Rémi Munos

Publication Activity (10 Years)

Years Active: 1996-2024
Publications (10 Years): 167

Top Venues

CoRR

ICML

NeurIPS

NIPS

Publications

Mohammad Gheshlaghi Azar, Zhaohan Daniel Guo, Bilal Piot, Rémi Munos, Mark Rowland, Michal Valko, Daniele Calandriello
A General Theoretical Paradigm to Understand Learning from Human Preferences. AISTATS (2024)
Yunhao Tang, Zhaohan Daniel Guo, Zeyu Zheng, Daniele Calandriello, Yuan Cao, Eugene Tarassov, Rémi Munos, Bernardo Ávila Pires, Michal Valko, Yong Cheng, Will Dabney
Understanding the performance gap between online and offline alignment algorithms. CoRR (2024)
Yunhao Tang, Zhaohan Daniel Guo, Zeyu Zheng, Daniele Calandriello, Rémi Munos, Mark Rowland, Pierre Harvey Richemond, Michal Valko, Bernardo Ávila Pires, Bilal Piot
Generalized Preference Optimization: A Unified Approach to Offline Alignment. CoRR (2024)
Yunhao Tang, Mark Rowland, Rémi Munos, Bernardo Ávila Pires, Will Dabney
Off-policy Distributional Q(λ): Distributional RL without Importance Sampling. CoRR (2024)
Lior Shani, Aviv Rosenberg, Asaf Cassel, Oran Lang, Daniele Calandriello, Avital Zipori, Hila Noga, Orgad Keller, Bilal Piot, Idan Szpektor, Avinatan Hassidim, Yossi Matias, Rémi Munos
Multi-turn Reinforcement Learning from Preference Human Feedback. CoRR (2024)
Daniele Calandriello, Daniel Guo, Rémi Munos, Mark Rowland, Yunhao Tang, Bernardo Ávila Pires, Pierre Harvey Richemond, Charline Le Lan, Michal Valko, Tianqi Liu, Rishabh Joshi, Zeyu Zheng, Bilal Piot
Human Alignment of Large Language Models through Online Preference Optimisation. CoRR (2024)
Pierre Harvey Richemond, Yunhao Tang, Daniel Guo, Daniele Calandriello, Mohammad Gheshlaghi Azar, Rafael Rafailov, Bernardo Ávila Pires, Eugene Tarassov, Lucas Spangher, Will Ellsworth, Aliaksei Severyn, Jonathan Mallinson, Lior Shani, Gil Shamir, Rishabh Joshi, Tianqi Liu, Rémi Munos, Bilal Piot
Offline Regularised Reinforcement Learning for Large Language Models Alignment. CoRR (2024)
Mark Rowland, Li Kevin Wenliang, Rémi Munos, Clare Lyle, Yunhao Tang, Will Dabney
Near-Minimax-Optimal Distributional Reinforcement Learning with a Generative Model. CoRR (2024)
Yunhao Tang, Rémi Munos
Towards a Better Understanding of Representation Dynamics under TD-learning. CoRR (2023)
Rémi Munos, Michal Valko, Daniele Calandriello, Mohammad Gheshlaghi Azar, Mark Rowland, Zhaohan Daniel Guo, Yunhao Tang, Matthieu Geist, Thomas Mesnard, Andrea Michi, Marco Selvi, Sertan Girgin, Nikola Momchev, Olivier Bachem, Daniel J. Mankowitz, Doina Precup, Bilal Piot
Nash Learning from Human Feedback. CoRR (2023)
Yash Chandak, Shantanu Thakoor, Zhaohan Daniel Guo, Yunhao Tang, Rémi Munos, Will Dabney, Diana L. Borsa
Representations and Exploration for Deep Reinforcement Learning using Singular Value Decomposition. CoRR (2023)
Yunhao Tang, Rémi Munos, Mark Rowland, Michal Valko
VA-learning as a more efficient alternative to Q-learning. CoRR (2023)
Mark Rowland, Yunhao Tang, Clare Lyle, Rémi Munos, Marc G. Bellemare, Will Dabney
