PettingZoo: Gym for Multi-Agent Reinforcement Learning
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 the games commonly used with MARL, that they promote severe bugs that are hard to detect, and that the AEC games model addresses these problems.
Cite
Text
KTerry et al. "PettingZoo: Gym for Multi-Agent Reinforcement Learning." Neural Information Processing Systems, 2021.Markdown
[KTerry et al. "PettingZoo: Gym for Multi-Agent Reinforcement Learning." Neural Information Processing Systems, 2021.](https://mlanthology.org/neurips/2021/kterry2021neurips-pettingzoo/)BibTeX
@inproceedings{kterry2021neurips-pettingzoo,
title = {{PettingZoo: Gym for Multi-Agent Reinforcement Learning}},
author = {KTerry, J and Black, Benjamin and Grammel, Nathaniel and Jayakumar, Mario and Hari, Ananth and Sullivan, Ryan and Santos, Luis S and Dieffendahl, Clemens and Horsch, Caroline and Perez-Vicente, Rodrigo and Williams, Niall and Lokesh, Yashas and Ravi, Praveen},
booktitle = {Neural Information Processing Systems},
year = {2021},
url = {https://mlanthology.org/neurips/2021/kterry2021neurips-pettingzoo/}
}