• DocumentCode
    1947792
  • Title

    Improvement with Joint Rewards on Multi-agent Cooperative Reinforcement Learning

  • Author

    Ying, Pan ; Dehua, Li

  • Author_Institution
    Inst. for Pattern Recognition & Artificial Intell., Huangzhong Univ. of Sci. & Technol., Wuhan
  • Volume
    1
  • fYear
    2008
  • fDate
    12-14 Dec. 2008
  • Firstpage
    536
  • Lastpage
    539
  • Abstract
    Cooperation among agents is important for multi-agent system. In this paper, an improved cooperative reinforcement learning algorithm is proposed, which based on joint rewards to insure agents to learn cooperative behavior. Furthermore, a symmetry idea is included in the algorithm to reduce the states size of reinforcement learning. The experiment results show the efficiency and well convergence of the algorithm.
  • Keywords
    learning (artificial intelligence); multi-agent systems; joint reward; multiagent cooperative reinforcement learning; Artificial intelligence; Computer science; Learning systems; Multiagent systems; Pattern recognition; Pursuit algorithms; Software engineering; State-space methods; Testing; Cooperative behavior; Joint reward; Reinforcement learning; Symmetry;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer Science and Software Engineering, 2008 International Conference on
  • Conference_Location
    Wuhan, Hubei
  • Print_ISBN
    978-0-7695-3336-0
  • Type

    conf

  • DOI
    10.1109/CSSE.2008.920
  • Filename
    4721805