An Accelerated k-Certainty Exploration Method

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概要

• 論文の詳細を見る

• Reinforcement learning aims to adapt an agent system to an unknown environment according to rewards. On Markov decision processes(MDPs), if a correct environment model is identified, an optimal policy can be derived by applying policy iteration algorithm. For identifying an environment on MDPs, k-Certainty Exploration Method has been proposed. But the k-certainty exploration does not give the direct path to the states with k-uncertainty rules that may result in an agent taking useless steps as exploring an environment. In this paper, we propose an Accelerated k-Certainty Exploration Method which speeds up the k-certainty exploration rate. By simulations, this method is demonstrated to be efficient, and, an satisfied exploration result is also gotten in larger environment with rules numbet 5x10^4.

• 社団法人人工知能学会の論文
• 1999-05-01

著者

• TATSUMI Shoji

  Faculty of Engineering, Osaka City University

• Tatsumi Shoji

  Faculty Of Engineering Osaka City University

• Tatsumi S

  Osaka City Univ. Osaka‐shi Jpn

• Zhao Gang

  Fujitsu Kansai-chubu Net-tech Limited

• Zhao Gang

  National Astronomical Observatories Chinese Acad. Of Sci. Beijing Chn

• SUN Ruoying

  Faculty of Engineering, Osaka City University

• ZHAO Gang

  Faculty of Engineering, Osaka City University

• SUN Ruoying

  College of Industry and Commerce Management, Liaoning University

• Sun Ruoying

  Faculty Of Engineering Osaka City University

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