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Many artificial intelligence (AI) applications often require multipleintelligent agents to work in a collaborative effort. Efficient learning forintra-agent communication and coordination is an indispensable step towardsgeneral AI. In this paper, we take StarCraft combat game as a case study, wherethe task is to coordinate multiple agents as a team to defeat their enemies. Tomaintain a scalable yet effective communication protocol, we introduce aMultiagent Bidirectionally-Coordinated Network (BiCNet ['bIknet]) with avectorised extension of actor-critic formulation. We show that BiCNet canhandle different types of combats with arbitrary numbers of AI agents for bothsides. Our analysis demonstrates that without any supervisions such as humandemonstrations or labelled data, BiCNet could learn various types of advancedcoordination strategies that have been commonly used by experienced gameplayers. In our experiments, we evaluate our approach against multiplebaselines under different scenarios; it shows state-of-the-art performance, andpossesses potential values for large-scale real-world applications.

Multiagent Bidirectionally-Coordinated Nets: Emergence of Human-level Coordination in Learning to Play StarCraft Combat Games