Minimum risk probability for finite horizon semi-Markov decision processes

JOURNAL OF MATHEMATICAL ANALYSIS AND APPLICATIONS

卷:402

Article

Huang, Yonghui¹  Guo, Xianping¹  Li, Zhongfei² 

[1] Sun Yat Sen Univ, Sch Math & Computat Sci, Guangzhou 510275, Guangdong, Peoples R China

[2] Sun Yat Sen Univ, Sch Business, Guangzhou 510275, Guangdong, Peoples R China

关键词: Finite horizon semi-Markov decision processes;    Risk probability;    Optimal value function;    Iteration algorithm;    Optimal policy;   

DOI  :  10.1016/j.jmaa.2013.01.021

来源: Elsevier

【 摘 要 】

This paper studies the risk probability criteria for finite horizon semi-Markov decision processes. The goal is to find an optimal policy with the minimum risk probability that the total reward produced by a system during a finite horizon does not exceed a reward level, where the optimality is over the class of all randomized historic policies which include states, planning horizons and also reward levels. Under mild conditions, the optimality equation and the existence of optimal policies are established, and in addition, an iteration algorithm for solving optimal policies is developed. Our main results are applied to a manufacturing system. (C) 2013 Elsevier Inc. All rights reserved.

【 授权许可】

Free