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In this paper, we consider the problem of multi-agent navigation in partiallyobservable grid environments. This problem is challenging for centralizedplanning approaches as they, typically, rely on the full knowledge of theenvironment. We suggest utilizing the reinforcement learning approach when theagents, first, learn the policies that map observations to actions and thenfollow these policies to reach their goals. To tackle the challenge associatedwith learning cooperative behavior, i.e. in many cases agents need to yield toeach other to accomplish a mission, we use a mixing Q-network that complementslearning individual policies. In the experimental evaluation, we show that suchapproach leads to plausible results and scales well to large number of agents.

Q-Mixing Network for Multi-Agent Pathfinding in Partially Observable Grid Environments