: 27
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent2: 0.5936412172669783
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: 0.6670528908375236
team_policy eval average team episode rewards of agent3: 85.0
team_policy eval idv catch total num of agent3: 28
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent4: 0.32570340146860494
team_policy eval average team episode rewards of agent4: 85.0
team_policy eval idv catch total num of agent4: 15
team_policy eval team catch total num: 34
idv_policy eval average step individual rewards of agent0: 0.5047073703071068
idv_policy eval average team episode rewards of agent0: 107.5
idv_policy eval idv catch total num of agent0: 22
idv_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent1: 0.6609615554559573
idv_policy eval average team episode rewards of agent1: 107.5
idv_policy eval idv catch total num of agent1: 28
idv_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent2: 1.0547484742287458
idv_policy eval average team episode rewards of agent2: 107.5
idv_policy eval idv catch total num of agent2: 44
idv_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent3: 0.5514689127821939
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: 0.5963195960769145
idv_policy eval average team episode rewards of agent4: 107.5
idv_policy eval idv catch total num of agent4: 26
idv_policy eval team catch total num: 43

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3876/10000 episodes, total num timesteps 775400/2000000, FPS 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.

team_policy eval average step individual rewards of agent0: 0.9937288610593439
team_policy eval average team episode rewards of agent0: 82.5
team_policy eval idv catch total num of agent0: 41
team_policy eval team catch total num: 33
team_policy eval average step individual rewards of agent1: 0.635920873885921
team_policy eval average team episode rewards of agent1: 82.5
team_policy eval idv catch total num of agent1: 27
team_policy eval team catch total num: 33
team_policy eval average step individual rewards of agent2: 0.4571538650573691
team_policy eval average team episode rewards of agent2: 82.5
team_policy eval idv catch total num of agent2: 20
team_policy eval team catch total num: 33
team_policy eval average step individual rewards of agent3: 0.6428961158620717
team_policy eval average team episode rewards of agent3: 82.5
team_policy eval idv catch total num of agent3: 27
team_policy eval team catch total num: 33
team_policy eval average step individual rewards of agent4: 0.6385304017379075
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: 0.4795339641151655
idv_policy eval average team episode rewards of agent0: 65.0
idv_policy eval idv catch total num of agent0: 21
idv_policy eval team catch total num: 26
idv_policy eval average step individual rewards of agent1: 0.5472894903268775
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: 0.2044089301122581
idv_policy eval average team episode rewards of agent2: 65.0
idv_policy eval idv catch total num of agent2: 10
idv_policy eval team catch total num: 26
idv_policy eval average step individual rewards of agent3: 0.1731825749025965
idv_policy eval average team episode rewards of agent3: 65.0
idv_policy eval idv catch total num of agent3: 9
idv_policy eval team catch total num: 26
idv_policy eval average step individual rewards of agent4: 0.8880222496521324
idv_policy eval average team episode rewards of agent4: 65.0
idv_policy eval idv catch total num of agent4: 37
idv_policy eval team catch total num: 26

 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.

team_policy eval average step individual rewards of agent0: 0.906355845317008
team_policy eval average team episode rewards of agent0: 97.5
team_policy eval idv catch total num of agent0: 38
team_policy eval team catch total num: 39
team_policy eval average step individual rewards of agent1: 0.8745905748450988
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: 0.5884964239152434
team_policy eval average team episode rewards of agent2: 97.5
team_policy eval idv catch total num of agent2: 25
team_policy eval team catch total num: 39
team_policy eval average step individual rewards of agent3: 0.5060467050174601
team_policy eval average team episode rewards of agent3: 97.5
team_policy eval idv catch total num of agent3: 22
team_policy eval team catch total num: 39
team_policy eval average step individual rewards of agent4: 0.6834653100059771
team_policy eval average team episode rewards of agent4: 97.5
team_policy eval idv catch total num of agent4: 29
team_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent0: 1.1421733287378983
idv_policy eval average team episode rewards of agent0: 132.5
idv_policy eval idv catch total num of agent0: 47
idv_policy eval team catch total num: 53
idv_policy eval average step individual rewards of agent1: 0.9657323748030493
idv_policy eval average team episode rewards of agent1: 132.5
idv_policy eval idv catch total num of agent1: 40
idv_policy eval team catch total num: 53
idv_policy eval average step individual rewards of agent2: 0.733146661574969
idv_policy eval average team episode rewards of agent2: 132.5
idv_policy eval idv catch total num of agent2: 31
idv_policy eval team catch total num: 53
idv_policy eval average step individual rewards of agent3: 1.1623194076333616
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: 0.7845892663765243
idv_policy eval average team episode rewards of agent4: 132.5
idv_policy eval idv catch total num of agent4: 33
idv_policy eval team catch total num: 53

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3926/10000 episodes, total num timesteps 785400/2000000, FPS 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 249.


 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 250.


 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 250.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.

team_policy eval average step individual rewards of agent0: 0.7124467896659046
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: 0.6317711905339738
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: 0.42856769147964063
team_policy eval average team episode rewards of agent2: 102.5
team_policy eval idv catch total num of agent2: 19
team_policy eval team catch total num: 41
team_policy eval average step individual rewards of agent3: 0.5829722339644847
team_policy eval average team episode rewards of agent3: 102.5
team_policy eval idv catch total num of agent3: 25
team_policy eval team catch total num: 41
team_policy eval average step individual rewards of agent4: 0.827379126929998
team_policy eval average team episode rewards of agent4: 102.5
team_policy eval idv catch total num of agent4: 35
team_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent0: 0.9888994345969434
idv_policy eval average team episode rewards of agent0: 82.5
idv_policy eval idv catch total num of agent0: 41
idv_policy eval team catch total num: 33
idv_policy eval average step individual rewards of agent1: 0.8214596463131297
idv_policy eval average team episode rewards of agent1: 82.5
idv_policy eval idv catch total num of agent1: 34
idv_policy eval team catch total num: 33
idv_policy eval average step individual rewards of agent2: 0.27798052286127567
idv_policy eval average team episode rewards of agent2: 82.5
idv_policy eval idv catch total num of agent2: 13
idv_policy eval team catch total num: 33
idv_policy eval average step individual rewards of agent3: 0.6302688447485827
idv_policy eval average team episode rewards of agent3: 82.5
idv_policy eval idv catch total num of agent3: 27
idv_policy eval team catch total num: 33
idv_policy eval average step individual rewards of agent4: 0.7370549236450262
idv_policy eval average team episode rewards of agent4: 82.5
idv_policy eval idv catch total num of agent4: 31
idv_policy eval team catch total num: 33

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 3951/10000 episodes, total num timesteps 790400/2000000, FPS 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.

team_policy eval average step individual rewards of agent0: 0.8408540916258146
team_policy eval average team episode rewards of agent0: 117.5
team_policy eval idv catch total num of agent0: 35
team_policy eval team catch total num: 47
team_policy eval average step individual rewards of agent1: 0.5749959871689502
team_policy eval average team episode rewards of agent1: 117.5
team_policy eval idv catch total num of agent1: 25
team_policy eval team catch total num: 47
team_policy eval average step individual rewards of agent2: 0.9077399815233876
team_policy eval average team episode rewards of agent2: 117.5
team_policy eval idv catch total num of agent2: 38
team_policy eval team catch total num: 47
team_policy eval average step individual rewards of agent3: 0.9619840593291049
team_policy eval average team episode rewards of agent3: 117.5
team_policy eval idv catch total num of agent3: 40
team_policy eval team catch total num: 47
team_policy eval average step individual rewards of agent4: 0.5827517183623504
team_policy eval average team episode rewards of agent4: 117.5
team_policy eval idv catch total num of agent4: 25
team_policy eval team catch total num: 47
idv_policy eval average step individual rewards of agent0: 0.9831441441997286
idv_policy eval average team episode rewards of agent0: 100.0
idv_policy eval idv catch total num of agent0: 41
idv_policy eval team catch total num: 40
idv_policy eval average step individual rewards of agent1: 0.48853599550711285
idv_policy eval average team episode rewards of agent1: 100.0
idv_policy eval idv catch total num of agent1: 21
idv_policy eval team catch total num: 40
idv_policy eval average step individual rewards of agent2: 1.0707032160572174
idv_policy eval average team episode rewards of agent2: 100.0
idv_policy eval idv catch total num of agent2: 44
idv_policy eval team catch total num: 40
idv_policy eval average step individual rewards of agent3: 0.6810719476722735
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: 0.5549718991412477
idv_policy eval average team episode rewards of agent4: 100.0
idv_policy eval idv catch total num of agent4: 24
idv_policy eval team catch total num: 40

 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.

team_policy eval average step individual rewards of agent0: 0.6783326413654766
team_policy eval average team episode rewards of agent0: 112.5
team_policy eval idv catch total num of agent0: 29
team_policy eval team catch total num: 45
team_policy eval average step individual rewards of agent1: 0.9618432657045495
team_policy eval average team episode rewards of agent1: 112.5
team_policy eval idv catch total num of agent1: 40
team_policy eval team catch total num: 45
team_policy eval average step individual rewards of agent2: 0.9175592863397855
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: 0.5378227774607492
team_policy eval average team episode rewards of agent3: 112.5
team_policy eval idv catch total num of agent3: 23
team_policy eval team catch total num: 45
team_policy eval average step individual rewards of agent4: 1.093761254361502
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: 0.6801623354337168
idv_policy eval average team episode rewards of agent0: 75.0
idv_policy eval idv catch total num of agent0: 29
idv_policy eval team catch total num: 30
idv_policy eval average step individual rewards of agent1: 0.47293962610794354
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: 0.7408749013964538
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: 0.3575344815100381
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: 0.5323345346364959
idv_policy eval average team episode rewards of agent4: 75.0
idv_policy eval idv catch total num of agent4: 23
idv_policy eval team catch total num: 30

 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.

team_policy eval average step individual rewards of agent0: 0.737436459523006
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: 0.5451458366824535
team_policy eval average team episode rewards of agent1: 95.0
team_policy eval idv catch total num of agent1: 24
team_policy eval team catch total num: 38
team_policy eval average step individual rewards of agent2: 0.4102065336179032
team_policy eval average team episode rewards of agent2: 95.0
team_policy eval idv catch total num of agent2: 18
team_policy eval team catch total num: 38
team_policy eval average step individual rewards of agent3: 1.1635727911762273
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: 0.5797192453784373
team_policy eval average team episode rewards of agent4: 95.0
team_policy eval idv catch total num of agent4: 25
team_policy eval team catch total num: 38
idv_policy eval average step individual rewards of agent0: 0.948900666742948
idv_policy eval average team episode rewards of agent0: 87.5
idv_policy eval idv catch total num of agent0: 39
idv_policy eval team catch total num: 35
idv_policy eval average step individual rewards of agent1: 0.6121623142620201
idv_policy eval average team episode rewards of agent1: 87.5
idv_policy eval idv catch total num of agent1: 26
idv_policy eval team catch total num: 35
idv_policy eval average step individual rewards of agent2: 0.5927886474820678
idv_policy eval average team episode rewards of agent2: 87.5
idv_policy eval idv catch total num of agent2: 25
idv_policy eval team catch total num: 35
idv_policy eval average step individual rewards of agent3: 0.9427351068498073
idv_policy eval average team episode rewards of agent3: 87.5
idv_policy eval idv catch total num of agent3: 39
idv_policy eval team catch total num: 35
idv_policy eval average step individual rewards of agent4: 0.5033989761781654
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 4026/10000 episodes, total num timesteps 805400/2000000, FPS 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.

team_policy eval average step individual rewards of agent0: 0.666970240783334
team_policy eval average team episode rewards of agent0: 112.5
team_policy eval idv catch total num of agent0: 28
team_policy eval team catch total num: 45
team_policy eval average step individual rewards of agent1: 0.5362179516559152
team_policy eval average team episode rewards of agent1: 112.5
team_policy eval idv catch total num of agent1: 23
team_policy eval team catch total num: 45
team_policy eval average step individual rewards of agent2: 0.741582925878412
team_policy eval average team episode rewards of agent2: 112.5
team_policy eval idv catch total num of agent2: 31
team_policy eval team catch total num: 45
team_policy eval average step individual rewards of agent3: 1.2714058140093019
team_policy eval average team episode rewards of agent3: 112.5
team_policy eval idv catch total num of agent3: 52
team_policy eval team catch total num: 45
team_policy eval average step individual rewards of agent4: 0.8676769921617961
team_policy eval average team episode rewards of agent4: 112.5
team_policy eval idv catch total num of agent4: 36
team_policy eval team catch total num: 45
idv_policy eval average step individual rewards of agent0: 0.4160772716322766
idv_policy eval average team episode rewards of agent0: 107.5
idv_policy eval idv catch total num of agent0: 18
idv_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent1: 0.7160666070107127
idv_policy eval average team episode rewards of agent1: 107.5
idv_policy eval idv catch total num of agent1: 30
idv_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent2: 0.9989207343971995
idv_policy eval average team episode rewards of agent2: 107.5
idv_policy eval idv catch total num of agent2: 41
idv_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent3: 1.0204084167028649
idv_policy eval average team episode rewards of agent3: 107.5
idv_policy eval idv catch total num of agent3: 42
idv_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent4: 0.9403617593249058
idv_policy eval average team episode rewards of agent4: 107.5
idv_policy eval idv catch total num of agent4: 39
idv_policy eval team catch total num: 43

 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.

team_policy eval average step individual rewards of agent0: 0.6564522867719252
team_policy eval average team episode rewards of agent0: 57.5
team_policy eval idv catch total num of agent0: 28
team_policy eval team catch total num: 23
team_policy eval average step individual rewards of agent1: 0.22124354358187467
team_policy eval average team episode rewards of agent1: 57.5
team_policy eval idv catch total num of agent1: 11
team_policy eval team catch total num: 23
team_policy eval average step individual rewards of agent2: 0.2760524587562288
team_policy eval average team episode rewards of agent2: 57.5
team_policy eval idv catch total num of agent2: 13
team_policy eval team catch total num: 23
team_policy eval average step individual rewards of agent3: 0.3579434356604569
team_policy eval average team episode rewards of agent3: 57.5
team_policy eval idv catch total num of agent3: 17
team_policy eval team catch total num: 23
team_policy eval average step individual rewards of agent4: 0.5512591722210295
team_policy eval average team episode rewards of agent4: 57.5
team_policy eval idv catch total num of agent4: 24
team_policy eval team catch total num: 23
idv_policy eval average step individual rewards of agent0: 1.1232026058984994
idv_policy eval average team episode rewards of agent0: 107.5
idv_policy eval idv catch total num of agent0: 46
idv_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent1: 0.8216892972488458
idv_policy eval average team episode rewards of agent1: 107.5
idv_policy eval idv catch total num of agent1: 34
idv_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent2: 0.5851418994813743
idv_policy eval average team episode rewards of agent2: 107.5
idv_policy eval idv catch total num of agent2: 25
idv_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent3: 0.7212738507255831
idv_policy eval average team episode rewards of agent3: 107.5
idv_policy eval idv catch total num of agent3: 30
idv_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent4: 0.564277860566026
idv_policy eval average team episode rewards of agent4: 107.5
idv_policy eval idv catch total num of agent4: 24
idv_policy eval team catch total num: 43

 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.

team_policy eval average step individual rewards of agent0: 0.4661618497828551
team_policy eval average team episode rewards of agent0: 105.0
team_policy eval idv catch total num of agent0: 20
team_policy eval team catch total num: 42
team_policy eval average step individual rewards of agent1: 0.9148176328019981
team_policy eval average team episode rewards of agent1: 105.0
team_policy eval idv catch total num of agent1: 38
team_policy eval team catch total num: 42
team_policy eval average step individual rewards of agent2: 1.0456738609312304
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: 0.5341836772779982
team_policy eval average team episode rewards of agent3: 105.0
team_policy eval idv catch total num of agent3: 23
team_policy eval team catch total num: 42
team_policy eval average step individual rewards of agent4: 0.8410843155550564
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: 0.8475201851796581
idv_policy eval average team episode rewards of agent0: 130.0
idv_policy eval idv catch total num of agent0: 35
idv_policy eval team catch total num: 52
idv_policy eval average step individual rewards of agent1: 0.8610709594399075
idv_policy eval average team episode rewards of agent1: 130.0
idv_policy eval idv catch total num of agent1: 36
idv_policy eval team catch total num: 52
idv_policy eval average step individual rewards of agent2: 0.9675238746671317
idv_policy eval average team episode rewards of agent2: 130.0
idv_policy eval idv catch total num of agent2: 40
idv_policy eval team catch total num: 52
idv_policy eval average step individual rewards of agent3: 0.8144757515856175
idv_policy eval average team episode rewards of agent3: 130.0
idv_policy eval idv catch total num of agent3: 34
idv_policy eval team catch total num: 52
idv_policy eval average step individual rewards of agent4: 0.9147100878344909
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 4101/10000 episodes, total num timesteps 820400/2000000, FPS 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 248.


 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 249.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.

team_policy eval average step individual rewards of agent0: 0.7933323809375797
team_policy eval average team episode rewards of agent0: 77.5
team_policy eval idv catch total num of agent0: 33
team_policy eval team catch total num: 31
team_policy eval average step individual rewards of agent1: 0.40986209122461253
team_policy eval average team episode rewards of agent1: 77.5
team_policy eval idv catch total num of agent1: 18
team_policy eval team catch total num: 31
team_policy eval average step individual rewards of agent2: 0.6562673378921501
team_policy eval average team episode rewards of agent2: 77.5
team_policy eval idv catch total num of agent2: 28
team_policy eval team catch total num: 31
team_policy eval average step individual rewards of agent3: 0.7613766877340146
team_policy eval average team episode rewards of agent3: 77.5
team_policy eval idv catch total num of agent3: 32
team_policy eval team catch total num: 31
team_policy eval average step individual rewards of agent4: 0.4313415133552917
team_policy eval average team episode rewards of agent4: 77.5
team_policy eval idv catch total num of agent4: 19
team_policy eval team catch total num: 31
idv_policy eval average step individual rewards of agent0: 0.41546423763640705
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: 0.8673680958806732
idv_policy eval average team episode rewards of agent1: 95.0
idv_policy eval idv catch total num of agent1: 36
idv_policy eval team catch total num: 38
idv_policy eval average step individual rewards of agent2: 0.6627267243942786
idv_policy eval average team episode rewards of agent2: 95.0
idv_policy eval idv catch total num of agent2: 28
idv_policy eval team catch total num: 38
idv_policy eval average step individual rewards of agent3: 0.695713868586207
idv_policy eval average team episode rewards of agent3: 95.0
idv_policy eval idv catch total num of agent3: 29
idv_policy eval team catch total num: 38
idv_policy eval average step individual rewards of agent4: 0.9731145967073485
idv_policy eval average team episode rewards of agent4: 95.0
idv_policy eval idv catch total num of agent4: 40
idv_policy eval team catch total num: 38

 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.

team_policy eval average step individual rewards of agent0: 1.1941921282943297
team_policy eval average team episode rewards of agent0: 90.0
team_policy eval idv catch total num of agent0: 49
team_policy eval team catch total num: 36
team_policy eval average step individual rewards of agent1: 0.46042413576362207
team_policy eval average team episode rewards of agent1: 90.0
team_policy eval idv catch total num of agent1: 20
team_policy eval team catch total num: 36
team_policy eval average step individual rewards of agent2: 0.5419935968801425
team_policy eval average team episode rewards of agent2: 90.0
team_policy eval idv catch total num of agent2: 23
team_policy eval team catch total num: 36
team_policy eval average step individual rewards of agent3: 0.6087880036774075
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: 0.6867288281732326
team_policy eval average team episode rewards of agent4: 90.0
team_policy eval idv catch total num of agent4: 29
team_policy eval team catch total num: 36
idv_policy eval average step individual rewards of agent0: 0.783623005253475
idv_policy eval average team episode rewards of agent0: 130.0
idv_policy eval idv catch total num of agent0: 33
idv_policy eval team catch total num: 52
idv_policy eval average step individual rewards of agent1: 0.6895586660682644
idv_policy eval average team episode rewards of agent1: 130.0
idv_policy eval idv catch total num of agent1: 29
idv_policy eval team catch total num: 52
idv_policy eval average step individual rewards of agent2: 0.9903860515177141
idv_policy eval average team episode rewards of agent2: 130.0
idv_policy eval idv catch total num of agent2: 41
idv_policy eval team catch total num: 52
idv_policy eval average step individual rewards of agent3: 0.7112934547213692
idv_policy eval average team episode rewards of agent3: 130.0
idv_policy eval idv catch total num of agent3: 30
idv_policy eval team catch total num: 52
idv_policy eval average step individual rewards of agent4: 1.1455021724289234
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 4151/10000 episodes, total num timesteps 830400/2000000, FPS 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.

team_policy eval average step individual rewards of agent0: 0.5349671864171804
team_policy eval average team episode rewards of agent0: 102.5
team_policy eval idv catch total num of agent0: 23
team_policy eval team catch total num: 41
team_policy eval average step individual rewards of agent1: 0.9808977977945398
team_policy eval average team episode rewards of agent1: 102.5
team_policy eval idv catch total num of agent1: 41
team_policy eval team catch total num: 41
team_policy eval average step individual rewards of agent2: 0.6358876116425576
team_policy eval average team episode rewards of agent2: 102.5
team_policy eval idv catch total num of agent2: 27
team_policy eval team catch total num: 41
team_policy eval average step individual rewards of agent3: 0.4551595222909126
team_policy eval average team episode rewards of agent3: 102.5
team_policy eval idv catch total num of agent3: 20
team_policy eval team catch total num: 41
team_policy eval average step individual rewards of agent4: 0.866818132936723
team_policy eval average team episode rewards of agent4: 102.5
team_policy eval idv catch total num of agent4: 36
team_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent0: 0.9410205373777768
idv_policy eval average team episode rewards of agent0: 112.5
idv_policy eval idv catch total num of agent0: 39
idv_policy eval team catch total num: 45
idv_policy eval average step individual rewards of agent1: 0.7377940603267451
idv_policy eval average team episode rewards of agent1: 112.5
idv_policy eval idv catch total num of agent1: 31
idv_policy eval team catch total num: 45
idv_policy eval average step individual rewards of agent2: 0.5924422799891867
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: 0.9078166774186252
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: 1.171529507826913
idv_policy eval average team episode rewards of agent4: 112.5
idv_policy eval idv catch total num of agent4: 48
idv_policy eval team catch total num: 45

 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.

team_policy eval average step individual rewards of agent0: 0.9122648677651355
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: 0.8573573988056049
team_policy eval average team episode rewards of agent1: 117.5
team_policy eval idv catch total num of agent1: 36
team_policy eval team catch total num: 47
team_policy eval average step individual rewards of agent2: 0.7070568103906412
team_policy eval average team episode rewards of agent2: 117.5
team_policy eval idv catch total num of agent2: 30
team_policy eval team catch total num: 47
team_policy eval average step individual rewards of agent3: 0.768697917216656
team_policy eval average team episode rewards of agent3: 117.5
team_policy eval idv catch total num of agent3: 32
team_policy eval team catch total num: 47
team_policy eval average step individual rewards of agent4: 0.9909935894552098
team_policy eval average team episode rewards of agent4: 117.5
team_policy eval idv catch total num of agent4: 41
team_policy eval team catch total num: 47
idv_policy eval average step individual rewards of agent0: 0.63253351523466
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: 0.8109113625954977
idv_policy eval average team episode rewards of agent1: 112.5
idv_policy eval idv catch total num of agent1: 34
idv_policy eval team catch total num: 45
idv_policy eval average step individual rewards of agent2: 0.6035770207096635
idv_policy eval average team episode rewards of agent2: 112.5
idv_policy eval idv catch total num of agent2: 26
idv_policy eval team catch total num: 45
idv_policy eval average step individual rewards of agent3: 1.1671449577268074
idv_policy eval average team episode rewards of agent3: 112.5
idv_policy eval idv catch total num of agent3: 48
idv_policy eval team catch total num: 45
idv_policy eval average step individual rewards of agent4: 0.7368153018388174
idv_policy eval average team episode rewards of agent4: 112.5
idv_policy eval idv catch total num of agent4: 31
idv_policy eval team catch total num: 45

