Learning and control for networked systems

JING Gang-shan; WANG Long; SHI Da-wei; CHEN Tong-wen

doi:10.7641/CTA.2025.50259

Abstract：A networked system refers to a system composed of multiple subsystems with the capability of interaction and task execution,connected through a network.Due to the properties of high dimension,multiple constraints,non-convexity,and nonlinearity in various application scenarios,the analysis and control of networked systems have received widespread attention from different communities in this century.To address uncertainties in dynamic environments and complex sys-tems,end-to-end methods like reinforcement learning have been introduced to learn control policies of networked systems.However,the high-dimensional nature of networked systems poses significant challenges to the learning efficiency.In fa-ct,many studies have found that the performance of networked systems is often closely related to the network structures.By approaching the problem from a graph perspective,complex optimization and control problems can be transformed into simple combinatorial optimization problems,enabling the scalability of the method to practical large-scale networks.In light of this,this paper systematically reviews learning-based control methods for networked systems from the graph perspective.By examining optimal control problems formulated by linear quadratic regulation and Markov games,respec-tively,it highlights the critical role of graph structures in learning control policies for networked systems.Additionally,some challenges in this field are outlined and a prospective outlook on future directions is provided.