Distributed Control of Network Systems in the Space of Stabilizing Graph Neural Network Policies
arXiv:2512.18540v2 Announce Type: replace-cross 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 acti
The rapid advancement in Graph Neural Networks and demand for robust, scalable AI in critical infrastructure systems are driving innovation in distributed control mechanisms.
This development offers a pathway to more reliable, stable, and autonomous control systems for complex networks, crucial for various 'AI Agents' and 'defence-tech-recapitalisation' applications.
The ability to design AI policies that guarantee stability in networked systems by design, rather than through extensive post-deployment validation, significantly de-risks deployment of autonomous control.
- · AI developers
- · Robotics companies
- · Critical infrastructure operators
- · Defence contractors
- · Companies relying on brittle or centralized control systems
More widespread adoption of AI for autonomous control in large-scale systems.
Reduced operational costs and increased efficiency in complex networked environments.
Acceleration of truly autonomous defence and industrial systems, potentially leading to new adversarial dynamics.
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Read at arXiv cs.LG