Before revision: In the multi-robot collaboration domain, training with Reinforcement Learning (RL) can become intractable, and performance starts to deteriorate drastically as the number of robots increases. In this work , we proposed a distributed multi-robot learning architecture with a scalable communication structure capable of learning a robust communication policy for time-varying communication topology.
After revision: We consider the problem of classifying a map using a team of communicating robots. It is assumed that all robots have localized visual sensing capabilities and can exchange their information with neighboring robots. Using a graph decomposition technique , we proposed a distributed multi-robot learning architecture with a scalable communication structure capable of learning a robust communication policy for time-varying communication topology.