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On undiscounted semi-Markov decision processes with absorbing states

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  • Prasenjit Mondal

    (Chandernagore Government College)

Abstract

Limiting ratio average (undiscounted) reward finite (state and action spaces) semi-Markov decision processes (SMDPs) with absorbing states are considered where all but one states are absorbing. We propose a realistic inspection model that suitably fits into the class of undiscounted SMDPs with absorbing states. Existence of an optimal semi-stationary policy (i.e., a semi-Markov policy independent of decision epoch counts) is proved. A linear programming algorithm is provided to compute such an optimal policy.

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  • Prasenjit Mondal, 2016. "On undiscounted semi-Markov decision processes with absorbing states," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 83(2), pages 161-177, April.
    • Handle: RePEc:spr:mathme:v:83:y:2016:i:2:d:10.1007_s00186-015-0524-y
    DOI: 10.1007/s00186-015-0524-y
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    References listed on IDEAS

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    Cited by:

    1. Prasenjit Mondal, 2020. "Computing semi-stationary optimal policies for multichain semi-Markov decision processes," Annals of Operations Research, Springer, vol. 287(2), pages 843-865, April.
    2. Prasenjit Mondal, 2018. "Completely mixed strategies for single controller unichain semi-Markov games with undiscounted payoffs," Operational Research, Springer, vol. 18(2), pages 451-468, July.

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