A phased reinforcement learning algorithm for complex control problems

Takakuni Goto, Noriyasu Homma, Makoto Yoshizawa, Kenichi Abe

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)

Abstract

In this article, a phased reinforcement learning algorithm for controlling complex systems is proposed. The key element of the proposed algorithm is a shaping function defined on a novel position-direction space. The shaping function is autonomously constructed once the goal is reached, and constrains the exploration area to optimize the policy. The efficiency of the proposed shaping function was demonstrated by using a complex control problem of positioning a 2-link planar underactuated manipulator.

Original languageEnglish
Pages (from-to)190-196
Number of pages7
JournalArtificial Life and Robotics
Volume11
Issue number2
DOIs
Publication statusPublished - 2007 Jul

Keywords

  • Human exploration-exploitation strategy
  • Promising zone
  • Reinforcement learning
  • Shaping function

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)
  • Artificial Intelligence

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