Markov programming by successive approximations with respect to weighted supremum norms

J. Wessels

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    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
    Place of PublicationEindhoven
    PublisherTechnische Hogeschool Eindhoven
    Number of pages12
    Publication statusPublished - 1974

    Publication series

    NameMemorandum COSOR
    Volume7413
    ISSN (Print)0926-4493

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