Variable Resolution Discretization in the Joint Space

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Permanent URL	http://hdl.lib.byu.edu/1877/2607
Title	Variable Resolution Discretization in the Joint Space
BYU Creator	Monson, Christopher K. Seppi, Kevin D. Wingate, David
Creator	Peterson, Todd S.
Keywords	reinforcement learning; continuous domain; control
Description/Abstract	We present JoSTLe, an algorithm that performs value iteration on control problems with continuous actions, allowing this useful reinforcement learning technique to be applied to problems where a priori action discretization is inadequate. The algorithm is an extension of a variable resolution technique that works for problems with continuous states and discrete actions. Results are given that indicate that JoSTLe is a promising step toward reinforcement learning in a fully continuous domain.
Source	Christopher K. Monson, David Wingate, Kevin D. Seppi, and Todd S. Peterson. "Variable Resolution Discretization in the Joint Space." In Proceedings of the International Conference on Machine Learning and Applications, Louisville, Kentucky, 2004.;
Publisher	IEEE
Date Original	2004
Date Digital	2010-10-21
Language	English eng en
Publication Type	Peer-Reviewed Articles Conference Proceedings
Type	Text
Format	application/pdf
Owning Institution	Brigham Young University
BYU College	College of Physical and Mathematical Sciences
BYU Department	Computer Science
Copyright Status	(c) 2004 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.;
Copyright Use Information	http://www.lib.byu.edu/generic_copyright.html

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Variable Resolution Discretization in the Joint Space

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