強化学習による能動認識能力の学習 Learning of Active Perception Based on Reinforcement Learning

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著者

    • 西野 哲生 NISHINO Tetsuo
    • 東京大学先端科学技術研究センター Research Center for Advanced Science and Technology (RCAST), University of Tokyo
    • 岡部 洋一 OKABE Yoichi
    • 東京大学先端科学技術研究センター Research Center for Advanced Science and Technology (RCAST), University of Tokyo

抄録

The aim is to perform to realize a learning system for active perception using a neural network. It obtains inputs only from a movable visual sensor and learns both appropriate recognition and sensor motions for effective perception. The proposed learning method is based on reinforcement learning using reinforcement signals calculated from the recognition results. We conclude from simulations that it enables the system to move a visual sensor to the appropriate location and finally classify presented patterns correctly. The recognition rate was better than that in a simulation for comparison where the sensor was fixed at the initial location.

収録刊行物

  • 日本神経回路学会誌 = The Brain & neural networks

    日本神経回路学会誌 = The Brain & neural networks 3(4), 126-134, 1996-12-05

    Japanese Neural Network Society

参考文献:  13件中 1-13件 を表示

被引用文献:  2件中 1-2件 を表示

各種コード

  • NII論文ID(NAID)
    10008841198
  • NII書誌ID(NCID)
    AA11658570
  • 本文言語コード
    JPN
  • 資料種別
    ART
  • ISSN
    1340766X
  • データ提供元
    CJP書誌  CJP引用  J-STAGE 
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