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A Counterexample on Sample-Path Optimality in Stable Markov Decision Chains with the Average Reward Criterion

Author

Listed:
  • Rolando Cavazos-Cadena

    (Universidad Autónoma Agraria Antonio Narro)

  • Raúl Montes-de-Oca

    (Universidad Autónoma Metropolitana)

  • Karel Sladký

    (Institute of Information Theory and Automation)

Abstract

This note deals with Markov decision chains evolving on a denumerable state space. Under standard continuity-compactness requirements, an explicit example is provided to show that, with respect to a strong sample-path average reward criterion, the Lyapunov function condition does not ensure the existence of an optimal stationary policy.

Suggested Citation

  • Rolando Cavazos-Cadena & Raúl Montes-de-Oca & Karel Sladký, 2014. "A Counterexample on Sample-Path Optimality in Stable Markov Decision Chains with the Average Reward Criterion," Journal of Optimization Theory and Applications, Springer, vol. 163(2), pages 674-684, November.
  • Handle: RePEc:spr:joptap:v:163:y:2014:i:2:d:10.1007_s10957-013-0474-6
    DOI: 10.1007/s10957-013-0474-6
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    References listed on IDEAS

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    1. Hunt, F.Y., 2005. "Sample path optimality for a Markov optimization problem," Stochastic Processes and their Applications, Elsevier, vol. 115(5), pages 769-779, May.
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    Cited by:

    1. Ghosh, Mrinal K. & Golui, Subrata & Pal, Chandan & Pradhan, Somnath, 2023. "Discrete-time zero-sum games for Markov chains with risk-sensitive average cost criterion," Stochastic Processes and their Applications, Elsevier, vol. 158(C), pages 40-74.
    2. Amir Ahmadi-Javid & Mohsen Ebadi, 2021. "Economic design of memory-type control charts: The fallacy of the formula proposed by Lorenzen and Vance (1986)," Computational Statistics, Springer, vol. 36(1), pages 661-690, March.

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