Arthur Guez

Publication Activity (10 Years)

Years Active: 2008-2024
Publications (10 Years): 40

Top Topics

Credit Assignment

Orders Of Magnitude

Function Approximation

Top Venues

Publications

Khimya Khetarpal, Zhaohan Daniel Guo, Bernardo Ávila Pires, Yunhao Tang, Clare Lyle, Mark Rowland, Nicolas Heess, Diana Borsa, Arthur Guez, Will Dabney
A Unifying Framework for Action-Conditional Self-Predictive Reinforcement Learning. CoRR (2024)
Veronica Chelu, Tom Zahavy, Arthur Guez, Doina Precup, Sebastian Flennerhag
Optimism and Adaptivity in Policy Optimization. CoRR (2023)
Anirudh Goyal, Abram L. Friesen, Andrea Banino, Theophane Weber, Nan Rosemary Ke, Adrià Puigdomènech Badia, Arthur Guez, Mehdi Mirza, Peter C. Humphreys, Ksenia Konyushkova, Michal Valko, Simon Osindero, Timothy P. Lillicrap, Nicolas Heess, Charles Blundell
Retrieval-Augmented Reinforcement Learning. ICML (2022)
Jongmin Lee, Cosmin Paduraru, Daniel J. Mankowitz, Nicolas Heess, Doina Precup, Kee-Eung Kim, Arthur Guez
COptiDICE: Offline Constrained Reinforcement Learning via Stationary Distribution Correction Estimation. CoRR (2022)
Peter C. Humphreys, Arthur Guez, Olivier Tieleman, Laurent Sifre, Théophane Weber, Timothy P. Lillicrap
Large-Scale Retrieval for Reinforcement Learning. CoRR (2022)
Anirudh Goyal, Abram L. Friesen, Andrea Banino, Theophane Weber, Nan Rosemary Ke, Adrià Puigdomènech Badia, Arthur Guez, Mehdi Mirza, Ksenia Konyushkova, Michal Valko, Simon Osindero, Timothy P. Lillicrap, Nicolas Heess, Charles Blundell
Retrieval-Augmented Reinforcement Learning. CoRR (2022)
Peter C. Humphreys, Arthur Guez, Olivier Tieleman, Laurent Sifre, Theophane Weber, Timothy P. Lillicrap
Large-Scale Retrieval for Reinforcement Learning. NeurIPS (2022)
Jongmin Lee, Cosmin Paduraru, Daniel J. Mankowitz, Nicolas Heess, Doina Precup, Kee-Eung Kim, Arthur Guez
COptiDICE: Offline Constrained Reinforcement Learning via Stationary Distribution Correction Estimation. ICLR (2022)
Ivo Danihelka, Arthur Guez, Julian Schrittwieser, David Silver
Policy improvement by planning with Gumbel. ICLR (2022)
Thomas Mesnard, Theophane Weber, Fabio Viola, Shantanu Thakoor, Alaa Saade, Anna Harutyunyan, Will Dabney, Thomas S. Stepleton, Nicolas Heess, Arthur Guez, Eric Moulines, Marcus Hutter, Lars Buesing, Rémi Munos
Counterfactual Credit Assignment in Model-Free Reinforcement Learning. ICML (2021)
Matteo Hessel, Ivo Danihelka, Fabio Viola, Arthur Guez, Simon Schmitt, Laurent Sifre, Theophane Weber, David Silver, Hado van Hasselt
Muesli: Combining Improvements in Policy Optimization. ICML (2021)
Jessica B. Hamrick, Abram L. Friesen, Feryal Behbahani, Arthur Guez, Fabio Viola, Sims Witherspoon, Thomas Anthony, Lars Holger Buesing, Petar Velickovic, Theophane Weber
On the role of planning in model-based deep reinforcement learning. ICLR (2021)
Matteo Hessel, Ivo Danihelka, Fabio Viola, Arthur Guez, Simon Schmitt, Laurent Sifre, Theophane Weber, David Silver, Hado van Hasselt
Muesli: Combining Improvements in Policy Optimization. CoRR (2021)
Arthur Guez, Fabio Viola, Théophane Weber, Lars Buesing, Steven Kapturowski, Doina Precup, David Silver, Nicolas Heess
Value-driven Hindsight Modelling. CoRR (2020)
Péter Karkus, Mehdi Mirza, Arthur Guez, Andrew Jaegle, Timothy P. Lillicrap, Lars Buesing, Nicolas Heess, Theophane Weber
Beyond Tabula-Rasa: a Modular Reinforcement Learning Approach for Physically Embedded 3D Sokoban. CoRR (2020)
