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Accelerated modified policy iteration algorithms for Markov decision processes

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  • Oleksandr Shlakhter
  • Chi-Guhn Lee

Abstract

We propose a new approach to accelerate the convergence of the modified policy iteration method for Markov decision processes with the total expected discounted reward. In the new policy iteration an additional operator is applied to the iterate generated by Markov operator, resulting in a bigger improvement in each iteration. Copyright Springer-Verlag Berlin Heidelberg 2013

Suggested Citation

  • Oleksandr Shlakhter & Chi-Guhn Lee, 2013. "Accelerated modified policy iteration algorithms for Markov decision processes," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 78(1), pages 61-76, August.
    • Handle: RePEc:spr:mathme:v:78:y:2013:i:1:p:61-76
    DOI: 10.1007/s00186-013-0432-y
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    References listed on IDEAS

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    1. Herzberg, Meir & Yechiali, Uri, 1996. "A K-step look-ahead analysis of value iteration algorithms for Markov decision processes," European Journal of Operational Research, Elsevier, vol. 88(3), pages 622-636, February.
    2. Meir Herzberg & Uri Yechiali, 1994. "Accelerating Procedures of the Value Iteration Algorithm for Discounted Markov Decision Processes, Based on a One-Step Lookahead Analysis," Operations Research, INFORMS, vol. 42(5), pages 940-946, October.
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    5. Oleksandr Shlakhter & Chi-Guhn Lee & Dmitry Khmelev & Nasser Jaber, 2010. "Acceleration Operators in the Value Iteration Algorithms for Markov Decision Processes," Operations Research, INFORMS, vol. 58(1), pages 193-202, February.
    6. M. A. Pollatschek & B. Avi-Itzhak, 1969. "Algorithms for Stochastic Games with Geometrical Interpretation," Management Science, INFORMS, vol. 15(7), pages 399-415, March.
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