https://github.com/ffelten/masac

Jax and Torch Multi-Agent SAC on PettingZoo API
https://github.com/ffelten/masac

multi-agent-reinforcement-learning pettingzoo reinforcement-learning

Last synced: 6 months ago
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Jax and Torch Multi-Agent SAC on PettingZoo API

• Host: GitHub
• URL: https://github.com/ffelten/masac
• Owner: ffelten
• License: mit
• Created: 2023-02-14T08:23:00.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2024-11-23T15:16:17.000Z (11 months ago)
• Last Synced: 2025-04-02T20:05:53.222Z (6 months ago)
• Topics: multi-agent-reinforcement-learning, pettingzoo, reinforcement-learning
• Language: Python
• Homepage:
• Size: 237 KB
• Stars: 75
• Watchers: 1
• Forks: 7
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE
  • Citation: CITATION.bib

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README

          
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# MASAC

:warning: Work in progress, I did not extensively test the algorithms (especially the jax version).:warning:

Simple, yet useful [Jax](https://github.com/google/jax) and [Torch](https://pytorch.org/) Multi-Agent SAC for Parallel [PettingZoo](https://pettingzoo.farama.org/) environments.

It is assumed that the agents are homogeneous (actions and observations) and all have the same global reward.

The implementation is based on the SAC implementation from the excellent [cleanRL](https://github.com/vwxyzjn/cleanrl) repo.

## Multi-Agent features

Shared parameters:

  * Shared critic between all agents;

  * Shared actor (conditioned on agent ID).

## Install & run

```shell

poetry install

poetry run python masac/masac.py

```

## Citation

If you use this code for your research, please cite this using:

```bibtex

@misc{masac,

    author = {Florian Felten},

    title = {MASAC: A Multi-Agent Soft-Actor-Critic implementation for PettingZoo},

    year = {2023},

    publisher = {GitHub},

    journal = {GitHub repository},

    howpublished = {\url{https://github.com/ffelten/MASAC}},

}

```