Presentation 2007-03-14
Prediction of the optimal parameter values in reinforcement learning based on interdependency
Keiji KAMEI, Masumi ISHIKAWA,
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Abstract(in English) Reinfoecement learning is that we have to specify parameters values without prior information. We proposed to optimize the values of parameters in RL with the help of a genetic algorithm. The parameter values in RL depend on the environments. Since then, the method is impractical due to huge computational cost. In this paper, we propose to predict of the optimal parameter values in RL as a function of measures for the complexity of the environment, which are multiple regression analysis and Supervise SOM. As a result of expriments, we succeed in estimating the optimal parameter values in RL.
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Keyword(in English) reinforcement learning / genetic algorithm / mobile robot / optimal path / parameter dependency / parameter prediction
Paper # NC2006-150
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Committee NC
Conference Date 2007/3/7(1days)
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Registration To Neurocomputing (NC)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Prediction of the optimal parameter values in reinforcement learning based on interdependency
Sub Title (in English)
Keyword(1) reinforcement learning
Keyword(2) genetic algorithm
Keyword(3) mobile robot
Keyword(4) optimal path
Keyword(5) parameter dependency
Keyword(6) parameter prediction
1st Author's Name Keiji KAMEI
1st Author's Affiliation Dept. of Brain Science and Engineering Graduate School of Life Science and Systems Engineering Kyushu Institute of Technology()
2nd Author's Name Masumi ISHIKAWA
2nd Author's Affiliation Dept. of Brain Science and Engineering Graduate School of Life Science and Systems Engineering Kyushu Institute of Technology
Date 2007-03-14
Paper # NC2006-150
Volume (vol) vol.106
Number (no) 588
Page pp.pp.-
#Pages 6
Date of Issue