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ORCIDTakeshi Shibuya
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2020 – today
- 2022
[c21]Chenyang Zhao, Takeshi Shibuya, Naoki Igo, Kiyoshi Hoshino:
Method of Controlling a Tankendo Robot Using a Mecanum Wheel Trolley Robot. ICIIT 2022: 126-130- [c20]Masato Nakai, Takeshi Shibuya:
Efficiency of Reinforcement Learning using Polarized Regime by Variational Autoencoder. SICE 2022: 128-134 - [c19]Takuma Ikeda, Takeshi Shibuya:
Centralized Training with Decentralized Execution Reinforcement Learning for Cooperative Multi-agent Systems with Communication Delay. SICE 2022: 135-140 - [c18]Seong-In Kim, Takeshi Shibuya:
Disturbance Observable Reinforcement Learning that Compensates for Changes in Environment. SICE 2022: 141-145 - [c17]Shoki Kobayashi, Takeshi Shibuya:
Reinforcement Learning to Efficiently Recover Control Performance of Robots Using Imitation Learning After Failure. SMC 2022: 1147-1154 - 2021
- [j5]Teppei Iwata
, Takeshi Shibuya:
Adaptive Modular Reinforcement Learning for Robot Controlled in Multiple Environments. IEEE Access 9: 103032-103043 (2021) - 2020
- [c16]Yuki Nakamura, Takeshi Shibuya:
Topological Visualization Method for Understanding the Landscape of Value Functions and Structure of the State Space in Reinforcement Learning. ICAART (2) 2020: 370-377 - [c15]Noritaka Yamada, Takeshi Shibuya:
Inferring Underlying Manifold of Low Density Data using Adaptive Interpolation. ICAART (2) 2020: 395-402 - [c14]Shoki Kobayashi, Takeshi Shibuya:
Reinforcement Learning Compensator Robust to the Time Constants of First Order Delay Elements. SMC 2020: 141-146
2010 – 2019
- 2019
- [c13]Rentaro Futagami, Noritaka Yamada, Takeshi Shibuya:
Inferring Underlying Manifold of Data by the Use of Persistent Homology Analysis. CTIC 2019: 40-53 - [c12]Tomoyuki Yamaguchi, Takeshi Shibuya, Masashi Kanda, Akira Yasojima:
Crack Inspection Support System for Concrete Structures Using Head Mounted Display in Mixed Reality Space. SICE 2019: 791-796 - [c11]Koki Hagiwara, Akira Yasojima, Takeshi Shibuya, Tomoyuki Yamaguchi:
Real-time Defect Detection Method without Training Data for Hammering Test Support System. SII 2019: 205-209 - 2015
- [j4]Youhei Kawamura, Markus Wagner, Hyongdoo Jang, Hajime Nobuhara, Takeshi Shibuya, Itaru Kitahara, Ashraf M. Dewan, Bert Veenendaal:
A Multimedia Data Visualization Based on Ad Hoc Communication Networks and Its Application to Disaster Management. ISPRS Int. J. Geo Inf. 4(4): 2004-2018 (2015) - [j3]Takaaki Kobayashi, Takeshi Shibuya, Masahiko Morita:
Q-Learning in Continuous State-Action Space with Noisy and Redundant Inputs by Using a Selective Desensitization Neural Network. J. Adv. Comput. Intell. Intell. Informatics 19(6): 825-832 (2015) - [c10]Junki Tamaru, Takeshi Shibuya:
Profit sharing reducing the occurrences of accidents by predicted action-safety degree. ASCC 2015: 1-6 - 2014
- [j2]Youhei Kawamura, Ashraf M. Dewan, Bert Veenendaal, Masahiro Hayashi, Takeshi Shibuya, Itaru Kitahara, Hajime Nobuhara, Kento Ishii:
Using GIS to develop a mobile communications network for disaster-damaged areas. Int. J. Digit. Earth 7(4): 279-293 (2014) - [c9]Takaaki Kobayashi, Takeshi Shibuya, Masahiko Morita:
Q-learning in continuous state-action space with redundant dimensions by using a selective desensitization neural network. SCIS&ISIS 2014: 801-806 - 2013
- [c8]Xiang Liu, Takeshi Shibuya, Seiji Yasunobu:
Space-time support system using simplified time-change fuzzy set. IFSA/NAFIPS 2013: 1502-1507 - 2012
- [j1]Syafiq Fauzi Kamarulzaman, Takeshi Shibuya, Seiji Yasunobu:
Substitute Target Learning Based Control System for Control Knowledge Acquisition Within Constrained Environment. J. Adv. Comput. Intell. Intell. Informatics 16(3): 397-403 (2012) - [c7]Xiang Liu, Takeshi Shibuya, Seiji Yasunobu:
Space-time fuzzy inference and applications using simplified time-change fuzzy set. SCIS&ISIS 2012: 1606-1609 - 2011
- [c6]Takeshi Shibuya, Seiji Yasunobu:
Reinforcement learning with nonstationary reward depending on the episode. SMC 2011: 2145-2150 - 2010
- [c5]Takeshi Shibuya, Hideaki Arita, Tomoki Hamagami:
Reinforcement learning in continuous state space with perceptual aliasing by using complex-valued RBF network. SMC 2010: 1799-1803 - [c4]Atsuhiro Yamazaki, Tomoki Hamagami, Takeshi Shibuya:
Complex-valued reinforcement learning with hierarchical architecture. SMC 2010: 1925-1931
2000 – 2009
- 2007
- [c3]Takeshi Shibuya, Shingo Shimada, Tomoki Hamagami:
Experimental study of the eligibility traces in complex valued reinforcement learning. SMC 2007: 1630-1635 - 2006
- [c2]Tomoki Hamagami, Takeshi Shibuya, Shingo Shimada:
Complex-Valued Reinforcement Learning. SMC 2006: 4175-4179 - 2005
- [c1]Fumio Nakaya, Akihiro Ito, Makoto Qunigoh, Hirokatsu Shimada, Kenji Fukasawa, Takeshi Shibuya, Hirohisa Yaguchi:
Suitable Printer Color Reproduction for Office Environment (1). CIC 2005: 185-188
Coauthor Index
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