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Technical Note—Improved Conditions for Convergence in Undiscounted Markov Renewal Programming

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  • Loren Platzman

    (Massachusetts Institute of Technology, Cambridge, Massachusetts)

Abstract

In a simply connected Markov renewal problem, each state is either transient under all policies or an element of a single chain under some policy. This property is easily verified; it implies invariance of the maximal long-term average return (gain) with respect to the initial state, which in turn assures convergence of Odoni's bounds in the damped value-iteration algorithm due to Schweitzer, even when the maximal-gain process is multiple-chained and/or periodic.

Suggested Citation

  • Loren Platzman, 1977. "Technical Note—Improved Conditions for Convergence in Undiscounted Markov Renewal Programming," Operations Research, INFORMS, vol. 25(3), pages 529-533, June.
    • Handle: RePEc:inm:oropre:v:25:y:1977:i:3:p:529-533
    DOI: 10.1287/opre.25.3.529
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    Cited by:

    1. L. Jianyong & Z. Xiaobo, 2004. "On Average Reward Semi-Markov Decision Processes with a General Multichain Structure," Mathematics of Operations Research, INFORMS, vol. 29(2), pages 339-352, May.
    2. Arie Leizarowitz, 2003. "An Algorithm to Identify and Compute Average Optimal Policies in Multichain Markov Decision Processes," Mathematics of Operations Research, INFORMS, vol. 28(3), pages 553-586, August.

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