Adaptive Treatment of Epilepsy via Batch-mode Reinforcement Learning. - Arthur Guez; Robert D. Vincent; Massimo Avoli; Joelle Pineau (AAAI, 2008)

Treating Epilepsy via Adaptive Neurostimulation: a Reinforcement Learning Approach. - Joelle Pineau; Arthur Guez; Robert D. Vincent; Gabriella Panuccio; Massimo Avoli (Int. J. Neural Syst., 2009)

Multi-tasking SLAM. - Arthur Guez; Joelle Pineau (ICRA, 2010)

Efficient Bayes-Adaptive Reinforcement Learning using Sample-Based Search. - Arthur Guez; David Silver; Peter Dayan (NIPS, 2012)

Scalable and Efficient Bayes-Adaptive Reinforcement Learning Based on Monte-Carlo Tree Search. - Arthur Guez; David Silver; Peter Dayan (J. Artif. Intell. Res., 2013)

Bayes-Adaptive Simulation-based Search with Value Function Approximation. - Arthur Guez; Nicolas Heess; David Silver; Peter Dayan (NIPS, 2014)

Better Optimism By Bayes: Adaptive Planning with Rich Models. - Arthur Guez; David Silver; Peter Dayan (CoRR, 2014)

Increasing the Action Gap: New Operators for Reinforcement Learning. - Marc G. Bellemare; Georg Ostrovski; Arthur Guez; Philip S. Thomas; Rémi Munos (AAAI, 2016)

Deep Reinforcement Learning with Double Q-Learning. - Hado van Hasselt; Arthur Guez; David Silver (AAAI, 2016)

Learning values across many orders of magnitude. - Hado van Hasselt; Arthur Guez; Matteo Hessel; Volodymyr Mnih; David Silver (NIPS, 2016)

Learning functions across many orders of magnitudes. - Hado van Hasselt; Arthur Guez; Matteo Hessel; David Silver (CoRR, 2016)

Mastering the game of Go with deep neural networks and tree search. - David Silver; Aja Huang; Chris J. Maddison; Arthur Guez; Laurent Sifre; George van den Driessche 002; Julian Schrittwieser; Ioannis Antonoglou; Vedavyas Panneershelvam; Marc Lanctot; Sander Dieleman; Dominik Grewe; et al. (Nat., 2016)

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 001; Thomas Degris (ICML, 2017)

Imagination-Augmented Agents for Deep Reinforcement Learning. - Sébastien Racanière; Theophane Weber; David P. Reichert; Lars Buesing; Arthur Guez; Danilo Jimenez Rezende; Adrià Puigdomènech Badia; Oriol Vinyals; Nicolas Heess; Yujia Li 001; Razvan Pascanu; Peter W. Battaglia; et al. (NIPS, 2017)

Imagination-Augmented Agents for Deep Reinforcement Learning. - Theophane Weber; Sébastien Racanière; David P. Reichert; Lars Buesing; Arthur Guez; Danilo Jimenez Rezende; Adrià Puigdomènech Badia; Oriol Vinyals; Nicolas Heess; Yujia Li 001; Razvan Pascanu; Peter W. Battaglia; et al. (CoRR, 2017)

Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm. - 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; et al. (CoRR, 2017)

Mastering the game of Go without human knowledge. - David Silver; Julian Schrittwieser; Karen Simonyan; Ioannis Antonoglou; Aja Huang; Arthur Guez; Thomas Hubert; Lucas Baker; Matthew Lai; Adrian Bolton; Yutian Chen 001; Timothy P. Lillicrap; et al. (Nat., 2017)

Adaptive planning in human search. - Moritz Krusche; Eric Schulz; Arthur Guez; Maarten Speekenbrink (CogSci, 2018)

Learning to Search with MCTSnets. - Arthur Guez; Theophane Weber; Ioannis Antonoglou; Karen Simonyan; Oriol Vinyals; Daan Wierstra; Rémi Munos; David Silver (ICML, 2018)

Woulda, Coulda, Shoulda: Counterfactually-Guided Policy Search. - Lars Buesing; Theophane Weber; Yori Zwols; Nicolas Heess; Sébastien Racanière; Arthur Guez; Jean-Baptiste Lespiau (ICLR (Poster), 2019)

