Počet záznamů: 1  

Sample-Path Optimal Stationary Policies in Stable Markov Decision Chains with Average Reward Criterion

  1. 1.
    0449029 - ÚTIA 2016 RIV GB eng J - Článek v odborném periodiku
    Cavazos-Cadena, R. - Montes-de-Oca, R. - Sladký, Karel
    Sample-Path Optimal Stationary Policies in Stable Markov Decision Chains with Average Reward Criterion.
    Journal of Applied Probability. Roč. 52, č. 2 (2015), s. 419-440. ISSN 0021-9002. E-ISSN 1475-6072
    Grant ostatní: GA AV ČR(CZ) 171396
    Institucionální podpora: RVO:67985556
    Klíčová slova: Dominated Convergence theorem for the expected average criterion * Discrepancy function * Kolmogorov inequality * Innovations * Strong sample-path optimality
    Kód oboru RIV: BC - Teorie a systémy řízení
    Impakt faktor: 0.665, rok: 2015
    http://library.utia.cas.cz/separaty/2015/E/sladky-0449029.pdf

    This work concerns discrete-time Markov decision chains with denumerable state and compact action sets. Besides standard continuity requirements, the main assumption on the model is that it admits a Lyapunov function m. In this context the average reward criterion is analyzed from the sample-path point of view. The main conclusion is that, if the expected average reward associated to m^2 is finite under any policy, then a stationary policy obtained from the optimality equation in the standard way is sample-path average optimal in a strong sense.
    Trvalý link: http://hdl.handle.net/11104/0250631

     
     
Počet záznamů: 1  

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