Finite state approximations for denumerable state infinite horizon discounted Markov decision processes with unbounded rewards
type: Item
instance of: scholarly article
title: Finite state approximations for denumerable state infinite horizon discounted Markov decision processes with unbounded rewards
zbMATH Open document ID: 0533.90094
DOI: 10.1016/0022-247X(82)90271-2
published in: Journal of Mathematical Analysis and Applications
publication date: 1982-01-01T00:00:00Z
review text: In very many Markov decision problems with denumerable states, the reward vector is unbounded. This paper deals with the approach to unbounded problems of \textit{J. M. Harrison} [Ann. Math. Staist. 43, 636-644 (1972; Zbl 0262.90064)], \textit{J. Wessels} [J. Math. Anal. Appl. 58, 326-335 (1977; Zbl 0354.90087)] and \textit{S. A. Lippman} [Manage. Sci., Theory 21, 1225-1233 (1975; Zbl 0309.90017)]. The approach is to use the results of these authors to convert the unbounded problems to bounded problems for which results in our earlier papers [e.g., J. Math. Anal. Appl. 74, 292- 295 (1980; Zbl 0428.90082); ibid. 72, 512-523 (1979; Zbl 0431.90080)] will then apply. The approximation errors are dependent on the appropriate contraction ratio factor for each case considered.
Mathematics Subject Classification ID: 90C40
zbMATH DE Number: 3847252
zbMATH Keywords: unbounded reward vector
zbMATH Keywords: Markov decision problems
zbMATH Keywords: denumerable states
zbMATH Keywords: 262.90064
zbMATH Keywords: 354.90087
zbMATH Keywords: 309.90017
zbMATH Keywords: 428.90082
zbMATH Keywords: 431.90080
zbMATH Keywords: approximation errors
author: D. J. White
MaRDI profile type: Publication
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description: scientific article; zbMATH DE number 3847252