Learning Attentional Communication for Multi-Agent Cooperation
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Communication could potentially be an effective way for multi-agent cooperation. However, information sharing among all agents or in predefined communication architectures that existing methods adopt can be problematic. When there is a large number of agents, agents hardly differentiate valuable information that helps cooperative decision making from globally shared information. Therefore, communication barely help, and could even impair the learning of multi-agent cooperation. Predefined communication architectures, on the other hand, restrict communication among agents and thus restrain potential cooperation. To tackle these difficulties, in this paper, we propose an attentional communication model that learns when communication is needed and how to integrates shared information for cooperative decision making. Our model leads to efficient and effective communication for large-scale multi-agent cooperation. Empirically, we show the strength of our model in various cooperative scenarios, where agents are able to develop more coordinated and sophisticated strategies than existing methods.
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