強化学習におけるオンラインセンサ選択  [in Japanese] Online Sensor Selection in Reinforcement Learning Environment  [in Japanese]

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Author(s)

    • 櫻井 彰人 SAKURAI Akito
    • 慶應義塾大学 理工学部 管理工学科 Department of Administration Engineering, Faculty of Science and Technology, Keio University
    • 藤波 努 [他] FUJINAMI Tsutomu
    • 北陸先端科学技術大学院大学 知識科学研究科 School of Knowledge Science, Japan Advanced Institute of Science and Technology
    • 國藤 進 KUNIFUJI Susumu
    • 北陸先端科学技術大学院大学 知識科学研究科 School of Knowledge Science, Japan Advanced Institute of Science and Technology

Abstract

More sensors do not necessarily result in more appropriate state descriptions, so that a mobile robot has to select an appropriate set of sensors besides learning a state-action function in a reinforcement learning environment. We present a multi-armed bandit formulation of the problem and apply it to mobile robot navigation task. We modified the reinforcement comparison method to suit our problem and build a system where the selection of optimal set of sensors and the learning of state-action functions are done simultaneously. Our approach is evaluated on a Khepera robot simulator and the results reveal that our approach works well as an integrated learning system to identify the best set of sensors and reduce learning time.

Journal

  • IEEJ Transactions on Electronics, Information and Systems

    IEEJ Transactions on Electronics, Information and Systems 125(6), 870-878, 2005-06-01

    The Institute of Electrical Engineers of Japan

References:  15

Cited by:  1

Codes

  • NII Article ID (NAID)
    10015672071
  • NII NACSIS-CAT ID (NCID)
    AN10065950
  • Text Lang
    JPN
  • Article Type
    Journal Article
  • ISSN
    03854221
  • NDL Article ID
    7387374
  • NDL Source Classification
    ZN31(科学技術--電気工学・電気機械工業)
  • NDL Call No.
    Z16-795
  • Data Source
    CJP  CJPref  NDL  J-STAGE 
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