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Semi-infinite discounted Markov decision processes: Policy improvement and singular perturbations

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  • Mohammed Abbad
  • Khalid Rahhali

Abstract

In this paper, Discounted Markov Decision Processes with finite state and countable action set (semi-infinite DMDP for short) are considered. A policy improvement finite algorithm which finds a nearly optimal deterministic strategy is presented. The steps of the algorithm are based on the classical policy improvement algorithm for finite DMDPs. Singularly perturbed semi-infinite DMDPs are investigated. In case of perturbations, some sufficient condition is given to guarantee that there exists a nearly optimal deterministic strategy which can approximate nearly optimal strategies for a whole family of singularly perturbed semi-infinite DMDP. Copyright Springer-Verlag Berlin Heidelberg 2001

Suggested Citation

  • Mohammed Abbad & Khalid Rahhali, 2001. "Semi-infinite discounted Markov decision processes: Policy improvement and singular perturbations," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 54(2), pages 279-290, December.
    • Handle: RePEc:spr:mathme:v:54:y:2001:i:2:p:279-290
    DOI: 10.1007/s001860100143
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