An extension of the rational policy making algorithm to continuous state spaces

Kazuteru Miyazaki, Hajime Kimura, Shigenobu Kobayashi

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

Reinforcement Learning is a kind of machine learning. We know Profit Sharing, the Rational Policy Making algorithm (RPM), the Penalty Avoiding Rational Policy Making algorithm and PS-r* to guarantee the rationality in a typical class of the Partially Observable Markov Decision Processes. However they cannot treat continuous state spaces. In this paper, we present a solution to adapt them in continuous state spaces. We give RPM a mechanism to treat continuous state spaces in the environment that has the same type of a reward. We show the effectiveness of the proposed method in numerical examples.

Original languageEnglish
Pages (from-to)332-341
Number of pages10
JournalTransactions of the Japanese Society for Artificial Intelligence
Volume22
Issue number3
DOIs
Publication statusPublished - Jan 1 2007

All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence

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