Toshinori Kitamura
Toshinori Kitamura
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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
ShinRL: A Library for Evaluating RL Algorithms from Theoretical and Practical Perspectives
T Kitamura, R Yonetani
arXiv preprint arXiv:2112.04123, 2021
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
Cautious policy programming: exploiting KL regularization for monotonic policy improvement in reinforcement learning
L Zhu, T Matsubara
Machine Learning 112 (11), 4527-4562, 2023
Cautious Actor-Critic
L Zhu, T Kitamura, M Takamitsu
Asian Conference on Machine Learning, 220-235, 2021
Geometric Value Iteration: Dynamic Error-Aware KL Regularization for Reinforcement Learning
T Kitamura, L Zhu, T Matsubara
Asian Conference on Machine Learning, 918-931, 2021
A Policy Gradient Primal-Dual Algorithm for Constrained MDPs with Uniform PAC Guarantees
T Kitamura, T Kozuno, M Kato, Y Ichihara, S Nishimori, A Sannai, ...
arXiv preprint arXiv:2401.17780, 2024
Dynamic KL Regularization in Reinforcement Learning: Theoretical Error Propagation Analysis and an Algorithm
T Kitamura
Nara Institute of Science and Technology, 2022
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