• DocumentCode
    1648093
  • Title

    Reinforcement Strategy Using Quantum Amplitude Amplification for Robot Learning

  • Author

    Daoyi, Dong ; Chunlin, Chen ; Hanxiong, Li

  • Author_Institution
    Chinese Acad. of Sci., Beijing
  • fYear
    2007
  • Firstpage
    571
  • Lastpage
    575
  • Abstract
    Quantum amplitude amplification is a kind of useful technique in quantum computation and it can boost the success probability of some quantum algorithms. Reinforcement strategy in reinforcement learning is essentially to boost the selection probability of "good" action. Considering the common characteristics, this paper uses the idea of amplitude amplification to reinforcement learning as a new reinforcement strategy, proposes a learning algorithm based on quantum amplitude amplification and demonstrates its effectiveness through simulated experiments.
  • Keywords
    learning (artificial intelligence); probability; quantum computing; robots; quantum algorithm; quantum amplitude amplification; quantum computation; reinforcement learning; robot learning; selection probability; success probability; Artificial intelligence; Control systems; Fuzzy logic; Information processing; Intelligent robots; Learning; Mobile robots; Quantum computing; Quantum mechanics; Robot sensing systems; Quantum Amplitude Amplification; Reinforcement Learning; Reinforcement Strategy; Robot Learning;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Control Conference, 2007. CCC 2007. Chinese
  • Conference_Location
    Hunan
  • Print_ISBN
    978-7-81124-055-9
  • Electronic_ISBN
    978-7-900719-22-5
  • Type

    conf

  • DOI
    10.1109/CHICC.2006.4347206
  • Filename
    4347206