Abstract
In this study,the Pareto optimal strategy problem was investigated for multi-player mean-field stochastic systems governed by Itô differential equations using the reinforcement learning(RL)method.A partially model-free solution for Pareto-optimal control was derived.First,by applying the convexity of cost functions,the Pareto optimal control problem was solved using a weighted-sum optimal control problem.Subsequently,using on-policy RL,we present a novel policy iteration(PI)algorithm based on the H-representation technique.In particular,by alternating between the policy evaluation and policy update steps,the Pareto optimal control policy is obtained when no further improvement occurs in system performance,which eliminates directly solving complicated cross-coupled generalized algebraic Riccati equations(GAREs).Practical numerical examples are presented to demonstrate the effectiveness of the proposed algorithm.
基金项目
国家自然科学基金(62103442)
国家自然科学基金(12326343)
国家自然科学基金(62373229)
山东省自然科学基金(ZR2021QF080)
中央高校基本科研业务费专项(23CX06024A)
Outstanding Youth Innovation Team in Shandong Higher Education Institutions(2023KJ061)
维普
万方数据