• DocumentCode
    3472789
  • Title

    Variability sensitive Markov decision processes

  • Author

    Baykal-Gursoy, Melike ; Ross, Keith W.

  • Author_Institution
    Dept. of Ind. Eng., Rutgers Univ., Piscataway, NJ, USA
  • fYear
    1989
  • fDate
    13-15 Dec 1989
  • Firstpage
    1261
  • Abstract
    Time-average Markov decision processes with finite state and action spaces are considered. Several definitions of variability are introduced and compared. It is shown that a stationary policy maximizes one of these criteria, namely, the expected long-run average variability. An algorithm that produces such an optimal stationary policy is given
  • Keywords
    Markov processes; decision theory; state-space methods; Markov decision processes; action spaces; finite state; variability; Artificial intelligence; Frequency; Industrial engineering; Random variables; Space stations; State-space methods;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Decision and Control, 1989., Proceedings of the 28th IEEE Conference on
  • Conference_Location
    Tampa, FL
  • Type

    conf

  • DOI
    10.1109/CDC.1989.70339
  • Filename
    70339
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3472789