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Decentralized cooperation in partially-observable multi-agent systemsrequires effective communications among agents. To support this effort, thiswork focuses on the class of problems where global communications are availablebut may be unreliable, thus precluding differentiable communication learningmethods. We introduce FCMNet, a reinforcement learning based approach thatallows agents to simultaneously learn a) an effective multi-hop communicationsprotocol and b) a common, decentralized policy that enables team-leveldecision-making. Specifically, our proposed method utilizes the hidden statesof multiple directional recurrent neural networks as communication messagesamong agents. Using a simple multi-hop topology, we endow each agent with theability to receive information sequentially encoded by every other agent ateach time step, leading to improved global cooperation. We demonstrate FCMNeton a challenging set of StarCraft II micromanagement tasks with shared rewards,as well as a collaborative multi-agent pathfinding task with individualrewards. There, our comparison results show that FCMNet outperformsstate-of-the-art communication-based reinforcement learning methods in allStarCraft II micromanagement tasks, and value decomposition methods in certaintasks. We further investigate the robustness of FCMNet under realisticcommunication disturbances, such as random message loss or binarized messages(i.e., non-differentiable communication channels), to showcase FMCNet'spotential applicability to robotic tasks under a variety of real-worldconditions.

FCMNet: Full Communication Memory Net for Team-Level Cooperation in Multi-Agent Systems