  Dynamical systems consisting of a set of autonomous agents face the challenge
of having to accomplish a global task, relying only on local information. While
centralized controllers are readily available, they face limitations in terms
of scalability and implementation, as they do not respect the distributed
information structure imposed by the network system of agents. Given the
difficulties in finding optimal decentralized controllers, we propose a novel
framework using graph neural networks (GNNs) to learn these controllers. GNNs
are well-suited for the task since they are naturally distributed architectures
and exhibit good scalability and transferability properties. The problems of
flocking and multi-agent path planning are explored to illustrate the potential
of GNNs in learning decentralized controllers.
