We study distributed control of networked systems through reinforcement learning, where neural policies must be simultaneously scalable, expressive and stabilizing. We introduce a policy parameterization that embeds Graph Neural Networks (GNNs) into a Youla-like magnitude-direction parameterization, yielding distributed stochastic controllers that guarantee network-level closed-loop stability by design. The magnitude is implemented as a stable operator consisting of a GNN acting on disturbance feedback, while the direction is a GNN acting on local observations. We prove robustness of the policy to perturbations in both the graph topology and model parameters. Numerical experiments validate the effectiveness of the proposed approach.
@article{arxiv.2512.18540,
title = {Distributed Control of Network Systems in the Space of Stabilizing Graph Neural Network Policies},
author = {John Cao and Luca Furieri},
journal= {arXiv preprint arXiv:2512.18540},
year = {2026}
}