ML_AGENT POCA Training, One side learning well, other side bad

Question

I am trying to train competitive 2 vs 2 hockey game. It is symetrical game. I am using MA-POCA. My problem one of the teams learning very well but other side is very bad. How this can happen? MA-POCA algorthm using self-play and if one side is playing good, other team also should play well because it is using old policy. But this is not happening.

I also want to ask you one more thing. I add “footballer begining side” input which is 0 or 1. I do this because agent can start right sided or left sided so it should know which post he should score. Should I remove this king of input? Thanks for answers.

I check my team_id’s → They should be different so I arrange like that

I am sharing my config file

behaviors: SoccerAgent: trainer_type: poca hyperparameters: batch_size: 1024 buffer_size: 10240 learning_rate: 0.0003 beta: 0.005 epsilon: 0.2 lambd: 0.95 num_epoch: 3 learning_rate_schedule: constant network_settings: normalize: false hidden_units: 512 num_layers: 2 vis_encode_type: simple goal_conditioning_type: none reward_signals: extrinsic: gamma: 0.99 strength: 1.0 keep_checkpoints: 5 max_steps: 5000000 time_horizon: 1024 summary_freq: 20000 self_play: save_steps: 100000 team_change: 500000 swap_steps: 10000 window: 10 play_against_latest_model_ratio: 0.5 initial_elo: 1200.0