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We consider the problem of multi-agent navigation and collision avoidancewhen observations are limited to the local neighborhood of each agent. Wepropose InforMARL, a novel architecture for multi-agent reinforcement learning(MARL) which uses local information intelligently to compute paths for all theagents in a decentralized manner. Specifically, InforMARL aggregatesinformation about the local neighborhood of agents for both the actor and thecritic using a graph neural network and can be used in conjunction with anystandard MARL algorithm. We show that (1) in training, InforMARL has bettersample efficiency and performance than baseline approaches, despite using lessinformation, and (2) in testing, it scales well to environments with arbitrarynumbers of agents and obstacles. We illustrate these results using four taskenvironments, including one with predetermined goals for each agent, and one inwhich the agents collectively try to cover all goals. Code available athttps://github.com/nsidn98/InforMARL.

Scalable Multi-Agent Reinforcement Learning through Intelligent Information Aggregation