AI 中文总结
本文针对多智能体线性系统的JCCO问题，基于CIB框架建立形式化模型，提出基于动态规划的方法求解开环及闭环通信策略下的最优策略，可导出里卡蒂方程并拓展至分散线性二次控制场景。
AI 中文摘要
本文针对多智能体线性系统中二次代价下的通信与控制策略联合优化（JCCO）问题，基于分散随机控制中的公共信息基（CIB）框架进行形式化建模。为保证计算可处理性，本文聚焦于具有部分嵌套（PN）信息结构（IS）的JCCO问题。具体而言，针对一种可导出PN IS的基线通信协议，本文建立了一系列条件，使得待优化的（附加）通信策略能保持部分嵌套性；若违反这些条件，结合开环通信策略时，最优策略通常会呈现非线性。随后，本文开发了一种基于动态规划的方法，用于计算采用开环通信策略的JCCO的最优控制策略，该方法可导出一组闭式里卡蒂方程。作为具有独立研究价值的副产品，该方法还提供了一种在CIB框架下求解具有PN IS和输出反馈的分散线性二次控制的途径。最后，本文将该方法扩展至采用闭环通信策略的JCCO，得到了比基于无限维CIB信念的方法更易处理的动态规划模型。
英文摘要
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.
CommentsPreliminary version accepted to IEEE CDC 2026
