STRONG N-DISCOUNT AND FINITE-HORIZON OPTIMALITY FOR CONTINUOUS-TIME MARKOV DECISION PROCESSES

作     者：ZHU Quanxin GUO Xianping 

作者机构：School of Mathematical Sciences and Institute of Finance and StatisticsNanjing Normal University School of Mathematics and Computational ScienceZhongshan University 

出 版 物：《Journal of Systems Science & Complexity》 (系统科学与复杂性学报（英文版）)

年 卷 期：2014年第27卷第5期

页      面：1045-1063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)] 07[理学] 070105[理学-运筹学与控制论] 0701[理学-数学] 

基　　金：supported by the National Natural Science Foundation of China under Grant Nos.61374080 and 61374067 the Natural Science Foundation of Zhejiang Province under Grant No.LY12F03010 the Natural Science Foundation of Ningbo under Grant No.2012A610032 Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions 

主　　题：Continuous time Markov decision process expected average reward criterion finite horizon optimality Polish space strong n discount optimality 

摘      要：This paper studies the strong n(n =—1,0)-discount and finite horizon criteria for continuoustime Markov decision processes in Polish *** corresponding transition rates are allowed to be unbounded,and the reward rates may have neither upper nor lower *** mild conditions,the authors prove the existence of strong n(n =—1,0)-discount optimal stationary policies by developing two equivalence relations:One is between the standard expected average reward and strong—1-discount optimality,and the other is between the bias and strong 0-discount *** authors also prove the existence of an optimal policy for a finite horizon control problem by developing an interesting characterization of a canonical triplet.