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Technical Note: On Ordinal Comparison of Policies in Markov Reward Processes

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  • H. S. Chang

    (Sogang University)

Abstract

An asymptotic exponential convergence rate of ordinal comparison from large deviations theory is well known for selecting the true best solution from the candidate solutions sample means. This note supplements the theories developed by Dai within the framework of ergodic Markov reward processes for ε-ordinal comparison of policies, establishing an asymptotic exponential convergence rate for the infinite-horizon average criterion.

Suggested Citation

  • H. S. Chang, 2004. "Technical Note: On Ordinal Comparison of Policies in Markov Reward Processes," Journal of Optimization Theory and Applications, Springer, vol. 122(1), pages 207-217, July.
    • Handle: RePEc:spr:joptap:v:122:y:2004:i:1:d:10.1023_b:jota.0000041736.82051.f1
    DOI: 10.1023/B:JOTA.0000041736.82051.f1
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    References listed on IDEAS

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    1. Y-C Ho & C G Cassandras & C-H Chen & L Dai, 2000. "Ordinal optimisation and simulation," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 51(4), pages 490-500, April.
    2. Glynn, Peter W. & Ormoneit, Dirk, 2002. "Hoeffding's inequality for uniformly ergodic Markov chains," Statistics & Probability Letters, Elsevier, vol. 56(2), pages 143-146, January.
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