Gym for multi-agent reinforcement learning
Project description
PettingZoo is a Python library for conducting research in multi-agent reinforcement learning. It's akin to a multi-agent version of OpenAI's Gym library.
We model environments as Agent Environment Cycle (AEC) games, in order to be able to support all types of multi-agent RL environments under one API.
Our website with comprehensive documentation is pettingzoo.ml
Environments and Installation
PettingZoo includes the following families of environments:
- Atari: Multi-player Atari 2600 games (both cooperative and competitive)
- Butterfly: Cooperative graphical games developed by us, requiring a high degree of coordination
- Classic: Classical games including card games, board games, etc.
- MAgent: Configurable environments with massive numbers of particle agents, originally from https://github.com/geek-ai/MAgent
- MPE: A set of simple nongraphical communication tasks, originally from https://github.com/openai/multiagent-particle-envs
- SISL: 3 cooperative environments, originally from https://github.com/sisl/MADRL
To install the pettingzoo base library, use pip install pettingzoo.
This does not include dependencies for all families of environments (there's a massive number, and some can be problematic to install on certain systems). You can install these dependencies for one family like pip install pettingzoo[atari] or use pip install pettingzoo[all] to install all dependencies.
We support Python 3.6, 3.7 and 3.8 on Linux and macOS.
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