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Jesse Farebrother

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Stop Regressing: Training Value Functions via Classification for Scalable Deep RL

Mar 06, 2024
Jesse Farebrother, Jordi Orbay, Quan Vuong, Adrien Ali Taïga, Yevgen Chebotar, Ted Xiao, Alex Irpan, Sergey Levine, Pablo Samuel Castro, Aleksandra Faust, Aviral Kumar, Rishabh Agarwal

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Mixtures of Experts Unlock Parameter Scaling for Deep RL

Feb 13, 2024
Johan Obando-Ceron, Ghada Sokar, Timon Willi, Clare Lyle, Jesse Farebrother, Jakob Foerster, Gintare Karolina Dziugaite, Doina Precup, Pablo Samuel Castro

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A Distributional Analogue to the Successor Representation

Feb 13, 2024
Harley Wiltzer, Jesse Farebrother, Arthur Gretton, Yunhao Tang, André Barreto, Will Dabney, Marc G. Bellemare, Mark Rowland

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Learning and Controlling Silicon Dopant Transitions in Graphene using Scanning Transmission Electron Microscopy

Nov 21, 2023
Max Schwarzer, Jesse Farebrother, Joshua Greaves, Ekin Dogus Cubuk, Rishabh Agarwal, Aaron Courville, Marc G. Bellemare, Sergei Kalinin, Igor Mordatch, Pablo Samuel Castro, Kevin M. Roccapriore

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Proto-Value Networks: Scaling Representation Learning with Auxiliary Tasks

Apr 25, 2023
Jesse Farebrother, Joshua Greaves, Rishabh Agarwal, Charline Le Lan, Ross Goroshin, Pablo Samuel Castro, Marc G. Bellemare

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A Novel Stochastic Gradient Descent Algorithm for Learning Principal Subspaces

Dec 08, 2022
Charline Le Lan, Joshua Greaves, Jesse Farebrother, Mark Rowland, Fabian Pedregosa, Rishabh Agarwal, Marc G. Bellemare

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Generalization and Regularization in DQN

Sep 29, 2018
Jesse Farebrother, Marlos C. Machado, Michael Bowling

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