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Happy 6c85147db7 | 1 year ago | |
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README.md | 1 year ago | |
requirements.txt | 1 year ago | |
simple_agent.py | 1 year ago | |
simple_model.py | 2 years ago | |
train.py | 1 year ago |
Based on PARL, the MADDPG algorithm of deep reinforcement learning has been reproduced.
Paper: MADDPG in Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
A simple multi-agent particle world based on gym. Please see here to install and know more about the environment.
Mean episode reward in training process (totally 25000 episodes).
simple |
simple_adversary |
simple_push |
simple_crypto |
simple_speaker_listener |
simple_spread |
simple_tag |
simple_world_comm |
# To train an agent for simple_speaker_listener scenario
python train.py
# To train for other scenario, model is automatically saved every 1000 episodes
python train.py --env [ENV_NAME]
# To show animation effects after training
python train.py --env [ENV_NAME] --show --restore
# To train and evaluate scenarios with continuous action spaces
python train.py --env [ENV_NAME] --continuous_actions
python train.py --env [ENV_NAME] --continuous_actions --show --restore
PARL 是一个高性能、灵活的强化学习框架
Python C++ JavaScript Shell Markdown other
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