Unifying Value Iteration, Advantage Learning, and Dynamic Policy Programming. - Tadashi Kozuno; Eiji Uchibe; Kenji Doya (CoRR, 2017)

Theoretical Analysis of Efficiency and Robustness of Softmax and Gap-Increasing Operators in Reinforcement Learning. - Tadashi Kozuno; Eiji Uchibe; Kenji Doya (AISTATS, 2019)

Gap-Increasing Policy Evaluation for Efficient and Noise-Tolerant Reinforcement Learning. - Tadashi Kozuno; Dongqi Han; Kenji Doya (CoRR, 2019)

Leverage the Average: an Analysis of KL Regularization in Reinforcement Learning. - Nino Vieillard; Tadashi Kozuno; Bruno Scherrer; Olivier Pietquin; Rémi Munos; Matthieu Geist (NeurIPS, 2020)

Leverage the Average: an Analysis of Regularization in RL. - Nino Vieillard; Tadashi Kozuno; Bruno Scherrer; Olivier Pietquin; Rémi Munos; Matthieu Geist (CoRR, 2020)

Policy Information Capacity: Information-Theoretic Measure for Task Complexity in Deep Reinforcement Learning. - Hiroki Furuta; Tatsuya Matsushima; Tadashi Kozuno; Yutaka Matsuo; Sergey Levine; Ofir Nachum; Shixiang Shane Gu (ICML, 2021)

Revisiting Peng's Q(λ) for Modern Reinforcement Learning. - Tadashi Kozuno; Yunhao Tang; Mark Rowland 001; Rémi Munos; Steven Kapturowski; Will Dabney; Michal Valko; David Abel (ICML, 2021)

Co-Adaptation of Algorithmic and Implementational Innovations in Inference-based Deep Reinforcement Learning. - Hiroki Furuta; Tadashi Kozuno; Tatsuya Matsushima; Yutaka Matsuo; Shixiang Shane Gu (NeurIPS, 2021)

Learning in two-player zero-sum partially observable Markov games with perfect recall. - Tadashi Kozuno; Pierre Ménard; Rémi Munos; Michal Valko (NeurIPS, 2021)

Unifying Gradient Estimators for Meta-Reinforcement Learning via Off-Policy Evaluation. - Yunhao Tang; Tadashi Kozuno; Mark Rowland 001; Rémi Munos; Michal Valko (NeurIPS, 2021)

Identifying Co-Adaptation of Algorithmic and Implementational Innovations in Deep Reinforcement Learning: A Taxonomy and Case Study of Inference-based Algorithms. - Hiroki Furuta; Tadashi Kozuno; Tatsuya Matsushima; Yutaka Matsuo; Shixiang Shane Gu (CoRR, 2021)

Model-Free Learning for Two-Player Zero-Sum Partially Observable Markov Games with Perfect Recall. - Tadashi Kozuno; Pierre Ménard; Rémi Munos; Michal Valko (CoRR, 2021)

Variational oracle guiding for reinforcement learning. - Dongqi Han; Tadashi Kozuno; Xufang Luo; Zhao-Yun Chen; Kenji Doya; Yuqing Yang 001; Dongsheng Li 002 (ICLR, 2022)

Confident Approximate Policy Iteration for Efficient Local Planning in $q^ pi$-realizable MDPs. - Gellért Weisz; András György 001; Tadashi Kozuno; Csaba Szepesvári (NeurIPS, 2022)

Deep Learning-based Nonlinear Quantizer for Fronthaul Compression. - Shinnosuke Yagi; Mutsuki Nakahara; Kosuke Suzuoki; Tadashi Kozuno; Daisuke Hisano (OECC/PSC, 2022)

KL-Entropy-Regularized RL with a Generative Model is Minimax Optimal. - Tadashi Kozuno; Wenhao Yang; Nino Vieillard; Toshinori Kitamura; Yunhao Tang; Jincheng Mei; Pierre Ménard; Mohammad Gheshlaghi Azar; Michal Valko; Rémi Munos; Olivier Pietquin; Matthieu Geist; et al. (CoRR, 2022)

Confident Approximate Policy Iteration for Efficient Local Planning in qπ-realizable MDPs. - Gellért Weisz; András György 001; Tadashi Kozuno; Csaba Szepesvári (CoRR, 2022)

Greedification Operators for Policy Optimization: Investigating Forward and Reverse KL Divergences. - Alan Chan 001; Hugo Silva; Sungsu Lim; Tadashi Kozuno; A. Rupam Mahmood; Martha White (J. Mach. Learn. Res., 2022)

No More Pesky Hyperparameters: Offline Hyperparameter Tuning for RL. - Han Wang; Archit Sakhadeo; Adam M. White; James Bell 004; Vincent Liu; Xutong Zhao; Puer Liu; Tadashi Kozuno; Alona Fyshe; Martha White (Trans. Mach. Learn. Res., 2022)

Counterfactual Fairness Filter for Fair-Delay Multi-Robot Navigation. - Hikaru Asano; Ryo Yonetani; Mai Nishimura; Tadashi Kozuno (AAMAS, 2023)