 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.

team_policy eval average step individual rewards of agent0: 0.6028109039174616
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: 0.6833373316619297
team_policy eval average team episode rewards of agent1: 100.0
team_policy eval idv catch total num of agent1: 29
team_policy eval team catch total num: 40
team_policy eval average step individual rewards of agent2: 0.7361719253477196
team_policy eval average team episode rewards of agent2: 100.0
team_policy eval idv catch total num of agent2: 31
team_policy eval team catch total num: 40
team_policy eval average step individual rewards of agent3: 0.6121282323283579
team_policy eval average team episode rewards of agent3: 100.0
team_policy eval idv catch total num of agent3: 26
team_policy eval team catch total num: 40
team_policy eval average step individual rewards of agent4: 0.49904323877447476
team_policy eval average team episode rewards of agent4: 100.0
team_policy eval idv catch total num of agent4: 22
team_policy eval team catch total num: 40
idv_policy eval average step individual rewards of agent0: 0.5933051573173457
idv_policy eval average team episode rewards of agent0: 75.0
idv_policy eval idv catch total num of agent0: 25
idv_policy eval team catch total num: 30
idv_policy eval average step individual rewards of agent1: 0.4128111971983203
idv_policy eval average team episode rewards of agent1: 75.0
idv_policy eval idv catch total num of agent1: 18
idv_policy eval team catch total num: 30
idv_policy eval average step individual rewards of agent2: 0.6379837544546896
idv_policy eval average team episode rewards of agent2: 75.0
idv_policy eval idv catch total num of agent2: 27
idv_policy eval team catch total num: 30
idv_policy eval average step individual rewards of agent3: 0.7019661707606099
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: 0.5808866876094272
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 4226/10000 episodes, total num timesteps 845400/2000000, FPS 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.

team_policy eval average step individual rewards of agent0: 0.6338965993943257
team_policy eval average team episode rewards of agent0: 80.0
team_policy eval idv catch total num of agent0: 27
team_policy eval team catch total num: 32
team_policy eval average step individual rewards of agent1: 0.5898904527531661
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: 0.7082322807606175
team_policy eval average team episode rewards of agent2: 80.0
team_policy eval idv catch total num of agent2: 30
team_policy eval team catch total num: 32
team_policy eval average step individual rewards of agent3: 0.5300659586448604
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: 0.5132460460528616
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: 0.9434666521144703
idv_policy eval average team episode rewards of agent0: 112.5
idv_policy eval idv catch total num of agent0: 39
idv_policy eval team catch total num: 45
idv_policy eval average step individual rewards of agent1: 1.171284589982807
idv_policy eval average team episode rewards of agent1: 112.5
idv_policy eval idv catch total num of agent1: 48
idv_policy eval team catch total num: 45
idv_policy eval average step individual rewards of agent2: 0.8404065001016713
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: 0.7644310865020877
idv_policy eval average team episode rewards of agent3: 112.5
idv_policy eval idv catch total num of agent3: 32
idv_policy eval team catch total num: 45
idv_policy eval average step individual rewards of agent4: 0.4628320721720564
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 4251/10000 episodes, total num timesteps 850400/2000000, FPS 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.

team_policy eval average step individual rewards of agent0: 0.8444011253862275
team_policy eval average team episode rewards of agent0: 107.5
team_policy eval idv catch total num of agent0: 35
team_policy eval team catch total num: 43
team_policy eval average step individual rewards of agent1: 0.8337480501186966
team_policy eval average team episode rewards of agent1: 107.5
team_policy eval idv catch total num of agent1: 35
team_policy eval team catch total num: 43
team_policy eval average step individual rewards of agent2: 1.1206629366049226
team_policy eval average team episode rewards of agent2: 107.5
team_policy eval idv catch total num of agent2: 46
team_policy eval team catch total num: 43
team_policy eval average step individual rewards of agent3: 0.37983361941674
team_policy eval average team episode rewards of agent3: 107.5
team_policy eval idv catch total num of agent3: 17
team_policy eval team catch total num: 43
team_policy eval average step individual rewards of agent4: 0.6344718294830234
team_policy eval average team episode rewards of agent4: 107.5
team_policy eval idv catch total num of agent4: 27
team_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent0: 0.48732510760898073
idv_policy eval average team episode rewards of agent0: 82.5
idv_policy eval idv catch total num of agent0: 21
idv_policy eval team catch total num: 33
idv_policy eval average step individual rewards of agent1: 0.8448104029201093
idv_policy eval average team episode rewards of agent1: 82.5
idv_policy eval idv catch total num of agent1: 35
idv_policy eval team catch total num: 33
idv_policy eval average step individual rewards of agent2: 0.3110279877638227
idv_policy eval average team episode rewards of agent2: 82.5
idv_policy eval idv catch total num of agent2: 14
idv_policy eval team catch total num: 33
idv_policy eval average step individual rewards of agent3: 0.842504568296669
idv_policy eval average team episode rewards of agent3: 82.5
idv_policy eval idv catch total num of agent3: 35
idv_policy eval team catch total num: 33
idv_policy eval average step individual rewards of agent4: 0.6843929006604497
idv_policy eval average team episode rewards of agent4: 82.5
idv_policy eval idv catch total num of agent4: 29
idv_policy eval team catch total num: 33

 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 248.


 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 248.


 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 248.


 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 248.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.

team_policy eval average step individual rewards of agent0: 1.3476507381077567
team_policy eval average team episode rewards of agent0: 132.5
team_policy eval idv catch total num of agent0: 55
team_policy eval team catch total num: 53
team_policy eval average step individual rewards of agent1: 0.4534138295479972
team_policy eval average team episode rewards of agent1: 132.5
team_policy eval idv catch total num of agent1: 20
team_policy eval team catch total num: 53
team_policy eval average step individual rewards of agent2: 1.045733110856415
team_policy eval average team episode rewards of agent2: 132.5
team_policy eval idv catch total num of agent2: 43
team_policy eval team catch total num: 53
team_policy eval average step individual rewards of agent3: 0.9494840577192776
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: 0.7621420118018745
team_policy eval average team episode rewards of agent4: 132.5
team_policy eval idv catch total num of agent4: 32
team_policy eval team catch total num: 53
idv_policy eval average step individual rewards of agent0: 0.5263200970474362
idv_policy eval average team episode rewards of agent0: 45.0
idv_policy eval idv catch total num of agent0: 23
idv_policy eval team catch total num: 18
idv_policy eval average step individual rewards of agent1: 0.2227269918025747
idv_policy eval average team episode rewards of agent1: 45.0
idv_policy eval idv catch total num of agent1: 11
idv_policy eval team catch total num: 18
idv_policy eval average step individual rewards of agent2: 0.5385074697455021
idv_policy eval average team episode rewards of agent2: 45.0
idv_policy eval idv catch total num of agent2: 23
idv_policy eval team catch total num: 18
idv_policy eval average step individual rewards of agent3: 0.3340491777004754
idv_policy eval average team episode rewards of agent3: 45.0
idv_policy eval idv catch total num of agent3: 15
idv_policy eval team catch total num: 18
idv_policy eval average step individual rewards of agent4: 0.22621911904274536
idv_policy eval average team episode rewards of agent4: 45.0
idv_policy eval idv catch total num of agent4: 11
idv_policy eval team catch total num: 18

 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.

team_policy eval average step individual rewards of agent0: 0.5333104826206008
team_policy eval average team episode rewards of agent0: 70.0
team_policy eval idv catch total num of agent0: 23
team_policy eval team catch total num: 28
team_policy eval average step individual rewards of agent1: 0.3529375107916089
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: 0.5310477590038334
team_policy eval average team episode rewards of agent2: 70.0
team_policy eval idv catch total num of agent2: 23
team_policy eval team catch total num: 28
team_policy eval average step individual rewards of agent3: 0.5090920091132553
team_policy eval average team episode rewards of agent3: 70.0
team_policy eval idv catch total num of agent3: 22
team_policy eval team catch total num: 28
team_policy eval average step individual rewards of agent4: 0.6631862189606045
team_policy eval average team episode rewards of agent4: 70.0
team_policy eval idv catch total num of agent4: 28
team_policy eval team catch total num: 28
idv_policy eval average step individual rewards of agent0: 0.4102513417829671
idv_policy eval average team episode rewards of agent0: 80.0
idv_policy eval idv catch total num of agent0: 18
idv_policy eval team catch total num: 32
idv_policy eval average step individual rewards of agent1: 0.43235524742786424
idv_policy eval average team episode rewards of agent1: 80.0
idv_policy eval idv catch total num of agent1: 19
idv_policy eval team catch total num: 32
idv_policy eval average step individual rewards of agent2: 0.6141071563629583
idv_policy eval average team episode rewards of agent2: 80.0
idv_policy eval idv catch total num of agent2: 26
idv_policy eval team catch total num: 32
idv_policy eval average step individual rewards of agent3: 0.8432127463597593
idv_policy eval average team episode rewards of agent3: 80.0
idv_policy eval idv catch total num of agent3: 35
idv_policy eval team catch total num: 32
idv_policy eval average step individual rewards of agent4: 0.4856606708507815
idv_policy eval average team episode rewards of agent4: 80.0
idv_policy eval idv catch total num of agent4: 21
idv_policy eval team catch total num: 32

 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.

team_policy eval average step individual rewards of agent0: 0.7713426631963596
team_policy eval average team episode rewards of agent0: 55.0
team_policy eval idv catch total num of agent0: 32
team_policy eval team catch total num: 22
team_policy eval average step individual rewards of agent1: 0.4388368573674653
team_policy eval average team episode rewards of agent1: 55.0
team_policy eval idv catch total num of agent1: 19
team_policy eval team catch total num: 22
team_policy eval average step individual rewards of agent2: 0.22475686727032948
team_policy eval average team episode rewards of agent2: 55.0
team_policy eval idv catch total num of agent2: 11
team_policy eval team catch total num: 22
team_policy eval average step individual rewards of agent3: 0.514050141257681
team_policy eval average team episode rewards of agent3: 55.0
team_policy eval idv catch total num of agent3: 22
team_policy eval team catch total num: 22
team_policy eval average step individual rewards of agent4: 0.6605254190418383
team_policy eval average team episode rewards of agent4: 55.0
team_policy eval idv catch total num of agent4: 28
team_policy eval team catch total num: 22
idv_policy eval average step individual rewards of agent0: 0.4786181057312708
idv_policy eval average team episode rewards of agent0: 60.0
idv_policy eval idv catch total num of agent0: 21
idv_policy eval team catch total num: 24
idv_policy eval average step individual rewards of agent1: 0.4610761334278752
idv_policy eval average team episode rewards of agent1: 60.0
idv_policy eval idv catch total num of agent1: 20
idv_policy eval team catch total num: 24
idv_policy eval average step individual rewards of agent2: 0.5353111624544176
idv_policy eval average team episode rewards of agent2: 60.0
idv_policy eval idv catch total num of agent2: 23
idv_policy eval team catch total num: 24
idv_policy eval average step individual rewards of agent3: 0.24634892316177168
idv_policy eval average team episode rewards of agent3: 60.0
idv_policy eval idv catch total num of agent3: 12
idv_policy eval team catch total num: 24
idv_policy eval average step individual rewards of agent4: 0.7595868916871155
idv_policy eval average team episode rewards of agent4: 60.0
idv_policy eval idv catch total num of agent4: 32
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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.

team_policy eval average step individual rewards of agent0: 0.9380991322255465
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: 0.8476939850762409
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: 0.700390348619284
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: 0.8145386944208454
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: 0.5869457037468885
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: 0.35198802840149407
idv_policy eval average team episode rewards of agent0: 112.5
idv_policy eval idv catch total num of agent0: 16
idv_policy eval team catch total num: 45
idv_policy eval average step individual rewards of agent1: 0.666382867336599
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: 0.9893476816344079
idv_policy eval average team episode rewards of agent2: 112.5
idv_policy eval idv catch total num of agent2: 41
idv_policy eval team catch total num: 45
idv_policy eval average step individual rewards of agent3: 0.6423210006435863
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: 0.7636119900534334
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 4376/10000 episodes, total num timesteps 875400/2000000, FPS 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.

team_policy eval average step individual rewards of agent0: 0.4322523183047292
team_policy eval average team episode rewards of agent0: 67.5
team_policy eval idv catch total num of agent0: 19
team_policy eval team catch total num: 27
team_policy eval average step individual rewards of agent1: 0.7121220259995854
team_policy eval average team episode rewards of agent1: 67.5
team_policy eval idv catch total num of agent1: 30
team_policy eval team catch total num: 27
team_policy eval average step individual rewards of agent2: 0.688852462942823
team_policy eval average team episode rewards of agent2: 67.5
team_policy eval idv catch total num of agent2: 29
team_policy eval team catch total num: 27
team_policy eval average step individual rewards of agent3: 0.45284139598779033
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: 0.31172319104726953
team_policy eval average team episode rewards of agent4: 67.5
team_policy eval idv catch total num of agent4: 14
team_policy eval team catch total num: 27
idv_policy eval average step individual rewards of agent0: 0.5382868881338674
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: 0.33204926248858924
idv_policy eval average team episode rewards of agent1: 97.5
idv_policy eval idv catch total num of agent1: 15
idv_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent2: 0.564167358164505
idv_policy eval average team episode rewards of agent2: 97.5
idv_policy eval idv catch total num of agent2: 24
idv_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent3: 1.220706284334896
idv_policy eval average team episode rewards of agent3: 97.5
idv_policy eval idv catch total num of agent3: 50
idv_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent4: 0.6661335455764639
idv_policy eval average team episode rewards of agent4: 97.5
idv_policy eval idv catch total num of agent4: 28
idv_policy eval team catch total num: 39

 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.

team_policy eval average step individual rewards of agent0: 0.7700533651151386
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: 0.6897755134132157
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: 0.8441272143988721
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: 0.8392042024174329
team_policy eval average team episode rewards of agent3: 95.0
team_policy eval idv catch total num of agent3: 35
team_policy eval team catch total num: 38
team_policy eval average step individual rewards of agent4: 0.6973016461177325
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: 0.530497476060424
idv_policy eval average team episode rewards of agent0: 95.0
idv_policy eval idv catch total num of agent0: 23
idv_policy eval team catch total num: 38
idv_policy eval average step individual rewards of agent1: 0.7645291632916691
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: 0.5197193931222135
idv_policy eval average team episode rewards of agent2: 95.0
idv_policy eval idv catch total num of agent2: 22
idv_policy eval team catch total num: 38
idv_policy eval average step individual rewards of agent3: 1.149173769701716
idv_policy eval average team episode rewards of agent3: 95.0
idv_policy eval idv catch total num of agent3: 47
idv_policy eval team catch total num: 38
idv_policy eval average step individual rewards of agent4: 0.5546195366012513
idv_policy eval average team episode rewards of agent4: 95.0
idv_policy eval idv catch total num of agent4: 24
idv_policy eval team catch total num: 38

 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.

team_policy eval average step individual rewards of agent0: 0.560067554304967
team_policy eval average team episode rewards of agent0: 90.0
team_policy eval idv catch total num of agent0: 24
team_policy eval team catch total num: 36
team_policy eval average step individual rewards of agent1: 0.7064455075156088
team_policy eval average team episode rewards of agent1: 90.0
team_policy eval idv catch total num of agent1: 30
team_policy eval team catch total num: 36
team_policy eval average step individual rewards of agent2: 0.7709289259152975
team_policy eval average team episode rewards of agent2: 90.0
team_policy eval idv catch total num of agent2: 32
team_policy eval team catch total num: 36
team_policy eval average step individual rewards of agent3: 0.6173789136607308
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: 0.5062124935934751
team_policy eval average team episode rewards of agent4: 90.0
team_policy eval idv catch total num of agent4: 22
team_policy eval team catch total num: 36
idv_policy eval average step individual rewards of agent0: 0.7665563728875469
idv_policy eval average team episode rewards of agent0: 50.0
idv_policy eval idv catch total num of agent0: 32
idv_policy eval team catch total num: 20
idv_policy eval average step individual rewards of agent1: 0.5610426768947475
idv_policy eval average team episode rewards of agent1: 50.0
idv_policy eval idv catch total num of agent1: 24
idv_policy eval team catch total num: 20
idv_policy eval average step individual rewards of agent2: 0.2731056401880273
idv_policy eval average team episode rewards of agent2: 50.0
idv_policy eval idv catch total num of agent2: 13
idv_policy eval team catch total num: 20
idv_policy eval average step individual rewards of agent3: 0.39997618762447557
idv_policy eval average team episode rewards of agent3: 50.0
idv_policy eval idv catch total num of agent3: 18
idv_policy eval team catch total num: 20
idv_policy eval average step individual rewards of agent4: 0.48348882015600436
idv_policy eval average team episode rewards of agent4: 50.0
idv_policy eval idv catch total num of agent4: 21
idv_policy eval team catch total num: 20

 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.

team_policy eval average step individual rewards of agent0: 0.8118119075158158
team_policy eval average team episode rewards of agent0: 100.0
team_policy eval idv catch total num of agent0: 34
team_policy eval team catch total num: 40
team_policy eval average step individual rewards of agent1: 0.5642846074328834
team_policy eval average team episode rewards of agent1: 100.0
team_policy eval idv catch total num of agent1: 24
team_policy eval team catch total num: 40
team_policy eval average step individual rewards of agent2: 1.047020848504982
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: 0.9984388104748657
team_policy eval average team episode rewards of agent3: 100.0
team_policy eval idv catch total num of agent3: 41
team_policy eval team catch total num: 40
team_policy eval average step individual rewards of agent4: 0.40887043971152887
team_policy eval average team episode rewards of agent4: 100.0
team_policy eval idv catch total num of agent4: 18
team_policy eval team catch total num: 40
idv_policy eval average step individual rewards of agent0: 0.6075513477106195
idv_policy eval average team episode rewards of agent0: 117.5
idv_policy eval idv catch total num of agent0: 26
idv_policy eval team catch total num: 47
idv_policy eval average step individual rewards of agent1: 1.068322101387153
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: 0.9682943001876041
idv_policy eval average team episode rewards of agent2: 117.5
idv_policy eval idv catch total num of agent2: 40
idv_policy eval team catch total num: 47
idv_policy eval average step individual rewards of agent3: 0.7877044016290791
idv_policy eval average team episode rewards of agent3: 117.5
idv_policy eval idv catch total num of agent3: 33
idv_policy eval team catch total num: 47
idv_policy eval average step individual rewards of agent4: 0.5248441048936031
idv_policy eval average team episode rewards of agent4: 117.5
idv_policy eval idv catch total num of agent4: 23
idv_policy eval team catch total num: 47

 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.

team_policy eval average step individual rewards of agent0: 0.5559865542587553
team_policy eval average team episode rewards of agent0: 65.0
team_policy eval idv catch total num of agent0: 24
team_policy eval team catch total num: 26
team_policy eval average step individual rewards of agent1: 0.6096488478254222
team_policy eval average team episode rewards of agent1: 65.0
team_policy eval idv catch total num of agent1: 26
team_policy eval team catch total num: 26
team_policy eval average step individual rewards of agent2: 0.6411546169567179
team_policy eval average team episode rewards of agent2: 65.0
team_policy eval idv catch total num of agent2: 27
team_policy eval team catch total num: 26
team_policy eval average step individual rewards of agent3: 0.18258050643322285
team_policy eval average team episode rewards of agent3: 65.0
team_policy eval idv catch total num of agent3: 9
team_policy eval team catch total num: 26
team_policy eval average step individual rewards of agent4: 0.5146248716919151
team_policy eval average team episode rewards of agent4: 65.0
team_policy eval idv catch total num of agent4: 22
team_policy eval team catch total num: 26
idv_policy eval average step individual rewards of agent0: 0.8940691336351173
idv_policy eval average team episode rewards of agent0: 102.5
idv_policy eval idv catch total num of agent0: 37
idv_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent1: 0.7327547829897719
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: 0.6091581465722988
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: 0.7578652338967712
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: 0.5607278493941772
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 4501/10000 episodes, total num timesteps 900400/2000000, FPS 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.

team_policy eval average step individual rewards of agent0: 0.7657042527463929
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: 0.6112176653154697
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: 0.9343497596343576
team_policy eval average team episode rewards of agent2: 115.0
team_policy eval idv catch total num of agent2: 39
team_policy eval team catch total num: 46
team_policy eval average step individual rewards of agent3: 0.7441063887688472
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: 0.957584005604238
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: 0.42331760959683234
idv_policy eval average team episode rewards of agent0: 102.5
idv_policy eval idv catch total num of agent0: 19
idv_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent1: 0.741072272677141
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: 0.9444509155536305
idv_policy eval average team episode rewards of agent2: 102.5
idv_policy eval idv catch total num of agent2: 39
idv_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent3: 0.5393938399376027
idv_policy eval average team episode rewards of agent3: 102.5
idv_policy eval idv catch total num of agent3: 23
idv_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent4: 0.9200888255972169
idv_policy eval average team episode rewards of agent4: 102.5
idv_policy eval idv catch total num of agent4: 38
idv_policy eval team catch total num: 41

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4526/10000 episodes, total num timesteps 905400/2000000, FPS 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.

team_policy eval average step individual rewards of agent0: 0.7641571603782381
team_policy eval average team episode rewards of agent0: 107.5
team_policy eval idv catch total num of agent0: 32
team_policy eval team catch total num: 43
team_policy eval average step individual rewards of agent1: 0.5292773445962392
team_policy eval average team episode rewards of agent1: 107.5
team_policy eval idv catch total num of agent1: 23
team_policy eval team catch total num: 43
team_policy eval average step individual rewards of agent2: 0.557108011591026
team_policy eval average team episode rewards of agent2: 107.5
team_policy eval idv catch total num of agent2: 24
team_policy eval team catch total num: 43
team_policy eval average step individual rewards of agent3: 1.2376043155524323
team_policy eval average team episode rewards of agent3: 107.5
team_policy eval idv catch total num of agent3: 51
team_policy eval team catch total num: 43
team_policy eval average step individual rewards of agent4: 0.726362864813671
team_policy eval average team episode rewards of agent4: 107.5
team_policy eval idv catch total num of agent4: 31
team_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent0: 0.48140770686831225
idv_policy eval average team episode rewards of agent0: 85.0
idv_policy eval idv catch total num of agent0: 21
idv_policy eval team catch total num: 34
idv_policy eval average step individual rewards of agent1: 0.656269047029443
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: 0.8673593124625478
idv_policy eval average team episode rewards of agent2: 85.0
idv_policy eval idv catch total num of agent2: 36
idv_policy eval team catch total num: 34
idv_policy eval average step individual rewards of agent3: 0.49512433868094063
idv_policy eval average team episode rewards of agent3: 85.0
idv_policy eval idv catch total num of agent3: 22
idv_policy eval team catch total num: 34
idv_policy eval average step individual rewards of agent4: 0.6430142348574986
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 4551/10000 episodes, total num timesteps 910400/2000000, FPS 247.


