• DocumentCode
    3597164
  • Title

    Cooperative Q-learning with heterogeneity in actions

  • Author

    Mirfattah, S. M Reza ; Ahmadabadi, Majid Nili

  • Author_Institution
    Robotics & AI Lab., Tehran Univ., Iran
  • Volume
    4
  • fYear
    2002
  • Abstract
    Cooperation in learning improves the speed of convergence and the quality of learning. Special care is needed when heterogeneous agents cooperate in learning. It is discussed that, cooperation in learning may cause the learning process to diverge if heterogeneity is not handled properly. In this paper, it is assumed that two heterogeneous Q-learning agents cooperate to learn. The heterogeneity is assumed in their action order (and not in their action set). A Q-learning-based method is introduced for the agents to learn the mapping among their actions. It is shown that, the agents are able to learn this mapping while cooperating in learning. Some simulation results are reported to show the effectiveness of the proposed method.
  • Keywords
    learning (artificial intelligence); multi-agent systems; action heterogeneity; action order; convergence; cooperative Q-learning; heterogeneous agent cooperation; multi-agent systems; simulation; Artificial intelligence; Convergence; Humans; Intelligent agent; Intelligent robots; Intelligent systems; Laboratories; Mathematics; Physics; Telecommunication control;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Systems, Man and Cybernetics, 2002 IEEE International Conference on
  • ISSN
    1062-922X
  • Print_ISBN
    0-7803-7437-1
  • Type

    conf

  • DOI
    10.1109/ICSMC.2002.1173250
  • Filename
    1173250