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.

Our website, with comprehensive documentation, is pettingzoo.ml

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.

PettingZoo model environments as Agent Environment Cycle (AEC) games, in order to be able to cleanly support all types of multi-agent RL environments under one API and to minimize the potential for certain classes of common bugs.

Using environments in PettingZoo is very similar to Gym, i.e. you initialize an environment via:

from pettingzoo.butterfly import pistonball_v3
env = pistonball_v3.env()

Environments can be interacted with in a manner very similar to Gym:

env.reset()
for agent in env.agent_iter():
    observation, reward, done, info = env.last()
    action = policy(observation)
    env.step(action)

For the complete API documentation, please see https://www.pettingzoo.ml/api

In certain environments, it's a valid to assume that agents take their actions at the same time. For these games, we offer a secondary API to allow for parallel actions, documented at https://www.pettingzoo.ml/api#parallel-api

SuperSuit is a library that includes all commonly used wrappers in RL (frame stacking, observation, normalization, etc.) for PettingZoo and Gym environments with a nice API. We developed it in lieu of wrappers built into PettingZoo. https://github.com/PettingZoo-Team/SuperSuit

PettingZoo keeps strict versioning for reproducibility reasons. All environments end in a suffix like "_v0". When changes are made to environments that might impact learning results, the number is increased by one to prevent potential confusion.

To cite this project in publication, please use

@article{terry2020pettingzoo,
  Title = {PettingZoo: Gym for Multi-Agent Reinforcement Learning},
  Author = {Terry, Justin K and Black, Benjamin and Jayakumar, Mario and Hari, Ananth and Santos, Luis and Dieffendahl, Clemens and Williams, Niall L and Lokesh, Yashas and Sullivan, Ryan and Horsch, Caroline and Ravi, Praveen},
  journal={arXiv preprint arXiv:2009.14471},
  year={2020}
}

We support Linux and macOS, and conduct CI testing on both. We will accept PRs related to Windows, but do not officially support it.

We have a sort bug/documentation error bounty program, inspired by Donald Knuth's reward checks. People who make mergable PRs which properly address meaningful problems in the code, or which make meaningful improvements to the documentation, can receive a negotiable check for "hexadecimal dollar" ($2.56) mailed to them, or sent to them via PayPal. To redeem this, just send an email to [email protected] with your mailing address or PayPal address. We also pay out 32 cents for small fixes. This reward extends to libraries maintained by the PettingZoo team that PettingZoo depends on.