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 Results 21 - 26 of 26 [Previous]  /   
Committee Date Time Place Paper Title / Authors Abstract Paper #
NC, MBE
(Joint)		 2009-03-13
09:20		 Tokyo Tamagawa Univ. Modular Reinforcement Learning based on Adaptive Model Complexity
Yu Hiei (Nara Inst. of Sci and Tech.), Takeshi Mori (Kyoto Univ.), Shin Ishii (Kyoto Univ./Nara Institute of Science and Technology) NC2008-149		 In real-world problems such as robot control, the environment surrounding a controlled system is nonstationary, and the ... [more] NC2008-149
pp.273-278
NC, NLP 2008-06-27
17:05		 Okinawa University of the Ryukyus Self-organized Reinforcement Learning in Nonstationary Environment
Yu Hiei (NAIST), Takeshi Mori, Shin Ishii (Kyoto Univ.) NC2008-30		 In real-world problems, the environment surrounding a controlled system is nonstationary, and the optimal control may ch... [more] NC2008-30
pp.97-101
NC, MBE
(Joint)		 2008-03-14
13:20		 Tokyo Tamagawa Univ Active sampling based on Gaussian Process for reinforcement learning
Kazuhiro Takeda, Takeshi Mori (ＮＡＩＳＴ), Shin Ishii (Kyoto Univ.) NC2007-192		 In reinforcement learning (RL), many samples are necessary in
every policy improvement, which requires the robot actual... [more]		 NC2007-192
pp.473-478
NC 2006-03-16
14:55		 Tokyo Tamagawa University Multiobjective Reinforcement Learning based on Multiple Value Function
Takumi Kamioka (OIST/NAIST), Eiji Uchibe (OIST), Kenji Doya (OIST/ATR) 		Standard Reinforcement Learning(RL) is formulated
for optimization of a single objective function.
However in most re... [more]		 NC2005-146
pp.127-132
NC 2006-03-17
11:00		 Tokyo Tamagawa University Considering model error when applying an reinforcement learning method to control of a real robot
Yoichi Tokita, Yutaka Nakamura (NAIST), Junichiro Yoshimoto (OIST), Shin Ishii (NAIST) 		Because reinforcement learning (RL) methods have an advantage such that a control rule can be obtained autonomously with... [more] NC2005-154
pp.19-24
NLP 2005-06-23
13:55		 Hiroshima Hiroshima City Univ. n/a
n/a, Takeshi Kamio, Kunihiko Mitsubori, Hisato Fujisaka (n/a) 		The trade-off between exploration and exploitation has often been discussed in studies on reinforcement learning (RL). T... [more] NLP2005-20
pp.25-30
 Results 21 - 26 of 26 [Previous]  /   
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