  We study the problem of inferring communication structures that can solve
cooperative multi-agent planning problems while minimizing the amount of
communication. We quantify the amount of communication as the maximum degree of
the communication graph; this metric captures settings where agents have
limited bandwidth. Minimizing communication is challenging due to the
combinatorial nature of both the decision space and the objective; for
instance, we cannot solve this problem by training neural networks using
gradient descent. We propose a novel algorithm that synthesizes a control
policy that combines a programmatic communication policy used to generate the
communication graph with a transformer policy network used to choose actions.
Our algorithm first trains the transformer policy, which implicitly generates a
"soft" communication graph; then, it synthesizes a programmatic communication
policy that "hardens" this graph, forming a neurosymbolic transformer. Our
experiments demonstrate how our approach can synthesize policies that generate
low-degree communication graphs while maintaining near-optimal performance.
