Metadata-Version: 2.1
Name: PettingZoo
Version: 0.1.2
Summary: Gym for multi-agent reinforcement learning
Home-page: https://github.com/PettingZoo-Team/PettingZoo
Author: PettingZoo Team
Author-email: justinkterry@gmail.com
License: UNKNOWN
Keywords: Reinforcement Learning,game,RL,AI,gym
Platform: UNKNOWN
Requires-Python: >=3.5
Description-Content-Type: text/markdown
Requires-Dist: gym (>=0.15.4)
Requires-Dist: pygame (==2.0.0.dev6)
Requires-Dist: scikit-image (>=0.16.2)
Requires-Dist: numpy (>=1.18.0)
Requires-Dist: matplotlib (>=3.1.2)
Requires-Dist: pymunk (>=5.6.0)
Requires-Dist: gym[box2d] (>=0.15.4)
Requires-Dist: python-chess
Requires-Dist: rlcard (>=0.1.14)
Requires-Dist: pynput
Requires-Dist: opencv-python

# PettingZoo
PettingZoo is 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.

## Environment Types and Installation

PettingZoo includes the following sets of games:

* atari: Multi-player Atari 2600 games (both cooperative and competitive)
* classic: Classical, nongraphical, competitive games (i.e. chess, Texas hold 'em, and go)
* gamma: Cooperative graphical games developed by us. Policies for these must learn very coordinated behaviors.
* magent: 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 3.5, 3.6, 3.7 and 3.8