Arthur Guez, Fabio Viola, Theophane Weber, Lars Buesing, Steven Kapturowski, Doina Precup, David Silver, Nicolas Heess
Value-driven Hindsight Modelling. NeurIPS (2020)
Mehdi Mirza, Andrew Jaegle, Jonathan J. Hunt, Arthur Guez, Saran Tunyasuvunakool, Alistair Muldal, Théophane Weber, Péter Karkus, Sébastien Racanière, Lars Buesing, Timothy P. Lillicrap, Nicolas Heess
Physically Embedded Planning Problems: New Challenges for Reinforcement Learning. CoRR (2020)
Julian Schrittwieser, Ioannis Antonoglou, Thomas Hubert, Karen Simonyan, Laurent Sifre, Simon Schmitt, Arthur Guez, Edward Lockhart, Demis Hassabis, Thore Graepel, Timothy P. Lillicrap, David Silver
Mastering Atari, Go, chess and shogi by planning with a learned model. Nat. 588 (7839) (2020)
Thomas Mesnard, Théophane Weber, Fabio Viola, Shantanu Thakoor, Alaa Saade, Anna Harutyunyan, Will Dabney, Tom Stepleton, Nicolas Heess, Arthur Guez, Marcus Hutter, Lars Buesing, Rémi Munos
Counterfactual Credit Assignment in Model-Free Reinforcement Learning. CoRR (2020)
Jessica B. Hamrick, Abram L. Friesen, Feryal Behbahani, Arthur Guez, Fabio Viola, Sims Witherspoon, Thomas Anthony, Lars Buesing, Petar Velickovic, Théophane Weber
On the role of planning in model-based deep reinforcement learning. CoRR (2020)
Julian Schrittwieser, Ioannis Antonoglou, Thomas Hubert, Karen Simonyan, Laurent Sifre, Simon Schmitt, Arthur Guez, Edward Lockhart, Demis Hassabis, Thore Graepel, Timothy P. Lillicrap, David Silver
Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model. CoRR (2019)
Shruti Mishra, Abbas Abdolmaleki, Arthur Guez, Piotr Trochim, Doina Precup
Augmenting learning using symmetry in a biologically-inspired domain. CoRR (2019)
Arthur Guez, Mehdi Mirza, Karol Gregor, Rishabh Kabra, Sébastien Racanière, Théophane Weber, David Raposo, Adam Santoro, Laurent Orseau, Tom Eccles, Greg Wayne, David Silver, Timothy P. Lillicrap
An investigation of model-free planning. CoRR (2019)
Arthur Guez, Mehdi Mirza, Karol Gregor, Rishabh Kabra, Sébastien Racanière, Theophane Weber, David Raposo, Adam Santoro, Laurent Orseau, Tom Eccles, Greg Wayne, David Silver, Timothy P. Lillicrap
An Investigation of Model-Free Planning. ICML (2019)
Lars Buesing, Theophane Weber, Yori Zwols, Nicolas Heess, Sébastien Racanière, Arthur Guez, Jean-Baptiste Lespiau
Woulda, Coulda, Shoulda: Counterfactually-Guided Policy Search. ICLR (Poster) (2019)
Lars Buesing, Theophane Weber, Yori Zwols, Sébastien Racanière, Arthur Guez, Jean-Baptiste Lespiau, Nicolas Heess
Woulda, Coulda, Shoulda: Counterfactually-Guided Policy Search. CoRR (2018)
Moritz Krusche, Eric Schulz, Arthur Guez, Maarten Speekenbrink
Adaptive planning in human search. CogSci (2018)
Arthur Guez, Theophane Weber, Ioannis Antonoglou, Karen Simonyan, Oriol Vinyals, Daan Wierstra, Rémi Munos, David Silver
Learning to Search with MCTSnets. ICML (2018)
Arthur Guez, Théophane Weber, Ioannis Antonoglou, Karen Simonyan, Oriol Vinyals, Daan Wierstra, Rémi Munos, David Silver
Learning to Search with MCTSnets. CoRR (2018)
David Silver, Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Matthew Lai, Arthur Guez, Marc Lanctot, Laurent Sifre, Dharshan Kumaran, Thore Graepel, Timothy P. Lillicrap, Karen Simonyan, Demis Hassabis
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm. CoRR (2017)
