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Finite state Markov decision models with average reward criteria

Author

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  • Feinberg, Eugene A.
  • Park, Haechurl

Abstract

This paper deals with a discrete time Markov decision model with a finite state space, arbitrary action space, and bounded reward function under the average reward criteria. We consider four average reward criteria and prove the existence of persistently nearly optimal strategies in various classes of strategies for models with complete state information. We show that such strategies exist in any class of strategies satisfying the following condition: along any trajectory at different epochs the controller knows different information about the past. Though neither optimal nor stationary nearly optimal strategies may exist, we show that for some nonempty set of states the described nearly optimal strategies may be chosen either stationary or optimal.

Suggested Citation

  • Feinberg, Eugene A. & Park, Haechurl, 1994. "Finite state Markov decision models with average reward criteria," Stochastic Processes and their Applications, Elsevier, vol. 49(1), pages 159-177, January.
    • Handle: RePEc:eee:spapps:v:49:y:1994:i:1:p:159-177
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    Cited by:

    1. Matthew Sobel, 2013. "Discounting axioms imply risk neutrality," Annals of Operations Research, Springer, vol. 208(1), pages 417-432, September.
    2. Rolando Cavazos-Cadena & Eugene A. Feinberg & Raúl Montes-de-Oca, 2000. "A Note on the Existence of Optimal Policies in Total Reward Dynamic Programs with Compact Action Sets," Mathematics of Operations Research, INFORMS, vol. 25(4), pages 657-666, November.

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