PARTIALLY OBSERVABLE MARKOV DECISION PROCESSES WITH PARTIALLY OBSERVABLE RANDOM DISCOUNT FACTORS

E. Everardo Martinez-Garcia, J. Adolfo Minjárez-Sosa, Oscar Vega-Amaya

Research output: Contribution to journalArticlepeer-review

Abstract

This paper deals with a class of partially observable discounted Markov decision processes defined on Borel state and action spaces, under unbounded one-stage cost. The discount rate is a stochastic process evolving according to a difference equation, which is also assumed to be partially observable. Introducing a suitable control model and filtering processes, we prove the existence of optimal control policies. In addition, we illustrate our results in a class of GI/GI/1 queueing systems where we obtain explicitly the corresponding optimality equation and the filtering process.

Original languageEnglish
Pages (from-to)960-983
Number of pages24
JournalKybernetika
Volume58
Issue number6
DOIs
StatePublished - 2022

Bibliographical note

Publisher Copyright:
© 2022 Institute of Information Theory and Automation of The Czech Academy of Sciences. All rights reserved.

Keywords

  • discounted criterion
  • optimal policies
  • partially observable systems
  • queueing models
  • random discount factors

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