| 2024 | ISAGA | 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 |
| 2024 | SMC | Action Robust Reinforcement Learning with Highly Expressive Policy. | Seong-in Kim, Takeshi Shibuya |
| 2024 | SMC | Explainable Reinforcement Learning via Causal Model Considering Agent's Intention. | Seong-in Kim, Takeshi Shibuya |
| 2022 | SMC | Reinforcement Learning to Efficiently Recover Control Performance of Robots Using Imitation Learning After Failure. | Shoki Kobayashi, Takeshi Shibuya |
| 2020 | ICAART | Topological Visualization Method for Understanding the Landscape of Value Functions and Structure of the State Space in Reinforcement Learning. | Yuki Nakamura, Takeshi Shibuya |
| 2020 | ICAART | Inferring Underlying Manifold of Low Density Data using Adaptive Interpolation. | Noritaka Yamada, Takeshi Shibuya |
| 2020 | SMC | Reinforcement Learning Compensator Robust to the Time Constants of First Order Delay Elements. | Shoki Kobayashi, Takeshi Shibuya |
| 2013 | IFSA | Space-time support system using simplified time-change fuzzy set. | Xiang Liu, Takeshi Shibuya, Seiji Yasunobu |
| 2011 | SMC | Reinforcement learning with nonstationary reward depending on the episode. | Takeshi Shibuya, Seiji Yasunobu |
| 2010 | SMC | Reinforcement learning in continuous state space with perceptual aliasing by using complex-valued RBF network. | Takeshi Shibuya, Hideaki Arita, Tomoki Hamagami |
| 2010 | SMC | Complex-valued reinforcement learning with hierarchical architecture. | Atsuhiro Yamazaki, Tomoki Hamagami, Takeshi Shibuya |
| 2007 | SMC | Experimental study of the eligibility traces in complex valued reinforcement learning. | Takeshi Shibuya, Shingo Shimada, Tomoki Hamagami |
| 2006 | SMC | Complex-Valued Reinforcement Learning. | Tomoki Hamagami, Takeshi Shibuya, Shingo Shimada |