LIMITED RANDOMIZATION FOR MARKOV DECISION PROCESSES WITH MULTIPLE CONSTRAINTS.

K. W. Ross

    Research output: Contribution to conferencePaperpeer-review

    Abstract

    We consider the Markov decision problem of finding a policy to maximize the long-run average reward subject to K long-run average cost constraints. We show that there exists an optimal policy with a degree of randomization less than or equal to K. Consequently, it is never necessary to randomize in more than K states. An algorithm employing linear programming is shown to produce the optimal policy with the limited-randomization property.

    Original languageEnglish (US)
    Pages649-651
    Number of pages3
    StatePublished - 1986

    ASJC Scopus subject areas

    • Engineering(all)

    Fingerprint Dive into the research topics of 'LIMITED RANDOMIZATION FOR MARKOV DECISION PROCESSES WITH MULTIPLE CONSTRAINTS.'. Together they form a unique fingerprint.

    Cite this