強化学習における適応的状態空間構成法 Adaptive State Space Formation Method for Reinforcement Learning

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For the application of reinforcement learning to real-world problems, an internal state space has to be constructed from a high dimensional observation space. The algorithm presented here constructs the internal state space during the course of learning desirable actions, and assigns local basis functions adaptively depending on the task requirement. The internal state space initially has only one basis function over the entire observation space, and that basis is eventually divided into smaller ones due to the statistical property of locally weighted temporal difference error. The algorithm was applied to an autonomous robot collision avoidance problem, and the validity of the algorithm was evaluated to show, for instance, the need of a smaller number of basis functions in comparison to other method.

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 6(3), 144-154, 1999-09-05

    Japanese Neural Network Society

参考文献:  21件中 1-21件 を表示

被引用文献:  6件中 1-6件 を表示

各種コード

  • NII論文ID(NAID)
    10008841609
  • 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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