AUTHOR=Rivera Corban , Staley Edward , Llorens Ashley TITLE=Learning multi-agent cooperation JOURNAL=Frontiers in Neurorobotics VOLUME=16 YEAR=2022 URL=https://www.frontiersin.org/journals/neurorobotics/articles/10.3389/fnbot.2022.932671 DOI=10.3389/fnbot.2022.932671 ISSN=1662-5218 ABSTRACT=

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 associating agents with policies and policies with learning algorithms. Our results highlight the strengths of our approach, illustrate the importance of curriculum design, and measure the impact of multi-agent learning paradigms on the emergence of cooperation.