Gabriel Dulac-Arnold
Gabriel first joined Google a Research Scientist at DeepMind where he worked on bringing reinforcement learning into real-world problems. While there he worked on many Google-related problems, namely reducing the energy usage of Google data centers using reinforcement learning. At Brain, Gabriel now works on general problems related to using reinforcement learning in real-world systems, and more generally in algorithmic barriers to wider adoption of machine learning in real systems.
Research Areas
Authored Publications
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Google
AI-based mobile application to fight antibiotic resistance
Marco Pascucci
Guilhem Royer
Jakub Adámek
Mai Al Asmar
David Aristizabal
Laetitia Blanche
Amine Bezzarga
Guillaume Boniface-Chang
Alex Brunner
Christian Curel
Rasheed M. Fakhri
Nada Malou
Clara Nordon
Vincent Runge
Franck Samson
Ellen Marie Sebastian
Dena Soukieh
Jean-Philippe Vert
Christophe Ambroise
Mohammed-Amin Madoui
Nature Communications, 12 (2021), pp. 1173
Challenges of Real-World Reinforcement Learning:Definitions, Benchmarks & Analysis
Cosmin Paduraru
Daniel J. Mankowitz
Jerry Li
Nir Levine
Todd Hester
Machine Learning Journal (2021)
A Geometric Perspective on Self-Supervised Policy Adaptation
Cristian Bodnar
Karol Hausman
Rico Jonschkowski
NeurIPS Workshop on Challenges of Real-World RL (2020)
Challenges of Real-World Reinforcement Learning
Daniel J. Mankowitz
Todd Hester
ICML Workshop on Real-Life Reinforcement Learning (2019)
Deep Q-learning from Demonstrations
Todd Hester
Matej Vecerik
Olivier Pietquin
Marc Lanctot
Tom Schaul
Bilal Piot
Dan Horgan
John Quan
Andrew Sendonaris
Ian Osband
John Agapiou
Joel Z Leibo
Audrunas Gruslys
Annual Meeting of the Association for the Advancement of Artificial Intelligence (AAAI), New Orleans (USA) (2018)
