  Dynamical systems comprised of autonomous agents arise in many relevant
problems such as multi-agent robotics, smart grids, or smart cities.
Controlling these systems is of paramount importance to guarantee a successful
deployment. Optimal centralized controllers are readily available but face
limitations in terms of scalability and practical implementation. Optimal
decentralized controllers, on the other hand, are difficult to find. In this
paper, we propose a framework using graph neural networks (GNNs) to learn
decentralized controllers from data. While GNNs are naturally distributed
architectures, making them perfectly suited for the task, we adapt them to
handle delayed communications as well. Furthermore, they are equivariant and
stable, leading to good scalability and transferability properties. The problem
of flocking is explored to illustrate the potential of GNNs in learning
decentralized controllers.
