Joint Communication-Control Strategy Optimization with Partially Nested Information Structures: The Linear-Quadratic Case
2026-08-13 • Multiagent Systems
Multiagent Systems
AI summaryⓘ
The authors study how multiple agents can coordinate their communication and control actions in a system where each agent has limited shared information. They focus on cases where the information structure is partially nested, meaning agents have some hierarchy in what they know. They find conditions that keep this structure intact when optimizing communication strategies, which helps keep the problem simpler. They also develop an approach using dynamic programming to find the best control strategies, providing explicit equations for solutions. Finally, they extend their method to more complex communication strategies, making the problem easier to solve than previous methods.
multi-agent systemslinear quadratic controldecentralized controlcommon informationpartially nested informationdynamic programmingRiccati equationsopen-loop communicationclosed-loop communicationstochastic control
Authors
Haoyi You, Kaiqing Zhang
Abstract
In this paper, we formalize a joint communication-control strategy optimization (JCCO) problem in multi-agent linear systems with quadratic costs, under the common-information-based (CIB) framework from decentralized stochastic control. For computational tractability, we focus on such JCCO problems with partially nested (PN) information structures (ISs). In particular, with a baseline communication protocol that leads to a PN IS, we establish a series of conditions under which the partial nestedness is preserved under the (additional) communication strategies to be optimized, while violating them may cause nonlinearity of the optimal strategies in general, with open-loop communication strategies. We then develop a dynamic-programming-based approach to compute the optimal control strategies of JCCO with open-loop communication strategies, which yields a set of closed-form Riccati Equations. As a byproduct of independent interest, such an approach also offers a way to solve decentralized linear-quadratic control with PN ISs and output feedback, under the CIB framework. Finally, we extend such an approach to JCCOs with closed-loop communication strategies, yielding a more tractable dynamic program than an infinite-dimensional CIB-belief-based one.