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A basic formula for performance gradient estimation of semi-Markov decision processes

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  • Li, Yanjie
  • Cao, Fang

Abstract

This paper presents a basic formula for performance gradient estimation of semi-Markov decision processes (SMDPs) under average-reward criterion. This formula directly follows from a sensitivity equation in perturbation analysis. With this formula, we develop three sample-path-based gradient estimation algorithms by using a single sample path. These algorithms naturally extend many gradient estimation algorithms for discrete-time Markov systems to continuous time semi-Markov models. In particular, they require less storage than the algorithm in the literature.

Suggested Citation

  • Li, Yanjie & Cao, Fang, 2013. "A basic formula for performance gradient estimation of semi-Markov decision processes," European Journal of Operational Research, Elsevier, vol. 224(2), pages 333-339.
  • Handle: RePEc:eee:ejores:v:224:y:2013:i:2:p:333-339
    DOI: 10.1016/j.ejor.2012.08.010
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    References listed on IDEAS

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    1. Singh, Sumeetpal S. & Tadic, Vladislav B. & Doucet, Arnaud, 2007. "A policy gradient method for semi-Markov decision processes with application to call admission control," European Journal of Operational Research, Elsevier, vol. 178(3), pages 808-818, May.
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    Cited by:

    1. Tang, Hao & Xu, Lingling & Sun, Jing & Chen, Yingjun & Zhou, Lei, 2015. "Modeling and optimization control of a demand-driven, conveyor-serviced production station," European Journal of Operational Research, Elsevier, vol. 243(3), pages 839-851.
    2. Xia, Li & Shihada, Basem, 2015. "A Jackson network model and threshold policy for joint optimization of energy and delay in multi-hop wireless networks," European Journal of Operational Research, Elsevier, vol. 242(3), pages 778-787.

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