 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 247.


 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 247.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 247.


 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 247.


 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 246.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 246.

team_policy eval average step individual rewards of agent0: 0.8387020624951943
team_policy eval average team episode rewards of agent0: 102.5
team_policy eval idv catch total num of agent0: 35
team_policy eval team catch total num: 41
team_policy eval average step individual rewards of agent1: 0.5635164768984153
team_policy eval average team episode rewards of agent1: 102.5
team_policy eval idv catch total num of agent1: 24
team_policy eval team catch total num: 41
team_policy eval average step individual rewards of agent2: 0.792632584227521
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: 0.7406586157211262
team_policy eval average team episode rewards of agent3: 102.5
team_policy eval idv catch total num of agent3: 31
team_policy eval team catch total num: 41
team_policy eval average step individual rewards of agent4: 0.40790361503552913
team_policy eval average team episode rewards of agent4: 102.5
team_policy eval idv catch total num of agent4: 18
team_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent0: 1.0872100850818576
idv_policy eval average team episode rewards of agent0: 127.5
idv_policy eval idv catch total num of agent0: 45
idv_policy eval team catch total num: 51
idv_policy eval average step individual rewards of agent1: 0.9933642985600685
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: 0.9456626762566713
idv_policy eval average team episode rewards of agent2: 127.5
idv_policy eval idv catch total num of agent2: 39
idv_policy eval team catch total num: 51
idv_policy eval average step individual rewards of agent3: 0.6895490559080164
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: 0.707534436037644
idv_policy eval average team episode rewards of agent4: 127.5
idv_policy eval idv catch total num of agent4: 30
idv_policy eval team catch total num: 51

 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 247.


 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 247.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.

team_policy eval average step individual rewards of agent0: 0.589734072316164
team_policy eval average team episode rewards of agent0: 115.0
team_policy eval idv catch total num of agent0: 25
team_policy eval team catch total num: 46
team_policy eval average step individual rewards of agent1: 0.8439616295555998
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: 1.0426691277379785
team_policy eval average team episode rewards of agent2: 115.0
team_policy eval idv catch total num of agent2: 43
team_policy eval team catch total num: 46
team_policy eval average step individual rewards of agent3: 0.9162302813156872
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: 0.6063751664273458
team_policy eval average team episode rewards of agent4: 115.0
team_policy eval idv catch total num of agent4: 26
team_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent0: 0.6586243967661481
idv_policy eval average team episode rewards of agent0: 67.5
idv_policy eval idv catch total num of agent0: 28
idv_policy eval team catch total num: 27
idv_policy eval average step individual rewards of agent1: 0.4598573760641102
idv_policy eval average team episode rewards of agent1: 67.5
idv_policy eval idv catch total num of agent1: 20
idv_policy eval team catch total num: 27
idv_policy eval average step individual rewards of agent2: 0.6839839311775961
idv_policy eval average team episode rewards of agent2: 67.5
idv_policy eval idv catch total num of agent2: 29
idv_policy eval team catch total num: 27
idv_policy eval average step individual rewards of agent3: 0.49916531324599683
idv_policy eval average team episode rewards of agent3: 67.5
idv_policy eval idv catch total num of agent3: 22
idv_policy eval team catch total num: 27
idv_policy eval average step individual rewards of agent4: 0.5125184082197297
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 4601/10000 episodes, total num timesteps 920400/2000000, FPS 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.

team_policy eval average step individual rewards of agent0: 0.7357997079184934
team_policy eval average team episode rewards of agent0: 152.5
team_policy eval idv catch total num of agent0: 31
team_policy eval team catch total num: 61
team_policy eval average step individual rewards of agent1: 1.1323806927975117
team_policy eval average team episode rewards of agent1: 152.5
team_policy eval idv catch total num of agent1: 47
team_policy eval team catch total num: 61
team_policy eval average step individual rewards of agent2: 0.7594698577267713
team_policy eval average team episode rewards of agent2: 152.5
team_policy eval idv catch total num of agent2: 32
team_policy eval team catch total num: 61
team_policy eval average step individual rewards of agent3: 1.222238316020056
team_policy eval average team episode rewards of agent3: 152.5
team_policy eval idv catch total num of agent3: 50
team_policy eval team catch total num: 61
team_policy eval average step individual rewards of agent4: 0.7870490881256355
team_policy eval average team episode rewards of agent4: 152.5
team_policy eval idv catch total num of agent4: 33
team_policy eval team catch total num: 61
idv_policy eval average step individual rewards of agent0: 0.7377640290250979
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: 0.7443370196471584
idv_policy eval average team episode rewards of agent1: 95.0
idv_policy eval idv catch total num of agent1: 31
idv_policy eval team catch total num: 38
idv_policy eval average step individual rewards of agent2: 0.9080664825685425
idv_policy eval average team episode rewards of agent2: 95.0
idv_policy eval idv catch total num of agent2: 38
idv_policy eval team catch total num: 38
idv_policy eval average step individual rewards of agent3: 0.36269068569029966
idv_policy eval average team episode rewards of agent3: 95.0
idv_policy eval idv catch total num of agent3: 16
idv_policy eval team catch total num: 38
idv_policy eval average step individual rewards of agent4: 0.8121283860306684
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 4626/10000 episodes, total num timesteps 925400/2000000, FPS 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.

team_policy eval average step individual rewards of agent0: 0.48283532620544617
team_policy eval average team episode rewards of agent0: 85.0
team_policy eval idv catch total num of agent0: 21
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent1: 0.7290629427246748
team_policy eval average team episode rewards of agent1: 85.0
team_policy eval idv catch total num of agent1: 31
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent2: 0.6335696321082254
team_policy eval average team episode rewards of agent2: 85.0
team_policy eval idv catch total num of agent2: 27
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent3: 0.6047100701986355
team_policy eval average team episode rewards of agent3: 85.0
team_policy eval idv catch total num of agent3: 26
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent4: 0.7069345375791155
team_policy eval average team episode rewards of agent4: 85.0
team_policy eval idv catch total num of agent4: 30
team_policy eval team catch total num: 34
idv_policy eval average step individual rewards of agent0: 0.6726166665996174
idv_policy eval average team episode rewards of agent0: 80.0
idv_policy eval idv catch total num of agent0: 28
idv_policy eval team catch total num: 32
idv_policy eval average step individual rewards of agent1: 0.47993208475563054
idv_policy eval average team episode rewards of agent1: 80.0
idv_policy eval idv catch total num of agent1: 21
idv_policy eval team catch total num: 32
idv_policy eval average step individual rewards of agent2: 0.3815593571019903
idv_policy eval average team episode rewards of agent2: 80.0
idv_policy eval idv catch total num of agent2: 17
idv_policy eval team catch total num: 32
idv_policy eval average step individual rewards of agent3: 0.9190529554111087
idv_policy eval average team episode rewards of agent3: 80.0
idv_policy eval idv catch total num of agent3: 38
idv_policy eval team catch total num: 32
idv_policy eval average step individual rewards of agent4: 0.6889009697224391
idv_policy eval average team episode rewards of agent4: 80.0
idv_policy eval idv catch total num of agent4: 29
idv_policy eval team catch total num: 32

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4651/10000 episodes, total num timesteps 930400/2000000, FPS 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.

team_policy eval average step individual rewards of agent0: 0.7352572459340155
team_policy eval average team episode rewards of agent0: 140.0
team_policy eval idv catch total num of agent0: 31
team_policy eval team catch total num: 56
team_policy eval average step individual rewards of agent1: 0.7811701963582582
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: 0.7939115801357864
team_policy eval average team episode rewards of agent2: 140.0
team_policy eval idv catch total num of agent2: 33
team_policy eval team catch total num: 56
team_policy eval average step individual rewards of agent3: 1.0406356402285113
team_policy eval average team episode rewards of agent3: 140.0
team_policy eval idv catch total num of agent3: 43
team_policy eval team catch total num: 56
team_policy eval average step individual rewards of agent4: 1.1513024032723884
team_policy eval average team episode rewards of agent4: 140.0
team_policy eval idv catch total num of agent4: 47
team_policy eval team catch total num: 56
idv_policy eval average step individual rewards of agent0: 0.682629535252381
idv_policy eval average team episode rewards of agent0: 82.5
idv_policy eval idv catch total num of agent0: 29
idv_policy eval team catch total num: 33
idv_policy eval average step individual rewards of agent1: 0.7606778175850804
idv_policy eval average team episode rewards of agent1: 82.5
idv_policy eval idv catch total num of agent1: 32
idv_policy eval team catch total num: 33
idv_policy eval average step individual rewards of agent2: 0.7564761825133826
idv_policy eval average team episode rewards of agent2: 82.5
idv_policy eval idv catch total num of agent2: 32
idv_policy eval team catch total num: 33
idv_policy eval average step individual rewards of agent3: 0.532336194916147
idv_policy eval average team episode rewards of agent3: 82.5
idv_policy eval idv catch total num of agent3: 23
idv_policy eval team catch total num: 33
idv_policy eval average step individual rewards of agent4: 0.44781940387857666
idv_policy eval average team episode rewards of agent4: 82.5
idv_policy eval idv catch total num of agent4: 20
idv_policy eval team catch total num: 33

 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.

team_policy eval average step individual rewards of agent0: 0.4556693638236766
team_policy eval average team episode rewards of agent0: 62.5
team_policy eval idv catch total num of agent0: 20
team_policy eval team catch total num: 25
team_policy eval average step individual rewards of agent1: 0.5842829419754216
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: 0.5002169821550234
team_policy eval average team episode rewards of agent2: 62.5
team_policy eval idv catch total num of agent2: 22
team_policy eval team catch total num: 25
team_policy eval average step individual rewards of agent3: 0.4827809174259609
team_policy eval average team episode rewards of agent3: 62.5
team_policy eval idv catch total num of agent3: 21
team_policy eval team catch total num: 25
team_policy eval average step individual rewards of agent4: 0.30246551337953403
team_policy eval average team episode rewards of agent4: 62.5
team_policy eval idv catch total num of agent4: 14
team_policy eval team catch total num: 25
idv_policy eval average step individual rewards of agent0: 1.043867779524101
idv_policy eval average team episode rewards of agent0: 135.0
idv_policy eval idv catch total num of agent0: 43
idv_policy eval team catch total num: 54
idv_policy eval average step individual rewards of agent1: 0.718836998285129
idv_policy eval average team episode rewards of agent1: 135.0
idv_policy eval idv catch total num of agent1: 30
idv_policy eval team catch total num: 54
idv_policy eval average step individual rewards of agent2: 1.1242624504102
idv_policy eval average team episode rewards of agent2: 135.0
idv_policy eval idv catch total num of agent2: 46
idv_policy eval team catch total num: 54
idv_policy eval average step individual rewards of agent3: 0.9984658139264053
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: 0.81653779937121
idv_policy eval average team episode rewards of agent4: 135.0
idv_policy eval idv catch total num of agent4: 34
idv_policy eval team catch total num: 54

 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.

team_policy eval average step individual rewards of agent0: 0.7942109072162725
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: 0.6577742244372893
team_policy eval average team episode rewards of agent1: 102.5
team_policy eval idv catch total num of agent1: 28
team_policy eval team catch total num: 41
team_policy eval average step individual rewards of agent2: 0.641183735501835
team_policy eval average team episode rewards of agent2: 102.5
team_policy eval idv catch total num of agent2: 28
team_policy eval team catch total num: 41
team_policy eval average step individual rewards of agent3: 0.6361063747328932
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: 0.7918446573017109
team_policy eval average team episode rewards of agent4: 102.5
team_policy eval idv catch total num of agent4: 33
team_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent0: 0.9408014076936982
idv_policy eval average team episode rewards of agent0: 115.0
idv_policy eval idv catch total num of agent0: 39
idv_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent1: 0.9419837489928707
idv_policy eval average team episode rewards of agent1: 115.0
idv_policy eval idv catch total num of agent1: 39
idv_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent2: 1.1448206691273857
idv_policy eval average team episode rewards of agent2: 115.0
idv_policy eval idv catch total num of agent2: 47
idv_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent3: 0.6490168555009822
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: 0.7574098459825065
idv_policy eval average team episode rewards of agent4: 115.0
idv_policy eval idv catch total num of agent4: 32
idv_policy eval team catch total num: 46

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4726/10000 episodes, total num timesteps 945400/2000000, FPS 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.

team_policy eval average step individual rewards of agent0: 0.6287290974142566
team_policy eval average team episode rewards of agent0: 95.0
team_policy eval idv catch total num of agent0: 27
team_policy eval team catch total num: 38
team_policy eval average step individual rewards of agent1: 0.5628089491330417
team_policy eval average team episode rewards of agent1: 95.0
team_policy eval idv catch total num of agent1: 24
team_policy eval team catch total num: 38
team_policy eval average step individual rewards of agent2: 0.6867470645520146
team_policy eval average team episode rewards of agent2: 95.0
team_policy eval idv catch total num of agent2: 29
team_policy eval team catch total num: 38
team_policy eval average step individual rewards of agent3: 0.6598577226286078
team_policy eval average team episode rewards of agent3: 95.0
team_policy eval idv catch total num of agent3: 28
team_policy eval team catch total num: 38
team_policy eval average step individual rewards of agent4: 1.057269481089675
team_policy eval average team episode rewards of agent4: 95.0
team_policy eval idv catch total num of agent4: 44
team_policy eval team catch total num: 38
idv_policy eval average step individual rewards of agent0: 0.3726353400592181
idv_policy eval average team episode rewards of agent0: 95.0
idv_policy eval idv catch total num of agent0: 17
idv_policy eval team catch total num: 38
idv_policy eval average step individual rewards of agent1: 0.6505180832953856
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: 0.574780296706353
idv_policy eval average team episode rewards of agent2: 95.0
idv_policy eval idv catch total num of agent2: 25
idv_policy eval team catch total num: 38
idv_policy eval average step individual rewards of agent3: 0.9866178067754043
idv_policy eval average team episode rewards of agent3: 95.0
idv_policy eval idv catch total num of agent3: 41
idv_policy eval team catch total num: 38
idv_policy eval average step individual rewards of agent4: 0.6872313678877802
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 4751/10000 episodes, total num timesteps 950400/2000000, FPS 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.

team_policy eval average step individual rewards of agent0: 0.6402148316300459
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: 0.6101292903260362
team_policy eval average team episode rewards of agent1: 75.0
team_policy eval idv catch total num of agent1: 26
team_policy eval team catch total num: 30
team_policy eval average step individual rewards of agent2: 0.6631379646555218
team_policy eval average team episode rewards of agent2: 75.0
team_policy eval idv catch total num of agent2: 28
team_policy eval team catch total num: 30
team_policy eval average step individual rewards of agent3: 0.5552217004275242
team_policy eval average team episode rewards of agent3: 75.0
team_policy eval idv catch total num of agent3: 24
team_policy eval team catch total num: 30
team_policy eval average step individual rewards of agent4: 0.43003004976630804
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: 0.670411327403967
idv_policy eval average team episode rewards of agent0: 122.5
idv_policy eval idv catch total num of agent0: 28
idv_policy eval team catch total num: 49
idv_policy eval average step individual rewards of agent1: 1.093419687790325
idv_policy eval average team episode rewards of agent1: 122.5
idv_policy eval idv catch total num of agent1: 45
idv_policy eval team catch total num: 49
idv_policy eval average step individual rewards of agent2: 0.6879853977395531
idv_policy eval average team episode rewards of agent2: 122.5
idv_policy eval idv catch total num of agent2: 29
idv_policy eval team catch total num: 49
idv_policy eval average step individual rewards of agent3: 0.8724016156863548
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: 0.8849997495730234
idv_policy eval average team episode rewards of agent4: 122.5
idv_policy eval idv catch total num of agent4: 37
idv_policy eval team catch total num: 49

 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.

team_policy eval average step individual rewards of agent0: 1.3910905648382408
team_policy eval average team episode rewards of agent0: 97.5
team_policy eval idv catch total num of agent0: 57
team_policy eval team catch total num: 39
team_policy eval average step individual rewards of agent1: 0.6330180566305257
team_policy eval average team episode rewards of agent1: 97.5
team_policy eval idv catch total num of agent1: 27
team_policy eval team catch total num: 39
team_policy eval average step individual rewards of agent2: 0.8904985922758643
team_policy eval average team episode rewards of agent2: 97.5
team_policy eval idv catch total num of agent2: 37
team_policy eval team catch total num: 39
team_policy eval average step individual rewards of agent3: 0.6286245304991044
team_policy eval average team episode rewards of agent3: 97.5
team_policy eval idv catch total num of agent3: 27
team_policy eval team catch total num: 39
team_policy eval average step individual rewards of agent4: 0.5617187542083011
team_policy eval average team episode rewards of agent4: 97.5
team_policy eval idv catch total num of agent4: 24
team_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent0: 0.7457509291820499
idv_policy eval average team episode rewards of agent0: 110.0
idv_policy eval idv catch total num of agent0: 31
idv_policy eval team catch total num: 44
idv_policy eval average step individual rewards of agent1: 1.0154851261321285
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: 0.9711429350454546
idv_policy eval average team episode rewards of agent2: 110.0
idv_policy eval idv catch total num of agent2: 40
idv_policy eval team catch total num: 44
idv_policy eval average step individual rewards of agent3: 0.554707466424838
idv_policy eval average team episode rewards of agent3: 110.0
idv_policy eval idv catch total num of agent3: 24
idv_policy eval team catch total num: 44
idv_policy eval average step individual rewards of agent4: 0.8301347979417905
idv_policy eval average team episode rewards of agent4: 110.0
idv_policy eval idv catch total num of agent4: 35
idv_policy eval team catch total num: 44

 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.

team_policy eval average step individual rewards of agent0: 0.6338260380564374
team_policy eval average team episode rewards of agent0: 80.0
team_policy eval idv catch total num of agent0: 27
team_policy eval team catch total num: 32
team_policy eval average step individual rewards of agent1: 0.5148821813909755
team_policy eval average team episode rewards of agent1: 80.0
team_policy eval idv catch total num of agent1: 22
team_policy eval team catch total num: 32
team_policy eval average step individual rewards of agent2: 0.4404381195560203
team_policy eval average team episode rewards of agent2: 80.0
team_policy eval idv catch total num of agent2: 19
team_policy eval team catch total num: 32
team_policy eval average step individual rewards of agent3: 0.7876524237269815
team_policy eval average team episode rewards of agent3: 80.0
team_policy eval idv catch total num of agent3: 33
team_policy eval team catch total num: 32
team_policy eval average step individual rewards of agent4: 0.745579619238639
team_policy eval average team episode rewards of agent4: 80.0
team_policy eval idv catch total num of agent4: 31
team_policy eval team catch total num: 32
idv_policy eval average step individual rewards of agent0: 0.6641718208482552
idv_policy eval average team episode rewards of agent0: 82.5
idv_policy eval idv catch total num of agent0: 28
idv_policy eval team catch total num: 33
idv_policy eval average step individual rewards of agent1: 0.6425994479804963
idv_policy eval average team episode rewards of agent1: 82.5
idv_policy eval idv catch total num of agent1: 27
idv_policy eval team catch total num: 33
idv_policy eval average step individual rewards of agent2: 0.7554548799161793
idv_policy eval average team episode rewards of agent2: 82.5
idv_policy eval idv catch total num of agent2: 32
idv_policy eval team catch total num: 33
idv_policy eval average step individual rewards of agent3: 0.7105425986492325
idv_policy eval average team episode rewards of agent3: 82.5
idv_policy eval idv catch total num of agent3: 30
idv_policy eval team catch total num: 33
idv_policy eval average step individual rewards of agent4: 0.6371652034212633
idv_policy eval average team episode rewards of agent4: 82.5
idv_policy eval idv catch total num of agent4: 27
idv_policy eval team catch total num: 33

 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.

team_policy eval average step individual rewards of agent0: 0.25331485904111867
team_policy eval average team episode rewards of agent0: 57.5
team_policy eval idv catch total num of agent0: 12
team_policy eval team catch total num: 23
team_policy eval average step individual rewards of agent1: 0.6577182775442286
team_policy eval average team episode rewards of agent1: 57.5
team_policy eval idv catch total num of agent1: 28
team_policy eval team catch total num: 23
team_policy eval average step individual rewards of agent2: 0.38815168211070883
team_policy eval average team episode rewards of agent2: 57.5
team_policy eval idv catch total num of agent2: 17
team_policy eval team catch total num: 23
team_policy eval average step individual rewards of agent3: 0.4503470930908064
team_policy eval average team episode rewards of agent3: 57.5
team_policy eval idv catch total num of agent3: 20
team_policy eval team catch total num: 23
team_policy eval average step individual rewards of agent4: 0.48344071988792836
team_policy eval average team episode rewards of agent4: 57.5
team_policy eval idv catch total num of agent4: 21
team_policy eval team catch total num: 23
idv_policy eval average step individual rewards of agent0: 1.1126346263146782
idv_policy eval average team episode rewards of agent0: 107.5
idv_policy eval idv catch total num of agent0: 46
idv_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent1: 0.6378010220807228
idv_policy eval average team episode rewards of agent1: 107.5
idv_policy eval idv catch total num of agent1: 27
idv_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent2: 0.7122117757303746
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: 0.7437285735077991
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: 0.48765172043327326
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 4851/10000 episodes, total num timesteps 970400/2000000, FPS 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.

team_policy eval average step individual rewards of agent0: 0.9432613332561811
team_policy eval average team episode rewards of agent0: 100.0
team_policy eval idv catch total num of agent0: 39
team_policy eval team catch total num: 40
team_policy eval average step individual rewards of agent1: 0.6610517893562675
team_policy eval average team episode rewards of agent1: 100.0
team_policy eval idv catch total num of agent1: 28
team_policy eval team catch total num: 40
team_policy eval average step individual rewards of agent2: 0.7891710457791408
team_policy eval average team episode rewards of agent2: 100.0
team_policy eval idv catch total num of agent2: 33
team_policy eval team catch total num: 40
team_policy eval average step individual rewards of agent3: 0.6347006527118512
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: 0.7105988813549294
team_policy eval average team episode rewards of agent4: 100.0
team_policy eval idv catch total num of agent4: 30
team_policy eval team catch total num: 40
idv_policy eval average step individual rewards of agent0: 0.3721759820972214
idv_policy eval average team episode rewards of agent0: 82.5
idv_policy eval idv catch total num of agent0: 17
idv_policy eval team catch total num: 33
idv_policy eval average step individual rewards of agent1: 0.7358567194853777
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: 0.42673858790431934
idv_policy eval average team episode rewards of agent2: 82.5
idv_policy eval idv catch total num of agent2: 19
idv_policy eval team catch total num: 33
idv_policy eval average step individual rewards of agent3: 0.8122720773324535
idv_policy eval average team episode rewards of agent3: 82.5
idv_policy eval idv catch total num of agent3: 34
idv_policy eval team catch total num: 33
idv_policy eval average step individual rewards of agent4: 0.6112140289890721
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 4876/10000 episodes, total num timesteps 975400/2000000, FPS 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.

