Risk-sensitive finite-horizon piecewise deterministic Markov decision processes
Yonghui Huang1,3; Zhaotong Lian2; Xianping Guo1,3
2020-01
Source PublicationOperations Research Letters
ABS Journal Level2
ISSN0167-6377
Volume48Issue:1Pages:96-103
Abstract

This paper deals with risk-sensitive piecewise deterministic Markov decision processes, where the expected exponential utility of a finite-horizon reward is to be maximized. Both the transition rates and reward functions are allowed to be unbounded. Feynman–Kac’s formula is developed in our setup, using which along with an approximation technique, we establish the associated Hamilton–Jacobi–Bellman equation and the existence of risk-sensitive optimal policies under suitable conditions.

KeywordPiecewise Deterministic Markov Decision Processes Unbounded Transition Rates Hjb Equation Optimal Policy Risk Sensitive Finite Horizon
DOIhttps://doi.org/10.1016/j.orl.2019.05.001
Indexed BySCIE
Language英語English
WOS Research AreaOperations Research & Management Science
WOS SubjectOperations Research & Management Science
WOS IDWOS:000512484300017
PublisherELSEVIER, RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
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Cited Times [WOS]:0   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionDEPARTMENT OF ACCOUNTING AND INFORMATION MANAGEMENT
Corresponding AuthorXianping Guo
Affiliation1.School of Mathematics, Sun Yat-Sen University, Guangzhou 510275, China
2.Faculty of Business Administration, University of Macau, Macau
3.Guangdong Province Key Laboratory of Computational Science, Sun Yat-Sen University, Guangzhou 510275, China
Recommended Citation
GB/T 7714
Yonghui Huang,Zhaotong Lian,Xianping Guo. Risk-sensitive finite-horizon piecewise deterministic Markov decision processes[J]. Operations Research Letters,2020,48(1):96-103.
APA Yonghui Huang,Zhaotong Lian,&Xianping Guo.(2020).Risk-sensitive finite-horizon piecewise deterministic Markov decision processes.Operations Research Letters,48(1),96-103.
MLA Yonghui Huang,et al."Risk-sensitive finite-horizon piecewise deterministic Markov decision processes".Operations Research Letters 48.1(2020):96-103.
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