Value iteration and rolling plans for Markov control processes with unbounded rewards
type: Item
instance of: scholarly article
title: Value iteration and rolling plans for Markov control processes with unbounded rewards
zbMATH Open document ID: 0781.90093
author: Onésimo Hernández-Lerma
author: Jean B. Lasserre
published in: Journal of Mathematical Analysis and Applications
publication date: 1993-09-05T00:00:00Z
review text: The purpose is to extend known results for discounted Markov decision processes to the convergence of the value-iteration and the existence of error bounds for rolling horizon procedures to the case of a general state space and unbounded rewards. Now the error bounds are pointwise [w.r.t. the initial states] in contrast to the known uniform bounds. Uniformness is then obtained by using weighted norms. Further, under a strong ergodicity condition the bounds can be improved. The condition assumes a positive measure as lower bound for the distributions of states.
Mathematics Subject Classification ID: 90C40
zbMATH DE Number: 399094
zbMATH Keywords: discounted Markov decision processes
zbMATH Keywords: convergence of the value-iteration
zbMATH Keywords: strong ergodicity condition
MaRDI profile type: Publication
full work available at URL: jmaa.1993.1242
OpenAlex ID: W2027960972
DOI: 10.1006/JMAA.1993.1242
description: scientific article; zbMATH DE number 399094