A model-based approximate λ-policy iteration approach to online evasive path planning and the video game ***-Man

作     者：Greg FODERARO Vikram RAJU Silvia FERRARI 

作者机构：Department of Mechanical Engineering and Materials ScienceDuke University 

出 版 物：《控制理论与应用（英文版）》 (Journal of Control Theory and Applications)

年 卷 期：2011年第9卷第3期

页      面：391-399页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 0808[工学-电气工程] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0802[工学-机械工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the National Science Foundation (No.ECS 0925407) 

主　　题：Approximate dynamic programming Reinforcement learning Path planning Pursuit evasion games 

摘      要：This paper presents a model-based approximate λ-policy iteration approach using temporal differences for optimizing paths online for a pursuit-evasion problem,where an agent must visit several target positions within a region of interest while simultaneously avoiding one or more actively pursuing *** method is relevant to applications,such as robotic path planning,mobile-sensor applications,and path *** methodology described utilizes cell decomposition to construct a decision tree and implements a temporal difference-based approximate λ-policy iteration to combine online learning with prior knowledge through modeling to achieve the objectives of minimizing the risk of being caught by an adversary and maximizing a reward associated with visiting target *** learning and frequent decision tree updates allow the algorithm to quickly adapt to unexpected movements by the adversaries or dynamic *** approach is illustrated through a modified version of the video game ***-Man,which is shown to be a benchmark example of the pursuit-evasion *** results show that the approach presented in this paper outperforms several other methods as well as most human players.