• DocumentCode
    1743635
  • Title

    A learning algorithm for Markov decision processes with adaptive state aggregation

  • Author

    Baras, J.S. ; Borkar, V.S.

  • Author_Institution
    Inst. for Syst. Res., Maryland Univ., College Park, MD, USA
  • Volume
    4
  • fYear
    2000
  • fDate
    2000
  • Firstpage
    3351
  • Abstract
    We propose a simulation-based algorithm for learning good policies for a Markov decision process with unknown transition law, with aggregated states. The state aggregation itself can be adapted on a slower time scale by an auxiliary learning algorithm. Rigorous justifications are provided for both algorithms
  • Keywords
    Markov processes; adaptive systems; decision theory; learning (artificial intelligence); stochastic systems; Markov decision processes; adaptive state aggregation; learning algorithm; state aggregation; unknown transition law; Algorithm design and analysis; Clustering algorithms; Communication system control; Computational modeling; Computer science; Data compression; Educational institutions; Learning; State estimation; Stochastic processes;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Decision and Control, 2000. Proceedings of the 39th IEEE Conference on
  • Conference_Location
    Sydney, NSW
  • ISSN
    0191-2216
  • Print_ISBN
    0-7803-6638-7
  • Type

    conf

  • DOI
    10.1109/CDC.2000.912220
  • Filename
    912220
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1743635