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In decentralized multi-robot navigation, the agents lack the world knowledgeto make safe and (near-)optimal plans reliably and make their decisions ontheir neighbors' observable states. We present a reinforcement learning basedmulti-agent navigation algorithm that performs inter-agent communications. Inorder to deal with the variable number of neighbors for each agent, we use amulti-head self-attention mechanism to encode neighbor information and create afixed-length observation vector. We pose communication selection as a linkprediction problem, where the network predicts whether communication isnecessary given the observable information. The communicated informationaugments the observed neighbor information and is used to select a suitablenavigation plan. We highlight the benefits of our approach by performing safeand efficient navigation among multiple robots in dense and challengingbenchmarks. We also compare the performance with other learning-based methodsand highlight improvements in terms of fewer collisions and time-to-goal indense scenarios.

DMCA: Dense Multi-agent Navigation using Attention and Communication