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Learning Energy Networks with Generalized Fenchel-Young Losses


May 19, 2022
Mathieu Blondel, Felipe Llinares-LĂłpez, Robert Dadashi, LĂ©onard Hussenot, Matthieu Geist

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Continuous Control with Action Quantization from Demonstrations


Oct 19, 2021
Robert Dadashi, LĂ©onard Hussenot, Damien Vincent, Sertan Girgin, Anton Raichuk, Matthieu Geist, Olivier Pietquin

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Offline Reinforcement Learning as Anti-Exploration


Jun 11, 2021
Shideh Rezaeifar, Robert Dadashi, Nino Vieillard, LĂ©onard Hussenot, Olivier Bachem, Olivier Pietquin, Matthieu Geist

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What Matters for Adversarial Imitation Learning?


Jun 01, 2021
Manu Orsini, Anton Raichuk, LĂ©onard Hussenot, Damien Vincent, Robert Dadashi, Sertan Girgin, Matthieu Geist, Olivier Bachem, Olivier Pietquin, Marcin Andrychowicz

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Hyperparameter Selection for Imitation Learning


May 25, 2021
Leonard Hussenot, Marcin Andrychowicz, Damien Vincent, Robert Dadashi, Anton Raichuk, Lukasz Stafiniak, Sertan Girgin, Raphael Marinier, Nikola Momchev, Sabela Ramos, Manu Orsini, Olivier Bachem, Matthieu Geist, Olivier Pietquin

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* ICML 2021 

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Offline Reinforcement Learning with Pseudometric Learning


Mar 02, 2021
Robert Dadashi, Shideh Rezaeifar, Nino Vieillard, LĂ©onard Hussenot, Olivier Pietquin, Matthieu Geist

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Show me the Way: Intrinsic Motivation from Demonstrations


Jun 23, 2020
LĂ©onard Hussenot, Robert Dadashi, Matthieu Geist, Olivier Pietquin

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Primal Wasserstein Imitation Learning


Jun 08, 2020
Robert Dadashi, LĂ©onard Hussenot, Matthieu Geist, Olivier Pietquin

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The Value-Improvement Path: Towards Better Representations for Reinforcement Learning


Jun 03, 2020
Will Dabney, André Barreto, Mark Rowland, Robert Dadashi, John Quan, Marc G. Bellemare, David Silver

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Statistics and Samples in Distributional Reinforcement Learning


Feb 21, 2019
Mark Rowland, Robert Dadashi, Saurabh Kumar, RĂ©mi Munos, Marc G. Bellemare, Will Dabney

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