| Leverage the Average: an Analysis of KL Regularization in Reinforcement Learning N Vieillard, T Kozuno, B Scherrer, O Pietquin, R Munos, M Geist The 34th Conference on Neural Information Processing Systems, 2020 | 118* | 2020 |
| Theoretical analysis of efficiency and robustness of softmax and gap-increasing operators in reinforcement learning T Kozuno, E Uchibe, K Doya The 22nd International Conference on Artificial Intelligence and Statistics …, 2019 | 44 | 2019 |
| Model-Free Learning for Two-Player Zero-Sum Partially Observable Markov Games with Perfect Recall T Kozuno, P Ménard, R Munos, M Valko Advances in Neural Information Processing Systems 35, 2021 | 37* | 2021 |
| Greedification operators for policy optimization: Investigating forward and reverse kl divergences A Chan, H Silva, S Lim, T Kozuno, AR Mahmood, M White Journal of Machine Learning Research 23 (253), 1-79, 2022 | 25 | 2022 |
| Revisiting Peng's Q () for Modern Reinforcement Learning T Kozuno, Y Tang, M Rowland, R Munos, S Kapturowski, W Dabney, ... The 38th International Conference on Machine Learning, 2021 | 22 | 2021 |
| Policy Information Capacity: Information-Theoretic Measure for Task Complexity in Deep Reinforcement Learning H Furuta, T Matsushima, T Kozuno, Y Matsuo, S Levine, O Nachum, ... The 38th International Conference on Machine Learning, 2021 | 19 | 2021 |
| Identifying Co-Adaptation of Algorithmic and Implementational Innovations in Deep Reinforcement Learning: A Taxonomy and Case Study of Inference-based Algorithms H Furuta, T Kozuno, T Matsushima, Y Matsuo, SS Gu Advances in Neural Information Processing Systems 35, 2021 | 16* | 2021 |
| Unifying Gradient Estimators for Meta-Reinforcement Learning via Off-Policy Evaluation Y Tang, T Kozuno, M Rowland, R Munos, M Valko Advances in Neural Information Processing Systems 35, 2021 | 11 | 2021 |
| Avoiding model estimation in robust markov decision processes with a generative model W Yang, H Wang, T Kozuno, SM Jordan, Z Zhang arXiv preprint arXiv:2302.01248 23, 2023 | 10 | 2023 |
| Confident Approximate Policy Iteration for Efficient Local Planning in -realizable MDPs G Weisz, A György, T Kozuno, C Szepesvári Advances in Neural Information Processing Systems 35, 25547-25559, 2022 | 10 | 2022 |
| Benchmarking Actor-Critic Deep Reinforcement Learning Algorithms for Robotics Control with Action Constraints K Kasaura, S Miura, T Kozuno, R Yonetani, K Hoshino, Y Hosoe IEEE Robotics and Automation Letters, 2023 | 9 | 2023 |
| Adapting to game trees in zero-sum imperfect information games C Fiegel, P Ménard, T Kozuno, R Munos, V Perchet, M Valko International Conference on Machine Learning, 10093-10135, 2023 | 8 | 2023 |
| KL-Entropy-Regularized RL with a Generative Model is Minimax Optimal T Kozuno, W Yang, N Vieillard, T Kitamura, Y Tang, J Mei, P Ménard, ... arXiv preprint arXiv:2205.14211, 2022 | 7 | 2022 |
| No More Pesky Hyperparameters: Offline Hyperparameter Tuning for RL H Wang, A Sakhadeo, A White, J Bell, V Liu, X Zhao, P Liu, T Kozuno, ... Transactions on Machine Learning Research, 2022 | 7 | 2022 |
| Variational oracle guiding for reinforcement learning D Han, T Kozuno, X Luo, ZY Chen, K Doya, Y Yang, D Li International Conference on Learning Representations, 2021 | 6 | 2021 |
| Study of White-LED Using Amorphous Carbon Nitride Grown by RF-sputtering and ECR-plasma CVD T Kozuno, S Kishimoto, K Tachibana, K Itoh, Y Iwano, S Kunitsugu, ... Journal of Light & Visual Environment 35 (1), 86-89, 2011 | 6 | 2011 |
| Symmetry-aware Reinforcement Learning for Robotic Assembly under Partial Observability with a Soft Wrist H Nguyen, T Kozuno, CC Beltran-Hernandez, M Hamaya arXiv preprint arXiv:2402.18002, 2024 | 4 | 2024 |
| Gap-Increasing Policy Evaluation for Efficient and Noise-Tolerant Reinforcement Learning T Kozuno, D Han, K Doya arXiv preprint arXiv:1906.07586, 2019 | 3 | 2019 |
| Unifying Value Iteration, Advantage Learning, and Dynamic Policy Programming T Kozuno, E Uchibe, K Doya arXiv preprint arXiv:1710.10866, 2017 | 3 | 2017 |
| Regularization and Variance-Weighted Regression Achieves Minimax Optimality in Linear MDPs: Theory and Practice T Kitamura, T Kozuno, Y Tang, N Vieillard, M Valko, W Yang, J Mei, ... International Conference on Machine Learning, 17135-17175, 2023 | 2 | 2023 |
Rémi MunosGoogle DeepMind在 inria.fr 的电子邮件经过验证
