Reinforcement learning in multi-dimensional state-action space using random rectangular coarse coding and Gibbs sampling

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

13 被引用数 (Scopus)

抄録

This paper presents a coarse coding technique and an action selection scheme for reinforcement learning (RL) in multi-dimensional and continuous state-action spaces following conventional and sound RL manners. RL in high-dimensional continuous domains includes two issues: One is a generalization problem for value-function approximation, and the other is a sampling problem for action selection over multi-dimensional continuous action spaces. The proposed method combines random rectangular coarse coding with an action selection scheme using Gibbs-sampling. The random rectangular coarse coding is very simple and quite suited both to approximate Q-functions in high-dimensional spaces and to execute Gibbs sampling. Gibbs sampling enables us to execute action selection following Boltsmann distribution over high-dimensional action space. The algorithm is demonstrated through Rod in maze problem and a redundant-arm reaching task comparing with conventional regular grid approaches.

本文言語英語
ホスト出版物のタイトルProceedings of the 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2007
ページ88-95
ページ数8
DOI
出版ステータス出版済み - 2007
イベント2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2007 - San Diego, CA, 米国
継続期間: 10 29 200711 2 2007

出版物シリーズ

名前IEEE International Conference on Intelligent Robots and Systems

その他

その他2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2007
国/地域米国
CitySan Diego, CA
Period10/29/0711/2/07

All Science Journal Classification (ASJC) codes

  • 制御およびシステム工学
  • ソフトウェア
  • コンピュータ ビジョンおよびパターン認識
  • コンピュータ サイエンスの応用

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