The Statistical Benefits of Quantile Temporal-Difference Learning for Value Estimation. CoRR (2023)
Yunhao Tang, Rémi Munos, Mark Rowland, Michal Valko
VA-learning as a more efficient alternative to Q-learning. ICML (2023)
Côme Fiegel, Pierre Ménard, Tadashi Kozuno, Rémi Munos, Vianney Perchet, Michal Valko
Local and adaptive mirror descents in extensive-form games. CoRR (2023)
Mark Rowland, Yunhao Tang, Clare Lyle, Rémi Munos, Marc G. Bellemare, Will Dabney
The Statistical Benefits of Quantile Temporal-Difference Learning for Value Estimation. ICML (2023)
Daniel Jarrett, Corentin Tallec, Florent Altché, Thomas Mesnard, Rémi Munos, Michal Valko
Curiosity in Hindsight: Intrinsic Exploration in Stochastic Environments. ICML (2023)
Yunhao Tang, Tadashi Kozuno, Mark Rowland, Anna Harutyunyan, Rémi Munos, Bernardo Ávila Pires, Michal Valko
DoMo-AC: Doubly Multi-step Off-policy Actor-Critic Algorithm. ICML (2023)
Côme Fiegel, Pierre Ménard, Tadashi Kozuno, Rémi Munos, Vianney Perchet, Michal Valko
Adapting to game trees in zero-sum imperfect information games. ICML (2023)
Yunhao Tang, Rémi Munos
Towards a better understanding of representation dynamics under TD-learning. ICML (2023)
Yunhao Tang, Zhaohan Daniel Guo, Pierre Harvey Richemond, Bernardo Ávila Pires, Yash Chandak, Rémi Munos, Mark Rowland, Mohammad Gheshlaghi Azar, Charline Le Lan, Clare Lyle, András György, Shantanu Thakoor, Will Dabney, Bilal Piot, Daniele Calandriello, Michal Valko
Understanding Self-Predictive Learning for Reinforcement Learning. ICML (2023)
Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Rémi Munos, Alexey Naumov, Pierre Perrault, Yunhao Tang, Michal Valko, Pierre Ménard
Fast Rates for Maximum Entropy Exploration. CoRR (2023)
Yash Chandak, Shantanu Thakoor, Zhaohan Daniel Guo, Yunhao Tang, Rémi Munos, Will Dabney, Diana L. Borsa
Representations and Exploration for Deep Reinforcement Learning using Singular Value Decomposition. ICML (2023)
Toshinori Kitamura, Tadashi Kozuno, Yunhao Tang, Nino Vieillard, Michal Valko, Wenhao Yang, Jincheng Mei, Pierre Ménard, Mohammad Gheshlaghi Azar, Rémi Munos, Olivier Pietquin, Matthieu Geist, Csaba Szepesvári, Wataru Kumagai, Yutaka Matsuo
Regularization and Variance-Weighted Regression Achieves Minimax Optimality in Linear MDPs: Theory and Practice. CoRR (2023)
Mark Rowland, Rémi Munos, Mohammad Gheshlaghi Azar, Yunhao Tang, Georg Ostrovski, Anna Harutyunyan, Karl Tuyls, Marc G. Bellemare, Will Dabney
An Analysis of Quantile Temporal-Difference Learning. CoRR (2023)
Thomas Mesnard, Wenqi Chen, Alaa Saade, Yunhao Tang, Mark Rowland, Theophane Weber, Clare Lyle, Audrunas Gruslys, Michal Valko, Will Dabney, Georg Ostrovski, Eric Moulines, Rémi Munos
Quantile Credit Assignment. ICML (2023)
Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Rémi Munos, Alexey Naumov, Pierre Perrault, Michal Valko, Pierre Ménard
Model-free Posterior Sampling via Learning Rate Randomization. CoRR (2023)
Mohammad Gheshlaghi Azar, Mark Rowland, Bilal Piot, Daniel Guo, Daniele Calandriello, Michal Valko, Rémi Munos
A General Theoretical Paradigm to Understand Learning from Human Preferences. CoRR (2023)
