{"ID":2826853,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.18540","arxiv_id":"2512.18540","title":"Distributed Control of Network Systems in the Space of Stabilizing Graph Neural Network Policies","abstract":"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.","short_abstract":"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 distribute...","url_abs":"https://arxiv.org/abs/2512.18540","url_pdf":"https://arxiv.org/pdf/2512.18540v2","authors":"[\"John Cao\",\"Luca Furieri\"]","published":"2025-12-20T23:35:07Z","proceeding":"eess.SY","tasks":"[\"eess.SY\",\"cs.LG\",\"math.OC\"]","methods":"[\"Graph Neural Network\",\"Reinforcement Learning\"]","has_code":false}