team_policy eval average step individual rewards of agent0: 0.4137105401897773
team_policy eval average team episode rewards of agent0: 72.5
team_policy eval idv catch total num of agent0: 18
team_policy eval team catch total num: 29
team_policy eval average step individual rewards of agent1: 0.581520800888691
team_policy eval average team episode rewards of agent1: 72.5
team_policy eval idv catch total num of agent1: 25
team_policy eval team catch total num: 29
team_policy eval average step individual rewards of agent2: 0.45996784565726756
team_policy eval average team episode rewards of agent2: 72.5
team_policy eval idv catch total num of agent2: 20
team_policy eval team catch total num: 29
team_policy eval average step individual rewards of agent3: 1.0430422672249322
team_policy eval average team episode rewards of agent3: 72.5
team_policy eval idv catch total num of agent3: 43
team_policy eval team catch total num: 29
team_policy eval average step individual rewards of agent4: 0.5651346312645557
team_policy eval average team episode rewards of agent4: 72.5
team_policy eval idv catch total num of agent4: 24
team_policy eval team catch total num: 29
idv_policy eval average step individual rewards of agent0: 0.7396974563988783
idv_policy eval average team episode rewards of agent0: 100.0
idv_policy eval idv catch total num of agent0: 31
idv_policy eval team catch total num: 40
idv_policy eval average step individual rewards of agent1: 0.8887244692992005
idv_policy eval average team episode rewards of agent1: 100.0
idv_policy eval idv catch total num of agent1: 37
idv_policy eval team catch total num: 40
idv_policy eval average step individual rewards of agent2: 0.7558420282966936
idv_policy eval average team episode rewards of agent2: 100.0
idv_policy eval idv catch total num of agent2: 32
idv_policy eval team catch total num: 40
idv_policy eval average step individual rewards of agent3: 0.7188423866841398
idv_policy eval average team episode rewards of agent3: 100.0
idv_policy eval idv catch total num of agent3: 30
idv_policy eval team catch total num: 40
idv_policy eval average step individual rewards of agent4: 0.6299461570868249
idv_policy eval average team episode rewards of agent4: 100.0
idv_policy eval idv catch total num of agent4: 27
idv_policy eval team catch total num: 40

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 4901/10000 episodes, total num timesteps 980400/2000000, FPS 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.

team_policy eval average step individual rewards of agent0: 0.7992693857839417
team_policy eval average team episode rewards of agent0: 120.0
team_policy eval idv catch total num of agent0: 33
team_policy eval team catch total num: 48
team_policy eval average step individual rewards of agent1: 0.9961698116221904
team_policy eval average team episode rewards of agent1: 120.0
team_policy eval idv catch total num of agent1: 41
team_policy eval team catch total num: 48
team_policy eval average step individual rewards of agent2: 0.6411542901772339
team_policy eval average team episode rewards of agent2: 120.0
team_policy eval idv catch total num of agent2: 27
team_policy eval team catch total num: 48
team_policy eval average step individual rewards of agent3: 0.9418667663740696
team_policy eval average team episode rewards of agent3: 120.0
team_policy eval idv catch total num of agent3: 39
team_policy eval team catch total num: 48
team_policy eval average step individual rewards of agent4: 0.43578499937665116
team_policy eval average team episode rewards of agent4: 120.0
team_policy eval idv catch total num of agent4: 19
team_policy eval team catch total num: 48
idv_policy eval average step individual rewards of agent0: 0.5827986057609398
idv_policy eval average team episode rewards of agent0: 67.5
idv_policy eval idv catch total num of agent0: 25
idv_policy eval team catch total num: 27
idv_policy eval average step individual rewards of agent1: 0.5343940707524358
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: 0.6581431886783266
idv_policy eval average team episode rewards of agent2: 67.5
idv_policy eval idv catch total num of agent2: 28
idv_policy eval team catch total num: 27
idv_policy eval average step individual rewards of agent3: 0.5735541257500028
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: 0.5266160715795712
idv_policy eval average team episode rewards of agent4: 67.5
idv_policy eval idv catch total num of agent4: 23
idv_policy eval team catch total num: 27

 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.

team_policy eval average step individual rewards of agent0: 0.5581239660997092
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: 0.6082805878715374
team_policy eval average team episode rewards of agent1: 72.5
team_policy eval idv catch total num of agent1: 26
team_policy eval team catch total num: 29
team_policy eval average step individual rewards of agent2: 0.7084563291255275
team_policy eval average team episode rewards of agent2: 72.5
team_policy eval idv catch total num of agent2: 30
team_policy eval team catch total num: 29
team_policy eval average step individual rewards of agent3: 0.7605097141404943
team_policy eval average team episode rewards of agent3: 72.5
team_policy eval idv catch total num of agent3: 32
team_policy eval team catch total num: 29
team_policy eval average step individual rewards of agent4: 0.3976470891790367
team_policy eval average team episode rewards of agent4: 72.5
team_policy eval idv catch total num of agent4: 18
team_policy eval team catch total num: 29
idv_policy eval average step individual rewards of agent0: 0.4036925993548781
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: 0.6477068079448859
idv_policy eval average team episode rewards of agent1: 95.0
idv_policy eval idv catch total num of agent1: 27
idv_policy eval team catch total num: 38
idv_policy eval average step individual rewards of agent2: 1.1913230125721066
idv_policy eval average team episode rewards of agent2: 95.0
idv_policy eval idv catch total num of agent2: 49
idv_policy eval team catch total num: 38
idv_policy eval average step individual rewards of agent3: 0.8953523867071753
idv_policy eval average team episode rewards of agent3: 95.0
idv_policy eval idv catch total num of agent3: 37
idv_policy eval team catch total num: 38
idv_policy eval average step individual rewards of agent4: 0.7578121315494326
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 4951/10000 episodes, total num timesteps 990400/2000000, FPS 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.

team_policy eval average step individual rewards of agent0: 0.8260766649117562
team_policy eval average team episode rewards of agent0: 77.5
team_policy eval idv catch total num of agent0: 35
team_policy eval team catch total num: 31
team_policy eval average step individual rewards of agent1: 0.5023115327250344
team_policy eval average team episode rewards of agent1: 77.5
team_policy eval idv catch total num of agent1: 22
team_policy eval team catch total num: 31
team_policy eval average step individual rewards of agent2: 0.6442195901967306
team_policy eval average team episode rewards of agent2: 77.5
team_policy eval idv catch total num of agent2: 27
team_policy eval team catch total num: 31
team_policy eval average step individual rewards of agent3: 0.3801859800243285
team_policy eval average team episode rewards of agent3: 77.5
team_policy eval idv catch total num of agent3: 17
team_policy eval team catch total num: 31
team_policy eval average step individual rewards of agent4: 0.4817323484059666
team_policy eval average team episode rewards of agent4: 77.5
team_policy eval idv catch total num of agent4: 21
team_policy eval team catch total num: 31
idv_policy eval average step individual rewards of agent0: 0.9325509098941059
idv_policy eval average team episode rewards of agent0: 97.5
idv_policy eval idv catch total num of agent0: 39
idv_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent1: 0.5049141393422037
idv_policy eval average team episode rewards of agent1: 97.5
idv_policy eval idv catch total num of agent1: 22
idv_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent2: 0.4996688376289767
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: 0.6106195958493564
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: 0.7254010880050876
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 4976/10000 episodes, total num timesteps 995400/2000000, FPS 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.

team_policy eval average step individual rewards of agent0: 0.7077852094594524
team_policy eval average team episode rewards of agent0: 132.5
team_policy eval idv catch total num of agent0: 30
team_policy eval team catch total num: 53
team_policy eval average step individual rewards of agent1: 0.9610758123775146
team_policy eval average team episode rewards of agent1: 132.5
team_policy eval idv catch total num of agent1: 40
team_policy eval team catch total num: 53
team_policy eval average step individual rewards of agent2: 0.8154810094188072
team_policy eval average team episode rewards of agent2: 132.5
team_policy eval idv catch total num of agent2: 34
team_policy eval team catch total num: 53
team_policy eval average step individual rewards of agent3: 0.9125321746379427
team_policy eval average team episode rewards of agent3: 132.5
team_policy eval idv catch total num of agent3: 38
team_policy eval team catch total num: 53
team_policy eval average step individual rewards of agent4: 0.9303345199540192
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: 0.737923778816645
idv_policy eval average team episode rewards of agent0: 97.5
idv_policy eval idv catch total num of agent0: 31
idv_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent1: 0.8188737073599551
idv_policy eval average team episode rewards of agent1: 97.5
idv_policy eval idv catch total num of agent1: 34
idv_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent2: 0.4827774666200105
idv_policy eval average team episode rewards of agent2: 97.5
idv_policy eval idv catch total num of agent2: 21
idv_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent3: 0.5544944620021589
idv_policy eval average team episode rewards of agent3: 97.5
idv_policy eval idv catch total num of agent3: 24
idv_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent4: 0.6386789438692532
idv_policy eval average team episode rewards of agent4: 97.5
idv_policy eval idv catch total num of agent4: 27
idv_policy eval team catch total num: 39

 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.

team_policy eval average step individual rewards of agent0: 1.0496366335444587
team_policy eval average team episode rewards of agent0: 97.5
team_policy eval idv catch total num of agent0: 43
team_policy eval team catch total num: 39
team_policy eval average step individual rewards of agent1: 0.6695088860872367
team_policy eval average team episode rewards of agent1: 97.5
team_policy eval idv catch total num of agent1: 28
team_policy eval team catch total num: 39
team_policy eval average step individual rewards of agent2: 0.562676185336094
team_policy eval average team episode rewards of agent2: 97.5
team_policy eval idv catch total num of agent2: 24
team_policy eval team catch total num: 39
team_policy eval average step individual rewards of agent3: 0.5645560186021643
team_policy eval average team episode rewards of agent3: 97.5
team_policy eval idv catch total num of agent3: 24
team_policy eval team catch total num: 39
team_policy eval average step individual rewards of agent4: 0.8072941723143799
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: 0.8376685289535782
idv_policy eval average team episode rewards of agent0: 92.5
idv_policy eval idv catch total num of agent0: 35
idv_policy eval team catch total num: 37
idv_policy eval average step individual rewards of agent1: 0.2069563418493213
idv_policy eval average team episode rewards of agent1: 92.5
idv_policy eval idv catch total num of agent1: 10
idv_policy eval team catch total num: 37
idv_policy eval average step individual rewards of agent2: 0.6893059276904464
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: 0.7152716607896689
idv_policy eval average team episode rewards of agent3: 92.5
idv_policy eval idv catch total num of agent3: 30
idv_policy eval team catch total num: 37
idv_policy eval average step individual rewards of agent4: 1.0951608316018813
idv_policy eval average team episode rewards of agent4: 92.5
idv_policy eval idv catch total num of agent4: 45
idv_policy eval team catch total num: 37

 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.

team_policy eval average step individual rewards of agent0: 1.3999339086767066
team_policy eval average team episode rewards of agent0: 120.0
team_policy eval idv catch total num of agent0: 57
team_policy eval team catch total num: 48
team_policy eval average step individual rewards of agent1: 0.6840975123967666
team_policy eval average team episode rewards of agent1: 120.0
team_policy eval idv catch total num of agent1: 29
team_policy eval team catch total num: 48
team_policy eval average step individual rewards of agent2: 0.7661743323338063
team_policy eval average team episode rewards of agent2: 120.0
team_policy eval idv catch total num of agent2: 32
team_policy eval team catch total num: 48
team_policy eval average step individual rewards of agent3: 0.8685753148548446
team_policy eval average team episode rewards of agent3: 120.0
team_policy eval idv catch total num of agent3: 36
team_policy eval team catch total num: 48
team_policy eval average step individual rewards of agent4: 0.5796752664643883
team_policy eval average team episode rewards of agent4: 120.0
team_policy eval idv catch total num of agent4: 25
team_policy eval team catch total num: 48
idv_policy eval average step individual rewards of agent0: 0.7193856115789993
idv_policy eval average team episode rewards of agent0: 112.5
idv_policy eval idv catch total num of agent0: 30
idv_policy eval team catch total num: 45
idv_policy eval average step individual rewards of agent1: 0.5103654307615563
idv_policy eval average team episode rewards of agent1: 112.5
idv_policy eval idv catch total num of agent1: 22
idv_policy eval team catch total num: 45
idv_policy eval average step individual rewards of agent2: 0.8213045524811536
idv_policy eval average team episode rewards of agent2: 112.5
idv_policy eval idv catch total num of agent2: 34
idv_policy eval team catch total num: 45
idv_policy eval average step individual rewards of agent3: 0.7940052966246524
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: 0.7451930153878926
idv_policy eval average team episode rewards of agent4: 112.5
idv_policy eval idv catch total num of agent4: 31
idv_policy eval team catch total num: 45

 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.

team_policy eval average step individual rewards of agent0: 0.636846320328559
team_policy eval average team episode rewards of agent0: 95.0
team_policy eval idv catch total num of agent0: 27
team_policy eval team catch total num: 38
team_policy eval average step individual rewards of agent1: 0.5432133903466717
team_policy eval average team episode rewards of agent1: 95.0
team_policy eval idv catch total num of agent1: 23
team_policy eval team catch total num: 38
team_policy eval average step individual rewards of agent2: 0.6894540119226167
team_policy eval average team episode rewards of agent2: 95.0
team_policy eval idv catch total num of agent2: 29
team_policy eval team catch total num: 38
team_policy eval average step individual rewards of agent3: 0.7115204328424007
team_policy eval average team episode rewards of agent3: 95.0
team_policy eval idv catch total num of agent3: 30
team_policy eval team catch total num: 38
team_policy eval average step individual rewards of agent4: 1.0166238880228562
team_policy eval average team episode rewards of agent4: 95.0
team_policy eval idv catch total num of agent4: 42
team_policy eval team catch total num: 38
idv_policy eval average step individual rewards of agent0: 0.8642961158003106
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: 0.86196584685999
idv_policy eval average team episode rewards of agent1: 120.0
idv_policy eval idv catch total num of agent1: 36
idv_policy eval team catch total num: 48
idv_policy eval average step individual rewards of agent2: 1.1226000668835878
idv_policy eval average team episode rewards of agent2: 120.0
idv_policy eval idv catch total num of agent2: 46
idv_policy eval team catch total num: 48
idv_policy eval average step individual rewards of agent3: 0.7624414650001304
idv_policy eval average team episode rewards of agent3: 120.0
idv_policy eval idv catch total num of agent3: 32
idv_policy eval team catch total num: 48
idv_policy eval average step individual rewards of agent4: 0.8657140488563708
idv_policy eval average team episode rewards of agent4: 120.0
idv_policy eval idv catch total num of agent4: 36
idv_policy eval team catch total num: 48

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5076/10000 episodes, total num timesteps 1015400/2000000, FPS 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.

team_policy eval average step individual rewards of agent0: 0.5051692281448298
team_policy eval average team episode rewards of agent0: 77.5
team_policy eval idv catch total num of agent0: 22
team_policy eval team catch total num: 31
team_policy eval average step individual rewards of agent1: 0.9692526869199053
team_policy eval average team episode rewards of agent1: 77.5
team_policy eval idv catch total num of agent1: 40
team_policy eval team catch total num: 31
team_policy eval average step individual rewards of agent2: 0.5595860500440769
team_policy eval average team episode rewards of agent2: 77.5
team_policy eval idv catch total num of agent2: 24
team_policy eval team catch total num: 31
team_policy eval average step individual rewards of agent3: 0.5591598948049563
team_policy eval average team episode rewards of agent3: 77.5
team_policy eval idv catch total num of agent3: 24
team_policy eval team catch total num: 31
team_policy eval average step individual rewards of agent4: 0.5634746377030849
team_policy eval average team episode rewards of agent4: 77.5
team_policy eval idv catch total num of agent4: 24
team_policy eval team catch total num: 31
idv_policy eval average step individual rewards of agent0: 0.31251328361242997
idv_policy eval average team episode rewards of agent0: 42.5
idv_policy eval idv catch total num of agent0: 14
idv_policy eval team catch total num: 17
idv_policy eval average step individual rewards of agent1: 0.43823582912158104
idv_policy eval average team episode rewards of agent1: 42.5
idv_policy eval idv catch total num of agent1: 19
idv_policy eval team catch total num: 17
idv_policy eval average step individual rewards of agent2: 0.5301733627055359
idv_policy eval average team episode rewards of agent2: 42.5
idv_policy eval idv catch total num of agent2: 23
idv_policy eval team catch total num: 17
idv_policy eval average step individual rewards of agent3: 0.6898665561028641
idv_policy eval average team episode rewards of agent3: 42.5
idv_policy eval idv catch total num of agent3: 29
idv_policy eval team catch total num: 17
idv_policy eval average step individual rewards of agent4: 0.17078226610913916
idv_policy eval average team episode rewards of agent4: 42.5
idv_policy eval idv catch total num of agent4: 9
idv_policy eval team catch total num: 17

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5101/10000 episodes, total num timesteps 1020400/2000000, FPS 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.

team_policy eval average step individual rewards of agent0: 0.6161846082707556
team_policy eval average team episode rewards of agent0: 85.0
team_policy eval idv catch total num of agent0: 26
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent1: 0.2904299840463851
team_policy eval average team episode rewards of agent1: 85.0
team_policy eval idv catch total num of agent1: 14
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent2: 0.4776698393129158
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: 0.610668012935865
team_policy eval average team episode rewards of agent3: 85.0
team_policy eval idv catch total num of agent3: 26
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent4: 0.6492856203614177
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: 0.4062809032474213
idv_policy eval average team episode rewards of agent0: 87.5
idv_policy eval idv catch total num of agent0: 18
idv_policy eval team catch total num: 35
idv_policy eval average step individual rewards of agent1: 0.6360132242725192
idv_policy eval average team episode rewards of agent1: 87.5
idv_policy eval idv catch total num of agent1: 27
idv_policy eval team catch total num: 35
idv_policy eval average step individual rewards of agent2: 0.6707533876316267
idv_policy eval average team episode rewards of agent2: 87.5
idv_policy eval idv catch total num of agent2: 28
idv_policy eval team catch total num: 35
idv_policy eval average step individual rewards of agent3: 1.1440766038475847
idv_policy eval average team episode rewards of agent3: 87.5
idv_policy eval idv catch total num of agent3: 47
idv_policy eval team catch total num: 35
idv_policy eval average step individual rewards of agent4: 0.7024752823833583
idv_policy eval average team episode rewards of agent4: 87.5
idv_policy eval idv catch total num of agent4: 30
idv_policy eval team catch total num: 35

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5126/10000 episodes, total num timesteps 1025400/2000000, FPS 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 245.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.

team_policy eval average step individual rewards of agent0: 0.7344808323538173
team_policy eval average team episode rewards of agent0: 110.0
team_policy eval idv catch total num of agent0: 31
team_policy eval team catch total num: 44
team_policy eval average step individual rewards of agent1: 1.0929509472137622
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: 0.597173876426435
team_policy eval average team episode rewards of agent2: 110.0
team_policy eval idv catch total num of agent2: 26
team_policy eval team catch total num: 44
team_policy eval average step individual rewards of agent3: 0.8386814120630198
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: 0.6043696041176944
team_policy eval average team episode rewards of agent4: 110.0
team_policy eval idv catch total num of agent4: 26
team_policy eval team catch total num: 44
idv_policy eval average step individual rewards of agent0: 0.7654087141839797
idv_policy eval average team episode rewards of agent0: 80.0
idv_policy eval idv catch total num of agent0: 32
idv_policy eval team catch total num: 32
idv_policy eval average step individual rewards of agent1: 0.6715398543421449
idv_policy eval average team episode rewards of agent1: 80.0
idv_policy eval idv catch total num of agent1: 28
idv_policy eval team catch total num: 32
idv_policy eval average step individual rewards of agent2: 0.9173146390293351
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: 0.5862382655047185
idv_policy eval average team episode rewards of agent3: 80.0
idv_policy eval idv catch total num of agent3: 25
idv_policy eval team catch total num: 32
idv_policy eval average step individual rewards of agent4: 0.32954924137993513
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 5151/10000 episodes, total num timesteps 1030400/2000000, FPS 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.

team_policy eval average step individual rewards of agent0: 0.7313442660093001
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: 0.669939444734007
team_policy eval average team episode rewards of agent1: 102.5
team_policy eval idv catch total num of agent1: 28
team_policy eval team catch total num: 41
team_policy eval average step individual rewards of agent2: 0.8617347409699717
team_policy eval average team episode rewards of agent2: 102.5
team_policy eval idv catch total num of agent2: 36
team_policy eval team catch total num: 41
team_policy eval average step individual rewards of agent3: 0.6835508699596313
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: 0.8633823831688605
team_policy eval average team episode rewards of agent4: 102.5
team_policy eval idv catch total num of agent4: 36
team_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent0: 0.9143302694077258
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: 0.4841424706685869
idv_policy eval average team episode rewards of agent1: 92.5
idv_policy eval idv catch total num of agent1: 21
idv_policy eval team catch total num: 37
idv_policy eval average step individual rewards of agent2: 0.5325140632828084
idv_policy eval average team episode rewards of agent2: 92.5
idv_policy eval idv catch total num of agent2: 23
idv_policy eval team catch total num: 37
idv_policy eval average step individual rewards of agent3: 0.6594402595339109
idv_policy eval average team episode rewards of agent3: 92.5
idv_policy eval idv catch total num of agent3: 28
idv_policy eval team catch total num: 37
idv_policy eval average step individual rewards of agent4: 0.8888630699330551
idv_policy eval average team episode rewards of agent4: 92.5
idv_policy eval idv catch total num of agent4: 37
idv_policy eval team catch total num: 37

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5176/10000 episodes, total num timesteps 1035400/2000000, FPS 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.

team_policy eval average step individual rewards of agent0: 0.7336155908786333
team_policy eval average team episode rewards of agent0: 115.0
team_policy eval idv catch total num of agent0: 31
team_policy eval team catch total num: 46
team_policy eval average step individual rewards of agent1: 1.1742633663839703
team_policy eval average team episode rewards of agent1: 115.0
team_policy eval idv catch total num of agent1: 48
team_policy eval team catch total num: 46
team_policy eval average step individual rewards of agent2: 0.6920000579858118
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: 0.6603745761194523
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: 0.8288612506663889
team_policy eval average team episode rewards of agent4: 115.0
team_policy eval idv catch total num of agent4: 35
team_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent0: 0.7415660766770518
idv_policy eval average team episode rewards of agent0: 92.5
idv_policy eval idv catch total num of agent0: 31
idv_policy eval team catch total num: 37
idv_policy eval average step individual rewards of agent1: 0.5491485276660143
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: 0.6584217259114646
idv_policy eval average team episode rewards of agent2: 92.5
idv_policy eval idv catch total num of agent2: 28
idv_policy eval team catch total num: 37
idv_policy eval average step individual rewards of agent3: 0.6605194808443398
idv_policy eval average team episode rewards of agent3: 92.5
idv_policy eval idv catch total num of agent3: 28
idv_policy eval team catch total num: 37
idv_policy eval average step individual rewards of agent4: 0.7601438331968967
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 5201/10000 episodes, total num timesteps 1040400/2000000, FPS 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.

