Gym for multi-agent reinforcement learning

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  • Author: PettingZoo Team
  • Tags Reinforcement Learning, game, RL, AI, gym
  • Requires: Python >=3.6

Project description

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 3.6, 3.7 and 3.8

Project details

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Avatar for jj_shoots from gravatar.com jj_shoots Avatar for jkterry from gravatar.com jkterry Avatar for pseudornd from gravatar.com pseudornd

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Project links
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  • Author: PettingZoo Team
  • Tags Reinforcement Learning, game, RL, AI, gym
  • Requires: Python >=3.6

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1.24.3

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Reason this release was yanked:

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screwed up

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