• DocumentCode
    493369
  • Title

    Adaptive computation of optimal nonrandomized policies in constrained average-reward MDPs

  • Author

    Feinberg, Eugene A.

  • Author_Institution
    Dept. of Appl. Math. & Stat., Stony Brook Univ., Stony Brook, NY
  • fYear
    2009
  • fDate
    March 30 2009-April 2 2009
  • Firstpage
    96
  • Lastpage
    100
  • Abstract
    This paper deals with computation of optimal nonrandomized nonstationary policies and mixed stationary policies for average-reward Markov decision processes with multiple criteria and constraints. We consider problems with finite state and action sets satisfying the unichain condition. The described procedure for computing optimal nonrandomized policies can also be used for adaptive control problems.
  • Keywords
    Markov processes; action sets; adaptive computation; adaptive control problems; average-reward Markov decision processes; constrained average-reward MDP; finite state; mixed stationary policies; optimal nonrandomized nonstationary policies; unichain condition; Adaptive control; Constraint theory; Frequency; Linear programming; Mathematics; Probability distribution; State-space methods; Statistics; Vectors;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Adaptive Dynamic Programming and Reinforcement Learning, 2009. ADPRL '09. IEEE Symposium on
  • Conference_Location
    Nashville, TN
  • Print_ISBN
    978-1-4244-2761-1
  • Type

    conf

  • DOI
    10.1109/ADPRL.2009.4927531
  • Filename
    4927531
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=493369