R-learning with multiple state-action value tables

Koichiro Ishikawa, Akito Sakurai, Tsutomu Fujinami, Susumu Kunifuji

Research output: Contribution to journalArticle

Abstract

We propose a method to improve the performance of R-learning, a reinforcement learning algorithm, by using multiple state-action value tables. Unlike Q- or Sarsa learning, R-learning learns a policy to maximize undiscounted rewards. Multiple state-action value tables cause substantial explorations as needed and make R-learnings to work well. Efficiency of the proposed method is verified through experiments in simulation environment.

Original languageEnglish
Pages (from-to)72-82
Number of pages11
JournalIEEJ Transactions on Electronics, Information and Systems
Volume126
Issue number1
DOIs
Publication statusPublished - 2006 Jan 1

Keywords

  • Autonomous mobile robot
  • R-learning
  • Reinforcement learning

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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