Tadashi Kozuno

According to our database, Tadashi Kozuno authored at least 31 papers between 2017 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

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

CoRR, 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

Symmetry-aware Reinforcement Learning for Robotic Assembly under Partial Observability with a Soft Wrist.

Hai Nguyen, Tadashi Kozuno, Cristian C. Beltran-Hernandez, Masashi Hamaya

Proceedings of the IEEE International Conference on Robotics and Automation, 2024

When to Replan? An Adaptive Replanning Strategy for Autonomous Navigation using Deep Reinforcement Learning.

Kohei Honda, Ryo Yonetani, Mai Nishimura, Tadashi Kozuno

Proceedings of the IEEE International Conference on Robotics and Automation, 2024

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

Multi-Agent Behavior Retrieval: Retrieval-Augmented Policy Training for Cooperative Manipulation by Mobile Robots.

So Kuroki, Mai Nishimura, Tadashi Kozuno

CoRR, 2023

Local and adaptive mirror descents in extensive-form games.

Côme Fiegel, Pierre Ménard, Tadashi Kozuno, Rémi Munos, Vianney Perchet, Michal Valko

CoRR, 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

DoMo-AC: Doubly Multi-step Off-policy Actor-Critic Algorithm.

Yunhao Tang, Tadashi Kozuno, Mark Rowland, Anna Harutyunyan, Rémi Munos, Bernardo Ávila Pires, Michal Valko

Proceedings of the International Conference on Machine Learning, 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, Csaba Szepesvári, Wataru Kumagai, Yutaka Matsuo

Proceedings of the International Conference on Machine Learning, 2023

Adapting to game trees in zero-sum imperfect information games.

Côme Fiegel, Pierre Ménard, Tadashi Kozuno, Rémi Munos, Vianney Perchet, Michal Valko

Proceedings of the International Conference on Machine Learning, 2023

Counterfactual Fairness Filter for Fair-Delay Multi-Robot Navigation.

Hikaru Asano, Ryo Yonetani, Mai Nishimura, Tadashi Kozuno

Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, 2023

2022

No More Pesky Hyperparameters: Offline Hyperparameter Tuning for RL.

Han Wang, Archit Sakhadeo, Adam M. White, James Bell, Vincent Liu, Xutong Zhao, Puer Liu, Tadashi Kozuno, Alona Fyshe, Martha White

Trans. Mach. Learn. Res., 2022

Greedification Operators for Policy Optimization: Investigating Forward and Reverse KL Divergences.

Alan Chan, Hugo Silva, Sungsu Lim, Tadashi Kozuno, A. Rupam Mahmood, Martha White

J. Mach. Learn. Res., 2022

Confident Approximate Policy Iteration for Efficient Local Planning in q^π-realizable MDPs.

Gellért Weisz, András György, Tadashi Kozuno, Csaba Szepesvári

CoRR, 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, Csaba Szepesvári

CoRR, 2022

Deep Learning-based Nonlinear Quantizer for Fronthaul Compression.

Shinnosuke Yagi, Mutsuki Nakahara, Kosuke Suzuoki, Tadashi Kozuno, Daisuke Hisano

Proceedings of the 2022 27th OptoElectronics and Communications Conference (OECC) and 2022 International Conference on Photonics in Switching and Computing (PSC), 2022

Confident Approximate Policy Iteration for Efficient Local Planning in $q^\pi$-realizable MDPs.

Gellért Weisz, András György, Tadashi Kozuno, Csaba Szepesvári

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Variational oracle guiding for reinforcement learning.

Dongqi Han, Tadashi Kozuno, Xufang Luo, Zhao-Yun Chen, Kenji Doya, Yuqing Yang, Dongsheng Li

Proceedings of the Tenth International Conference on Learning Representations, 2022

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

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

Unifying Gradient Estimators for Meta-Reinforcement Learning via Off-Policy Evaluation.

Yunhao Tang, Tadashi Kozuno, Mark Rowland, Rémi Munos, Michal Valko

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Learning in two-player zero-sum partially observable Markov games with perfect recall.

Tadashi Kozuno, Pierre Ménard, Rémi Munos, Michal Valko

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 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

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Revisiting Peng's Q(λ) for Modern Reinforcement Learning.

Tadashi Kozuno, Yunhao Tang, Mark Rowland, Rémi Munos, Steven Kapturowski, Will Dabney, Michal Valko, David Abel

Proceedings of the 38th International Conference on Machine Learning, 2021

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

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

Leverage the Average: an Analysis of Regularization in RL.

Nino Vieillard, Tadashi Kozuno, Bruno Scherrer, Olivier Pietquin, Rémi Munos, Matthieu Geist

CoRR, 2020

Leverage the Average: an Analysis of KL Regularization in Reinforcement Learning.

Nino Vieillard, Tadashi Kozuno, Bruno Scherrer, Olivier Pietquin, Rémi Munos, Matthieu Geist

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2019

Gap-Increasing Policy Evaluation for Efficient and Noise-Tolerant Reinforcement Learning.

Tadashi Kozuno, Dongqi Han, Kenji Doya

CoRR, 2019

Theoretical Analysis of Efficiency and Robustness of Softmax and Gap-Increasing Operators in Reinforcement Learning.

Tadashi Kozuno, Eiji Uchibe, Kenji Doya

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2017

Unifying Value Iteration, Advantage Learning, and Dynamic Policy Programming.

Tadashi Kozuno, Eiji Uchibe, Kenji Doya

CoRR, 2017