Markov programming by successive approximations with respect to weighted supremum norms

J. Wessels

    Research output: Contribution to journalArticleAcademicpeer-review

    52 Citations (Scopus)

    Abstract

    Markovian decision processes are considered in the situation of discrete time, countable state space, and general decision space. By introducing a Banach space with a weighted supremum norm, conditions are derived, which guarantee convergence of successive approximations to the value function. These conditions are weaker then those required by the usual supnorm approach. Several properties of the successive approximations are derived.
    Original languageEnglish
    Pages (from-to)326-335
    Number of pages10
    JournalJournal of Mathematical Analysis and Applications
    Volume58
    Issue number2
    DOIs
    Publication statusPublished - 1977

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