• DocumentCode
    2847563
  • Title

    Decentralized learning in two-player zero-sum games: A LR-I lagging anchor algorithm

  • Author

    Xiaosong Lu ; Schwartz, H.M.

  • Author_Institution
    Dept. of Syst. & Comput. Eng., Carleton Univ., Ottawa, ON, Canada
  • fYear
    2011
  • fDate
    June 29 2011-July 1 2011
  • Firstpage
    107
  • Lastpage
    112
  • Abstract
    This paper presents a LR-I lagging anchor algorithm that combines a lagging anchor method to the LR-I learning algorithm. We prove that this decentralized learning algorithm converges in strategies to a Nash equilibrium in two-player, zero-sum, two-action matrix games, while only needing knowledge of their own action and reward.
  • Keywords
    game theory; learning (artificial intelligence); matrix algebra; multi-agent systems; LR-I lagging anchor algorithm; LR-I learning algorithm; Nash equilibrium; decentralized learning algorithm; lagging anchor method; two-player zero-sum two-action matrix games; Algorithm design and analysis; Convergence; Games; Learning automata; Nash equilibrium;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    American Control Conference (ACC), 2011
  • Conference_Location
    San Francisco, CA
  • ISSN
    0743-1619
  • Print_ISBN
    978-1-4577-0080-4
  • Type

    conf

  • DOI
    10.1109/ACC.2011.5990832
  • Filename
    5990832
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2847563