team_policy eval average step individual rewards of agent0: 0.9185930800656504
team_policy eval average team episode rewards of agent0: 112.5
team_policy eval idv catch total num of agent0: 38
team_policy eval team catch total num: 45
team_policy eval average step individual rewards of agent1: 0.7842623245968758
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: 0.8446112475061269
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: 0.6203106770891994
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: 0.9183792059389547
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: 0.71912987939546
idv_policy eval average team episode rewards of agent0: 80.0
idv_policy eval idv catch total num of agent0: 30
idv_policy eval team catch total num: 32
idv_policy eval average step individual rewards of agent1: 0.5064888275239899
idv_policy eval average team episode rewards of agent1: 80.0
idv_policy eval idv catch total num of agent1: 22
idv_policy eval team catch total num: 32
idv_policy eval average step individual rewards of agent2: 0.6894446471176083
idv_policy eval average team episode rewards of agent2: 80.0
idv_policy eval idv catch total num of agent2: 29
idv_policy eval team catch total num: 32
idv_policy eval average step individual rewards of agent3: 0.6616483725872692
idv_policy eval average team episode rewards of agent3: 80.0
idv_policy eval idv catch total num of agent3: 28
idv_policy eval team catch total num: 32
idv_policy eval average step individual rewards of agent4: 0.6032961812699755
idv_policy eval average team episode rewards of agent4: 80.0
idv_policy eval idv catch total num of agent4: 26
idv_policy eval team catch total num: 32

 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.

team_policy eval average step individual rewards of agent0: 0.8090964826426719
team_policy eval average team episode rewards of agent0: 87.5
team_policy eval idv catch total num of agent0: 34
team_policy eval team catch total num: 35
team_policy eval average step individual rewards of agent1: 1.1356370518734642
team_policy eval average team episode rewards of agent1: 87.5
team_policy eval idv catch total num of agent1: 47
team_policy eval team catch total num: 35
team_policy eval average step individual rewards of agent2: 0.561209792852582
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: 0.43141503132112047
team_policy eval average team episode rewards of agent3: 87.5
team_policy eval idv catch total num of agent3: 19
team_policy eval team catch total num: 35
team_policy eval average step individual rewards of agent4: 0.9933074663432433
team_policy eval average team episode rewards of agent4: 87.5
team_policy eval idv catch total num of agent4: 41
team_policy eval team catch total num: 35
idv_policy eval average step individual rewards of agent0: 0.8664147326507958
idv_policy eval average team episode rewards of agent0: 57.5
idv_policy eval idv catch total num of agent0: 36
idv_policy eval team catch total num: 23
idv_policy eval average step individual rewards of agent1: 0.30744909665619674
idv_policy eval average team episode rewards of agent1: 57.5
idv_policy eval idv catch total num of agent1: 14
idv_policy eval team catch total num: 23
idv_policy eval average step individual rewards of agent2: 0.7714169462863905
idv_policy eval average team episode rewards of agent2: 57.5
idv_policy eval idv catch total num of agent2: 32
idv_policy eval team catch total num: 23
idv_policy eval average step individual rewards of agent3: 0.24465283746298044
idv_policy eval average team episode rewards of agent3: 57.5
idv_policy eval idv catch total num of agent3: 12
idv_policy eval team catch total num: 23
idv_policy eval average step individual rewards of agent4: 0.5022593229997011
idv_policy eval average team episode rewards of agent4: 57.5
idv_policy eval idv catch total num of agent4: 22
idv_policy eval team catch total num: 23

 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.

team_policy eval average step individual rewards of agent0: 0.5507999193704823
team_policy eval average team episode rewards of agent0: 110.0
team_policy eval idv catch total num of agent0: 24
team_policy eval team catch total num: 44
team_policy eval average step individual rewards of agent1: 0.7370859969786143
team_policy eval average team episode rewards of agent1: 110.0
team_policy eval idv catch total num of agent1: 31
team_policy eval team catch total num: 44
team_policy eval average step individual rewards of agent2: 1.066103031375511
team_policy eval average team episode rewards of agent2: 110.0
team_policy eval idv catch total num of agent2: 44
team_policy eval team catch total num: 44
team_policy eval average step individual rewards of agent3: 0.8428517157321158
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: 0.9406535116777317
team_policy eval average team episode rewards of agent4: 110.0
team_policy eval idv catch total num of agent4: 39
team_policy eval team catch total num: 44
idv_policy eval average step individual rewards of agent0: 0.7041631347636862
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: 0.9094438588323785
idv_policy eval average team episode rewards of agent1: 100.0
idv_policy eval idv catch total num of agent1: 38
idv_policy eval team catch total num: 40
idv_policy eval average step individual rewards of agent2: 0.754990149811722
idv_policy eval average team episode rewards of agent2: 100.0
idv_policy eval idv catch total num of agent2: 32
idv_policy eval team catch total num: 40
idv_policy eval average step individual rewards of agent3: 0.47515995553077717
idv_policy eval average team episode rewards of agent3: 100.0
idv_policy eval idv catch total num of agent3: 21
idv_policy eval team catch total num: 40
idv_policy eval average step individual rewards of agent4: 0.7021178935586095
idv_policy eval average team episode rewards of agent4: 100.0
idv_policy eval idv catch total num of agent4: 30
idv_policy eval team catch total num: 40

 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.

team_policy eval average step individual rewards of agent0: 0.8953166845022261
team_policy eval average team episode rewards of agent0: 92.5
team_policy eval idv catch total num of agent0: 37
team_policy eval team catch total num: 37
team_policy eval average step individual rewards of agent1: 0.7145169669496043
team_policy eval average team episode rewards of agent1: 92.5
team_policy eval idv catch total num of agent1: 30
team_policy eval team catch total num: 37
team_policy eval average step individual rewards of agent2: 0.5876926058415406
team_policy eval average team episode rewards of agent2: 92.5
team_policy eval idv catch total num of agent2: 25
team_policy eval team catch total num: 37
team_policy eval average step individual rewards of agent3: 0.8094719699172811
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: 0.5155472497437958
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: 0.78310507831027
idv_policy eval average team episode rewards of agent0: 87.5
idv_policy eval idv catch total num of agent0: 33
idv_policy eval team catch total num: 35
idv_policy eval average step individual rewards of agent1: 0.8369669293389639
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: 0.6331939479350776
idv_policy eval average team episode rewards of agent2: 87.5
idv_policy eval idv catch total num of agent2: 27
idv_policy eval team catch total num: 35
idv_policy eval average step individual rewards of agent3: 0.1079073439610234
idv_policy eval average team episode rewards of agent3: 87.5
idv_policy eval idv catch total num of agent3: 6
idv_policy eval team catch total num: 35
idv_policy eval average step individual rewards of agent4: 0.7612411562857208
idv_policy eval average team episode rewards of agent4: 87.5
idv_policy eval idv catch total num of agent4: 32
idv_policy eval team catch total num: 35

 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.

team_policy eval average step individual rewards of agent0: 0.33149129470792377
team_policy eval average team episode rewards of agent0: 82.5
team_policy eval idv catch total num of agent0: 15
team_policy eval team catch total num: 33
team_policy eval average step individual rewards of agent1: 0.8935612902422718
team_policy eval average team episode rewards of agent1: 82.5
team_policy eval idv catch total num of agent1: 37
team_policy eval team catch total num: 33
team_policy eval average step individual rewards of agent2: 0.7157062185374502
team_policy eval average team episode rewards of agent2: 82.5
team_policy eval idv catch total num of agent2: 30
team_policy eval team catch total num: 33
team_policy eval average step individual rewards of agent3: 0.4265151853659037
team_policy eval average team episode rewards of agent3: 82.5
team_policy eval idv catch total num of agent3: 19
team_policy eval team catch total num: 33
team_policy eval average step individual rewards of agent4: 0.5317460598027415
team_policy eval average team episode rewards of agent4: 82.5
team_policy eval idv catch total num of agent4: 23
team_policy eval team catch total num: 33
idv_policy eval average step individual rewards of agent0: 0.8370451441017147
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: 1.0150087467587283
idv_policy eval average team episode rewards of agent1: 100.0
idv_policy eval idv catch total num of agent1: 42
idv_policy eval team catch total num: 40
idv_policy eval average step individual rewards of agent2: 0.7522600207494982
idv_policy eval average team episode rewards of agent2: 100.0
idv_policy eval idv catch total num of agent2: 32
idv_policy eval team catch total num: 40
idv_policy eval average step individual rewards of agent3: 0.8320933970170489
idv_policy eval average team episode rewards of agent3: 100.0
idv_policy eval idv catch total num of agent3: 35
idv_policy eval team catch total num: 40
idv_policy eval average step individual rewards of agent4: 0.80843372121189
idv_policy eval average team episode rewards of agent4: 100.0
idv_policy eval idv catch total num of agent4: 34
idv_policy eval team catch total num: 40

 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 246.


 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 247.


 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 246.


 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 246.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.

team_policy eval average step individual rewards of agent0: 1.2685139037229525
team_policy eval average team episode rewards of agent0: 127.5
team_policy eval idv catch total num of agent0: 52
team_policy eval team catch total num: 51
team_policy eval average step individual rewards of agent1: 0.579563173920413
team_policy eval average team episode rewards of agent1: 127.5
team_policy eval idv catch total num of agent1: 25
team_policy eval team catch total num: 51
team_policy eval average step individual rewards of agent2: 0.7873655837249326
team_policy eval average team episode rewards of agent2: 127.5
team_policy eval idv catch total num of agent2: 33
team_policy eval team catch total num: 51
team_policy eval average step individual rewards of agent3: 1.0129382821219917
team_policy eval average team episode rewards of agent3: 127.5
team_policy eval idv catch total num of agent3: 42
team_policy eval team catch total num: 51
team_policy eval average step individual rewards of agent4: 0.6596479366100171
team_policy eval average team episode rewards of agent4: 127.5
team_policy eval idv catch total num of agent4: 28
team_policy eval team catch total num: 51
idv_policy eval average step individual rewards of agent0: 1.2237095822875679
idv_policy eval average team episode rewards of agent0: 145.0
idv_policy eval idv catch total num of agent0: 50
idv_policy eval team catch total num: 58
idv_policy eval average step individual rewards of agent1: 0.8923876289979714
idv_policy eval average team episode rewards of agent1: 145.0
idv_policy eval idv catch total num of agent1: 37
idv_policy eval team catch total num: 58
idv_policy eval average step individual rewards of agent2: 0.889556590563174
idv_policy eval average team episode rewards of agent2: 145.0
idv_policy eval idv catch total num of agent2: 37
idv_policy eval team catch total num: 58
idv_policy eval average step individual rewards of agent3: 0.9349678617038234
idv_policy eval average team episode rewards of agent3: 145.0
idv_policy eval idv catch total num of agent3: 39
idv_policy eval team catch total num: 58
idv_policy eval average step individual rewards of agent4: 0.9422531233016324
idv_policy eval average team episode rewards of agent4: 145.0
idv_policy eval idv catch total num of agent4: 39
idv_policy eval team catch total num: 58

 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.

team_policy eval average step individual rewards of agent0: 0.9944011115180567
team_policy eval average team episode rewards of agent0: 100.0
team_policy eval idv catch total num of agent0: 41
team_policy eval team catch total num: 40
team_policy eval average step individual rewards of agent1: 0.8715817896493181
team_policy eval average team episode rewards of agent1: 100.0
team_policy eval idv catch total num of agent1: 36
team_policy eval team catch total num: 40
team_policy eval average step individual rewards of agent2: 0.5947235592851081
team_policy eval average team episode rewards of agent2: 100.0
team_policy eval idv catch total num of agent2: 25
team_policy eval team catch total num: 40
team_policy eval average step individual rewards of agent3: 1.0162128279434484
team_policy eval average team episode rewards of agent3: 100.0
team_policy eval idv catch total num of agent3: 42
team_policy eval team catch total num: 40
team_policy eval average step individual rewards of agent4: 0.5414935569462361
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: 0.9374026308046975
idv_policy eval average team episode rewards of agent0: 122.5
idv_policy eval idv catch total num of agent0: 39
idv_policy eval team catch total num: 49
idv_policy eval average step individual rewards of agent1: 0.7312470592328262
idv_policy eval average team episode rewards of agent1: 122.5
idv_policy eval idv catch total num of agent1: 31
idv_policy eval team catch total num: 49
idv_policy eval average step individual rewards of agent2: 1.1162602319022243
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: 0.7857534515176299
idv_policy eval average team episode rewards of agent3: 122.5
idv_policy eval idv catch total num of agent3: 33
idv_policy eval team catch total num: 49
idv_policy eval average step individual rewards of agent4: 0.5643611020921135
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 5376/10000 episodes, total num timesteps 1075400/2000000, FPS 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.

team_policy eval average step individual rewards of agent0: 0.9113745521598569
team_policy eval average team episode rewards of agent0: 97.5
team_policy eval idv catch total num of agent0: 38
team_policy eval team catch total num: 39
team_policy eval average step individual rewards of agent1: 0.3079634151595064
team_policy eval average team episode rewards of agent1: 97.5
team_policy eval idv catch total num of agent1: 14
team_policy eval team catch total num: 39
team_policy eval average step individual rewards of agent2: 0.8365636065862492
team_policy eval average team episode rewards of agent2: 97.5
team_policy eval idv catch total num of agent2: 35
team_policy eval team catch total num: 39
team_policy eval average step individual rewards of agent3: 0.49426446124156054
team_policy eval average team episode rewards of agent3: 97.5
team_policy eval idv catch total num of agent3: 21
team_policy eval team catch total num: 39
team_policy eval average step individual rewards of agent4: 0.6345466419585973
team_policy eval average team episode rewards of agent4: 97.5
team_policy eval idv catch total num of agent4: 27
team_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent0: 0.5270683328064046
idv_policy eval average team episode rewards of agent0: 62.5
idv_policy eval idv catch total num of agent0: 23
idv_policy eval team catch total num: 25
idv_policy eval average step individual rewards of agent1: 0.5461506357054371
idv_policy eval average team episode rewards of agent1: 62.5
idv_policy eval idv catch total num of agent1: 24
idv_policy eval team catch total num: 25
idv_policy eval average step individual rewards of agent2: 0.5036692018326602
idv_policy eval average team episode rewards of agent2: 62.5
idv_policy eval idv catch total num of agent2: 22
idv_policy eval team catch total num: 25
idv_policy eval average step individual rewards of agent3: 0.2701929723763489
idv_policy eval average team episode rewards of agent3: 62.5
idv_policy eval idv catch total num of agent3: 13
idv_policy eval team catch total num: 25
idv_policy eval average step individual rewards of agent4: 0.44229286444350424
idv_policy eval average team episode rewards of agent4: 62.5
idv_policy eval idv catch total num of agent4: 20
idv_policy eval team catch total num: 25

 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.

team_policy eval average step individual rewards of agent0: 1.5062273450109354
team_policy eval average team episode rewards of agent0: 95.0
team_policy eval idv catch total num of agent0: 61
team_policy eval team catch total num: 38
team_policy eval average step individual rewards of agent1: 0.6558831231259523
team_policy eval average team episode rewards of agent1: 95.0
team_policy eval idv catch total num of agent1: 28
team_policy eval team catch total num: 38
team_policy eval average step individual rewards of agent2: 0.584733070104185
team_policy eval average team episode rewards of agent2: 95.0
team_policy eval idv catch total num of agent2: 25
team_policy eval team catch total num: 38
team_policy eval average step individual rewards of agent3: 0.5950158368731843
team_policy eval average team episode rewards of agent3: 95.0
team_policy eval idv catch total num of agent3: 25
team_policy eval team catch total num: 38
team_policy eval average step individual rewards of agent4: 0.6066509543388026
team_policy eval average team episode rewards of agent4: 95.0
team_policy eval idv catch total num of agent4: 26
team_policy eval team catch total num: 38
idv_policy eval average step individual rewards of agent0: 0.5272518347730275
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: 0.5323632089923818
idv_policy eval average team episode rewards of agent1: 82.5
idv_policy eval idv catch total num of agent1: 23
idv_policy eval team catch total num: 33
idv_policy eval average step individual rewards of agent2: 0.3495966442219283
idv_policy eval average team episode rewards of agent2: 82.5
idv_policy eval idv catch total num of agent2: 16
idv_policy eval team catch total num: 33
idv_policy eval average step individual rewards of agent3: 0.6535958218140041
idv_policy eval average team episode rewards of agent3: 82.5
idv_policy eval idv catch total num of agent3: 28
idv_policy eval team catch total num: 33
idv_policy eval average step individual rewards of agent4: 0.7141707219764183
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 5426/10000 episodes, total num timesteps 1085400/2000000, FPS 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.

team_policy eval average step individual rewards of agent0: 0.5928491153900278
team_policy eval average team episode rewards of agent0: 97.5
team_policy eval idv catch total num of agent0: 25
team_policy eval team catch total num: 39
team_policy eval average step individual rewards of agent1: 1.2394051360262994
team_policy eval average team episode rewards of agent1: 97.5
team_policy eval idv catch total num of agent1: 51
team_policy eval team catch total num: 39
team_policy eval average step individual rewards of agent2: 0.5609908501862576
team_policy eval average team episode rewards of agent2: 97.5
team_policy eval idv catch total num of agent2: 24
team_policy eval team catch total num: 39
team_policy eval average step individual rewards of agent3: 0.8654912494725145
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: 0.662231909543824
team_policy eval average team episode rewards of agent4: 97.5
team_policy eval idv catch total num of agent4: 28
team_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent0: 0.8083251035592874
idv_policy eval average team episode rewards of agent0: 115.0
idv_policy eval idv catch total num of agent0: 34
idv_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent1: 0.8440543631643683
idv_policy eval average team episode rewards of agent1: 115.0
idv_policy eval idv catch total num of agent1: 35
idv_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent2: 0.9698319923862713
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: 1.1690042663928175
idv_policy eval average team episode rewards of agent3: 115.0
idv_policy eval idv catch total num of agent3: 48
idv_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent4: 0.6882935657278861
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 5451/10000 episodes, total num timesteps 1090400/2000000, FPS 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.

team_policy eval average step individual rewards of agent0: 0.7684721058301867
team_policy eval average team episode rewards of agent0: 127.5
team_policy eval idv catch total num of agent0: 32
team_policy eval team catch total num: 51
team_policy eval average step individual rewards of agent1: 0.4436074046514083
team_policy eval average team episode rewards of agent1: 127.5
team_policy eval idv catch total num of agent1: 19
team_policy eval team catch total num: 51
team_policy eval average step individual rewards of agent2: 1.122604957570139
team_policy eval average team episode rewards of agent2: 127.5
team_policy eval idv catch total num of agent2: 46
team_policy eval team catch total num: 51
team_policy eval average step individual rewards of agent3: 0.6146800384543151
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: 1.2194615889603817
team_policy eval average team episode rewards of agent4: 127.5
team_policy eval idv catch total num of agent4: 50
team_policy eval team catch total num: 51
idv_policy eval average step individual rewards of agent0: 1.122887969181681
idv_policy eval average team episode rewards of agent0: 135.0
idv_policy eval idv catch total num of agent0: 46
idv_policy eval team catch total num: 54
idv_policy eval average step individual rewards of agent1: 0.7150555635176546
idv_policy eval average team episode rewards of agent1: 135.0
idv_policy eval idv catch total num of agent1: 30
idv_policy eval team catch total num: 54
idv_policy eval average step individual rewards of agent2: 0.660914416026131
idv_policy eval average team episode rewards of agent2: 135.0
idv_policy eval idv catch total num of agent2: 28
idv_policy eval team catch total num: 54
idv_policy eval average step individual rewards of agent3: 1.3509644129757774
idv_policy eval average team episode rewards of agent3: 135.0
idv_policy eval idv catch total num of agent3: 55
idv_policy eval team catch total num: 54
idv_policy eval average step individual rewards of agent4: 1.1263531985983881
idv_policy eval average team episode rewards of agent4: 135.0
idv_policy eval idv catch total num of agent4: 46
idv_policy eval team catch total num: 54

 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.

team_policy eval average step individual rewards of agent0: 0.4506288322309618
team_policy eval average team episode rewards of agent0: 90.0
team_policy eval idv catch total num of agent0: 20
team_policy eval team catch total num: 36
team_policy eval average step individual rewards of agent1: 0.9653153485778623
team_policy eval average team episode rewards of agent1: 90.0
team_policy eval idv catch total num of agent1: 40
team_policy eval team catch total num: 36
team_policy eval average step individual rewards of agent2: 0.25458628388296883
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: 1.0439279728603528
team_policy eval average team episode rewards of agent3: 90.0
team_policy eval idv catch total num of agent3: 43
team_policy eval team catch total num: 36
team_policy eval average step individual rewards of agent4: 0.6866465008706113
team_policy eval average team episode rewards of agent4: 90.0
team_policy eval idv catch total num of agent4: 29
team_policy eval team catch total num: 36
idv_policy eval average step individual rewards of agent0: 0.5902443864731657
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: 0.5589948750292959
idv_policy eval average team episode rewards of agent1: 120.0
idv_policy eval idv catch total num of agent1: 24
idv_policy eval team catch total num: 48
idv_policy eval average step individual rewards of agent2: 1.2644195568863372
idv_policy eval average team episode rewards of agent2: 120.0
idv_policy eval idv catch total num of agent2: 52
idv_policy eval team catch total num: 48
idv_policy eval average step individual rewards of agent3: 0.8452610890190587
idv_policy eval average team episode rewards of agent3: 120.0
idv_policy eval idv catch total num of agent3: 35
idv_policy eval team catch total num: 48
idv_policy eval average step individual rewards of agent4: 0.9158101295918738
idv_policy eval average team episode rewards of agent4: 120.0
idv_policy eval idv catch total num of agent4: 38
idv_policy eval team catch total num: 48

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5501/10000 episodes, total num timesteps 1100400/2000000, FPS 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.

team_policy eval average step individual rewards of agent0: 1.1073902909301279
team_policy eval average team episode rewards of agent0: 77.5
team_policy eval idv catch total num of agent0: 46
team_policy eval team catch total num: 31
team_policy eval average step individual rewards of agent1: 0.40732284481492753
team_policy eval average team episode rewards of agent1: 77.5
team_policy eval idv catch total num of agent1: 18
team_policy eval team catch total num: 31
team_policy eval average step individual rewards of agent2: 0.7128588380496598
team_policy eval average team episode rewards of agent2: 77.5
team_policy eval idv catch total num of agent2: 30
team_policy eval team catch total num: 31
team_policy eval average step individual rewards of agent3: 0.3006576690006247
team_policy eval average team episode rewards of agent3: 77.5
team_policy eval idv catch total num of agent3: 14
team_policy eval team catch total num: 31
team_policy eval average step individual rewards of agent4: 0.35057157589681087
team_policy eval average team episode rewards of agent4: 77.5
team_policy eval idv catch total num of agent4: 16
team_policy eval team catch total num: 31
idv_policy eval average step individual rewards of agent0: 0.6137206482336308
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: 1.0347764709718634
idv_policy eval average team episode rewards of agent1: 102.5
idv_policy eval idv catch total num of agent1: 43
idv_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent2: 0.5313675479689998
idv_policy eval average team episode rewards of agent2: 102.5
idv_policy eval idv catch total num of agent2: 23
idv_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent3: 1.07218130203099
idv_policy eval average team episode rewards of agent3: 102.5
idv_policy eval idv catch total num of agent3: 44
idv_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent4: 0.6366891858315925
idv_policy eval average team episode rewards of agent4: 102.5
idv_policy eval idv catch total num of agent4: 27
idv_policy eval team catch total num: 41

 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.

