Suitable Printer Color Reproduction for Office Environment (1). - Fumio Nakaya; Akihiro Ito; Makoto Qunigoh; Hirokatsu Shimada; Kenji Fukasawa; Takeshi Shibuya; Hirohisa Yaguchi (CIC, 2005)

Complex-Valued Reinforcement Learning. - Tomoki Hamagami; Takeshi Shibuya; Shingo Shimada (SMC, 2006)

Experimental study of the eligibility traces in complex valued reinforcement learning. - Takeshi Shibuya; Shingo Shimada; Tomoki Hamagami (SMC, 2007)

Reinforcement learning in continuous state space with perceptual aliasing by using complex-valued RBF network. - Takeshi Shibuya; Hideaki Arita; Tomoki Hamagami (SMC, 2010)

Complex-valued reinforcement learning with hierarchical architecture. - Atsuhiro Yamazaki; Tomoki Hamagami; Takeshi Shibuya (SMC, 2010)

Reinforcement learning with nonstationary reward depending on the episode. - Takeshi Shibuya; Seiji Yasunobu (SMC, 2011)

Space-time fuzzy inference and applications using simplified time-change fuzzy set. - Xiang Liu; Takeshi Shibuya; Seiji Yasunobu (SCIS&ISIS, 2012)

Substitute Target Learning Based Control System for Control Knowledge Acquisition Within Constrained Environment. - Syafiq Fauzi Kamarulzaman; Takeshi Shibuya; Seiji Yasunobu (J. Adv. Comput. Intell. Intell. Informatics, 2012)

Space-time support system using simplified time-change fuzzy set. - Xiang Liu; Takeshi Shibuya; Seiji Yasunobu (IFSA/NAFIPS, 2013)

Q-learning in continuous state-action space with redundant dimensions by using a selective desensitization neural network. - Takaaki Kobayashi; Takeshi Shibuya; Masahiko Morita (SCIS&ISIS, 2014)

Using GIS to develop a mobile communications network for disaster-damaged areas. - Youhei Kawamura; Ashraf M. Dewan; Bert Veenendaal; Masahiro Hayashi; Takeshi Shibuya; Itaru Kitahara; Hajime Nobuhara; Kento Ishii (Int. J. Digit. Earth, 2014)

Profit sharing reducing the occurrences of accidents by predicted action-safety degree. - Junki Tamaru; Takeshi Shibuya (ASCC, 2015)

A Multimedia Data Visualization Based on Ad Hoc Communication Networks and Its Application to Disaster Management. - Youhei Kawamura; Markus Wagner 007; Hyongdoo Jang; Hajime Nobuhara; Takeshi Shibuya; Itaru Kitahara; Ashraf M. Dewan; Bert Veenendaal (ISPRS Int. J. Geo Inf., 2015)

Q-Learning in Continuous State-Action Space with Noisy and Redundant Inputs by Using a Selective Desensitization Neural Network. - Takaaki Kobayashi; Takeshi Shibuya; Masahiko Morita (J. Adv. Comput. Intell. Intell. Informatics, 2015)

Inferring Underlying Manifold of Data by the Use of Persistent Homology Analysis. - Rentaro Futagami; Noritaka Yamada; Takeshi Shibuya (CTIC, 2019)

Crack Inspection Support System for Concrete Structures Using Head Mounted Display in Mixed Reality Space. - Tomoyuki Yamaguchi; Takeshi Shibuya; Masashi Kanda; Akira Yasojima (SICE, 2019)

Real-time Defect Detection Method without Training Data for Hammering Test Support System. - Koki Hagiwara; Akira Yasojima; Takeshi Shibuya; Tomoyuki Yamaguchi (SII, 2019)

Topological Visualization Method for Understanding the Landscape of Value Functions and Structure of the State Space in Reinforcement Learning. - Yuki Nakamura; Takeshi Shibuya (ICAART (2), 2020)

Inferring Underlying Manifold of Low Density Data using Adaptive Interpolation. - Noritaka Yamada; Takeshi Shibuya (ICAART (2), 2020)

Reinforcement Learning Compensator Robust to the Time Constants of First Order Delay Elements. - Shoki Kobayashi; Takeshi Shibuya (SMC, 2020)

Adaptive Modular Reinforcement Learning for Robot Controlled in Multiple Environments. - Teppei Iwata; Takeshi Shibuya (IEEE Access, 2021)

Method of Controlling a Tankendo Robot Using a Mecanum Wheel Trolley Robot. - Chenyang Zhao; Takeshi Shibuya; Naoki Igo; Kiyoshi Hoshino (ICIIT, 2022)

Centralized Training with Decentralized Execution Reinforcement Learning for Cooperative Multi-agent Systems with Communication Delay. - Takuma Ikeda; Takeshi Shibuya (SICE, 2022)

Disturbance Observable Reinforcement Learning that Compensates for Changes in Environment. - Seong-In Kim; Takeshi Shibuya (SICE, 2022)

Efficiency of Reinforcement Learning using Polarized Regime by Variational Autoencoder. - Masato Nakai; Takeshi Shibuya (SICE, 2022)

Reinforcement Learning to Efficiently Recover Control Performance of Robots Using Imitation Learning After Failure. - Shoki Kobayashi; Takeshi Shibuya (SMC, 2022)

Understanding Behavioral Differences Between Machine Agents and Human Participants Based on How They Play the Energy Transition Game. - Kengo Suzuki; Yuta Nakadegawa; Kento Miura; Takeshi Shibuya; Susumu Ohnuma (ISAGA, 2024)

Action Robust Reinforcement Learning with Highly Expressive Policy. - Seong-in Kim; Takeshi Shibuya (SMC, 2024)

Explainable Reinforcement Learning via Causal Model Considering Agent's Intention. - Seong-in Kim; Takeshi Shibuya (SMC, 2024)

Nonlinear Value Function Approximation Method With Easy Hyperparameter Tuning and Convergence Guarantee. - Yuuya Watabe; Takeshi Shibuya (IEEE Access, 2025)