Model-free method for LQ mean-field social control problems with one-dimensional state space
Zhenhui Xu1
Tielong Shen2
1.School of Engineering,Tokyo Institute of Technology,Tokyo 152-8550,Japan2.Department of Engineering and Applied Sciences,Sophia University,Tokyo 102-8554,Japan
摘要：This paper presents a novel model-free method to solve linear quadratic(LQ)mean-field control problems with one-dimensional state space and multiplicative noise.The focus is on the infinite horizon LQ setting,where the conditions for solution either stabilization or optimization can be formulated as two algebraic Riccati equations(AREs).The proposed approach leverages the integral reinforcement learning technique to iteratively solve the drift-coefficient-dependent stochastic ARE(SARE)and other indefinite ARE,without requiring knowledge of the system dynamics.A numerical example is given to demonstrate the effectiveness of the proposed algorithm.
机标关键词：model-freecontrolspacestatesocialmethodwithmean-field
论文发表日期：2024-08-05
在线出版日期：2025-08-15（本平台首次上网日期，不代表文献的发表时间）
页数：8( 479-486 )
英文信息