team_policy eval average step individual rewards of agent0: 0.5269624803135595
team_policy eval average team episode rewards of agent0: 77.5
team_policy eval idv catch total num of agent0: 23
team_policy eval team catch total num: 31
team_policy eval average step individual rewards of agent1: 0.7098827045756552
team_policy eval average team episode rewards of agent1: 77.5
team_policy eval idv catch total num of agent1: 30
team_policy eval team catch total num: 31
team_policy eval average step individual rewards of agent2: 0.644735687480867
team_policy eval average team episode rewards of agent2: 77.5
team_policy eval idv catch total num of agent2: 27
team_policy eval team catch total num: 31
team_policy eval average step individual rewards of agent3: 0.7251028754242967
team_policy eval average team episode rewards of agent3: 77.5
team_policy eval idv catch total num of agent3: 31
team_policy eval team catch total num: 31
team_policy eval average step individual rewards of agent4: 0.4863594652642552
team_policy eval average team episode rewards of agent4: 77.5
team_policy eval idv catch total num of agent4: 21
team_policy eval team catch total num: 31
idv_policy eval average step individual rewards of agent0: 1.2169744211759916
idv_policy eval average team episode rewards of agent0: 145.0
idv_policy eval idv catch total num of agent0: 50
idv_policy eval team catch total num: 58
idv_policy eval average step individual rewards of agent1: 1.01864768201184
idv_policy eval average team episode rewards of agent1: 145.0
idv_policy eval idv catch total num of agent1: 42
idv_policy eval team catch total num: 58
idv_policy eval average step individual rewards of agent2: 0.8945082310495087
idv_policy eval average team episode rewards of agent2: 145.0
idv_policy eval idv catch total num of agent2: 37
idv_policy eval team catch total num: 58
idv_policy eval average step individual rewards of agent3: 0.8939218647307925
idv_policy eval average team episode rewards of agent3: 145.0
idv_policy eval idv catch total num of agent3: 37
idv_policy eval team catch total num: 58
idv_policy eval average step individual rewards of agent4: 0.7307138858866793
idv_policy eval average team episode rewards of agent4: 145.0
idv_policy eval idv catch total num of agent4: 31
idv_policy eval team catch total num: 58

 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 247.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.

team_policy eval average step individual rewards of agent0: 1.2987478695240453
team_policy eval average team episode rewards of agent0: 150.0
team_policy eval idv catch total num of agent0: 53
team_policy eval team catch total num: 60
team_policy eval average step individual rewards of agent1: 1.3491172650168135
team_policy eval average team episode rewards of agent1: 150.0
team_policy eval idv catch total num of agent1: 55
team_policy eval team catch total num: 60
team_policy eval average step individual rewards of agent2: 1.3002895088946473
team_policy eval average team episode rewards of agent2: 150.0
team_policy eval idv catch total num of agent2: 53
team_policy eval team catch total num: 60
team_policy eval average step individual rewards of agent3: 0.7905394984118016
team_policy eval average team episode rewards of agent3: 150.0
team_policy eval idv catch total num of agent3: 33
team_policy eval team catch total num: 60
team_policy eval average step individual rewards of agent4: 0.6875737229001895
team_policy eval average team episode rewards of agent4: 150.0
team_policy eval idv catch total num of agent4: 29
team_policy eval team catch total num: 60
idv_policy eval average step individual rewards of agent0: 0.36565818363703145
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: 1.0427333738542164
idv_policy eval average team episode rewards of agent1: 87.5
idv_policy eval idv catch total num of agent1: 43
idv_policy eval team catch total num: 35
idv_policy eval average step individual rewards of agent2: 0.693691342456813
idv_policy eval average team episode rewards of agent2: 87.5
idv_policy eval idv catch total num of agent2: 29
idv_policy eval team catch total num: 35
idv_policy eval average step individual rewards of agent3: 0.5606249498308303
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: 0.6918840410861777
idv_policy eval average team episode rewards of agent4: 87.5
idv_policy eval idv catch total num of agent4: 29
idv_policy eval team catch total num: 35

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5576/10000 episodes, total num timesteps 1115400/2000000, FPS 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.

team_policy eval average step individual rewards of agent0: 0.5129070007442619
team_policy eval average team episode rewards of agent0: 117.5
team_policy eval idv catch total num of agent0: 22
team_policy eval team catch total num: 47
team_policy eval average step individual rewards of agent1: 1.0743440258045722
team_policy eval average team episode rewards of agent1: 117.5
team_policy eval idv catch total num of agent1: 44
team_policy eval team catch total num: 47
team_policy eval average step individual rewards of agent2: 0.9666899888844429
team_policy eval average team episode rewards of agent2: 117.5
team_policy eval idv catch total num of agent2: 40
team_policy eval team catch total num: 47
team_policy eval average step individual rewards of agent3: 0.8442364399318049
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: 1.0644633003020656
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: 0.715040181317418
idv_policy eval average team episode rewards of agent0: 122.5
idv_policy eval idv catch total num of agent0: 30
idv_policy eval team catch total num: 49
idv_policy eval average step individual rewards of agent1: 0.9931288441932676
idv_policy eval average team episode rewards of agent1: 122.5
idv_policy eval idv catch total num of agent1: 41
idv_policy eval team catch total num: 49
idv_policy eval average step individual rewards of agent2: 0.8594103049285425
idv_policy eval average team episode rewards of agent2: 122.5
idv_policy eval idv catch total num of agent2: 36
idv_policy eval team catch total num: 49
idv_policy eval average step individual rewards of agent3: 0.8661497090338741
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: 0.7086834453393532
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 5601/10000 episodes, total num timesteps 1120400/2000000, FPS 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.

team_policy eval average step individual rewards of agent0: 0.8852872041233559
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: 1.1483646985149887
team_policy eval average team episode rewards of agent1: 132.5
team_policy eval idv catch total num of agent1: 47
team_policy eval team catch total num: 53
team_policy eval average step individual rewards of agent2: 0.5494497334866761
team_policy eval average team episode rewards of agent2: 132.5
team_policy eval idv catch total num of agent2: 24
team_policy eval team catch total num: 53
team_policy eval average step individual rewards of agent3: 1.2188962173393016
team_policy eval average team episode rewards of agent3: 132.5
team_policy eval idv catch total num of agent3: 50
team_policy eval team catch total num: 53
team_policy eval average step individual rewards of agent4: 0.8625581050791614
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: 1.0660660333632956
idv_policy eval average team episode rewards of agent0: 150.0
idv_policy eval idv catch total num of agent0: 44
idv_policy eval team catch total num: 60
idv_policy eval average step individual rewards of agent1: 1.1735135018806357
idv_policy eval average team episode rewards of agent1: 150.0
idv_policy eval idv catch total num of agent1: 48
idv_policy eval team catch total num: 60
idv_policy eval average step individual rewards of agent2: 1.4499663248741075
idv_policy eval average team episode rewards of agent2: 150.0
idv_policy eval idv catch total num of agent2: 59
idv_policy eval team catch total num: 60
idv_policy eval average step individual rewards of agent3: 0.6912293611411432
idv_policy eval average team episode rewards of agent3: 150.0
idv_policy eval idv catch total num of agent3: 29
idv_policy eval team catch total num: 60
idv_policy eval average step individual rewards of agent4: 1.0166935486201107
idv_policy eval average team episode rewards of agent4: 150.0
idv_policy eval idv catch total num of agent4: 42
idv_policy eval team catch total num: 60

 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.

team_policy eval average step individual rewards of agent0: 0.7577740287703145
team_policy eval average team episode rewards of agent0: 77.5
team_policy eval idv catch total num of agent0: 32
team_policy eval team catch total num: 31
team_policy eval average step individual rewards of agent1: 0.35602095320872323
team_policy eval average team episode rewards of agent1: 77.5
team_policy eval idv catch total num of agent1: 16
team_policy eval team catch total num: 31
team_policy eval average step individual rewards of agent2: 0.7127034654760334
team_policy eval average team episode rewards of agent2: 77.5
team_policy eval idv catch total num of agent2: 30
team_policy eval team catch total num: 31
team_policy eval average step individual rewards of agent3: 0.5502298060276715
team_policy eval average team episode rewards of agent3: 77.5
team_policy eval idv catch total num of agent3: 24
team_policy eval team catch total num: 31
team_policy eval average step individual rewards of agent4: 0.612615787136792
team_policy eval average team episode rewards of agent4: 77.5
team_policy eval idv catch total num of agent4: 26
team_policy eval team catch total num: 31
idv_policy eval average step individual rewards of agent0: 0.49981666032474975
idv_policy eval average team episode rewards of agent0: 75.0
idv_policy eval idv catch total num of agent0: 22
idv_policy eval team catch total num: 30
idv_policy eval average step individual rewards of agent1: 0.8877103562039267
idv_policy eval average team episode rewards of agent1: 75.0
idv_policy eval idv catch total num of agent1: 37
idv_policy eval team catch total num: 30
idv_policy eval average step individual rewards of agent2: 0.5619228423951038
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: 0.5947648053820017
idv_policy eval average team episode rewards of agent3: 75.0
idv_policy eval idv catch total num of agent3: 25
idv_policy eval team catch total num: 30
idv_policy eval average step individual rewards of agent4: 0.5106108403722375
idv_policy eval average team episode rewards of agent4: 75.0
idv_policy eval idv catch total num of agent4: 22
idv_policy eval team catch total num: 30

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 5651/10000 episodes, total num timesteps 1130400/2000000, FPS 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.

team_policy eval average step individual rewards of agent0: 0.6898713355578195
team_policy eval average team episode rewards of agent0: 105.0
team_policy eval idv catch total num of agent0: 29
team_policy eval team catch total num: 42
team_policy eval average step individual rewards of agent1: 0.9986147131280171
team_policy eval average team episode rewards of agent1: 105.0
team_policy eval idv catch total num of agent1: 41
team_policy eval team catch total num: 42
team_policy eval average step individual rewards of agent2: 0.9115904429404545
team_policy eval average team episode rewards of agent2: 105.0
team_policy eval idv catch total num of agent2: 38
team_policy eval team catch total num: 42
team_policy eval average step individual rewards of agent3: 0.9666409351924594
team_policy eval average team episode rewards of agent3: 105.0
team_policy eval idv catch total num of agent3: 40
team_policy eval team catch total num: 42
team_policy eval average step individual rewards of agent4: 0.5828806005553662
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: 0.8849728415208001
idv_policy eval average team episode rewards of agent0: 112.5
idv_policy eval idv catch total num of agent0: 37
idv_policy eval team catch total num: 45
idv_policy eval average step individual rewards of agent1: 0.5808592471309781
idv_policy eval average team episode rewards of agent1: 112.5
idv_policy eval idv catch total num of agent1: 25
idv_policy eval team catch total num: 45
idv_policy eval average step individual rewards of agent2: 0.7634491045887871
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: 0.9614476901159236
idv_policy eval average team episode rewards of agent3: 112.5
idv_policy eval idv catch total num of agent3: 40
idv_policy eval team catch total num: 45
idv_policy eval average step individual rewards of agent4: 0.6634381879230636
idv_policy eval average team episode rewards of agent4: 112.5
idv_policy eval idv catch total num of agent4: 28
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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.

team_policy eval average step individual rewards of agent0: 0.29683406915125005
team_policy eval average team episode rewards of agent0: 77.5
team_policy eval idv catch total num of agent0: 14
team_policy eval team catch total num: 31
team_policy eval average step individual rewards of agent1: 0.6427906695536473
team_policy eval average team episode rewards of agent1: 77.5
team_policy eval idv catch total num of agent1: 27
team_policy eval team catch total num: 31
team_policy eval average step individual rewards of agent2: 0.7165811047621833
team_policy eval average team episode rewards of agent2: 77.5
team_policy eval idv catch total num of agent2: 30
team_policy eval team catch total num: 31
team_policy eval average step individual rewards of agent3: 0.508444849082255
team_policy eval average team episode rewards of agent3: 77.5
team_policy eval idv catch total num of agent3: 22
team_policy eval team catch total num: 31
team_policy eval average step individual rewards of agent4: 0.7185457895608398
team_policy eval average team episode rewards of agent4: 77.5
team_policy eval idv catch total num of agent4: 30
team_policy eval team catch total num: 31
idv_policy eval average step individual rewards of agent0: 0.4376918485728188
idv_policy eval average team episode rewards of agent0: 132.5
idv_policy eval idv catch total num of agent0: 19
idv_policy eval team catch total num: 53
idv_policy eval average step individual rewards of agent1: 1.141135995340303
idv_policy eval average team episode rewards of agent1: 132.5
idv_policy eval idv catch total num of agent1: 47
idv_policy eval team catch total num: 53
idv_policy eval average step individual rewards of agent2: 0.6864432207335652
idv_policy eval average team episode rewards of agent2: 132.5
idv_policy eval idv catch total num of agent2: 29
idv_policy eval team catch total num: 53
idv_policy eval average step individual rewards of agent3: 1.1961863450648764
idv_policy eval average team episode rewards of agent3: 132.5
idv_policy eval idv catch total num of agent3: 49
idv_policy eval team catch total num: 53
idv_policy eval average step individual rewards of agent4: 0.9586959539486122
idv_policy eval average team episode rewards of agent4: 132.5
idv_policy eval idv catch total num of agent4: 40
idv_policy eval team catch total num: 53

 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.

team_policy eval average step individual rewards of agent0: 0.6111758704988042
team_policy eval average team episode rewards of agent0: 120.0
team_policy eval idv catch total num of agent0: 26
team_policy eval team catch total num: 48
team_policy eval average step individual rewards of agent1: 0.8913808134881006
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: 0.6311163575513715
team_policy eval average team episode rewards of agent2: 120.0
team_policy eval idv catch total num of agent2: 27
team_policy eval team catch total num: 48
team_policy eval average step individual rewards of agent3: 0.9165273191873371
team_policy eval average team episode rewards of agent3: 120.0
team_policy eval idv catch total num of agent3: 38
team_policy eval team catch total num: 48
team_policy eval average step individual rewards of agent4: 1.0982411305670383
team_policy eval average team episode rewards of agent4: 120.0
team_policy eval idv catch total num of agent4: 45
team_policy eval team catch total num: 48
idv_policy eval average step individual rewards of agent0: 1.0126929724289027
idv_policy eval average team episode rewards of agent0: 107.5
idv_policy eval idv catch total num of agent0: 42
idv_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent1: 0.585244577519259
idv_policy eval average team episode rewards of agent1: 107.5
idv_policy eval idv catch total num of agent1: 25
idv_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent2: 0.436427530257529
idv_policy eval average team episode rewards of agent2: 107.5
idv_policy eval idv catch total num of agent2: 19
idv_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent3: 0.6658154667646108
idv_policy eval average team episode rewards of agent3: 107.5
idv_policy eval idv catch total num of agent3: 28
idv_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent4: 1.0488233253729353
idv_policy eval average team episode rewards of agent4: 107.5
idv_policy eval idv catch total num of agent4: 43
idv_policy eval team catch total num: 43

 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.

team_policy eval average step individual rewards of agent0: 1.2184126566799898
team_policy eval average team episode rewards of agent0: 120.0
team_policy eval idv catch total num of agent0: 50
team_policy eval team catch total num: 48
team_policy eval average step individual rewards of agent1: 0.8883688012712087
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: 1.2919653516143743
team_policy eval average team episode rewards of agent2: 120.0
team_policy eval idv catch total num of agent2: 53
team_policy eval team catch total num: 48
team_policy eval average step individual rewards of agent3: 0.5879406683580403
team_policy eval average team episode rewards of agent3: 120.0
team_policy eval idv catch total num of agent3: 25
team_policy eval team catch total num: 48
team_policy eval average step individual rewards of agent4: 0.7307268264385887
team_policy eval average team episode rewards of agent4: 120.0
team_policy eval idv catch total num of agent4: 31
team_policy eval team catch total num: 48
idv_policy eval average step individual rewards of agent0: 0.7331272258918857
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: 0.8170710225322668
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: 1.1458359842720363
idv_policy eval average team episode rewards of agent2: 102.5
idv_policy eval idv catch total num of agent2: 47
idv_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent3: 1.1193494717453076
idv_policy eval average team episode rewards of agent3: 102.5
idv_policy eval idv catch total num of agent3: 46
idv_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent4: 0.5820728570473676
idv_policy eval average team episode rewards of agent4: 102.5
idv_policy eval idv catch total num of agent4: 25
idv_policy eval team catch total num: 41

 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.

team_policy eval average step individual rewards of agent0: 0.7615024247935529
team_policy eval average team episode rewards of agent0: 125.0
team_policy eval idv catch total num of agent0: 32
team_policy eval team catch total num: 50
team_policy eval average step individual rewards of agent1: 0.8879872417484895
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: 1.4498564260568898
team_policy eval average team episode rewards of agent2: 125.0
team_policy eval idv catch total num of agent2: 59
team_policy eval team catch total num: 50
team_policy eval average step individual rewards of agent3: 0.8629589367488166
team_policy eval average team episode rewards of agent3: 125.0
team_policy eval idv catch total num of agent3: 36
team_policy eval team catch total num: 50
team_policy eval average step individual rewards of agent4: 0.7617545574718062
team_policy eval average team episode rewards of agent4: 125.0
team_policy eval idv catch total num of agent4: 32
team_policy eval team catch total num: 50
idv_policy eval average step individual rewards of agent0: 0.8616933408767803
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: 0.6361582818545731
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: 1.1748462358560368
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: 1.020192843587216
idv_policy eval average team episode rewards of agent3: 125.0
idv_policy eval idv catch total num of agent3: 42
idv_policy eval team catch total num: 50
idv_policy eval average step individual rewards of agent4: 0.9886158956964486
idv_policy eval average team episode rewards of agent4: 125.0
idv_policy eval idv catch total num of agent4: 41
idv_policy eval team catch total num: 50

 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.

team_policy eval average step individual rewards of agent0: 1.0353288842224047
team_policy eval average team episode rewards of agent0: 160.0
team_policy eval idv catch total num of agent0: 43
team_policy eval team catch total num: 64
team_policy eval average step individual rewards of agent1: 0.8599164154527452
team_policy eval average team episode rewards of agent1: 160.0
team_policy eval idv catch total num of agent1: 36
team_policy eval team catch total num: 64
team_policy eval average step individual rewards of agent2: 1.1160444115271009
team_policy eval average team episode rewards of agent2: 160.0
team_policy eval idv catch total num of agent2: 46
team_policy eval team catch total num: 64
team_policy eval average step individual rewards of agent3: 1.191913956844606
team_policy eval average team episode rewards of agent3: 160.0
team_policy eval idv catch total num of agent3: 49
team_policy eval team catch total num: 64
team_policy eval average step individual rewards of agent4: 0.9853307468408894
team_policy eval average team episode rewards of agent4: 160.0
team_policy eval idv catch total num of agent4: 41
team_policy eval team catch total num: 64
idv_policy eval average step individual rewards of agent0: 1.1203199788753326
idv_policy eval average team episode rewards of agent0: 127.5
idv_policy eval idv catch total num of agent0: 46
idv_policy eval team catch total num: 51
idv_policy eval average step individual rewards of agent1: 0.796202496184074
idv_policy eval average team episode rewards of agent1: 127.5
idv_policy eval idv catch total num of agent1: 33
idv_policy eval team catch total num: 51
idv_policy eval average step individual rewards of agent2: 0.6337761995365646
idv_policy eval average team episode rewards of agent2: 127.5
idv_policy eval idv catch total num of agent2: 27
idv_policy eval team catch total num: 51
idv_policy eval average step individual rewards of agent3: 1.0861815321413117
idv_policy eval average team episode rewards of agent3: 127.5
idv_policy eval idv catch total num of agent3: 45
idv_policy eval team catch total num: 51
idv_policy eval average step individual rewards of agent4: 1.092160143618594
idv_policy eval average team episode rewards of agent4: 127.5
idv_policy eval idv catch total num of agent4: 45
idv_policy eval team catch total num: 51

 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 248.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.

team_policy eval average step individual rewards of agent0: 0.7339667627965093
team_policy eval average team episode rewards of agent0: 85.0
team_policy eval idv catch total num of agent0: 31
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent1: 0.6500208369030972
team_policy eval average team episode rewards of agent1: 85.0
team_policy eval idv catch total num of agent1: 28
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent2: 0.9231854134487227
team_policy eval average team episode rewards of agent2: 85.0
team_policy eval idv catch total num of agent2: 38
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent3: 0.6070795972543006
team_policy eval average team episode rewards of agent3: 85.0
team_policy eval idv catch total num of agent3: 26
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent4: 0.0650181293091064
team_policy eval average team episode rewards of agent4: 85.0
team_policy eval idv catch total num of agent4: 5
team_policy eval team catch total num: 34
idv_policy eval average step individual rewards of agent0: 0.5085655811112645
idv_policy eval average team episode rewards of agent0: 97.5
idv_policy eval idv catch total num of agent0: 22
idv_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent1: 0.5951068499134103
idv_policy eval average team episode rewards of agent1: 97.5
idv_policy eval idv catch total num of agent1: 25
idv_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent2: 0.8189847234833242
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: 1.1192164545557193
idv_policy eval average team episode rewards of agent3: 97.5
idv_policy eval idv catch total num of agent3: 46
idv_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent4: 0.8211452657634821
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 5826/10000 episodes, total num timesteps 1165400/2000000, FPS 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.

team_policy eval average step individual rewards of agent0: 0.7708248786543581
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: 0.4165245697850261
team_policy eval average team episode rewards of agent1: 95.0
team_policy eval idv catch total num of agent1: 18
team_policy eval team catch total num: 38
team_policy eval average step individual rewards of agent2: 0.8379501106576199
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: 0.5121083930668344
team_policy eval average team episode rewards of agent3: 95.0
team_policy eval idv catch total num of agent3: 22
team_policy eval team catch total num: 38
team_policy eval average step individual rewards of agent4: 0.6729900177614286
team_policy eval average team episode rewards of agent4: 95.0
team_policy eval idv catch total num of agent4: 28
team_policy eval team catch total num: 38
idv_policy eval average step individual rewards of agent0: 0.7855750215366935
idv_policy eval average team episode rewards of agent0: 110.0
idv_policy eval idv catch total num of agent0: 33
idv_policy eval team catch total num: 44
idv_policy eval average step individual rewards of agent1: 0.8910818420478128
idv_policy eval average team episode rewards of agent1: 110.0
idv_policy eval idv catch total num of agent1: 37
idv_policy eval team catch total num: 44
idv_policy eval average step individual rewards of agent2: 0.7875521929649301
idv_policy eval average team episode rewards of agent2: 110.0
idv_policy eval idv catch total num of agent2: 33
idv_policy eval team catch total num: 44
idv_policy eval average step individual rewards of agent3: 0.9097328584239822
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: 0.7907028367435615
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 5851/10000 episodes, total num timesteps 1170400/2000000, FPS 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.

team_policy eval average step individual rewards of agent0: 0.688988166835014
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: 0.4438492059640922
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: 0.7166595432783822
team_policy eval average team episode rewards of agent2: 87.5
team_policy eval idv catch total num of agent2: 30
team_policy eval team catch total num: 35
team_policy eval average step individual rewards of agent3: 0.6926356897165412
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: 0.6031466895385478
team_policy eval average team episode rewards of agent4: 87.5
team_policy eval idv catch total num of agent4: 26
team_policy eval team catch total num: 35
idv_policy eval average step individual rewards of agent0: 0.8115996940005321
idv_policy eval average team episode rewards of agent0: 137.5
idv_policy eval idv catch total num of agent0: 34
idv_policy eval team catch total num: 55
idv_policy eval average step individual rewards of agent1: 1.0710447011351412
idv_policy eval average team episode rewards of agent1: 137.5
idv_policy eval idv catch total num of agent1: 44
idv_policy eval team catch total num: 55
idv_policy eval average step individual rewards of agent2: 0.9637304590311129
idv_policy eval average team episode rewards of agent2: 137.5
idv_policy eval idv catch total num of agent2: 40
idv_policy eval team catch total num: 55
idv_policy eval average step individual rewards of agent3: 0.7645741643105322
idv_policy eval average team episode rewards of agent3: 137.5
idv_policy eval idv catch total num of agent3: 32
idv_policy eval team catch total num: 55
idv_policy eval average step individual rewards of agent4: 1.0151962938435068
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 5876/10000 episodes, total num timesteps 1175400/2000000, FPS 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.

