PettingZoo: Gym for Multi-Agent Reinforcement Learning
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This paper introduces PettingZoo, a library of diverse sets of multi-agent environments under a single elegant Python API. PettingZoo was developed with the goal of acceleration research in multi-agent reinforcement learning, by creating a set of benchmark environments easily accessible to all researchers and a standardized API for the field. This goal is inspired by what OpenAI's Gym library did for accelerating research in single-agent reinforcement learning, and PettingZoo draws heavily from Gym in terms of API and user experience. PettingZoo is unique from other multi-agent environment libraries in that it's API is based on the model of Agent Environment Cycle ("AEC") games, which allows for the sensible representation all species of games under one API for the first time. While retaining a very simple and Gym-like API, PettingZoo still allows access to low-level environment properties required by non-traditional learning methods.
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