Takahisa Imagawa

15 papers A* 2C 1Journal 8Unranked 4

Year	Rank	Type	Title / Venue / Authors
2026	—	`conf`	Biased Exploration Q-Learning: A Simple Method for Embedding Knowledge into Reinforcement Learning. ICAART (2) Takahisa Imagawa, Shuichi Enokida
2023	J	`jnl`	Unsupervised Discovery of Continuous Skills on a Sphere. CoRR Takahisa Imagawa, Takuya Hiraoka, Yoshimasa Tsuruoka
2022	A*	`conf`	Dropout Q-Functions for Doubly Efficient Reinforcement Learning. ICLR Takuya Hiraoka, Takahisa Imagawa, Taisei Hashimoto, Takashi Onishi, Yoshimasa Tsuruoka
2022	J	`jnl`	Off-Policy Meta-Reinforcement Learning With Belief-Based Task Inference. IEEE Access Takahisa Imagawa, Takuya Hiraoka, Yoshimasa Tsuruoka
2021	J	`jnl`	Dropout Q-Functions for Doubly Efficient Reinforcement Learning. CoRR Takuya Hiraoka, Takahisa Imagawa, Taisei Hashimoto, Takashi Onishi, Yoshimasa Tsuruoka
2021	C	`conf`	Meta-Model-Based Meta-Policy Optimization. ACML Takuya Hiraoka, Takahisa Imagawa, Voot Tangkaratt, Takayuki Osa, Takashi Onishi, Yoshimasa Tsuruoka
2021	J	`jnl`	Off-Policy Meta-Reinforcement Learning Based on Feature Embedding Spaces. CoRR Takahisa Imagawa, Takuya Hiraoka, Yoshimasa Tsuruoka
2020	J	`jnl`	Meta-Model-Based Meta-Policy Optimization. CoRR Takuya Hiraoka, Takahisa Imagawa, Voot Tangkaratt, Takayuki Osa, Takashi Onishi, Yoshimasa Tsuruoka
2019	A*	`conf`	Learning Robust Options by Conditional Value at Risk Optimization. NeurIPS Takuya Hiraoka, Takahisa Imagawa, Tatsuya Mori, Takashi Onishi, Yoshimasa Tsuruoka
2019	J	`jnl`	Learning Robust Options by Conditional Value at Risk Optimization. CoRR Takuya Hiraoka, Takahisa Imagawa, Tatsuya Mori, Takashi Onishi, Yoshimasa Tsuruoka
2019	J	`jnl`	Optimistic Proximal Policy Optimization. CoRR Takahisa Imagawa, Takuya Hiraoka, Yoshimasa Tsuruoka
2018	J	`jnl`	Refining Manually-Designed Symbol Grounding and High-Level Planning by Policy Gradients. CoRR Takuya Hiraoka, Takashi Onishi, Takahisa Imagawa, Yoshimasa Tsuruoka
2017	—	`conf`	Estimating the Maximum Expected Value through Upper Confidence Bound of Likelihood. TAAI Takahisa Imagawa, Tomoyuki Kaneko
2016	—	`conf`	Monte Carlo Tree Search with Robust Exploration. Computers and Games Takahisa Imagawa, Tomoyuki Kaneko
2015	—	`conf`	Enhancements in Monte Carlo tree search algorithms for biased game trees. CIG Takahisa Imagawa, Tomoyuki Kaneko