team_policy eval average step individual rewards of agent0: 0.9179298926479491
team_policy eval average team episode rewards of agent0: 85.0
team_policy eval idv catch total num of agent0: 38
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent1: 0.789745057904024
team_policy eval average team episode rewards of agent1: 85.0
team_policy eval idv catch total num of agent1: 33
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent2: 0.6077587441081895
team_policy eval average team episode rewards of agent2: 85.0
team_policy eval idv catch total num of agent2: 26
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent3: 0.6570859651314881
team_policy eval average team episode rewards of agent3: 85.0
team_policy eval idv catch total num of agent3: 28
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent4: 0.5293778521647811
team_policy eval average team episode rewards of agent4: 85.0
team_policy eval idv catch total num of agent4: 23
team_policy eval team catch total num: 34
idv_policy eval average step individual rewards of agent0: 0.6608733429778267
idv_policy eval average team episode rewards of agent0: 122.5
idv_policy eval idv catch total num of agent0: 28
idv_policy eval team catch total num: 49
idv_policy eval average step individual rewards of agent1: 0.8962127427613599
idv_policy eval average team episode rewards of agent1: 122.5
idv_policy eval idv catch total num of agent1: 37
idv_policy eval team catch total num: 49
idv_policy eval average step individual rewards of agent2: 0.8682826617527419
idv_policy eval average team episode rewards of agent2: 122.5
idv_policy eval idv catch total num of agent2: 36
idv_policy eval team catch total num: 49
idv_policy eval average step individual rewards of agent3: 0.6147885905603342
idv_policy eval average team episode rewards of agent3: 122.5
idv_policy eval idv catch total num of agent3: 26
idv_policy eval team catch total num: 49
idv_policy eval average step individual rewards of agent4: 1.0158547835549454
idv_policy eval average team episode rewards of agent4: 122.5
idv_policy eval idv catch total num of agent4: 42
idv_policy eval team catch total num: 49

 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.

team_policy eval average step individual rewards of agent0: 0.8697891076037655
team_policy eval average team episode rewards of agent0: 102.5
team_policy eval idv catch total num of agent0: 36
team_policy eval team catch total num: 41
team_policy eval average step individual rewards of agent1: 0.6894072226282532
team_policy eval average team episode rewards of agent1: 102.5
team_policy eval idv catch total num of agent1: 29
team_policy eval team catch total num: 41
team_policy eval average step individual rewards of agent2: 0.8922733107455925
team_policy eval average team episode rewards of agent2: 102.5
team_policy eval idv catch total num of agent2: 37
team_policy eval team catch total num: 41
team_policy eval average step individual rewards of agent3: 0.8926312197729628
team_policy eval average team episode rewards of agent3: 102.5
team_policy eval idv catch total num of agent3: 37
team_policy eval team catch total num: 41
team_policy eval average step individual rewards of agent4: 0.3514216621663633
team_policy eval average team episode rewards of agent4: 102.5
team_policy eval idv catch total num of agent4: 16
team_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent0: 0.6287131025651675
idv_policy eval average team episode rewards of agent0: 70.0
idv_policy eval idv catch total num of agent0: 27
idv_policy eval team catch total num: 28
idv_policy eval average step individual rewards of agent1: 0.30950161058356396
idv_policy eval average team episode rewards of agent1: 70.0
idv_policy eval idv catch total num of agent1: 14
idv_policy eval team catch total num: 28
idv_policy eval average step individual rewards of agent2: 0.5883946327900734
idv_policy eval average team episode rewards of agent2: 70.0
idv_policy eval idv catch total num of agent2: 25
idv_policy eval team catch total num: 28
idv_policy eval average step individual rewards of agent3: 0.4793102104305948
idv_policy eval average team episode rewards of agent3: 70.0
idv_policy eval idv catch total num of agent3: 21
idv_policy eval team catch total num: 28
idv_policy eval average step individual rewards of agent4: 0.3454354013965281
idv_policy eval average team episode rewards of agent4: 70.0
idv_policy eval idv catch total num of agent4: 16
idv_policy eval team catch total num: 28

 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.

team_policy eval average step individual rewards of agent0: 0.6558634132483008
team_policy eval average team episode rewards of agent0: 97.5
team_policy eval idv catch total num of agent0: 28
team_policy eval team catch total num: 39
team_policy eval average step individual rewards of agent1: 0.6296119327197132
team_policy eval average team episode rewards of agent1: 97.5
team_policy eval idv catch total num of agent1: 27
team_policy eval team catch total num: 39
team_policy eval average step individual rewards of agent2: 0.43254049925937776
team_policy eval average team episode rewards of agent2: 97.5
team_policy eval idv catch total num of agent2: 19
team_policy eval team catch total num: 39
team_policy eval average step individual rewards of agent3: 0.4738926223217795
team_policy eval average team episode rewards of agent3: 97.5
team_policy eval idv catch total num of agent3: 21
team_policy eval team catch total num: 39
team_policy eval average step individual rewards of agent4: 1.0683300202119212
team_policy eval average team episode rewards of agent4: 97.5
team_policy eval idv catch total num of agent4: 44
team_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent0: 0.4849194479191492
idv_policy eval average team episode rewards of agent0: 37.5
idv_policy eval idv catch total num of agent0: 21
idv_policy eval team catch total num: 15
idv_policy eval average step individual rewards of agent1: 0.2554851859046302
idv_policy eval average team episode rewards of agent1: 37.5
idv_policy eval idv catch total num of agent1: 12
idv_policy eval team catch total num: 15
idv_policy eval average step individual rewards of agent2: 0.4834127045281777
idv_policy eval average team episode rewards of agent2: 37.5
idv_policy eval idv catch total num of agent2: 21
idv_policy eval team catch total num: 15
idv_policy eval average step individual rewards of agent3: 0.2674144910775642
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: 0.35621034809440266
idv_policy eval average team episode rewards of agent4: 37.5
idv_policy eval idv catch total num of agent4: 16
idv_policy eval team catch total num: 15

 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.

team_policy eval average step individual rewards of agent0: 1.1717171384483975
team_policy eval average team episode rewards of agent0: 132.5
team_policy eval idv catch total num of agent0: 48
team_policy eval team catch total num: 53
team_policy eval average step individual rewards of agent1: 1.11775333313311
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: 1.0673297056387339
team_policy eval average team episode rewards of agent2: 132.5
team_policy eval idv catch total num of agent2: 44
team_policy eval team catch total num: 53
team_policy eval average step individual rewards of agent3: 0.6846453789548209
team_policy eval average team episode rewards of agent3: 132.5
team_policy eval idv catch total num of agent3: 29
team_policy eval team catch total num: 53
team_policy eval average step individual rewards of agent4: 0.8429339431197005
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: 0.8426696520339507
idv_policy eval average team episode rewards of agent0: 82.5
idv_policy eval idv catch total num of agent0: 35
idv_policy eval team catch total num: 33
idv_policy eval average step individual rewards of agent1: 0.40561127012077486
idv_policy eval average team episode rewards of agent1: 82.5
idv_policy eval idv catch total num of agent1: 18
idv_policy eval team catch total num: 33
idv_policy eval average step individual rewards of agent2: 0.6830273565658388
idv_policy eval average team episode rewards of agent2: 82.5
idv_policy eval idv catch total num of agent2: 29
idv_policy eval team catch total num: 33
idv_policy eval average step individual rewards of agent3: 0.9150133978659061
idv_policy eval average team episode rewards of agent3: 82.5
idv_policy eval idv catch total num of agent3: 38
idv_policy eval team catch total num: 33
idv_policy eval average step individual rewards of agent4: 0.4040312221112788
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 5976/10000 episodes, total num timesteps 1195400/2000000, FPS 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.

team_policy eval average step individual rewards of agent0: 1.1448121903973816
team_policy eval average team episode rewards of agent0: 127.5
team_policy eval idv catch total num of agent0: 47
team_policy eval team catch total num: 51
team_policy eval average step individual rewards of agent1: 0.8463003103481084
team_policy eval average team episode rewards of agent1: 127.5
team_policy eval idv catch total num of agent1: 35
team_policy eval team catch total num: 51
team_policy eval average step individual rewards of agent2: 1.1212728786744521
team_policy eval average team episode rewards of agent2: 127.5
team_policy eval idv catch total num of agent2: 46
team_policy eval team catch total num: 51
team_policy eval average step individual rewards of agent3: 0.8693616255193578
team_policy eval average team episode rewards of agent3: 127.5
team_policy eval idv catch total num of agent3: 36
team_policy eval team catch total num: 51
team_policy eval average step individual rewards of agent4: 0.6871819229999651
team_policy eval average team episode rewards of agent4: 127.5
team_policy eval idv catch total num of agent4: 29
team_policy eval team catch total num: 51
idv_policy eval average step individual rewards of agent0: 0.71409907247525
idv_policy eval average team episode rewards of agent0: 62.5
idv_policy eval idv catch total num of agent0: 30
idv_policy eval team catch total num: 25
idv_policy eval average step individual rewards of agent1: 0.7390047242122151
idv_policy eval average team episode rewards of agent1: 62.5
idv_policy eval idv catch total num of agent1: 31
idv_policy eval team catch total num: 25
idv_policy eval average step individual rewards of agent2: 0.6301927360588386
idv_policy eval average team episode rewards of agent2: 62.5
idv_policy eval idv catch total num of agent2: 27
idv_policy eval team catch total num: 25
idv_policy eval average step individual rewards of agent3: 0.5181743147351348
idv_policy eval average team episode rewards of agent3: 62.5
idv_policy eval idv catch total num of agent3: 22
idv_policy eval team catch total num: 25
idv_policy eval average step individual rewards of agent4: 0.3838071470071216
idv_policy eval average team episode rewards of agent4: 62.5
idv_policy eval idv catch total num of agent4: 17
idv_policy eval team catch total num: 25

 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.

team_policy eval average step individual rewards of agent0: 0.7459258979512136
team_policy eval average team episode rewards of agent0: 105.0
team_policy eval idv catch total num of agent0: 31
team_policy eval team catch total num: 42
team_policy eval average step individual rewards of agent1: 0.9186461161271129
team_policy eval average team episode rewards of agent1: 105.0
team_policy eval idv catch total num of agent1: 38
team_policy eval team catch total num: 42
team_policy eval average step individual rewards of agent2: 0.6958717686820626
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: 0.8913865506601837
team_policy eval average team episode rewards of agent3: 105.0
team_policy eval idv catch total num of agent3: 37
team_policy eval team catch total num: 42
team_policy eval average step individual rewards of agent4: 0.7729465065630428
team_policy eval average team episode rewards of agent4: 105.0
team_policy eval idv catch total num of agent4: 32
team_policy eval team catch total num: 42
idv_policy eval average step individual rewards of agent0: 0.7385674493949552
idv_policy eval average team episode rewards of agent0: 120.0
idv_policy eval idv catch total num of agent0: 31
idv_policy eval team catch total num: 48
idv_policy eval average step individual rewards of agent1: 0.8164774817379993
idv_policy eval average team episode rewards of agent1: 120.0
idv_policy eval idv catch total num of agent1: 34
idv_policy eval team catch total num: 48
idv_policy eval average step individual rewards of agent2: 0.8148420233270747
idv_policy eval average team episode rewards of agent2: 120.0
idv_policy eval idv catch total num of agent2: 34
idv_policy eval team catch total num: 48
idv_policy eval average step individual rewards of agent3: 1.1223902853715
idv_policy eval average team episode rewards of agent3: 120.0
idv_policy eval idv catch total num of agent3: 46
idv_policy eval team catch total num: 48
idv_policy eval average step individual rewards of agent4: 0.8411633323986664
idv_policy eval average team episode rewards of agent4: 120.0
idv_policy eval idv catch total num of agent4: 35
idv_policy eval team catch total num: 48

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6026/10000 episodes, total num timesteps 1205400/2000000, FPS 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.

team_policy eval average step individual rewards of agent0: 0.9165709291672783
team_policy eval average team episode rewards of agent0: 97.5
team_policy eval idv catch total num of agent0: 38
team_policy eval team catch total num: 39
team_policy eval average step individual rewards of agent1: 1.0916220897778433
team_policy eval average team episode rewards of agent1: 97.5
team_policy eval idv catch total num of agent1: 45
team_policy eval team catch total num: 39
team_policy eval average step individual rewards of agent2: 0.6836176394318059
team_policy eval average team episode rewards of agent2: 97.5
team_policy eval idv catch total num of agent2: 29
team_policy eval team catch total num: 39
team_policy eval average step individual rewards of agent3: 0.6472779733726354
team_policy eval average team episode rewards of agent3: 97.5
team_policy eval idv catch total num of agent3: 27
team_policy eval team catch total num: 39
team_policy eval average step individual rewards of agent4: 0.6666742318205983
team_policy eval average team episode rewards of agent4: 97.5
team_policy eval idv catch total num of agent4: 28
team_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent0: 0.5848808046306369
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: 0.6071795354114888
idv_policy eval average team episode rewards of agent1: 102.5
idv_policy eval idv catch total num of agent1: 26
idv_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent2: 0.8642921647486349
idv_policy eval average team episode rewards of agent2: 102.5
idv_policy eval idv catch total num of agent2: 36
idv_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent3: 0.8967550126811639
idv_policy eval average team episode rewards of agent3: 102.5
idv_policy eval idv catch total num of agent3: 38
idv_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent4: 0.6696016028818152
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 6051/10000 episodes, total num timesteps 1210400/2000000, FPS 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.

team_policy eval average step individual rewards of agent0: 1.0913226678376617
team_policy eval average team episode rewards of agent0: 142.5
team_policy eval idv catch total num of agent0: 45
team_policy eval team catch total num: 57
team_policy eval average step individual rewards of agent1: 0.9102167849130453
team_policy eval average team episode rewards of agent1: 142.5
team_policy eval idv catch total num of agent1: 38
team_policy eval team catch total num: 57
team_policy eval average step individual rewards of agent2: 0.970286702465423
team_policy eval average team episode rewards of agent2: 142.5
team_policy eval idv catch total num of agent2: 40
team_policy eval team catch total num: 57
team_policy eval average step individual rewards of agent3: 0.6093313925661383
team_policy eval average team episode rewards of agent3: 142.5
team_policy eval idv catch total num of agent3: 26
team_policy eval team catch total num: 57
team_policy eval average step individual rewards of agent4: 1.12321635485883
team_policy eval average team episode rewards of agent4: 142.5
team_policy eval idv catch total num of agent4: 46
team_policy eval team catch total num: 57
idv_policy eval average step individual rewards of agent0: 0.942585819346315
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: 0.35823493897040315
idv_policy eval average team episode rewards of agent1: 107.5
idv_policy eval idv catch total num of agent1: 16
idv_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent2: 0.5596218033254478
idv_policy eval average team episode rewards of agent2: 107.5
idv_policy eval idv catch total num of agent2: 24
idv_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent3: 0.8651919981217503
idv_policy eval average team episode rewards of agent3: 107.5
idv_policy eval idv catch total num of agent3: 36
idv_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent4: 1.1715768656710213
idv_policy eval average team episode rewards of agent4: 107.5
idv_policy eval idv catch total num of agent4: 48
idv_policy eval team catch total num: 43

 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.


 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 249.

team_policy eval average step individual rewards of agent0: 0.5298054359130766
team_policy eval average team episode rewards of agent0: 65.0
team_policy eval idv catch total num of agent0: 23
team_policy eval team catch total num: 26
team_policy eval average step individual rewards of agent1: 0.3575908583066533
team_policy eval average team episode rewards of agent1: 65.0
team_policy eval idv catch total num of agent1: 16
team_policy eval team catch total num: 26
team_policy eval average step individual rewards of agent2: 0.540135371149737
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: 0.4326923401160795
team_policy eval average team episode rewards of agent3: 65.0
team_policy eval idv catch total num of agent3: 19
team_policy eval team catch total num: 26
team_policy eval average step individual rewards of agent4: 0.8563386881084116
team_policy eval average team episode rewards of agent4: 65.0
team_policy eval idv catch total num of agent4: 36
team_policy eval team catch total num: 26
idv_policy eval average step individual rewards of agent0: 0.642142404589442
idv_policy eval average team episode rewards of agent0: 107.5
idv_policy eval idv catch total num of agent0: 27
idv_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent1: 0.4094269005118797
idv_policy eval average team episode rewards of agent1: 107.5
idv_policy eval idv catch total num of agent1: 18
idv_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent2: 0.8743221543057319
idv_policy eval average team episode rewards of agent2: 107.5
idv_policy eval idv catch total num of agent2: 36
idv_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent3: 0.6114220472269801
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: 1.0483183269393817
idv_policy eval average team episode rewards of agent4: 107.5
idv_policy eval idv catch total num of agent4: 43
idv_policy eval team catch total num: 43

 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 249.


 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 249.


 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 249.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 249.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.

team_policy eval average step individual rewards of agent0: 0.5869954449269617
team_policy eval average team episode rewards of agent0: 77.5
team_policy eval idv catch total num of agent0: 25
team_policy eval team catch total num: 31
team_policy eval average step individual rewards of agent1: 0.6866865768855223
team_policy eval average team episode rewards of agent1: 77.5
team_policy eval idv catch total num of agent1: 29
team_policy eval team catch total num: 31
team_policy eval average step individual rewards of agent2: 0.20390884816570207
team_policy eval average team episode rewards of agent2: 77.5
team_policy eval idv catch total num of agent2: 10
team_policy eval team catch total num: 31
team_policy eval average step individual rewards of agent3: 0.7955263355719028
team_policy eval average team episode rewards of agent3: 77.5
team_policy eval idv catch total num of agent3: 33
team_policy eval team catch total num: 31
team_policy eval average step individual rewards of agent4: 0.7869999226941796
team_policy eval average team episode rewards of agent4: 77.5
team_policy eval idv catch total num of agent4: 33
team_policy eval team catch total num: 31
idv_policy eval average step individual rewards of agent0: 0.6362961397447613
idv_policy eval average team episode rewards of agent0: 77.5
idv_policy eval idv catch total num of agent0: 27
idv_policy eval team catch total num: 31
idv_policy eval average step individual rewards of agent1: 0.46158322260310597
idv_policy eval average team episode rewards of agent1: 77.5
idv_policy eval idv catch total num of agent1: 20
idv_policy eval team catch total num: 31
idv_policy eval average step individual rewards of agent2: 0.48225164244619906
idv_policy eval average team episode rewards of agent2: 77.5
idv_policy eval idv catch total num of agent2: 21
idv_policy eval team catch total num: 31
idv_policy eval average step individual rewards of agent3: 0.6129245118718313
idv_policy eval average team episode rewards of agent3: 77.5
idv_policy eval idv catch total num of agent3: 26
idv_policy eval team catch total num: 31
idv_policy eval average step individual rewards of agent4: 0.385219630999523
idv_policy eval average team episode rewards of agent4: 77.5
idv_policy eval idv catch total num of agent4: 17
idv_policy eval team catch total num: 31

 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.

team_policy eval average step individual rewards of agent0: 0.6043501351953575
team_policy eval average team episode rewards of agent0: 102.5
team_policy eval idv catch total num of agent0: 26
team_policy eval team catch total num: 41
team_policy eval average step individual rewards of agent1: 0.7683500518655911
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: 0.7779837441454237
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: 0.9173312860565547
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: 1.0642928624101116
team_policy eval average team episode rewards of agent4: 102.5
team_policy eval idv catch total num of agent4: 44
team_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent0: 0.9595739519977501
idv_policy eval average team episode rewards of agent0: 115.0
idv_policy eval idv catch total num of agent0: 40
idv_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent1: 1.0631959640573374
idv_policy eval average team episode rewards of agent1: 115.0
idv_policy eval idv catch total num of agent1: 44
idv_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent2: 0.7600992984923265
idv_policy eval average team episode rewards of agent2: 115.0
idv_policy eval idv catch total num of agent2: 32
idv_policy eval team catch total num: 46
idv_policy eval average step individual rewards of agent3: 0.6396008565654658
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: 1.1179439317199997
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 6151/10000 episodes, total num timesteps 1230400/2000000, FPS 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.

team_policy eval average step individual rewards of agent0: 0.6024059169033384
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: 0.3312125374366415
team_policy eval average team episode rewards of agent1: 82.5
team_policy eval idv catch total num of agent1: 15
team_policy eval team catch total num: 33
team_policy eval average step individual rewards of agent2: 0.44860976037701406
team_policy eval average team episode rewards of agent2: 82.5
team_policy eval idv catch total num of agent2: 20
team_policy eval team catch total num: 33
team_policy eval average step individual rewards of agent3: 0.6624678337767277
team_policy eval average team episode rewards of agent3: 82.5
team_policy eval idv catch total num of agent3: 28
team_policy eval team catch total num: 33
team_policy eval average step individual rewards of agent4: 0.33761951812842866
team_policy eval average team episode rewards of agent4: 82.5
team_policy eval idv catch total num of agent4: 15
team_policy eval team catch total num: 33
idv_policy eval average step individual rewards of agent0: 0.6896073487420951
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: 0.12742985257678105
idv_policy eval average team episode rewards of agent1: 72.5
idv_policy eval idv catch total num of agent1: 7
idv_policy eval team catch total num: 29
idv_policy eval average step individual rewards of agent2: 0.507270272438681
idv_policy eval average team episode rewards of agent2: 72.5
idv_policy eval idv catch total num of agent2: 22
idv_policy eval team catch total num: 29
idv_policy eval average step individual rewards of agent3: 0.9943333991345117
idv_policy eval average team episode rewards of agent3: 72.5
idv_policy eval idv catch total num of agent3: 41
idv_policy eval team catch total num: 29
idv_policy eval average step individual rewards of agent4: 0.3729987665731337
idv_policy eval average team episode rewards of agent4: 72.5
idv_policy eval idv catch total num of agent4: 17
idv_policy eval team catch total num: 29

 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.

team_policy eval average step individual rewards of agent0: 0.8407415810545499
team_policy eval average team episode rewards of agent0: 72.5
team_policy eval idv catch total num of agent0: 35
team_policy eval team catch total num: 29
team_policy eval average step individual rewards of agent1: 0.382486192426433
team_policy eval average team episode rewards of agent1: 72.5
team_policy eval idv catch total num of agent1: 17
team_policy eval team catch total num: 29
team_policy eval average step individual rewards of agent2: 0.553507697953663
team_policy eval average team episode rewards of agent2: 72.5
team_policy eval idv catch total num of agent2: 24
team_policy eval team catch total num: 29
team_policy eval average step individual rewards of agent3: 0.6564324874105004
team_policy eval average team episode rewards of agent3: 72.5
team_policy eval idv catch total num of agent3: 28
team_policy eval team catch total num: 29
team_policy eval average step individual rewards of agent4: 0.570128129358118
team_policy eval average team episode rewards of agent4: 72.5
team_policy eval idv catch total num of agent4: 24
team_policy eval team catch total num: 29
idv_policy eval average step individual rewards of agent0: 1.0657536910017196
idv_policy eval average team episode rewards of agent0: 97.5
idv_policy eval idv catch total num of agent0: 44
idv_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent1: 0.6336230433291441
idv_policy eval average team episode rewards of agent1: 97.5
idv_policy eval idv catch total num of agent1: 27
idv_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent2: 0.6300811721097698
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: 0.7782265845081655
idv_policy eval average team episode rewards of agent3: 97.5
idv_policy eval idv catch total num of agent3: 33
idv_policy eval team catch total num: 39
idv_policy eval average step individual rewards of agent4: 0.6358901138175646
idv_policy eval average team episode rewards of agent4: 97.5
idv_policy eval idv catch total num of agent4: 27
idv_policy eval team catch total num: 39

