  Learning cooperative policies for multi-agent systems is often challenged by
partial observability and a lack of coordination. In some settings, the
structure of a problem allows a distributed solution with limited
communication. Here, we consider a scenario where no communication is
available, and instead we learn local policies for all agents that collectively
mimic the solution to a centralized multi-agent static optimization problem.
Our main contribution is an information theoretic framework based on rate
distortion theory which facilitates analysis of how well the resulting fully
decentralized policies are able to reconstruct the optimal solution. Moreover,
this framework provides a natural extension that addresses which nodes an agent
should communicate with to improve the performance of its individual policy.
