{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/pettingzoo-gym-for-multi-agent-reinforcement","title":"PettingZoo: Gym for Multi-Agent Reinforcement Learning","arxiv_id":"2009.14471","date":"2020-09-30","proceeding":"NeurIPS 2021 12","authors":["J. K. Terry","Benjamin Black","Nathaniel Grammel","Mario Jayakumar","Ananth Hari","Ryan Sullivan","Luis Santos","Rodrigo Perez","Caroline Horsch","Clemens Dieffendahl","Niall L. Williams","Yashas Lokesh","Praveen Ravi"],"abstract":"This paper introduces the PettingZoo library and the accompanying Agent Environment Cycle (\"AEC\") games model. PettingZoo is a library of diverse sets of multi-agent environments with a universal, elegant Python API. PettingZoo was developed with the goal of accelerating research in Multi-Agent Reinforcement Learning (\"MARL\"), by making work more interchangeable, accessible and reproducible akin to what OpenAI's Gym library did for single-agent reinforcement learning. PettingZoo's API, while inheriting many features of Gym, is unique amongst MARL APIs in that it's based around the novel AEC games model. We argue, in part through case studies on major problems in popular MARL environments, that the popular game models are poor conceptual models of games commonly used in MARL and accordingly can promote confusing bugs that are hard to detect, and that the AEC games model addresses these problems.","url_abs":"https://arxiv.org/abs/2009.14471v7","url_pdf":"https://arxiv.org/pdf/2009.14471v7.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"pettingzoo-gym-for-multi-agent-reinforcement","repo_url":"https://github.com/Farama-Foundation/PettingZoo","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"pettingzoo-gym-for-multi-agent-reinforcement","repo_url":"https://github.com/PettingZoo-Team/PettingZoo","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"multi-agent-reinforcement-learning","task_name":"Multi-agent Reinforcement Learning"},{"task_slug":"reinforcement-learning","task_name":"Reinforcement Learning"},{"task_slug":"reinforcement-learning-1","task_name":"Reinforcement Learning (RL)"},{"task_slug":"reinforcement-learning-2","task_name":"reinforcement-learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=2009.14471","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2009.14471"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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