  Cooperative multi-agent reinforcement learning (MARL) has achieved
significant results, most notably by leveraging the representation learning
abilities of deep neural networks. However, large centralized approaches
quickly become infeasible as the number of agents scale, and fully
decentralized approaches can miss important opportunities for information
sharing and coordination. Furthermore, not all agents are equal - in some
cases, individual agents may not even have the ability to send communication to
other agents or explicitly model other agents. This paper considers the case
where there is a single, powerful, central agent that can observe the entire
observation space, and there are multiple, low powered, local agents that can
only receive local observations and cannot communicate with each other. The job
of the central agent is to learn what message to send to different local
agents, based on the global observations, not by centrally solving the entire
problem and sending action commands, but by determining what additional
information an individual agent should receive so that it can make a better
decision. After explaining our MARL algorithm, hammer, and where it would be
most applicable, we implement it in the cooperative navigation and multi-agent
walker domains. Empirical results show that 1) learned communication does
indeed improve system performance, 2) results generalize to multiple numbers of
agents, and 3) results generalize to different reward structures.