Toshinori Kitamura, Tadashi Kozuno, Yunhao Tang, Nino Vieillard, Michal Valko, Wenhao Yang, Jincheng Mei, Pierre Ménard, Mohammad Gheshlaghi Azar, Rémi Munos, Olivier Pietquin, Matthieu Geist, Csaba Szepesvári, Wataru Kumagai, Yutaka Matsuo
Regularization and Variance-Weighted Regression Achieves Minimax Optimality in Linear MDPs: Theory and Practice. ICML (2023)
Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Rémi Munos, Alexey Naumov, Pierre Perrault, Yunhao Tang, Michal Valko, Pierre Ménard
Fast Rates for Maximum Entropy Exploration. ICML (2023)
Yunhao Tang, Tadashi Kozuno, Mark Rowland, Anna Harutyunyan, Rémi Munos, Bernardo Ávila Pires, Michal Valko
DoMo-AC: Doubly Multi-step Off-policy Actor-Critic Algorithm. CoRR (2023)
Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Rémi Munos, Alexey Naumov, Pierre Perrault, Michal Valko, Pierre Ménard
Model-free Posterior Sampling via Learning Rate Randomization. NeurIPS (2023)
Yunhao Tang, Mark Rowland, Rémi Munos, Michal Valko
Marginalized Operators for Off-policy Reinforcement Learning. CoRR (2022)
Zhaohan Guo, Shantanu Thakoor, Miruna Pislar, Bernardo Ávila Pires, Florent Altché, Corentin Tallec, Alaa Saade, Daniele Calandriello, Jean-Bastien Grill, Yunhao Tang, Michal Valko, Rémi Munos, Mohammad Gheshlaghi Azar, Bilal Piot
BYOL-Explore: Exploration by Bootstrapped Prediction. NeurIPS (2022)
Yunhao Tang, Rémi Munos, Mark Rowland, Bernardo Ávila Pires, Will Dabney, Marc G. Bellemare
The Nature of Temporal Difference Errors in Multi-step Distributional Reinforcement Learning. NeurIPS (2022)
Shantanu Thakoor, Mark Rowland, Diana Borsa, Will Dabney, Rémi Munos, André Barreto
Generalised Policy Improvement with Geometric Policy Composition. ICML (2022)
Yunhao Tang, Mark Rowland, Rémi Munos, Michal Valko
Marginalized Operators for Off-policy Reinforcement Learning. AISTATS (2022)
Shantanu Thakoor, Mark Rowland, Diana Borsa, Will Dabney, Rémi Munos, André Barreto
Generalised Policy Improvement with Geometric Policy Composition. CoRR (2022)
Matthieu Geist, Julien Pérolat, Mathieu Laurière, Romuald Elie, Sarah Perrin, Olivier Bachem, Rémi Munos, Olivier Pietquin
Concave Utility Reinforcement Learning: The Mean-field Game Viewpoint. AAMAS (2022)
Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Rémi Munos, Alexey Naumov, Mark Rowland, Michal Valko, Pierre Ménard
Optimistic Posterior Sampling for Reinforcement Learning with Few Samples and Tight Guarantees. NeurIPS (2022)
Zhaohan Daniel Guo, Shantanu Thakoor, Miruna Pislar, Bernardo Ávila Pires, Florent Altché, Corentin Tallec, Alaa Saade, Daniele Calandriello, Jean-Bastien Grill, Yunhao Tang, Michal Valko, Rémi Munos, Mohammad Gheshlaghi Azar, Bilal Piot
BYOL-Explore: Exploration by Bootstrapped Prediction. CoRR (2022)
Daniel Jarrett, Corentin Tallec, Florent Altché, Thomas Mesnard, Rémi Munos, Michal Valko
Curiosity in hindsight. CoRR (2022)
Côme Fiegel, Pierre Ménard, Tadashi Kozuno, Rémi Munos, Vianney Perchet, Michal Valko
Adapting to game trees in zero-sum imperfect information games. CoRR (2022)