Adapting to game trees in zero-sum imperfect information games. - Côme Fiegel; Pierre Ménard; Tadashi Kozuno; Rémi Munos; Vianney Perchet; Michal Valko (ICML, 2023)

Regularization and Variance-Weighted Regression Achieves Minimax Optimality in Linear MDPs: Theory and Practice. - Toshinori Kitamura; Tadashi Kozuno; Yunhao Tang; Nino Vieillard; Michal Valko; Wenhao Yang; Jincheng Mei; Pierre Ménard; Mohammad Gheshlaghi Azar; Rémi Munos; Olivier Pietquin; Matthieu Geist; et al. (ICML, 2023)

DoMo-AC: Doubly Multi-step Off-policy Actor-Critic Algorithm. - Yunhao Tang; Tadashi Kozuno; Mark Rowland 001; Anna Harutyunyan; Rémi Munos; Bernardo Ávila Pires; Michal Valko (ICML, 2023)

Avoiding Model Estimation in Robust Markov Decision Processes with a Generative Model. - Wenhao Yang; Han Wang; Tadashi Kozuno; Scott M. Jordan; Zhihua Zhang (CoRR, 2023)

Multi-Agent Behavior Retrieval: Retrieval-Augmented Policy Training for Cooperative Manipulation by Mobile Robots. - So Kuroki; Mai Nishimura; Tadashi Kozuno (CoRR, 2023)

Benchmarking Actor-Critic Deep Reinforcement Learning Algorithms for Robotics Control With Action Constraints. - Kazumi Kasaura; Shuwa Miura; Tadashi Kozuno; Ryo Yonetani; Kenta Hoshino; Yohei Hosoe (IEEE Robotics Autom. Lett., 2023)

Hand2Any: Hand-to-Any Motion Mapping with Few-Shot User Adaptation for Avatar Manipulation. - Riku Shinohara; Atsushi Hashimoto; Tadashi Kozuno; Shigeo Yoshida; Yutaro Hirao; Monica Perusquía-Hernández; Hideaki Uchiyama; Kiyoshi Kiyokawa (ECCV Workshops (2), 2024)

When to Replan? An Adaptive Replanning Strategy for Autonomous Navigation using Deep Reinforcement Learning. - Kohei Honda 002; Ryo Yonetani; Mai Nishimura; Tadashi Kozuno (ICRA, 2024)

Symmetry-aware Reinforcement Learning for Robotic Assembly under Partial Observability with a Soft Wrist. - Hai Nguyen; Tadashi Kozuno; Cristian C. Beltran-Hernandez; Masashi Hamaya (ICRA, 2024)

Multi-Agent Behavior Retrieval: Retrieval-Augmented Policy Training for Cooperative Push Manipulation by Mobile Robots. - So Kuroki; Mai Nishimura; Tadashi Kozuno (IROS, 2024)

Language-Guided Pattern Formation for Swarm Robotics with Multi-Agent Reinforcement Learning. - Hsu-Shen Liu; So Kuroki; Tadashi Kozuno; Wei-Fang Sun; Chun-Yi Lee (IROS, 2024)

Hand2Any: Real-time Avatar Manipulation through Hand-to-Any Motion Mapping with Few-Shot User Adaptation. - Riku Shinohara; Atsushi Hashimoto; Tadashi Kozuno; Shigeo Yoshida; Yutaro Hirao; Monica Perusquía-Hernández; Hideaki Uchiyama; Kiyoshi Kiyokawa (ISMAR-Adjunct, 2024)

Local and Adaptive Mirror Descents in Extensive-Form Games. - Côme Fiegel; Pierre Ménard; Tadashi Kozuno; Rémi Munos; Vianney Perchet; Michal Valko (NeurIPS, 2024)

A Policy Gradient Primal-Dual Algorithm for Constrained MDPs with Uniform PAC Guarantees. - Toshinori Kitamura; Tadashi Kozuno; Masahiro Kato; Yuki Ichihara; Soichiro Nishimori; Akiyoshi Sannai; Sho Sonoda; Wataru Kumagai; Yutaka Matsuo (CoRR, 2024)

Near-Optimal Policy Identification in Robust Constrained Markov Decision Processes via Epigraph Form. - Toshinori Kitamura; Tadashi Kozuno; Wataru Kumagai; Kenta Hoshino; Yohei Hosoe; Kazumi Kasaura; Masashi Hamaya; Paavo Parmas; Yutaka Matsuo (ICLR, 2025)

Provably Efficient RL under Episode-Wise Safety in Constrained MDPs with Linear Function Approximation. - Toshinori Kitamura; Arnob Ghosh; Tadashi Kozuno; Wataru Kumagai; Kazumi Kasaura; Kenta Hoshino; Yohei Hosoe; Yutaka Matsuo (CoRR, 2025)

Self Iterative Label Refinement via Robust Unlabeled Learning. - Hikaru Asano; Tadashi Kozuno; Yukino Baba (CoRR, 2025)

MK2 at PBIG Competition: A Prompt Generation Solution. - Yuzheng Xu; Tosho Hirasawa; Seiya Kawano; Shota Kato; Tadashi Kozuno (CoRR, 2025)