论文标题
Pettingzoo：多代理增强学习的体育馆
PettingZoo: Gym for Multi-Agent Reinforcement Learning
论文作者
论文摘要
本文介绍了Pettingzoo库和随附的代理环境周期（“ AEC”）游戏模型。 Pettingzoo是一个多种多样的多机构环境的库，具有通用，优雅的Python API。 Pettingzoo的开发目的是通过使工作更加可互换，易于访问和可再现，类似于Openai的Gym图书馆对单机器人的增强学习所做的事情，以加速多代理增强学习（“ MARL”）的研究。 Pettingzoo的API虽然继承了健身房的许多功能，但在Marl API中是独一无二的，因为它基于新颖的AEC游戏模型。我们认为，在一定程度上，通过关于流行的MARL环境中的主要问题的案例研究，流行的游戏模型是MARL中常用的游戏的良好概念模型，因此可以促进难以检测到的混乱错误，并且AEC游戏模型解决了这些问题。
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.
