pettingzoo 0.1.14

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 https://pettingzoo-team.github.io/PettingZoo/

Environment Types and Installation

PettingZoo includes the following sets of games:

• 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, use pip install pettingzoo

We support Python 3.6, 3.7 and 3.8, on Linux and macOS.