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Gabriel Dulac-Arnold


Learning Dynamics Models for Model Predictive Agents

Sep 29, 2021
Michael Lutter, Leonard Hasenclever, Arunkumar Byravan, Gabriel Dulac-Arnold, Piotr Trochim, Nicolas Heess, Josh Merel, Yuval Tassa

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Residual Reinforcement Learning from Demonstrations

Jun 15, 2021
Minttu Alakuijala, Gabriel Dulac-Arnold, Julien Mairal, Jean Ponce, Cordelia Schmid

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Learning to run a Power Network Challenge: a Retrospective Analysis

Mar 02, 2021
Antoine Marot, Benjamin Donnot, Gabriel Dulac-Arnold, Adrian Kelly, Aïdan O'Sullivan, Jan Viebahn, Mariette Awad, Isabelle Guyon, Patrick Panciatici, Camilo Romero

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A Geometric Perspective on Self-Supervised Policy Adaptation

Nov 14, 2020
Cristian Bodnar, Karol Hausman, Gabriel Dulac-Arnold, Rico Jonschkowski

* Contains 17 pages, 18 figures 

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Model-Based Offline Planning

Aug 12, 2020
Arthur Argenson, Gabriel Dulac-Arnold

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RL Unplugged: Benchmarks for Offline Reinforcement Learning

Jul 02, 2020
Caglar Gulcehre, Ziyu Wang, Alexander Novikov, Tom Le Paine, Sergio Gomez Colmenarejo, Konrad Zolna, Rishabh Agarwal, Josh Merel, Daniel Mankowitz, Cosmin Paduraru, Gabriel Dulac-Arnold, Jerry Li, Mohammad Norouzi, Matt Hoffman, Ofir Nachum, George Tucker, Nicolas Heess, Nando de Freitas

* 21 pages including supplementary material, the github link for the datasets: 

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An empirical investigation of the challenges of real-world reinforcement learning

Mar 24, 2020
Gabriel Dulac-Arnold, Nir Levine, Daniel J. Mankowitz, Jerry Li, Cosmin Paduraru, Sven Gowal, Todd Hester

* arXiv admin note: text overlap with arXiv:1904.12901 

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Differentiable Deep Clustering with Cluster Size Constraints

Oct 20, 2019
Aude Genevay, Gabriel Dulac-Arnold, Jean-Philippe Vert

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Deep multi-class learning from label proportions

May 30, 2019
Gabriel Dulac-Arnold, Neil Zeghidour, Marco Cuturi, Lucas Beyer, Jean-Philippe Vert

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Challenges of Real-World Reinforcement Learning

Apr 29, 2019
Gabriel Dulac-Arnold, Daniel Mankowitz, Todd Hester

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Deep Q-learning from Demonstrations

Nov 22, 2017
Todd Hester, Matej Vecerik, Olivier Pietquin, Marc Lanctot, Tom Schaul, Bilal Piot, Dan Horgan, John Quan, Andrew Sendonaris, Gabriel Dulac-Arnold, Ian Osband, John Agapiou, Joel Z. Leibo, Audrunas Gruslys

* Published at AAAI 2018. Previously on arxiv as "Learning from Demonstrations for Real World Reinforcement Learning" 

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The Predictron: End-To-End Learning and Planning

Jul 20, 2017
David Silver, Hado van Hasselt, Matteo Hessel, Tom Schaul, Arthur Guez, Tim Harley, Gabriel Dulac-Arnold, David Reichert, Neil Rabinowitz, Andre Barreto, Thomas Degris

* Camera-ready version, ICML 2017, with supplement 

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Deep Reinforcement Learning in Large Discrete Action Spaces

Apr 04, 2016
Gabriel Dulac-Arnold, Richard Evans, Hado van Hasselt, Peter Sunehag, Timothy Lillicrap, Jonathan Hunt, Timothy Mann, Theophane Weber, Thomas Degris, Ben Coppin

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Deep Reinforcement Learning with Attention for Slate Markov Decision Processes with High-Dimensional States and Actions

Dec 16, 2015
Peter Sunehag, Richard Evans, Gabriel Dulac-Arnold, Yori Zwols, Daniel Visentin, Ben Coppin

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Sequentially Generated Instance-Dependent Image Representations for Classification

Feb 11, 2014
Gabriel Dulac-Arnold, Ludovic Denoyer, Nicolas Thome, Matthieu Cord, Patrick Gallinari

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Fast Reinforcement Learning with Large Action Sets using Error-Correcting Output Codes for MDP Factorization

Feb 29, 2012
Gabriel Dulac-Arnold, Ludovic Denoyer, Philippe Preux, Patrick Gallinari

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Datum-Wise Classification: A Sequential Approach to Sparsity

Aug 29, 2011
Gabriel Dulac-Arnold, Ludovic Denoyer, Philippe Preux, Patrick Gallinari

* Lecture Notes in Computer Science, 2011, Volume 6911/2011, 375-390 
* ECML2011 

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Text Classification: A Sequential Reading Approach

Aug 29, 2011
Gabriel Dulac-Arnold, Ludovic Denoyer, Patrick Gallinari

* Lecture Notes in Computer Science, 2011, Volume 6611/2011, 411-423 
* ECIR2011 

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