| --- |
| tags: |
| - ML-Agents-SoccerTwos |
| - Reinforcement Learning |
| - ml-agents |
| - ONNX |
| - reinforcement-learning |
| - unity |
| - unity-ml-agents |
| - poca |
| - self-play |
| - soccer |
| - deep-reinforcement-learning |
|
|
| library_name: ml-agents |
| pipeline_tag: reinforcement-learning |
|
|
| license: mit |
| --- |
| |
| # POCA SoccerTwos Self-Play Agent |
|
|
| This repository contains a trained :contentReference[oaicite:0]{index=0} POCA self-play agent for the SoccerTwos environment. |
|
|
| The model was trained using: |
| - Proximal Policy Optimization with Centralized Critic (POCA) |
| - Self-play training |
| - Unity ML-Agents |
| - Multi-agent reinforcement learning |
|
|
| --- |
|
|
| # Environment |
|
|
| The agent was trained on the official Unity SoccerTwos environment. |
|
|
| You can watch or run the environment here: |
|
|
| :contentReference[oaicite:1]{index=1} |
|
|
| --- |
|
|
| # Training Details |
|
|
| ## Trainer |
|
|
| - POCA (multi-agent centralized critic) |
|
|
| ## Environment |
|
|
| - SoccerTwos |
| - 2v2 competitive soccer environment |
|
|
| ## Training Setup |
|
|
| - Self-play enabled |
| - Multi-agent cooperative + competitive training |
| - Long-horizon reinforcement learning |
|
|
| ## Hyperparameters |
|
|
| ```yaml |
| behaviors: |
| SoccerTwos: |
| trainer_type: poca |
| |
| hyperparameters: |
| batch_size: 2048 |
| buffer_size: 20480 |
| 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 |
| |
| reward_signals: |
| extrinsic: |
| gamma: 0.99 |
| strength: 1.0 |
| |
| keep_checkpoints: 5 |
| max_steps: 50000000 |
| time_horizon: 1000 |
