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Cooperative multi-agent reinforcement learning (MARL) has achievedsignificant results, most notably by leveraging the representation-learningabilities of deep neural networks. However, large centralized approachesquickly become infeasible as the number of agents scale, and fullydecentralized approaches can miss important opportunities for informationsharing and coordination. Furthermore, not all agents are equal -- in somecases, individual agents may not even have the ability to send communication toother agents or explicitly model other agents. This paper considers the casewhere there is a single, powerful, \emph{central agent} that can observe theentire observation space, and there are multiple, low-powered \emph{localagents} that can only receive local observations and are not able tocommunicate with each other. The central agent's job is to learn what messageneeds to be sent to different local agents based on the global observations,not by centrally solving the entire problem and sending action commands, but bydetermining what additional information an individual agent should receive sothat it can make a better decision. In this work we present our MARL algorithm\algo, describe where it would be most applicable, and implement it in thecooperative navigation and multi-agent walker domains. Empirical results showthat 1) learned communication does indeed improve system performance, 2)results generalize to heterogeneous local agents, and 3) results generalize todifferent reward structures.

HAMMER: Multi-Level Coordination of Reinforcement Learning Agents via Learned Messaging