 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.

team_policy eval average step individual rewards of agent0: 0.9170588295139322
team_policy eval average team episode rewards of agent0: 112.5
team_policy eval idv catch total num of agent0: 38
team_policy eval team catch total num: 45
team_policy eval average step individual rewards of agent1: 0.9368456847340851
team_policy eval average team episode rewards of agent1: 112.5
team_policy eval idv catch total num of agent1: 39
team_policy eval team catch total num: 45
team_policy eval average step individual rewards of agent2: 0.7431253728312552
team_policy eval average team episode rewards of agent2: 112.5
team_policy eval idv catch total num of agent2: 31
team_policy eval team catch total num: 45
team_policy eval average step individual rewards of agent3: 0.7388385260495478
team_policy eval average team episode rewards of agent3: 112.5
team_policy eval idv catch total num of agent3: 31
team_policy eval team catch total num: 45
team_policy eval average step individual rewards of agent4: 0.8586701878397371
team_policy eval average team episode rewards of agent4: 112.5
team_policy eval idv catch total num of agent4: 36
team_policy eval team catch total num: 45
idv_policy eval average step individual rewards of agent0: 0.9211599279489018
idv_policy eval average team episode rewards of agent0: 107.5
idv_policy eval idv catch total num of agent0: 38
idv_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent1: 0.9449508767959593
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: 0.5870423698355276
idv_policy eval average team episode rewards of agent2: 107.5
idv_policy eval idv catch total num of agent2: 25
idv_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent3: 0.51039384867648
idv_policy eval average team episode rewards of agent3: 107.5
idv_policy eval idv catch total num of agent3: 22
idv_policy eval team catch total num: 43
idv_policy eval average step individual rewards of agent4: 0.7080666014584565
idv_policy eval average team episode rewards of agent4: 107.5
idv_policy eval idv catch total num of agent4: 30
idv_policy eval team catch total num: 43

 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.

team_policy eval average step individual rewards of agent0: 0.45416627803678766
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: 0.7182294928586477
team_policy eval average team episode rewards of agent1: 92.5
team_policy eval idv catch total num of agent1: 30
team_policy eval team catch total num: 37
team_policy eval average step individual rewards of agent2: 0.8173279353634112
team_policy eval average team episode rewards of agent2: 92.5
team_policy eval idv catch total num of agent2: 34
team_policy eval team catch total num: 37
team_policy eval average step individual rewards of agent3: 0.7420069284277307
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: 0.7911643047176925
team_policy eval average team episode rewards of agent4: 92.5
team_policy eval idv catch total num of agent4: 33
team_policy eval team catch total num: 37
idv_policy eval average step individual rewards of agent0: 0.19767584947435107
idv_policy eval average team episode rewards of agent0: 62.5
idv_policy eval idv catch total num of agent0: 10
idv_policy eval team catch total num: 25
idv_policy eval average step individual rewards of agent1: 0.35293571325153905
idv_policy eval average team episode rewards of agent1: 62.5
idv_policy eval idv catch total num of agent1: 16
idv_policy eval team catch total num: 25
idv_policy eval average step individual rewards of agent2: 0.6411569410712136
idv_policy eval average team episode rewards of agent2: 62.5
idv_policy eval idv catch total num of agent2: 27
idv_policy eval team catch total num: 25
idv_policy eval average step individual rewards of agent3: 0.38128586611379794
idv_policy eval average team episode rewards of agent3: 62.5
idv_policy eval idv catch total num of agent3: 17
idv_policy eval team catch total num: 25
idv_policy eval average step individual rewards of agent4: 0.8945232497555006
idv_policy eval average team episode rewards of agent4: 62.5
idv_policy eval idv catch total num of agent4: 37
idv_policy eval team catch total num: 25

 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.

team_policy eval average step individual rewards of agent0: 0.8002781319893885
team_policy eval average team episode rewards of agent0: 132.5
team_policy eval idv catch total num of agent0: 34
team_policy eval team catch total num: 53
team_policy eval average step individual rewards of agent1: 0.9865146512138199
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: 0.9435131935458145
team_policy eval average team episode rewards of agent2: 132.5
team_policy eval idv catch total num of agent2: 39
team_policy eval team catch total num: 53
team_policy eval average step individual rewards of agent3: 0.9913185369388077
team_policy eval average team episode rewards of agent3: 132.5
team_policy eval idv catch total num of agent3: 41
team_policy eval team catch total num: 53
team_policy eval average step individual rewards of agent4: 0.7091057930376499
team_policy eval average team episode rewards of agent4: 132.5
team_policy eval idv catch total num of agent4: 30
team_policy eval team catch total num: 53
idv_policy eval average step individual rewards of agent0: 0.7576716540359963
idv_policy eval average team episode rewards of agent0: 65.0
idv_policy eval idv catch total num of agent0: 32
idv_policy eval team catch total num: 26
idv_policy eval average step individual rewards of agent1: 0.5876620959020825
idv_policy eval average team episode rewards of agent1: 65.0
idv_policy eval idv catch total num of agent1: 25
idv_policy eval team catch total num: 26
idv_policy eval average step individual rewards of agent2: 0.5382160648739436
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: 0.43662854089799535
idv_policy eval average team episode rewards of agent3: 65.0
idv_policy eval idv catch total num of agent3: 19
idv_policy eval team catch total num: 26
idv_policy eval average step individual rewards of agent4: 0.3268530705478602
idv_policy eval average team episode rewards of agent4: 65.0
idv_policy eval idv catch total num of agent4: 15
idv_policy eval team catch total num: 26

 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.

team_policy eval average step individual rewards of agent0: 0.5877288148427661
team_policy eval average team episode rewards of agent0: 112.5
team_policy eval idv catch total num of agent0: 25
team_policy eval team catch total num: 45
team_policy eval average step individual rewards of agent1: 0.8647480548015076
team_policy eval average team episode rewards of agent1: 112.5
team_policy eval idv catch total num of agent1: 36
team_policy eval team catch total num: 45
team_policy eval average step individual rewards of agent2: 0.8150167147449225
team_policy eval average team episode rewards of agent2: 112.5
team_policy eval idv catch total num of agent2: 34
team_policy eval team catch total num: 45
team_policy eval average step individual rewards of agent3: 0.7367423676733851
team_policy eval average team episode rewards of agent3: 112.5
team_policy eval idv catch total num of agent3: 31
team_policy eval team catch total num: 45
team_policy eval average step individual rewards of agent4: 1.143678068408043
team_policy eval average team episode rewards of agent4: 112.5
team_policy eval idv catch total num of agent4: 47
team_policy eval team catch total num: 45
idv_policy eval average step individual rewards of agent0: 0.6108093826560856
idv_policy eval average team episode rewards of agent0: 90.0
idv_policy eval idv catch total num of agent0: 26
idv_policy eval team catch total num: 36
idv_policy eval average step individual rewards of agent1: 1.1000964656082446
idv_policy eval average team episode rewards of agent1: 90.0
idv_policy eval idv catch total num of agent1: 45
idv_policy eval team catch total num: 36
idv_policy eval average step individual rewards of agent2: 0.7807268716618377
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: 0.6550795257754536
idv_policy eval average team episode rewards of agent3: 90.0
idv_policy eval idv catch total num of agent3: 28
idv_policy eval team catch total num: 36
idv_policy eval average step individual rewards of agent4: 0.3999978213371494
idv_policy eval average team episode rewards of agent4: 90.0
idv_policy eval idv catch total num of agent4: 18
idv_policy eval team catch total num: 36

 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.

team_policy eval average step individual rewards of agent0: 0.35711320699918403
team_policy eval average team episode rewards of agent0: 82.5
team_policy eval idv catch total num of agent0: 16
team_policy eval team catch total num: 33
team_policy eval average step individual rewards of agent1: 0.8138325568728088
team_policy eval average team episode rewards of agent1: 82.5
team_policy eval idv catch total num of agent1: 34
team_policy eval team catch total num: 33
team_policy eval average step individual rewards of agent2: 0.6630610102326969
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: 0.33335248004070467
team_policy eval average team episode rewards of agent3: 82.5
team_policy eval idv catch total num of agent3: 15
team_policy eval team catch total num: 33
team_policy eval average step individual rewards of agent4: 0.3217297391249332
team_policy eval average team episode rewards of agent4: 82.5
team_policy eval idv catch total num of agent4: 15
team_policy eval team catch total num: 33
idv_policy eval average step individual rewards of agent0: 0.5107008028646579
idv_policy eval average team episode rewards of agent0: 127.5
idv_policy eval idv catch total num of agent0: 22
idv_policy eval team catch total num: 51
idv_policy eval average step individual rewards of agent1: 0.845339452367973
idv_policy eval average team episode rewards of agent1: 127.5
idv_policy eval idv catch total num of agent1: 35
idv_policy eval team catch total num: 51
idv_policy eval average step individual rewards of agent2: 0.94260974354415
idv_policy eval average team episode rewards of agent2: 127.5
idv_policy eval idv catch total num of agent2: 39
idv_policy eval team catch total num: 51
idv_policy eval average step individual rewards of agent3: 1.2746797260081129
idv_policy eval average team episode rewards of agent3: 127.5
idv_policy eval idv catch total num of agent3: 52
idv_policy eval team catch total num: 51
idv_policy eval average step individual rewards of agent4: 0.7936564470008604
idv_policy eval average team episode rewards of agent4: 127.5
idv_policy eval idv catch total num of agent4: 33
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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.

team_policy eval average step individual rewards of agent0: 1.2965367345301109
team_policy eval average team episode rewards of agent0: 125.0
team_policy eval idv catch total num of agent0: 53
team_policy eval team catch total num: 50
team_policy eval average step individual rewards of agent1: 0.5799674085731293
team_policy eval average team episode rewards of agent1: 125.0
team_policy eval idv catch total num of agent1: 25
team_policy eval team catch total num: 50
team_policy eval average step individual rewards of agent2: 0.5850248844797725
team_policy eval average team episode rewards of agent2: 125.0
team_policy eval idv catch total num of agent2: 25
team_policy eval team catch total num: 50
team_policy eval average step individual rewards of agent3: 0.9856536909088208
team_policy eval average team episode rewards of agent3: 125.0
team_policy eval idv catch total num of agent3: 41
team_policy eval team catch total num: 50
team_policy eval average step individual rewards of agent4: 0.9141912976490536
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: 0.7634000069125739
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: 0.8464404242733131
idv_policy eval average team episode rewards of agent1: 82.5
idv_policy eval idv catch total num of agent1: 35
idv_policy eval team catch total num: 33
idv_policy eval average step individual rewards of agent2: 0.7939728450692788
idv_policy eval average team episode rewards of agent2: 82.5
idv_policy eval idv catch total num of agent2: 33
idv_policy eval team catch total num: 33
idv_policy eval average step individual rewards of agent3: 0.41457027344841535
idv_policy eval average team episode rewards of agent3: 82.5
idv_policy eval idv catch total num of agent3: 18
idv_policy eval team catch total num: 33
idv_policy eval average step individual rewards of agent4: 0.3843660426308517
idv_policy eval average team episode rewards of agent4: 82.5
idv_policy eval idv catch total num of agent4: 17
idv_policy eval team catch total num: 33

 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.

team_policy eval average step individual rewards of agent0: 0.7401499988175105
team_policy eval average team episode rewards of agent0: 147.5
team_policy eval idv catch total num of agent0: 31
team_policy eval team catch total num: 59
team_policy eval average step individual rewards of agent1: 1.2293162481122266
team_policy eval average team episode rewards of agent1: 147.5
team_policy eval idv catch total num of agent1: 50
team_policy eval team catch total num: 59
team_policy eval average step individual rewards of agent2: 1.0224268062171027
team_policy eval average team episode rewards of agent2: 147.5
team_policy eval idv catch total num of agent2: 42
team_policy eval team catch total num: 59
team_policy eval average step individual rewards of agent3: 1.2257863898786334
team_policy eval average team episode rewards of agent3: 147.5
team_policy eval idv catch total num of agent3: 50
team_policy eval team catch total num: 59
team_policy eval average step individual rewards of agent4: 0.9702337866459971
team_policy eval average team episode rewards of agent4: 147.5
team_policy eval idv catch total num of agent4: 40
team_policy eval team catch total num: 59
idv_policy eval average step individual rewards of agent0: 0.755486859021009
idv_policy eval average team episode rewards of agent0: 80.0
idv_policy eval idv catch total num of agent0: 32
idv_policy eval team catch total num: 32
idv_policy eval average step individual rewards of agent1: 0.587122596327286
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: 0.5328288137542454
idv_policy eval average team episode rewards of agent2: 80.0
idv_policy eval idv catch total num of agent2: 23
idv_policy eval team catch total num: 32
idv_policy eval average step individual rewards of agent3: 0.5556579756300437
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: 0.8104928200390052
idv_policy eval average team episode rewards of agent4: 80.0
idv_policy eval idv catch total num of agent4: 34
idv_policy eval team catch total num: 32

 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.

team_policy eval average step individual rewards of agent0: 0.9122110803374502
team_policy eval average team episode rewards of agent0: 130.0
team_policy eval idv catch total num of agent0: 38
team_policy eval team catch total num: 52
team_policy eval average step individual rewards of agent1: 0.9407080844402393
team_policy eval average team episode rewards of agent1: 130.0
team_policy eval idv catch total num of agent1: 39
team_policy eval team catch total num: 52
team_policy eval average step individual rewards of agent2: 0.7961079640502584
team_policy eval average team episode rewards of agent2: 130.0
team_policy eval idv catch total num of agent2: 33
team_policy eval team catch total num: 52
team_policy eval average step individual rewards of agent3: 0.6415713521110803
team_policy eval average team episode rewards of agent3: 130.0
team_policy eval idv catch total num of agent3: 27
team_policy eval team catch total num: 52
team_policy eval average step individual rewards of agent4: 0.91690016336137
team_policy eval average team episode rewards of agent4: 130.0
team_policy eval idv catch total num of agent4: 38
team_policy eval team catch total num: 52
idv_policy eval average step individual rewards of agent0: 0.9105015372122388
idv_policy eval average team episode rewards of agent0: 155.0
idv_policy eval idv catch total num of agent0: 38
idv_policy eval team catch total num: 62
idv_policy eval average step individual rewards of agent1: 1.0610483801511599
idv_policy eval average team episode rewards of agent1: 155.0
idv_policy eval idv catch total num of agent1: 44
idv_policy eval team catch total num: 62
idv_policy eval average step individual rewards of agent2: 1.0112953975197376
idv_policy eval average team episode rewards of agent2: 155.0
idv_policy eval idv catch total num of agent2: 42
idv_policy eval team catch total num: 62
idv_policy eval average step individual rewards of agent3: 1.210692103301018
idv_policy eval average team episode rewards of agent3: 155.0
idv_policy eval idv catch total num of agent3: 50
idv_policy eval team catch total num: 62
idv_policy eval average step individual rewards of agent4: 1.3148379439626938
idv_policy eval average team episode rewards of agent4: 155.0
idv_policy eval idv catch total num of agent4: 54
idv_policy eval team catch total num: 62

 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.

team_policy eval average step individual rewards of agent0: 0.4023038480142591
team_policy eval average team episode rewards of agent0: 85.0
team_policy eval idv catch total num of agent0: 18
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent1: 0.6984167838298257
team_policy eval average team episode rewards of agent1: 85.0
team_policy eval idv catch total num of agent1: 29
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent2: 0.591776587060239
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: 0.5662568333611072
team_policy eval average team episode rewards of agent3: 85.0
team_policy eval idv catch total num of agent3: 24
team_policy eval team catch total num: 34
team_policy eval average step individual rewards of agent4: 0.7377412724629724
team_policy eval average team episode rewards of agent4: 85.0
team_policy eval idv catch total num of agent4: 31
team_policy eval team catch total num: 34
idv_policy eval average step individual rewards of agent0: 0.48477138960393984
idv_policy eval average team episode rewards of agent0: 60.0
idv_policy eval idv catch total num of agent0: 21
idv_policy eval team catch total num: 24
idv_policy eval average step individual rewards of agent1: 0.8660673590408285
idv_policy eval average team episode rewards of agent1: 60.0
idv_policy eval idv catch total num of agent1: 36
idv_policy eval team catch total num: 24
idv_policy eval average step individual rewards of agent2: 0.20158996877464155
idv_policy eval average team episode rewards of agent2: 60.0
idv_policy eval idv catch total num of agent2: 10
idv_policy eval team catch total num: 24
idv_policy eval average step individual rewards of agent3: 0.513160529445087
idv_policy eval average team episode rewards of agent3: 60.0
idv_policy eval idv catch total num of agent3: 22
idv_policy eval team catch total num: 24
idv_policy eval average step individual rewards of agent4: 0.7957567901263984
idv_policy eval average team episode rewards of agent4: 60.0
idv_policy eval idv catch total num of agent4: 33
idv_policy eval team catch total num: 24

 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.

team_policy eval average step individual rewards of agent0: 1.343383739263199
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: 0.7422582025773682
team_policy eval average team episode rewards of agent1: 127.5
team_policy eval idv catch total num of agent1: 31
team_policy eval team catch total num: 51
team_policy eval average step individual rewards of agent2: 0.6816005469035066
team_policy eval average team episode rewards of agent2: 127.5
team_policy eval idv catch total num of agent2: 29
team_policy eval team catch total num: 51
team_policy eval average step individual rewards of agent3: 0.9605618543373649
team_policy eval average team episode rewards of agent3: 127.5
team_policy eval idv catch total num of agent3: 40
team_policy eval team catch total num: 51
team_policy eval average step individual rewards of agent4: 0.9471620781741831
team_policy eval average team episode rewards of agent4: 127.5
team_policy eval idv catch total num of agent4: 39
team_policy eval team catch total num: 51
idv_policy eval average step individual rewards of agent0: 0.8868878293995541
idv_policy eval average team episode rewards of agent0: 102.5
idv_policy eval idv catch total num of agent0: 37
idv_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent1: 0.5845734101273742
idv_policy eval average team episode rewards of agent1: 102.5
idv_policy eval idv catch total num of agent1: 25
idv_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent2: 0.7108194695795518
idv_policy eval average team episode rewards of agent2: 102.5
idv_policy eval idv catch total num of agent2: 30
idv_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent3: 0.5771537857695431
idv_policy eval average team episode rewards of agent3: 102.5
idv_policy eval idv catch total num of agent3: 25
idv_policy eval team catch total num: 41
idv_policy eval average step individual rewards of agent4: 0.7562970972307436
idv_policy eval average team episode rewards of agent4: 102.5
idv_policy eval idv catch total num of agent4: 32
idv_policy eval team catch total num: 41

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_CMT_s2r2_v1 updates 6451/10000 episodes, total num timesteps 1290400/2000000, FPS 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.

team_policy eval average step individual rewards of agent0: 0.6264363881923459
team_policy eval average team episode rewards of agent0: 102.5
team_policy eval idv catch total num of agent0: 27
team_policy eval team catch total num: 41
team_policy eval average step individual rewards of agent1: 1.1208177519350913
team_policy eval average team episode rewards of agent1: 102.5
team_policy eval idv catch total num of agent1: 46
team_policy eval team catch total num: 41
team_policy eval average step individual rewards of agent2: 0.6923946257901795
team_policy eval average team episode rewards of agent2: 102.5
team_policy eval idv catch total num of agent2: 29
team_policy eval team catch total num: 41
team_policy eval average step individual rewards of agent3: 0.868641812466333
team_policy eval average team episode rewards of agent3: 102.5
team_policy eval idv catch total num of agent3: 36
team_policy eval team catch total num: 41
team_policy eval average step individual rewards of agent4: 0.5391389178988024
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: 0.3505566671734932
idv_policy eval average team episode rewards of agent0: 70.0
idv_policy eval idv catch total num of agent0: 16
idv_policy eval team catch total num: 28
idv_policy eval average step individual rewards of agent1: 0.5141491153983155
idv_policy eval average team episode rewards of agent1: 70.0
idv_policy eval idv catch total num of agent1: 22
idv_policy eval team catch total num: 28
idv_policy eval average step individual rewards of agent2: 0.7206914929631728
idv_policy eval average team episode rewards of agent2: 70.0
idv_policy eval idv catch total num of agent2: 30
idv_policy eval team catch total num: 28
idv_policy eval average step individual rewards of agent3: 0.48432093999059483
idv_policy eval average team episode rewards of agent3: 70.0
idv_policy eval idv catch total num of agent3: 21
idv_policy eval team catch total num: 28
idv_policy eval average step individual rewards of agent4: 0.40021169610542684
idv_policy eval average team episode rewards of agent4: 70.0
idv_policy eval idv catch total num of agent4: 18
idv_policy eval team catch total num: 28

 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.


 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 250.

team_policy eval average step individual rewards of agent0: 1.0636409837300458
team_policy eval average team episode rewards of agent0: 125.0
team_policy eval idv catch total num of agent0: 44
team_policy eval team catch total num: 50
team_policy eval average step individual rewards of agent1: 1.323513258090329
team_policy eval average team episode rewards of agent1: 125.0
team_policy eval idv catch total num of agent1: 54
team_policy eval team catch total num: 50
team_policy eval average step individual rewards of agent2: 0.8127082249478748
team_policy eval average team episode rewards of agent2: 125.0
team_policy eval idv catch total num of agent2: 34
team_policy eval team catch total num: 50
team_policy eval average step individual rewards of agent3: 1.1109986595887893
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: 0.6580845095041009
team_policy eval average team episode rewards of agent4: 125.0
team_policy eval idv catch total num of agent4: 28
team_policy eval team catch total num: 50
idv_policy eval average step individual rewards of agent0: 0.5517748614271188
idv_policy eval average team episode rewards of agent0: 87.5
idv_policy eval idv catch total num of agent0: 24
idv_policy eval team catch total num: 35
idv_policy eval average step individual rewards of agent1: 0.7915858958845271
idv_policy eval average team episode rewards of agent1: 87.5
idv_policy eval idv catch total num of agent1: 33
idv_policy eval team catch total num: 35
idv_policy eval average step individual rewards of agent2: 0.41729031031567
idv_policy eval average team episode rewards of agent2: 87.5
idv_policy eval idv catch total num of agent2: 18
idv_policy eval team catch total num: 35
idv_policy eval average step individual rewards of agent3: 0.6545769809507688
idv_policy eval average team episode rewards of agent3: 87.5
idv_policy eval idv catch total num of agent3: 28
idv_policy eval team catch total num: 35
idv_policy eval average step individual rewards of agent4: 0.7677616303633741
idv_policy eval average team episode rewards of agent4: 87.5
idv_policy eval idv catch total num of agent4: 32
idv_policy eval team catch total num: 35

 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 250.


 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 250.


 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 250.

