  We consider the problem of finding distributed controllers for large networks
of mobile robots with interacting dynamics and sparsely available
communications. Our approach is to learn local controllers that require only
local information and communications at test time by imitating the policy of
centralized controllers using global information at training time. By extending
aggregation graph neural networks to time varying signals and time varying
network support, we learn a single common local controller which exploits
information from distant teammates using only local communication interchanges.
We apply this approach to the problem of flocking to demonstrate performance on
communication graphs that change as the robots move. We examine how a
decreasing communication radius and faster velocities increase the value of
multi-hop information.
