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:hidden:
:caption: Introduction

content/basic_usage
content/environment_creation
content/environment_tests

:hidden:
:caption: API

api/aec
api/parallel
api/wrappers
api/utils

:hidden:
:caption: Environments

environments/atari
environments/butterfly
environments/classic
environments/mpe
environments/sisl
environments/third_party_envs

:hidden:
:caption: Tutorials

tutorials/custom_environment/index
tutorials/cleanrl/index
tutorials/tianshou/index
tutorials/rllib/index
tutorials/langchain/index
tutorials/sb3/index
tutorials/agilerl/index

:hidden:
:caption: Development

Github <https://github.com/Farama-Foundation/PettingZoo>
release_notes/index
Contribute to the Docs <https://github.com/Farama-Foundation/PettingZoo/tree/master/docs/>

:alt: PettingZoo Logo

An API standard for multi-agent reinforcement learning.

    :width: 480px
    :name: PettingZoo environments

PettingZoo is a simple, pythonic interface capable of representing general multi-agent reinforcement learning (MARL) problems. PettingZoo includes a wide variety of reference environments, helpful utilities, and tools for creating your own custom environments.

The AEC API supports sequential turn based environments, while the Parallel API supports environments with simultaneous actions.

Environments can be interacted with using a similar interface to Gymnasium:

  from pettingzoo.butterfly import knights_archers_zombies_v10
  env = knights_archers_zombies_v10.env(render_mode="human")
  env.reset(seed=42)
  for agent in env.agent_iter():
      observation, reward, termination, truncation, info = env.last()
      action = policy(observation, agent)
      env.step(action)