Using Communication to ee Locality in M

This paper attempts to bridge the fields of ma-chine learning, robotics, and distributed AI. It discusses the use of communication in reduc-ing the undesirable effects of locality in fully distributed multi-agent systems with multiple agents/robots learning in parallel while interact-ing with each other. Two key problems, hidden state and credit assignment, are addressed by ap-plying local undirected broadcast communication in a dual role: as sensing and as reinforcement. The methodology is demonstrated on two multi-robot learning experiments. The first describes learning a tightly-coupled coordination task with two robots, the second a loosely-coupled task with four robots learning social rules. Communi-cation is used to share sensory data to overcome hidden state and reinforcement to overcome the credit assignment problem between the agents and to bridge the gap between local and global payoff