计算机工程与应用2024，Vol.60Issue(20)：116-123,8.DOI:10.3778/j.issn.1002-8331.2401-0032
基于强化学习的舰船目标跟踪有限理性博弈算法研究
Research on Bounded Rational Game Algorithm for Ship Target Tracking Based on Reinforcement Learning
摘要
Abstract
Since decision-makers in reality are not always able to analyze problems perfectly rationally,a pursuit evasion game algorithm based on bounded rationality is proposed.It establishes a pursuit evasion game model and first solves the saddle point strategies of the two players under perfect rationality.Introducing the bounded rationality level-k model,a structural assumption is made on the level of thinking strategies for pursuers and evaders.It allows both parties to have different strategic reasoning abilities,and gives corresponding levels'value functions and strategies,which satisfy the HJI equation.As the level increases,the strategy will eventually tend towards Nash equilibrium.Due to the difficulty in directly solving the HJI equation,an actor critic algorithm based on reinforcement learning is used to solve it.The algorithm is designed to enable pursuers to estimate the thinking level of evaders and adopt appropriate strategies.Simplify the motion of a ship as a two-dimensional mathematical model,this paper establishes a ship pursuit and evasion game model,and per-forms algorithm simulation verification on it.关键词
追逃博弈/目标跟踪/强化学习/有限理性Key words
pursuit-evasion game/target tracking/reinforcement learning/bounded rationality分类
信息技术与安全科学引用本文复制引用
陈素霞,徐清雯,刘久富,解晖,刘向武..基于强化学习的舰船目标跟踪有限理性博弈算法研究[J].计算机工程与应用,2024,60(20):116-123,8.基金项目
国家自然科学基金(61473144). （61473144）
