Reinforcement Learning Based on Dynamic Construction of the Fuzzy State Space -Adjustment of Fuzzy Sets of States-

DOI
  • Hosoya Yu
    Department of Mathematics and Information Sciences, Graduate School of Science, Osaka Prefecture Univesity
  • Yamamura Tadayoshi
    Department of Mathematics and Information Sciences, Graduate School of Science, Osaka Prefecture Univesity
  • Umano Motohide
    Department of Mathematics and Information Sciences, Graduate School of Science, Osaka Prefecture Univesity
  • Seta Kazuhisa
    Department of Mathematics and Information Sciences, Graduate School of Science, Osaka Prefecture Univesity

Bibliographic Information

Other Title
  • 強化学習におけるファジィ状態空間の動的構築-状態のファジィ集合の調整-

Description

In the previous paper, we proposed a method with dynamic construction facility of the state space, where we initially have no state and gradually add a new state of fuzzy set with removing unnecessary actions. We adjusted Q values for actions but not fuzzy sets for states. In this paper, therefore, we propose a method to adjust fuzzy sets, the central value and width of its membership functions, by TD (Temporal Difference) error. Then, we apply this method to the pursuit problem in real number environment.

Journal

Keywords

Details 詳細情報について

  • CRID
    1390282680644306048
  • NII Article ID
    130004591348
  • DOI
    10.14864/fss.22.0.216.0
  • Text Lang
    ja
  • Data Source
    • JaLC
    • CiNii Articles
  • Abstract License Flag
    Disallowed

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