Preservation and Application of Acquired Knowledge Using Instance-based Reinforcement Learning

• Sakanoue Junki

  Hiroshima University

• Yasuda Toshiyuki

  Hiroshima University

• Ohkura Kazuhiro

  Hiroshima University

抄録

We have been developing a reinforcement learning technique called BRL as an approach to autonomous specialization, which is a new concept in cooperative multi-robot systems. BRL has a mechanism for autonomously segmenting the continuous state and action space. However, as in other machine learning approaches, overfitting is occasionally observed after successful learning. This paper proposes a technique to sophisticatedly utilize messy knowledge acquired using BRL. The proposed technique is expected to show better robustness against environmental changes. We investigate the proposed technique by conducting computer simulations of a cooperative carrying task.

収録刊行物

• SCIS & ISIS

  SCIS & ISIS 2010 (0), 576-581, 2010

  日本知能情報ファジィ学会

キーワード

• Multi-robot Systems
• Reinforcement Learning
• Support Vector Machine

詳細情報

• CRID: 1390001205589898368
• NII論文ID: 130005019631
• DOI: 10.14864/softscis.2010.0.576.0
• 本文言語コード: en
• データソース種別:
  • JaLC
  • CiNii Articles
• 抄録ライセンスフラグ: 使用不可