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 accelerating research in multi-agent reinforcement learning, by creating a set of benchmark environments that are easily accessible to all researchers and a standardized API for the field, akin to what OpenAI's Gym library did for single-agent reinforcement learning. PettingZoo's API is unique from other multi-agent environment libraries in that it's API is able to sensibly represent all forms of environments encountered on multi-agent reinforcement learning. In addition to this, PettingZoo's API is very similar to Gym's and can be immediately understood by novices, while still providing access to all low-level features that may be needed for novel research.

翻译：本文介绍PettingZoo(PettingZoo),这是一家单一优雅的Python API下多种试剂环境的图书馆。开发PettingZoo的目的是加速多剂强化学习的研究,通过建立一套所有研究人员都容易获得的基准环境,并建立一个标准化的实地API,类似于OpenAI的OpenAI Gym图书馆为单剂强化学习所做的工作。PettingZoo的API是其他多剂环境图书馆中独一无二的,因为它能够明智地代表在多剂强化学习中遇到的各种形式的环境。除此之外,PettingZoo的API与Gym非常相似,可以立即为新研究提供所有低层次的功能。