metadata
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
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