Tadashi Kozuno, Wenhao Yang, Nino Vieillard, Toshinori Kitamura, Yunhao Tang, Jincheng Mei, Pierre Ménard, Mohammad Gheshlaghi Azar, Michal Valko, Rémi Munos, Olivier Pietquin, Matthieu Geist, Csaba Szepesvári
KL-Entropy-Regularized RL with a Generative Model is Minimax Optimal. CoRR (2022)
Yunhao Tang, Zhaohan Daniel Guo, Pierre Harvey Richemond, Bernardo Ávila Pires, Yash Chandak, Rémi Munos, Mark Rowland, Mohammad Gheshlaghi Azar, Charline Le Lan, Clare Lyle, András György, Shantanu Thakoor, Will Dabney, Bilal Piot, Daniele Calandriello, Michal Valko
Understanding Self-Predictive Learning for Reinforcement Learning. CoRR (2022)
Julien Pérolat, Bart De Vylder, Daniel Hennes, Eugene Tarassov, Florian Strub, Vincent de Boer, Paul Muller, Jerome T. Connor, Neil Burch, Thomas W. Anthony, Stephen McAleer, Romuald Elie, Sarah H. Cen, Zhe Wang, Audrunas Gruslys, Aleksandra Malysheva, Mina Khan, Sherjil Ozair, Finbarr Timbers, Toby Pohlen, Tom Eccles, Mark Rowland, Marc Lanctot, Jean-Baptiste Lespiau, Bilal Piot, Shayegan Omidshafiei, Edward Lockhart, Laurent Sifre, Nathalie Beauguerlange, Rémi Munos, David Silver, Satinder Singh, Demis Hassabis, Karl Tuyls
Mastering the Game of Stratego with Model-Free Multiagent Reinforcement Learning. CoRR (2022)
Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Rémi Munos, Alexey Naumov, Mark Rowland, Michal Valko, Pierre Ménard
Optimistic Posterior Sampling for Reinforcement Learning with Few Samples and Tight Guarantees. CoRR (2022)
Yunhao Tang, Mark Rowland, Rémi Munos, Bernardo Ávila Pires, Will Dabney, Marc G. Bellemare
The Nature of Temporal Difference Errors in Multi-step Distributional Reinforcement Learning. CoRR (2022)
Shantanu Thakoor, Corentin Tallec, Mohammad Gheshlaghi Azar, Mehdi Azabou, Eva L. Dyer, Rémi Munos, Petar Velickovic, Michal Valko
Large-Scale Representation Learning on Graphs via Bootstrapping. ICLR (2022)
Yunhao Tang, Tadashi Kozuno, Mark Rowland, Rémi Munos, Michal Valko
Unifying Gradient Estimators for Meta-Reinforcement Learning via Off-Policy Evaluation. NeurIPS (2021)
Tadashi Kozuno, Yunhao Tang, Mark Rowland, Rémi Munos, Steven Kapturowski, Will Dabney, Michal Valko, David Abel
Revisiting Peng's Q(λ) for Modern Reinforcement Learning. CoRR (2021)
Shantanu Thakoor, Corentin Tallec, Mohammad Gheshlaghi Azar, Rémi Munos, Petar Velickovic, Michal Valko
Bootstrapped Representation Learning on Graphs. CoRR (2021)
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)
Tadashi Kozuno, Pierre Ménard, Rémi Munos, Michal Valko
Model-Free Learning for Two-Player Zero-Sum Partially Observable Markov Games with Perfect Recall. CoRR (2021)
Yunhao Tang, Mark Rowland, Rémi Munos, Michal Valko
Taylor Expansion of Discount Factors. ICML (2021)
Matthieu Geist, Julien Pérolat, Mathieu Laurière, Romuald Elie, Sarah Perrin, Olivier Bachem, Rémi Munos, Olivier Pietquin
Concave Utility Reinforcement Learning: the Mean-field Game viewpoint. CoRR (2021)
Tadashi Kozuno, Yunhao Tang, Mark Rowland, Rémi Munos, Steven Kapturowski, Will Dabney, Michal Valko, David Abel
