Online Sensor Selection in Reinforcement Learning Environment

Koichiro Ishikawa, Tsutomu Fujinami, Susumu Kunifuji, Akito Sakurai

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)870-878
Number of pages9
JournalIEEJ Transactions on Electronics, Information and Systems
Issue number6
Publication statusPublished - 2005
Externally publishedYes


  • Q-learning
  • autonomous mobile robot
  • reinforcement learning
  • sensor selection

ASJC Scopus subject areas

  • Electrical and Electronic Engineering


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