An Investigation of Model-Free Planning. - Arthur Guez; Mehdi Mirza; Karol Gregor; Rishabh Kabra; Sébastien Racanière; Theophane Weber; David Raposo; Adam Santoro; Laurent Orseau; Tom Eccles; Greg Wayne; David Silver; et al. (ICML, 2019)

Augmenting learning using symmetry in a biologically-inspired domain. - Shruti Mishra; Abbas Abdolmaleki; Arthur Guez; Piotr Trochim; Doina Precup (CoRR, 2019)

Value-driven Hindsight Modelling. - Arthur Guez; Fabio Viola; Theophane Weber; Lars Buesing; Steven Kapturowski; Doina Precup; David Silver; Nicolas Heess (NeurIPS, 2020)

Physically Embedded Planning Problems: New Challenges for Reinforcement Learning. - 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 (CoRR, 2020)

Beyond Tabula-Rasa: a Modular Reinforcement Learning Approach for Physically Embedded 3D Sokoban. - Péter Karkus; Mehdi Mirza; Arthur Guez; Andrew Jaegle; Timothy P. Lillicrap; Lars Buesing; Nicolas Heess; Theophane Weber (CoRR, 2020)

Counterfactual Credit Assignment in Model-Free Reinforcement Learning. - Thomas Mesnard; Théophane Weber; Fabio Viola; Shantanu Thakoor; Alaa Saade; Anna Harutyunyan; Will Dabney; Tom Stepleton; Nicolas Heess; Arthur Guez; Marcus Hutter; Lars Buesing; et al. (CoRR, 2020)

Mastering Atari, Go, chess and shogi by planning with a learned model. - 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 (Nat., 2020)

On the role of planning in model-based deep reinforcement learning. - Jessica B. Hamrick; Abram L. Friesen; Feryal M. P. Behbahani; Arthur Guez; Fabio Viola; Sims Witherspoon; Thomas Anthony 001; Lars Holger Buesing; Petar Velickovic; Theophane Weber (ICLR, 2021)

Muesli: Combining Improvements in Policy Optimization. - Matteo Hessel; Ivo Danihelka; Fabio Viola; Arthur Guez; Simon Schmitt; Laurent Sifre; Theophane Weber; David Silver; Hado van Hasselt (ICML, 2021)

Counterfactual Credit Assignment in Model-Free Reinforcement Learning. - 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; et al. (ICML, 2021)

COptiDICE: Offline Constrained Reinforcement Learning via Stationary Distribution Correction Estimation. - Jongmin Lee 004; Cosmin Paduraru; Daniel J. Mankowitz; Nicolas Heess; Doina Precup; Kee-Eung Kim; Arthur Guez (ICLR, 2022)

Policy improvement by planning with Gumbel. - Ivo Danihelka; Arthur Guez; Julian Schrittwieser; David Silver (ICLR, 2022)

Retrieval-Augmented Reinforcement Learning. - Anirudh Goyal; Abram L. Friesen; Andrea Banino; Theophane Weber; Nan Rosemary Ke; Adrià Puigdomènech Badia; Arthur Guez; Mehdi Mirza; Peter Conway Humphreys; Ksenia Konyushkova; Michal Valko; Simon Osindero; et al. (ICML, 2022)

Large-Scale Retrieval for Reinforcement Learning. - Peter Conway Humphreys; Arthur Guez; Olivier Tieleman; Laurent Sifre; Theophane Weber; Timothy P. Lillicrap (NeurIPS, 2022)

Retrieval-Augmented Reinforcement Learning. - 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; et al. (CoRR, 2022)

Optimism and Adaptivity in Policy Optimization. - Veronica Chelu; Tom Zahavy; Arthur Guez; Doina Precup; Sebastian Flennerhag (CoRR, 2023)

A Unifying Framework for Action-Conditional Self-Predictive Reinforcement Learning. - Khimya Khetarpal; Zhaohan Daniel Guo; Bernardo Ávila Pires; Yunhao Tang; Clare Lyle; Mark Rowland 001; Nicolas Heess; Diana L. Borsa; Arthur Guez; Will Dabney (AISTATS, 2025)