Advances in reinforcement learning (RL) have resulted in recent breakthroughs in the application of artificial intelligence (AI) across many different domains. An emerging landscape of development environments is making powerful RL techniques more accessible for a growing community of researchers. However, most existing frameworks do not directly address the problem of learning in complex operating environments, such as dense urban settings or defense-related scenarios, that incorporate distributed, heterogeneous teams of agents. To help enable AI research for this important class of applications, we introduce the AI Arena: a scalable framework with flexible abstractions for distributed multi-agent reinforcement learning. The AI Arena extends the OpenAI Gym interface to allow greater flexibility in learning control policies across multiple agents with heterogeneous learning strategies and localized views of the environment. To illustrate the utility of our framework, we present experimental results that demonstrate performance gains due to a distributed multi-agent learning approach over commonly-used RL techniques in several different learning environments.

翻译：在强化学习(RL)方面的进展导致最近在许多不同领域应用人工智能(AI)方面的突破。新兴的发展环境使越来越多的研究人员更容易获得强大的RL技术。然而,大多数现有框架并不直接解决在复杂的操作环境中学习的问题,例如在密集的城市环境或国防相关情景中学习的问题,这些环境包括分布式的、各式各样的代理团队。为了帮助AI研究这一重要的应用类别,我们引入了AI Arena:一个可扩展的框架,为分布式多试剂强化学习提供灵活的抽取。AI Arena扩展了OpenAI Gym界面,使具有不同学习战略和环境本地观点的多个代理商在学习控制政策上具有更大的灵活性。为了说明我们框架的效用,我们提出了实验结果,表明由于在若干不同的学习环境中对常用的RL技术采取分布式多剂学习方法而取得的业绩收益。