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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| NC, MBE (Joint) |
2011-03-07 16:25 |
Tokyo | Tamagawa University | Fusing Learning Strategies to Learn Various Tasks with Single Configuration Akihiko Yamaguchi, Jun Takamatsu, Tsukasa Ogasawara (NAIST)  NC2010-154 |
This paper proposes a method to fuse learning strategies (LSs) in reinforcement learning framework. Generally, we need ... [more] | NC2010-154 pp.159-164 |
| NC, MBE (Joint) |
2011-03-07 17:15 |
Tokyo | Tamagawa University | Spatial representation in reinforcement learning Tsubasa Asano, Satoshi Yamada (Okayama Univ. Sci.) NC2010-156 |
It is important to use appropriate spatial representations in the reinforcement learning. Since Incremental Normalized G... [more] | NC2010-156 pp.171-176 |
| NC, MBE (Joint) |
2010-03-09 16:50 |
Tokyo | Tamagawa University | Hierarchical Architecture with Evolving Modular Networks and Modular Reinforcement Learning Naoyuki Kanamoto, Masumi Ishikawa (Kyushu Inst. of Tech.) NC2009-112 |
We propose a hierarchical architecture composed of a characteristic learning layer which models characteristics of a tar... [more] | NC2009-112 pp.143-148 |
| 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, MBE (Joint) |
2009-03-13 11:35 |
Tokyo | Tamagawa Univ. | Application of modular reinforcement learning to the control of robot having three different sensors Hayato Nakama, Naoki Tanaka, Satoshi Yamada (Okayama Univ. of Sci.) NC2008-154 |
We apply modular reinforcement learning to the control of robot having three different sensors. The modular reinforcemen... [more] | NC2008-154 pp.301-306 |
| 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 | 2007-03-15 11:20 |
Tokyo | Tamagawa University | A Motor Control Learning Model of Degrees of Freedom in Postural Control Kenji Uematsu, Naohiro Fukumura (Toyohashi Univ. Tech.), Yoji Uno (Nagoya Univ.) |
In this study, we propose an efficient learning model that learns fast in a low dimensional state space using a low degr... [more] | NC2006-158 pp.31-36 |
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