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

Jul 17, 2021
Alan Chan, Hugo Silva, Sungsu Lim, Tadashi Kozuno, A. Rupam Mahmood, Martha White

* Submitted to JMLR 

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

Jun 24, 2021
Yunhao Tang, Tadashi Kozuno, Mark Rowland, R茅mi Munos, Michal Valko

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Model-Free Learning for Two-Player Zero-Sum Partially Observable Markov Games with Perfect Recall

Jun 11, 2021
Tadashi Kozuno, Pierre M茅nard, R茅mi Munos, Michal Valko

* 20 pages 

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Identifying Co-Adaptation of Algorithmic and Implementational Innovations in Deep Reinforcement Learning: A Taxonomy and Case Study of Inference-based Algorithms

Mar 31, 2021
Hiroki Furuta, Tadashi Kozuno, Tatsuya Matsushima, Yutaka Matsuo, Shixiang Shane Gu

* The implementation is available at: 

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Policy Information Capacity: Information-Theoretic Measure for Task Complexity in Deep Reinforcement Learning

Mar 23, 2021
Hiroki Furuta, Tatsuya Matsushima, Tadashi Kozuno, Yutaka Matsuo, Sergey Levine, Ofir Nachum, Shixiang Shane Gu

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Revisiting Peng's Q($位$) for Modern Reinforcement Learning

Feb 27, 2021
Tadashi Kozuno, Yunhao Tang, Mark Rowland, R茅mi Munos, Steven Kapturowski, Will Dabney, Michal Valko, David Abel

* 26 pages, 7 figures, 2 tables 

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Leverage the Average: an Analysis of Regularization in RL

Apr 10, 2020
Nino Vieillard, Tadashi Kozuno, Bruno Scherrer, Olivier Pietquin, R茅mi Munos, Matthieu Geist

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

Jun 18, 2019
Tadashi Kozuno, Dongqi Han, Kenji Doya

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Unifying Value Iteration, Advantage Learning, and Dynamic Policy Programming

Oct 30, 2017
Tadashi Kozuno, Eiji Uchibe, Kenji Doya

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