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Non-randomized policies for constrained Markov decision processes

Author

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  • Richard Chen
  • Eugene Feinberg

Abstract

This paper addresses constrained Markov decision processes, with expected discounted total cost criteria, which are controlled by non-randomized policies. A dynamic programming approach is used to construct optimal policies. The convergence of the series of finite horizon value functions to the infinite horizon value function is also shown. A simple example illustrating an application is presented. Copyright Springer-Verlag 2007

Suggested Citation

  • Richard Chen & Eugene Feinberg, 2007. "Non-randomized policies for constrained Markov decision processes," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 66(1), pages 165-179, August.
    • Handle: RePEc:spr:mathme:v:66:y:2007:i:1:p:165-179
    DOI: 10.1007/s00186-006-0133-x
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    Citations

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    Cited by:

    1. Guo, Xianping & Ye, Liuer & Yin, George, 2012. "A mean–variance optimization problem for discounted Markov decision processes," European Journal of Operational Research, Elsevier, vol. 220(2), pages 423-429.
    2. Armando F. Mendoza-Pérez & Héctor Jasso-Fuentes & Omar A. De-la-Cruz Courtois, 2016. "Constrained Markov decision processes in Borel spaces: from discounted to average optimality," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 84(3), pages 489-525, December.
    3. Richard Chen & Eugene Feinberg, 2010. "Compactness of the space of non-randomized policies in countable-state sequential decision processes," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 71(2), pages 307-323, April.
    4. Ohlmann, Jeffrey W. & Bean, James C., 2009. "Resource-constrained management of heterogeneous assets with stochastic deterioration," European Journal of Operational Research, Elsevier, vol. 199(1), pages 198-208, November.

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