• DocumentCode
    2987544
  • Title

    Reinforcement Learning for the N-Persons Iterated Prisoners´ Dilemma

  • Author

    Agudo, J. Enrique ; Fyfe, Colin

  • Author_Institution
    Univ. of Extremadura, Caceres, Spain
  • fYear
    2011
  • fDate
    3-4 Dec. 2011
  • Firstpage
    472
  • Lastpage
    476
  • Abstract
    This paper discusses an empirical investigation into the N-person´s Iterated Prisoners´ Dilemma, a standard problem from game theory. We use reinforcement learning and our experimental results give some insight into the circumstances where cooperation might develop.
  • Keywords
    game theory; iterative methods; learning (artificial intelligence); N-persons iterated prisoners dilemma; game theory; reinforcement learning; Computational intelligence; Finance; Games; Immune system; Learning; Oligopoly; iterated prisoners dilemma; reinforcement learning;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computational Intelligence and Security (CIS), 2011 Seventh International Conference on
  • Conference_Location
    Hainan
  • Print_ISBN
    978-1-4577-2008-6
  • Type

    conf

  • DOI
    10.1109/CIS.2011.111
  • Filename
    6128167
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2987544