• DocumentCode
    445893
  • Title

    The k-server problem: a reinforcement learning approach

  • Author

    Junior, M.L.L. ; Neto, Adrião D Doria ; Melo, Jorge D.

  • Author_Institution
    Departamento de Engenharia de Computacao e Automacao, Univ. Fed. do Rio Grande do Norte, Natal, Brazil
  • Volume
    2
  • fYear
    2005
  • fDate
    31 July-4 Aug. 2005
  • Firstpage
    798
  • Abstract
    This work presents an original algorithm, based on reinforcement learning, as a solution for the k-server problem. This problem was modelled as a multi-stage decision-making process, and subsequently the Q-learning algorithm was used as a means of solution. A series of experiments was conducted with the aim of evaluating the appropriateness and performance of the proposed solution. The results obtained show the efficiency of the suggested algorithm in comparison with other methods of solving the k-server problem frequently cited in the literature, whose rates of competitiveness have already been proven.
  • Keywords
    decision making; learning (artificial intelligence); queueing theory; Q-learning algorithm; k-server problem; multi-stage decision-making process; reinforcement learning; Algorithm design and analysis; Costs; Decision making; Learning;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 2005. IJCNN '05. Proceedings. 2005 IEEE International Joint Conference on
  • Print_ISBN
    0-7803-9048-2
  • Type

    conf

  • DOI
    10.1109/IJCNN.2005.1555954
  • Filename
    1555954