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Value iteration and approximately optimal stationary policies in finite-state average Markov decision chains

Author

Listed:
  • Rolando Cavazos-Cadena
  • Rolando Cavazos-Cadena

Abstract

This work concerns finte-state Markov decision chains endowed with the long-run average reward criterion. Assuming that the optimality equation has a solution, it is shown that a nearly optimal stationary policy, as well as an approximation to the optimal average reward within a specified error, can be obtained in a finite number of steps of the value iteration method. These results extend others already available in the literature, which were established under more stringent restrictions on the ergodic structure of the decision process. Copyright Springer-Verlag Berlin Heidelberg 2002

Suggested Citation

  • Rolando Cavazos-Cadena & Rolando Cavazos-Cadena, 2002. "Value iteration and approximately optimal stationary policies in finite-state average Markov decision chains," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 56(2), pages 181-196, November.
    • Handle: RePEc:spr:mathme:v:56:y:2002:i:2:p:181-196
    DOI: 10.1007/s001860200205
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    Cited by:

    1. Karel Sladký, 2013. "Risk-Sensitive and Mean Variance Optimality in Markov Decision Processes," Czech Economic Review, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, vol. 7(3), pages 146-161, November.
    2. Rolando Cavazos-Cadena & Raúl Montes-de-Oca, 2003. "The Value Iteration Algorithm in Risk-Sensitive Average Markov Decision Chains with Finite State Space," Mathematics of Operations Research, INFORMS, vol. 28(4), pages 752-776, November.

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