Gabriel Dulac-Arnold

Researcher, Google Research

Joined

September 2017

Names

Gabriel Dulac-Arnold (Preferred)

Emails

****@blacksheep.ai (Confirmed)

****@squirrelsoup.net (Confirmed)

****@google.com (Confirmed)

Personal Links

Homepage

Google Scholar

DBLP

Career & Education History

Researcher

Google Research (google.com)

2019 – Present

Researcher

Google DeepMind (google.com)

2014 – 2017

PhD student

LIP6 (lip6.fr)

2010 – 2014

Advisors, Relations & Conflicts

PhD Advisor

Ludovic Denoyer

2010 – 2014

Expertise

robotics

2017 – Present

reinforcement learning

2012 – Present

deep learning

2012 – Present

Publications

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 R Sanketi, Karol Hausman, Izhak Shafran, brian ichter et al. (1 additional authors not shown)
- LangRob @ CoRL 2023 Oral
- Readers: Everyone
On the importance of data collection for training general goal-reaching policies.
Alexis D. Jacq, Manu Orsini, Gabriel Dulac-Arnold, Olivier Pietquin, Matthieu Geist, Olivier Bachem
- ALOE 2023 Poster
- Readers: Everyone
RoboVQA: Multimodal Long-Horizon Reasoningfor Robotics
Pierre Sermanet, Tianli Ding, Jeffrey Zhao, Fei Xia, Debidatta Dwibedi, Keerthana Gopalakrishnan, Christine Chan, Gabriel Dulac-Arnold, sharath maddineni, Nikhil Joshi, Pete Florence, Wei Han, Robert Baruch, Yao Lu, Suvir Mirchandani, Peng Xu, Pannag Sanketi, Karol Hausman, Izhak Shafran, brian ichter et al. (1 additional authors not shown)
- FMDM@NeurIPS2023
- Readers: Everyone
On the importance of data collection for training general goal-reaching policies
Alexis D. Jacq, Manu Orsini, Gabriel Dulac-Arnold, Olivier Pietquin, Matthieu Geist, Olivier Bachem
- Published: 20 Jul 2023, Last Modified: 31 Aug 2023
- EWRL16
- Readers: Everyone
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
- ICML 2023 Poster
- Readers: Everyone
C3PO: Learning to Achieve Arbitrary Goals via Massively Entropic Pretraining
Alexis D. Jacq, Manu Orsini, Gabriel Dulac-Arnold, Olivier Pietquin, Matthieu Geist, Olivier Bachem
- Published: 01 Feb 2023, Last Modified: 13 Feb 2023
- Submitted to ICLR 2023
- Readers: Everyone
Learning Dynamics Models for Model Predictive Agents
Michael Lutter, Leonard Hasenclever, Arunkumar Byravan, Gabriel Dulac-Arnold, Piotr Trochim, Nicolas Heess, Josh Merel, Yuval Tassa
- Published: 28 Jan 2022, Last Modified: 13 Feb 2023
- ICLR 2022 Submitted
- Readers: Everyone
Model-Based Offline Planning
Arthur Argenson, Gabriel Dulac-Arnold
- Published: 12 Jan 2021, Last Modified: 05 May 2023
- ICLR 2021 Poster
- Readers: Everyone
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, Matthew D. Hoffman, Ofir Nachum, George Tucker, Nicolas Heess, Nando de Freitas
- 2020 (modified: 12 May 2025)
- CoRR 2020
- Readers: Everyone
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
- 2020 (modified: 06 Nov 2022)
- CoRR 2020
- Readers: Everyone

View all 23 publications

Co-Authors

View all 88 co-authors