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Risk-sensitive semi-Markov decision processes with general utilities and multiple criteria
Huang,Yonghui1; Lian,Zhaotong2; Guo,Xianping1
Source PublicationAdvances in Applied Probability

In this paper we investigate risk-sensitive semi-Markov decision processes with a Borel state space, unbounded cost rates, and general utility functions. The performance criteria are several expected utilities of the total cost in a finite horizon. Our analysis is based on a type of finite-horizon occupation measure. We express the distribution of the finite-horizon cost in terms of the occupation measure for each policy, wherein the discount is not needed. For unconstrained and constrained problems, we establish the existence and computation of optimal policies. In particular, we develop a linear program and its dual program for the constrained problem and, moreover, establish the strong duality between the two programs. Finally, we provide two special cases of our results, one of which concerns the discrete-time model, and the other the chance-constrained problem.

KeywordExpected Utility Finite-horizon Cost Linear Program Occupation Measure Semi-markov Decision Process
URLView the original
Indexed BySCI
WOS Research AreaMathematics
WOS SubjectStatistics & Probability
WOS IDWOS:000450373200006
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Document TypeJournal article
Corresponding AuthorGuo,Xianping
Affiliation1.School of Mathematics,SunYat-Sen University,,Guangzhou,510275,China
2.Faculty of Business Administration,University of Macau,,Macau,Macao
Recommended Citation
GB/T 7714
Huang,Yonghui,Lian,Zhaotong,Guo,Xianping. Risk-sensitive semi-Markov decision processes with general utilities and multiple criteria[J]. Advances in Applied Probability,2018,50(3):783-804.
APA Huang,Yonghui,Lian,Zhaotong,&Guo,Xianping.(2018).Risk-sensitive semi-Markov decision processes with general utilities and multiple criteria.Advances in Applied Probability,50(3),783-804.
MLA Huang,Yonghui,et al."Risk-sensitive semi-Markov decision processes with general utilities and multiple criteria".Advances in Applied Probability 50.3(2018):783-804.
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