PettingZoo
A Python library providing a standardized API for multi-agent reinforcement learning environments with diverse reference games.
What is PettingZoo?
PettingZoo is a Python library that provides a standardized API for multi-agent reinforcement learning environments, similar to a multi-agent version of Gymnasium. It includes diverse reference environments like Atari games, board games, and cooperative simulations to facilitate research and development in MARL. The library addresses the need for consistent, reproducible interfaces across different types of multi-agent problems.
Researchers and developers working on multi-agent reinforcement learning algorithms who need standardized environments for training and evaluation. It's particularly valuable for AI/ML practitioners conducting experiments that require multiple interacting agents.
Developers choose PettingZoo because it offers a clean, unified API that minimizes common bugs in MARL environment implementations, supports both sequential and parallel agent interactions, and comes with a diverse collection of pre-built environments. Its strict versioning ensures reproducibility, and compatibility with wrappers like SuperSuit streamlines preprocessing.
Overview
An API standard for multi-agent reinforcement learning environments, with popular reference environments and related utilities
Use Cases
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