Truncated policy iteration methods
type: Item
instance of: scholarly article
title: Truncated policy iteration methods
zbMATH Open document ID: 0567.90097
DOI: 10.1016/0167-6377(84)90054-3
author: Ron S. Dembo
author: Moshe Haviv
published in: Operations Research Letters
publication date: 1984-01-01T00:00:00Z
review text: Policy iteration methods are important but often computationally expensive approaches for solving certain stochastic optimization problems. Modified policy iteration methods have been proposed to reduce the storage and computational burden. The asymptotic speed-of-convergence of such methods is, however, not well understood. In this paper we show how modified policy iteration methods may be constructed to achieve a preassigned rate-of-convergence. Our analysis provides a framework for analyzing the local behavior of such methods and provides impetus for perhaps more computationally efficient procedures than currently exist.
Mathematics Subject Classification ID: 90C40
Mathematics Subject Classification ID: 90C15
Mathematics Subject Classification ID: 65K05
Mathematics Subject Classification ID: 90C39
zbMATH DE Number: 3906264
zbMATH Keywords: Markov chains
zbMATH Keywords: modified policy iteration methods
zbMATH Keywords: preassigned rate-of- convergence
MaRDI profile type: Publication
full work available at URL: 0167-6377(84)90054-3
OpenAlex ID: W1999380740
cites work: Inexact Newton Methods
cites work: Contraction Mappings in the Theory Underlying Dynamic Programming
cites work: Q3266141
cites work: Modified Policy Iteration Algorithms for Discounted Markov Decision Problems
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description: scientific article; zbMATH DE number 3906264