Optimal policy for minimizing risk models in Markov decision processes
type: Item
instance of: scholarly article
title: Optimal policy for minimizing risk models in Markov decision processes
zbMATH Open document ID: 1019.91012
DOI: 10.1016/S0022-247X(02)00097-5
author: Yoshio Ohtsubo
author: Kenji Toyonaga
published in: Journal of Mathematical Analysis and Applications
publication date: 2002-11-28T00:00:00Z
review text: The referred paper deals with the discounted Markov decision processes with countable state space and non-negative bounded general rewards. The attention is focused on the minimizing risk problem, the optimal policy and the optimal values for finite and infinite horizon case. The main results show two sufficient conditions for the existence of an optimal policy in an infinite horizon case. One of them improves an incorrectness existing in the accessible literature. The existence of an optimal policy and the uniqueness of the solution of an optimality equation are shown under the conditions mentioned above.
reviewed by: Milan Mareš
Mathematics Subject Classification ID: 91B06
Mathematics Subject Classification ID: 91B30
zbMATH DE Number: 1836739
zbMATH Keywords: Markov decision processes
zbMATH Keywords: decision making
zbMATH Keywords: minimizing risk model
zbMATH Keywords: maximal fixed point
zbMATH Keywords: optimal policy
MaRDI profile type: Publication
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full work available at URL: s0022-247x(02)00097-5
OpenAlex ID: W2071692771
description: scientific article; zbMATH DE number 1836739