Presentation 2006-07-04
Feature Space Construction and Function Approximation for Reinforcement Learning
Hideki SATOH,
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Abstract(in English) A feature space construction method for function approximation was developed and applied to reinforcement learning for multi-dimensional continuous state spaces. First, a non-linear function was approximated using a linear combination of elements of a basis. Next, the elements with small-absolute-value corresponding coefficients were replaced with other candidate elements. Making this replacement at periodic intervals resulted in the basis constructing an optimum feature space for function approximation. An example chaos control problem for multiple logistic maps was solved, demonstrating that reinforcement learning with feature space construction can not only construct an optimum feature space with the minimum degree of expansion but also reconstruct an optimum feature space based on changes in the environment.
Keyword(in Japanese) (See Japanese page)
Keyword(in English) feature space / function approximation / non-linear / reinforcement learning / multi-dimensional
Paper # NLP2006-41
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Conference Information
Committee NLP
Conference Date 2006/6/27(1days)
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Paper Information
Registration To Nonlinear Problems (NLP)
Language ENG
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Feature Space Construction and Function Approximation for Reinforcement Learning
Sub Title (in English)
Keyword(1) feature space
Keyword(2) function approximation
Keyword(3) non-linear
Keyword(4) reinforcement learning
Keyword(5) multi-dimensional
1st Author's Name Hideki SATOH
1st Author's Affiliation School of Systems Information Science, Future University-Hakodate()
Date 2006-07-04
Paper # NLP2006-41
Volume (vol) vol.106
Number (no) 136
Page pp.pp.-
#Pages 6
Date of Issue