Multiagent Bidirectionally-Coordinated Nets for Learning to Play StarCraft Combat Games

Multiagent Bidirectionally-Coordinated Nets for Learning to Play StarCraft Combat Games

Peng Peng (1), Quan Yuan (1), Ying Wen (2), Yaodong Yang (2), Zhenkun Tang (1), Haitao Long (1), Jun Wang (2)

(1) Alibaba Group, (2) University College London

(arXiv:1703.10069, Submitted on 29 Mar 2017)

Real-world artificial intelligence (AI) applications often require multiple agents to work in a collaborative effort. Efficient learning for intra-agent communication and coordination is an indispensable step towards general AI. In this paper, we take StarCraft combat game as the test scenario, where the task is to coordinate multiple agents as a team to defeat their enemies. To maintain a scalable yet effective communication protocol, we introduce a multiagent bidirectionally-coordinated network (BiCNet [‘bIknet]) with a vectorised extension of actor-critic formulation. We show that BiCNet can handle different types of combats under diverse terrains with arbitrary numbers of AI agents for both sides. Our analysis demonstrates that without any supervisions such as human demonstrations or labelled data, BiCNet could learn various types of coordination strategies that is similar to these of experienced game players. Moreover, BiCNet is easily adaptable to the tasks with heterogeneous agents. In our experiments, we evaluate our approach against multiple baselines under different scenarios; it shows state-of-the-art performance, and possesses potential values for large-scale real-world applications.








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