An Extension of the Rational Policy Making algorithm to Continuous State Spaces

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  • Miyazaki Kazuteru
    Department of Assessment and Research for Degree Awarding, National Institution for Academic Degrees and University Evaluation
  • Kimura Hajime
    Department of Marine Systems Engineering, Kyushu University
  • Kobayashi Shigenobu
    Graduate School of Interdisciplinary Science and Engineering, Tokyo Institute of Technology

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  • 合理的政策形成アルゴリズムの連続値入力への拡張
  • ゴウリテキ セイサク ケイセイ アルゴリズム ノ レンゾクチ ニュウリョク エノ カクチョウ

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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.

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