ipark-cs / pettingzoo

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.

API

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

from pettingzoo.butterfly import pistonball_v0
env = pistonball_v0.env()

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

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

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

Parallel 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

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

Release History

Version 1.3.0 (September 8, 2020):

Fixed how agent iter wrapper handles premature agent death. Bumped environments with death (joust, mario_bros, maze_craze, warlords, wizard_of_wor, knights_archers_zombies, battle, battlefield, combined_arms, gather, tiger_deer, multiwalker). Also switched MAgent to having a native parallel environment, making it much faster. We bumped adverserial pursuit as well due to this.

Version 1.2.1 (August 31, 2020):

Fixed ability to indefinitely stall in Double Dunk, Othello, Tennis and Video Checkers Atari environments, bumped versions to v1.

Version 1.2.0 (August 27, 2020):

Large fix to quadrapong, version bumped to v1.

Version 1.1.0 (August 20, 2020):

Added ParallelEnv API where all agents step at once. Fixed entombed_competitive rewards and bumped environment version to entombed_competitive_v1. Fixed prospector rewards and bumped version to prospector_v1.

Version 1.0.1 (August 12, 2020):

Fixes to continuous mode on pistonball and prison butterfly environments, along with a bad test that let the problems slip through. Versions bumped on both games.

Version 1.0.0 (August 5th, 2020):

This is the first official stable release of PettingZoo. Any changes to environments after this point will result in incrementing the environment version number. We currently plan to do three more things for PettingZoo beyond general maintenance: write a paper and put it on Arxiv, add Shogi as a classic environment using python-shogi, and add "colosseum"- an online tool for benchmarking competitive environments.

Citation

To cite this project in publication, please use

@misc{pettingZoo2020,
  author = {Terry, Justin K and Black, Benjamin and Jayakumar, Mario  and Hari, Ananth and Santos, Luis and Dieffendahl, Clemens and Williams, Niall and Ravi, Praveen and Lokesh, Yashas and Horsch, Caroline and Patel, Dipam},
  title = {Petting{Z}oo},
  year = {2020},
  publisher = {GitHub},
  note = {GitHub repository},
  howpublished = {\url{https://github.com/PettingZoo-Team/PettingZoo}}
}

OS Support

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're open to help properly supporting Windows.

Reward Program

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.