Gabriel Dulac-Arnold

According to our database, Gabriel Dulac-Arnold authored at least 30 papers between 2011 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

RoboVQA: Multimodal Long-Horizon Reasoning for Robotics.

Pierre Sermanet, Tianli Ding, Jeffrey Zhao, Fei Xia, Debidatta Dwibedi, Keerthana Gopalakrishnan, Christine Chan, Gabriel Dulac-Arnold, Sharath Maddineni, Nikhil J. Joshi, Pete Florence, Wei Han, Robert Baruch, Yao Lu, Suvir Mirchandani, Peng Xu, Pannag Sanketi, Karol Hausman, Izhak Shafran, Brian Ichter, Yuan Cao

Proceedings of the IEEE International Conference on Robotics and Automation, 2024

2023

Barkour: Benchmarking Animal-level Agility with Quadruped Robots.

Ken Caluwaerts, Atil Iscen, J. Chase Kew, Wenhao Yu, Tingnan Zhang, Daniel Freeman, Kuang-Huei Lee, Lisa Lee, Stefano Saliceti, Vincent Zhuang, Nathan Batchelor, Steven Bohez, Federico Casarini, José Enrique Chen, Omar Cortes, Erwin Coumans, Adil Dostmohamed, Gabriel Dulac-Arnold, Alejandro Escontrela, Erik Frey, Roland Hafner, Deepali Jain, Bauyrjan Jyenis, Yuheng Kuang, Tsang-Wei Edward Lee, Linda Luu, Ofir Nachum, Ken Oslund, Jason Powell, Diego Reyes, Francesco Romano, Fereshteh Sadeghi, Ron Sloat, Baruch Tabanpour, Daniel Zheng, Michael Neunert, Raia Hadsell, Nicolas Heess, Francesco Nori, Jeff Seto, Carolina Parada, Vikas Sindhwani, Vincent Vanhoucke, Jie Tan

CoRR, 2023

Get Back Here: Robust Imitation by Return-to-Distribution Planning.

Geoffrey Cideron, Baruch Tabanpour, Sebastian Curi, Sertan Girgin, Léonard Hussenot, Gabriel Dulac-Arnold, Matthieu Geist, Olivier Pietquin, Robert Dadashi

CoRR, 2023

Learning Reward Functions for Robotic Manipulation by Observing Humans.

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

Proceedings of the IEEE International Conference on Robotics and Automation, 2023

Investigating the Role of Model-Based Learning in Exploration and Transfer.

Jacob C. Walker, Eszter Vértes, Yazhe Li, Gabriel Dulac-Arnold, Ankesh Anand, Theophane Weber, Jessica B. Hamrick

Proceedings of the International Conference on Machine Learning, 2023

2022

C3PO: Learning to Achieve Arbitrary Goals via Massively Entropic Pretraining.

Alexis Jacq, Manu Orsini, Gabriel Dulac-Arnold, Olivier Pietquin, Matthieu Geist, Olivier Bachem

CoRR, 2022

2021

A Metric Space Perspective on Self-Supervised Policy Adaptation.

Cristian Bodnar, Karol Hausman, Gabriel Dulac-Arnold, Rico Jonschkowski

IEEE Robotics Autom. Lett., 2021

Challenges of real-world reinforcement learning: definitions, benchmarks and analysis.

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

Mach. Learn., 2021

Learning Dynamics Models for Model Predictive Agents.

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

CoRR, 2021

Residual Reinforcement Learning from Demonstrations.

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

CoRR, 2021

Model-Based Offline Planning.

Arthur Argenson, Gabriel Dulac-Arnold

Proceedings of the 9th International Conference on Learning Representations, 2021

2020

A Geometric Perspective on Self-Supervised Policy Adaptation.

Cristian Bodnar, Karol Hausman, Gabriel Dulac-Arnold, Rico Jonschkowski

CoRR, 2020

RL Unplugged: Benchmarks for Offline Reinforcement Learning.

Çaglar Gülçehre, Ziyu Wang, Alexander Novikov, Tom Le Paine, Sergio Gómez Colmenarejo, Konrad Zolna, Rishabh Agarwal, Josh Merel, Daniel J. Mankowitz, Cosmin Paduraru, Gabriel Dulac-Arnold, Jerry Li, Mohammad Norouzi, Matt Hoffman, Ofir Nachum, George Tucker, Nicolas Heess, Nando de Freitas

CoRR, 2020

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

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

CoRR, 2020

Learning to run a Power Network Challenge: a Retrospective Analysis.

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

Proceedings of the NeurIPS 2020 Competition and Demonstration Track, 2020

RL Unplugged: A Collection of Benchmarks for Offline Reinforcement Learning.

Çaglar Gülçehre, Ziyu Wang, Alexander Novikov, Thomas Paine, Sergio Gómez Colmenarejo, Konrad Zolna, Rishabh Agarwal, Josh Merel, Daniel J. Mankowitz, Cosmin Paduraru, Gabriel Dulac-Arnold, Jerry Li, Mohammad Norouzi, Matthew Hoffman, Nicolas Heess, Nando de Freitas

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2019

Differentiable Deep Clustering with Cluster Size Constraints.

Aude Genevay, Gabriel Dulac-Arnold, Jean-Philippe Vert

CoRR, 2019

Deep multi-class learning from label proportions.

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

CoRR, 2019

Challenges of Real-World Reinforcement Learning.

Gabriel Dulac-Arnold, Daniel J. Mankowitz, Todd Hester

CoRR, 2019

2018

Deep Q-learning From Demonstrations.

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

Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018

2017

Learning from Demonstrations for Real World Reinforcement Learning.

Todd Hester, Matej Vecerík, Olivier Pietquin, Marc Lanctot, Tom Schaul, Bilal Piot, Andrew Sendonaris, Gabriel Dulac-Arnold, Ian Osband, John P. Agapiou, Joel Z. Leibo, Audrunas Gruslys

CoRR, 2017

The Predictron: End-To-End Learning and Planning.

David Silver, Hado van Hasselt, Matteo Hessel, Tom Schaul, Arthur Guez, Tim Harley, Gabriel Dulac-Arnold, David P. Reichert, Neil C. Rabinowitz, André Barreto, Thomas Degris

Proceedings of the 34th International Conference on Machine Learning, 2017

2015

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

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

CoRR, 2015

Reinforcement Learning in Large Discrete Action Spaces.

Gabriel Dulac-Arnold, Richard Evans, Peter Sunehag, Ben Coppin

CoRR, 2015

2014

Sequentially Generated Instance-Dependent Image Representations for Classification.

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

Proceedings of the 2nd International Conference on Learning Representations, 2014

2012

Sequential approaches for learning datum-wise sparse representations.

Gabriel Dulac-Arnold, Ludovic Denoyer, Philippe Preux, Patrick Gallinari

Mach. Learn., 2012

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

Gabriel Dulac-Arnold, Ludovic Denoyer, Philippe Preux, Patrick Gallinari

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2012

Lecture Séquentielle de Documents pour la Classification.

Gabriel Dulac-Arnold, Ludovic Denoyer, Patrick Gallinari

Proceedings of the CORIA (Conférence en Recherche d'Infomations et Applications), 2012

2011

Datum-Wise Classification: A Sequential Approach to Sparsity.

Gabriel Dulac-Arnold, Ludovic Denoyer, Philippe Preux, Patrick Gallinari

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2011

Text Classification: A Sequential Reading Approach.

Gabriel Dulac-Arnold, Ludovic Denoyer, Patrick Gallinari

Proceedings of the Advances in Information Retrieval, 2011