Sébastien Racanière, Theophane Weber, David P. Reichert, Lars Buesing, Arthur Guez, Danilo Jimenez Rezende, Adrià Puigdomènech Badia, Oriol Vinyals, Nicolas Heess, Yujia Li, Razvan Pascanu, Peter W. Battaglia, Demis Hassabis, David Silver, Daan Wierstra
Imagination-Augmented Agents for Deep Reinforcement Learning. NIPS (2017)
Theophane Weber, Sébastien Racanière, David P. Reichert, Lars Buesing, Arthur Guez, Danilo Jimenez Rezende, Adrià Puigdomènech Badia, Oriol Vinyals, Nicolas Heess, Yujia Li, Razvan Pascanu, Peter W. Battaglia, David Silver, Daan Wierstra
Imagination-Augmented Agents for Deep Reinforcement Learning. CoRR (2017)
David Silver, Julian Schrittwieser, Karen Simonyan, Ioannis Antonoglou, Aja Huang, Arthur Guez, Thomas Hubert, Lucas Baker, Matthew Lai, Adrian Bolton, Yutian Chen, Timothy P. Lillicrap, Fan Hui, Laurent Sifre, George van den Driessche, Thore Graepel, Demis Hassabis
Mastering the game of Go without human knowledge. Nat. 550 (7676) (2017)
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
The Predictron: End-To-End Learning and Planning. ICML (2017)
Marc G. Bellemare, Georg Ostrovski, Arthur Guez, Philip S. Thomas, Rémi Munos
Increasing the Action Gap: New Operators for Reinforcement Learning. AAAI (2016)
David Silver, Aja Huang, Chris J. Maddison, Arthur Guez, Laurent Sifre, George van den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Vedavyas Panneershelvam, Marc Lanctot, Sander Dieleman, Dominik Grewe, John Nham, Nal Kalchbrenner, Ilya Sutskever, Timothy P. Lillicrap, Madeleine Leach, Koray Kavukcuoglu, Thore Graepel, Demis Hassabis
Mastering the game of Go with deep neural networks and tree search. Nat. 529 (7587) (2016)
Hado van Hasselt, Arthur Guez, Matteo Hessel, David Silver
Learning functions across many orders of magnitudes. CoRR (2016)
Hado van Hasselt, Arthur Guez, David Silver
Deep Reinforcement Learning with Double Q-Learning. AAAI (2016)
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
The Predictron: End-To-End Learning and Planning. CoRR (2016)
Hado van Hasselt, Arthur Guez, Matteo Hessel, Volodymyr Mnih, David Silver
Learning values across many orders of magnitude. NIPS (2016)
Marc G. Bellemare, Georg Ostrovski, Arthur Guez, Philip S. Thomas, Rémi Munos
Increasing the Action Gap: New Operators for Reinforcement Learning. CoRR (2015)
Hado van Hasselt, Arthur Guez, David Silver
Deep Reinforcement Learning with Double Q-learning. CoRR (2015)
Arthur Guez, Nicolas Heess, David Silver, Peter Dayan
Bayes-Adaptive Simulation-based Search with Value Function Approximation. NIPS (2014)
Arthur Guez, David Silver, Peter Dayan
Better Optimism By Bayes: Adaptive Planning with Rich Models. CoRR (2014)
Arthur Guez, David Silver, Peter Dayan
Scalable and Efficient Bayes-Adaptive Reinforcement Learning Based on Monte-Carlo Tree Search. J. Artif. Intell. Res. 48 (2013)
Arthur Guez, David Silver, Peter Dayan
Efficient Bayes-Adaptive Reinforcement Learning using Sample-Based Search. NIPS (2012)
Arthur Guez, David Silver, Peter Dayan
Efficient Bayes-Adaptive Reinforcement Learning using Sample-Based Search CoRR (2012)
Arthur Guez, Joelle Pineau
Multi-tasking SLAM. ICRA (2010)
Joelle Pineau, Arthur Guez, Robert D. Vincent, Gabriella Panuccio, Massimo Avoli
Treating Epilepsy via Adaptive Neurostimulation: a Reinforcement Learning Approach. Int. J. Neural Syst. 19 (4) (2009)
Arthur Guez, Robert D. Vincent, Massimo Avoli, Joelle Pineau
Adaptive Treatment of Epilepsy via Batch-mode Reinforcement Learning. AAAI (2008)