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Learning classifier system equivalent with reinforcement learning with function approximation
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Authors:
Atsushi Wada
ATR NIS, Hikaridai, Seika-cho, Soraku-gun, Kyoto, Japan
Keiki Takadama
Tokyo Institute of Technology, Nagatsuta-cho, Midori-ku, Kanagawa, Japan
Katsunori Shimohara
ATR NIS, Hikaridai, Seika-cho, Soraku-gun, Kyoto, Japan
Published in:
· Proceeding
GECCO '05 Proceedings of the 2005 workshops on Genetic and evolutionary computation
Pages 92 - 93
ACM
New York, NY
, USA
©2005
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doi>
10.1145/1102256.1102277
2005 Article
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Tags:
algorithms
design
function approximation
genetic-based machine learning
learning
learning classifier systems
reinforcement learning
theory
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