{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/6"}],"enrichment":{"capability":"PettingZoo provides multi-agent reinforcement learning environments with a Gymnasium-like API, supporting both sequential (AEC) and parallel action modes across Atari, classic games, and custom environments.","skillfed_tags":["multi-agent-rl","game-environments","research-framework"],"use_cases":["Train cooperative multi-agent policies in environments like Pistonball where agents must coordinate to solve tasks.","Benchmark competitive multi-agent algorithms on classic games (Tic-Tac-Toe, Connect Four) and Atari titles.","Implement curriculum learning and self-play training using PettingZoo's environment versioning for reproducible experiments.","Research agent communication and emergent behavior in mixed-sum games combining cooperation and competition.","Prototype custom multi-agent environments using PettingZoo's API and environment creation tutorials."],"what_it_does":"PettingZoo is a multi-agent reinforcement learning library that extends Gymnasium's single-agent paradigm to support environments where multiple agents interact simultaneously or sequentially. It models environments as Agent Environment Cycle (AEC) games to provide a unified API for diverse multi-agent scenarios\u2014from competitive Atari games to cooperative puzzle tasks\u2014while maintaining strict environment versioning for reproducibility.\n\nThe library offers two interaction modes: a sequential AEC API where agents take turns, and a parallel API for simultaneous action environments. It depends on numpy for numerical operations, gymnasium for the base environment interface, and typing-extensions for type hints. The core install is lightweight, with optional extras for specific environment families that may have system-level dependencies.","worth_installing":"Yes. PettingZoo is actively maintained, has low install friction, carries a permissive MIT license, and is the standard library for multi-agent RL research. It has no known vulnerabilities, supports current Python versions (3.10\u20133.14), and integrates cleanly with popular RL training frameworks. Install it if you need a multi-agent environment interface; use base install for API exploration and add extras only for specific environment families."},"id":"pettingzoo","links":{"html":"https://skillfed.io/packages/pettingzoo","md":"https://skillfed.io/packages/pettingzoo.md","pypi":"https://pypi.org/project/pettingzoo/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-13","license_spdx":null,"license_treatment":"permissive","name":"pettingzoo","python_support":"supports_current","summary":"Gymnasium for multi-agent reinforcement learning."},"popularity":{"monthly_downloads":311622,"position":7729,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.27.0"}