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

2 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.

本文言語英語
ホスト出版物のタイトルSICE Annual Conference, SICE 2007
ページ2754-2761
ページ数8
DOI
出版ステータス出版済み - 2007
イベントSICE(Society of Instrument and Control Engineers)Annual Conference, SICE 2007 - Takamatsu, 日本
継続期間: 9 17 20079 20 2007

出版物シリーズ

名前Proceedings of the SICE Annual Conference

その他

その他SICE(Society of Instrument and Control Engineers)Annual Conference, SICE 2007
国/地域日本
CityTakamatsu
Period9/17/079/20/07

All Science Journal Classification (ASJC) codes

  • 制御およびシステム工学
  • コンピュータ サイエンスの応用
  • 電子工学および電気工学

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