  We present a framework combining hierarchical and multi-agent deep
reinforcement learning approaches to solve coordination problems among a
multitude of agents using a semi-decentralized model. The framework extends the
multi-agent learning setup by introducing a meta-controller that guides the
communication between agent pairs, enabling agents to focus on communicating
with only one other agent at any step. This hierarchical decomposition of the
task allows for efficient exploration to learn policies that identify globally
optimal solutions even as the number of collaborating agents increases. We show
promising initial experimental results on a simulated distributed scheduling
problem.
