用图神经网络设计可稳定分布控制，确保系统鲁棒性。
Distributed Control of Network Systems in the Space of Stabilizing Graph Neural Network Policies
- 将GNN嵌入类似Youla的参数化，实现分布式控制
- 通过扰动反馈与局部观测，保证闭环系统稳定
- 对拓扑和模型参数变化均具鲁棒性，适合大规模网络
我们研究通过强化学习实现网络系统的分布式控制，要求神经策略同时具备可扩展性、表达能力和稳定性。提出一种将图神经网络（GNN）嵌入类似Youla的幅值-方向参数化的策略，生成的分布式随机控制器能设计保证网络级闭环稳定。幅值部分由作用于扰动反馈的稳定算子（含GNN）实现，方向部分由作用于局部观测的GNN实现。证明了该策略对图拓扑和模型参数扰动具有鲁棒性。数值实验验证了所提方法的有效性。
原文摘要 · Abstract (English)
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.
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