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Pytorch implementation of the MARL algorithm, MADDPG, which correspondings to the paper "Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments".
| Date | Stars |
|---|---|
| 2026-07-31 | 682 |
| 2026-08-06 | 682 |
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# MADDPG This is a pytorch implementation of MADDPG on [Multi-Agent Particle Environment(MPE)](https://github.com/openai/multiagent-particle-envs), the corresponding paper of MADDPG is [Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments](https://arxiv.org/abs/1706.02275). ## Requirements - python=3.6.5 - [Multi-Agent Particle Environment(MPE)](https://github.com/openai/multiagent-particle-envs) - torch=1.1.0 ## Quick Start ```shell $ python main.py --scenario-name=simple_tag --evaluate-episodes=10 ``` Directly run the main.py, then the algrithm will be tested on scenario 'simple_tag' for 10 episodes, using the pretrained model. ## Note + We have train the agent on scenario 'simple_tag', but the model we provide is not the best because we don't want to waste time on training, you can keep training it for better performence. + There are 4 agents in simple_tag, including 3 predators and 1 prey. we use MADDPG to train predators to catch the prey. The prey's action can be controlled by you, in our case we set it random. + The default setting of Multi-Agent Particle Environment(MPE) is sparse reward, you can change it to dense reward by replacing 'shape=False' to 'shape=True' in file multiagent-particle-envs/multiagent/scenarios/simple_tag.py/.
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matched fp:4fbff68f02a7e359, desc:multi-agent, desc:multi agent