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Hierarchical algorithms for discounted and weighted Markov decision processes

Author

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  • M. Abbad
  • C. Daoui

Abstract

We consider a discrete time finite Markov decision process (MDP) with the discounted and weighted reward optimality criteria. In [1] the authors considered some decomposition of limiting average MDPs. In this paper, we use an analogous approach for discounted and weighted MDPs. Then, we construct some hierarchical decomposition algorithms for both discounted and weighted MDPs. Copyright Springer-Verlag 2003

Suggested Citation

  • M. Abbad & C. Daoui, 2003. "Hierarchical algorithms for discounted and weighted Markov decision processes," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 58(2), pages 237-245, November.
    • Handle: RePEc:spr:mathme:v:58:y:2003:i:2:p:237-245
    DOI: 10.1007/s001860300290
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    Cited by:

    1. Cherki Daoui & Dimitri Lefebvre, 2017. "Control design for untimed Petri nets using Markov Decision Processes," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 27(4), pages 27-43.

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