Block-successive approximation for a discounted Markov decision model

Moshe Haviv*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

In this paper we suggest a new successive approximation method to compute the optimal discounted reward for finite state and action, discrete time, discounted Markov decision chains. The method is based on a block partitioning of the (stochastic) matrices corresponding to the stationary policies. The method is particularly attractive when the transition matrices are jointly nearly decomposable or nearly completely decomposable.

Original languageEnglish
Pages (from-to)151-160
Number of pages10
JournalStochastic Processes and their Applications
Volume19
Issue number1
DOIs
StatePublished - Feb 1985

Keywords

  • Markov decision model
  • optimal reward
  • partitioning transition matrices
  • stationary policies
  • successive approximation

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