  Many algorithms for control of multi-robot teams operate under the assumption
that low-latency, global state information necessary to coordinate agent
actions can readily be disseminated among the team. However, in harsh
environments with no existing communication infrastructure, robots must form
ad-hoc networks, forcing the team to operate in a distributed fashion. To
overcome this challenge, we propose a task-agnostic, decentralized, low-latency
method for data distribution in ad-hoc networks using Graph Neural Networks
(GNN). Our approach enables multi-agent algorithms based on global state
information to function by ensuring it is available at each robot. To do this,
agents glean information about the topology of the network from packet
transmissions and feed it to a GNN running locally which instructs the agent
when and where to transmit the latest state information. We train the
distributed GNN communication policies via reinforcement learning using the
average Age of Information as the reward function and show that it improves
training stability compared to task-specific reward functions. Our approach
performs favorably compared to industry-standard methods for data distribution
such as random flooding and round robin. We also show that the trained policies
generalize to larger teams of both static and mobile agents.
