• DocumentCode
    3455691
  • Title

    Applying Reinforcement Learning for Game AI in a Tank-Battle Game

  • Author

    Fang, Yung-Ping ; Ting, I-Hsien

  • Author_Institution
    Dept. of Inf. Manage., Nat. Univ. of Kaohsiung, Kaohsiung, Taiwan
  • fYear
    2009
  • fDate
    7-9 Dec. 2009
  • Firstpage
    1031
  • Lastpage
    1034
  • Abstract
    Reinforcement learning is an unsupervised machine learning method in the area of artificial intelligence. It presents well performance in simulation of the thinking ability of human. However, it needs a trial-and-error process to achieve the goal. In the research field of game AI, it is a good approach to allow the non-player-characters (NPCs) of digital games to become more humanity. In this paper, we try to build a tank-battle computer game and use the methodology of reinforcement learning for the NPCs (tanks). The goal of this paper is to make this game become more interesting from the enhanced interactions with these intelligent NPCs.
  • Keywords
    computer games; unsupervised learning; artificial intelligence; digital games; game AI; intelligent nonplayer characters; reinforcement learning; tank-battle computer game; trial-and-error process; unsupervised machine learning method; Application software; Artificial intelligence; Computational modeling; Delay; Humans; Information management; Learning systems; Least squares approximation; Legged locomotion; Machine learning;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Innovative Computing, Information and Control (ICICIC), 2009 Fourth International Conference on
  • Conference_Location
    Kaohsiung
  • Print_ISBN
    978-1-4244-5543-0
  • Type

    conf

  • DOI
    10.1109/ICICIC.2009.114
  • Filename
    5412307