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.
翻译:本文介绍了宠物动物园图书馆和伴随的代理环境循环(“AEC”)游戏模式。 宠物动物园是多试剂环境各组的图书馆,拥有通用、优雅的 Python API。 开发宠物动物园的目的是加速多代理强化学习(“MARL ”)的研究, 使工作更便于互换、更方便和可复制, 类似于 OpenAI Gym 图书馆为单试剂强化学习所做的工作。 宠物动物园的 API 虽然继承了 Gym 的许多特征, 但它在MARL API 的多个特征中是独一无二的, 因为它以新颖的 AEC 游戏模式为基础。 我们在一定程度上通过对流行的 MARL 环境中的主要问题进行案例研究, 认为流行的游戏模式是与 MARL 常用的游戏的不良概念模型, 它们推广了难以检测的严重错误, 而 AEC 游戏模式解决这些问题。