Online Sensor Selection in Reinforcement Learning Environment

Koichiro Ishikawa, Tsutomu Fujinami, Susumu Kunifuji, Akito Sakurai

研究成果: Article査読

2 被引用数 (Scopus)

抄録

More sensors do not necessarily result in more appropriate state descriptions, so that a mobile robot has to select an appropriate set of sensors besides learning a state-action function in a reinforcement learning environment. We present a multi-armed bandit formulation of the problem and apply it to mobile robot navigation task. We modified the reinforcement comparison method to suit our problem and build a system where the selection of optimal set of sensors and the learning of state-action functions are done simultaneously. Our approach is evaluated on a Khepera robot simulator and the results reveal that our approach works well as an integrated learning system to identify the best set of sensors and reduce learning time.

本文言語English
ページ(範囲)870-878
ページ数9
ジャーナルIEEJ Transactions on Electronics, Information and Systems
125
6
DOI
出版ステータスPublished - 2005
外部発表はい

ASJC Scopus subject areas

  • 電子工学および電気工学

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