pettingzoo 1.3.2

Gym for multi-agent reinforcement learning

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.