Presentation 2011-06-24
Q-learning in Continuous State-Action Space by Using a Selective Desensitization Neural Network
Takaaki KOBAYASHI, Takeshi SHIBUYA, Fumihide TANAKA, Masahiko MORITA,
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Abstract(in English) Value function approximation takes an important role for reinforcement learning in continuous state-action space. Conventional methods such as radial basis function networks need considerable amount of computation in its learning as well as optimal action selection. This paper proposes a novel representation of the output layer of selective desensitization neural networks. By using the method, the efficiency of learning is increased and amount of computation is decreased. The effectiveness of proposed method is confirmed through computer simulation experiments using acrobot.
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Keyword(in English) Q-learning / continuous state-action space / function approximation / selective desensitization neural networks
Paper # NC2011-15
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Committee NC
Conference Date 2011/6/16(1days)
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Registration To Neurocomputing (NC)
Language JPN
Title (in Japanese) (See Japanese page)
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Title (in English) Q-learning in Continuous State-Action Space by Using a Selective Desensitization Neural Network
Sub Title (in English)
Keyword(1) Q-learning
Keyword(2) continuous state-action space
Keyword(3) function approximation
Keyword(4) selective desensitization neural networks
1st Author's Name Takaaki KOBAYASHI
1st Author's Affiliation Graduate School of System and Information Engineering, University of Tsukuba()
2nd Author's Name Takeshi SHIBUYA
2nd Author's Affiliation Graduate School of System and Information Engineering, University of Tsukuba
3rd Author's Name Fumihide TANAKA
3rd Author's Affiliation Graduate School of System and Information Engineering, University of Tsukuba
4th Author's Name Masahiko MORITA
4th Author's Affiliation Graduate School of System and Information Engineering, University of Tsukuba
Date 2011-06-24
Paper # NC2011-15
Volume (vol) vol.111
Number (no) 96
Page pp.pp.-
#Pages 5
Date of Issue