Enhancements in Monte Carlo tree search algorithms for biased game trees. - Takahisa Imagawa; Tomoyuki Kaneko (CIG, 2015)

Monte Carlo Tree Search with Robust Exploration. - Takahisa Imagawa; Tomoyuki Kaneko (Computers and Games, 2016)

Estimating the Maximum Expected Value through Upper Confidence Bound of Likelihood. - Takahisa Imagawa; Tomoyuki Kaneko (TAAI, 2017)

Refining Manually-Designed Symbol Grounding and High-Level Planning by Policy Gradients. - Takuya Hiraoka; Takashi Onishi; Takahisa Imagawa; Yoshimasa Tsuruoka (CoRR, 2018)

Learning Robust Options by Conditional Value at Risk Optimization. - Takuya Hiraoka; Takahisa Imagawa; Tatsuya Mori; Takashi Onishi; Yoshimasa Tsuruoka (NeurIPS, 2019)

Optimistic Proximal Policy Optimization. - Takahisa Imagawa; Takuya Hiraoka; Yoshimasa Tsuruoka (CoRR, 2019)

Meta-Model-Based Meta-Policy Optimization. - Takuya Hiraoka; Takahisa Imagawa; Voot Tangkaratt; Takayuki Osa; Takashi Onishi; Yoshimasa Tsuruoka (ACML, 2021)

Off-Policy Meta-Reinforcement Learning Based on Feature Embedding Spaces. - Takahisa Imagawa; Takuya Hiraoka; Yoshimasa Tsuruoka (CoRR, 2021)

Dropout Q-Functions for Doubly Efficient Reinforcement Learning. - Takuya Hiraoka; Takahisa Imagawa; Taisei Hashimoto; Takashi Onishi; Yoshimasa Tsuruoka (ICLR, 2022)

Off-Policy Meta-Reinforcement Learning With Belief-Based Task Inference. - Takahisa Imagawa; Takuya Hiraoka; Yoshimasa Tsuruoka (IEEE Access, 2022)

Unsupervised Discovery of Continuous Skills on a Sphere. - Takahisa Imagawa; Takuya Hiraoka; Yoshimasa Tsuruoka (CoRR, 2023)