R-learning with multiple state-action value tables

Koichiro Ishikawa; Akito Sakurai; Tsutomu Fujinami; Susumu Kunifuji

doi:10.1541/ieejeiss.126.72

R-learning with multiple state-action value tables

Koichiro Ishikawa, Akito Sakurai, Tsutomu Fujinami, Susumu Kunifuji

Research output: Contribution to journal › Article › peer-review

1 Citation (Scopus)

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 language	English
Pages (from-to)	72-82
Number of pages	11
Journal	IEEJ Transactions on Electronics, Information and Systems
Volume	126
Issue number	1
DOIs	https://doi.org/10.1541/ieejeiss.126.72
Publication status	Published - 2006
Externally published	Yes

Keywords

Autonomous mobile robot
R-learning
Reinforcement learning

ASJC Scopus subject areas

Electrical and Electronic Engineering

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10.1541/ieejeiss.126.72

Cite this

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title = "R-learning with multiple state-action value tables",

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.",

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AU - Ishikawa, Koichiro

AU - Sakurai, Akito

AU - Fujinami, Tsutomu

AU - Kunifuji, Susumu

PY - 2006

Y1 - 2006

N2 - 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.

AB - 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.

KW - Autonomous mobile robot

KW - R-learning

KW - Reinforcement learning

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IS - 1

ER -

R-learning with multiple state-action value tables

Abstract

Keywords

ASJC Scopus subject areas

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