Motions obtaining of multi-degree-freedom underwater robot by using reinforcement learning algorithms

Youkun Han, Hajime Kimura

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

This paper deals with motions obtaining of an underwater robot arm which have multi-degree of freedom by using reinforcement learning algorithms. A natural gradient Actor-Critic algorithm which uses Eligibility Traces is applied to the robot arm. In this algorithm, motion planning problems are modeled as finite state Markov decision processes. The robot arm is developed to have 4 joints, each joint consists 1 servo motor. The experiment results show the robot arm successfully learning to swim by feasible learning steps.

Original languageEnglish
Title of host publicationTENCON 2010 - 2010 IEEE Region 10 Conference
Pages1498-1502
Number of pages5
DOIs
Publication statusPublished - Dec 1 2010
Event2010 IEEE Region 10 Conference, TENCON 2010 - Fukuoka, Japan
Duration: Nov 21 2010Nov 24 2010

Other

Other2010 IEEE Region 10 Conference, TENCON 2010
CountryJapan
CityFukuoka
Period11/21/1011/24/10

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Electrical and Electronic Engineering

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