Revisiting Peng's Q(λ) for Modern Reinforcement Learning. ICML (2021)
Yunhao Tang, Tadashi Kozuno, Mark Rowland, Rémi Munos, Michal Valko
Unifying Gradient Estimators for Meta-Reinforcement Learning via Off-Policy Evaluation. CoRR (2021)
Zhaohan Daniel Guo, Mohammad Gheshlaghi Azar, Alaa Saade, Shantanu Thakoor, Bilal Piot, Bernardo Ávila Pires, Michal Valko, Thomas Mesnard, Tor Lattimore, Rémi Munos
Geometric Entropic Exploration. CoRR (2021)
Tadashi Kozuno, Pierre Ménard, Rémi Munos, Michal Valko
Learning in two-player zero-sum partially observable Markov games with perfect recall. NeurIPS (2021)
Karl Tuyls, Shayegan Omidshafiei, Paul Muller, Zhe Wang, Jerome T. Connor, Daniel Hennes, Ian Graham, William Spearman, Tim Waskett, Dafydd Steele, Pauline Luc, Adrià Recasens, Alexandre Galashov, Gregory Thornton, Romuald Elie, Pablo Sprechmann, Pol Moreno, Kris Cao, Marta Garnelo, Praneet Dutta, Michal Valko, Nicolas Heess, Alex Bridgland, Julien Pérolat, Bart De Vylder, S. M. Ali Eslami, Mark Rowland, Andrew Jaegle, Rémi Munos, Trevor Back, Razia Ahamed, Simon Bouton, Nathalie Beauguerlange, Jackson Broshear, Thore Graepel, Demis Hassabis
Game Plan: What AI can do for Football, and What Football can do for AI. J. Artif. Intell. Res. 71 (2021)
Prashanth L. A., Nathaniel Korda, Rémi Munos
Concentration bounds for temporal difference learning with linear function approximation: the case of batch data and uniform sampling. Mach. Learn. 110 (3) (2021)
Julien Pérolat, Rémi Munos, Jean-Baptiste Lespiau, Shayegan Omidshafiei, Mark Rowland, Pedro A. Ortega, Neil Burch, Thomas W. Anthony, David Balduzzi, Bart De Vylder, Georgios Piliouras, Marc Lanctot, Karl Tuyls
From Poincaré Recurrence to Convergence in Imperfect Information Games: Finding Equilibrium via Regularization. ICML (2021)
Yunhao Tang, Mark Rowland, Rémi Munos, Michal Valko
Taylor Expansion of Discount Factors. CoRR (2021)
Audrunas Gruslys, Marc Lanctot, Rémi Munos, Finbarr Timbers, Martin Schmid, Julien Pérolat, Dustin Morrill, Vinícius Flores Zambaldi, Jean-Baptiste Lespiau, John Schultz, Mohammad Gheshlaghi Azar, Michael Bowling, Karl Tuyls
The Advantage Regret-Matching Actor-Critic. CoRR (2020)
Daniel Hennes, Dustin Morrill, Shayegan Omidshafiei, Rémi Munos, Julien Pérolat, Marc Lanctot, Audrunas Gruslys, Jean-Baptiste Lespiau, Paavo Parmas, Edgar A. Duéñez-Guzmán, Karl Tuyls
Neural Replicator Dynamics: Multiagent Learning via Hedging Policy Gradients. AAMAS (2020)
Zhaohan Daniel Guo, Bernardo Ávila Pires, Bilal Piot, Jean-Bastien Grill, Florent Altché, Rémi Munos, Mohammad Gheshlaghi Azar
Bootstrap Latent-Predictive Representations for Multitask Reinforcement Learning. ICML (2020)
Yunhao Tang, Michal Valko, Rémi Munos
Taylor Expansion Policy Optimization. CoRR (2020)
Rémi Munos, Julien Pérolat, Jean-Baptiste Lespiau, Mark Rowland, Bart De Vylder, Marc Lanctot, Finbarr Timbers, Daniel Hennes, Shayegan Omidshafiei, Audrunas Gruslys, Mohammad Gheshlaghi Azar, Edward Lockhart, Karl Tuyls
Fast computation of Nash Equilibria in Imperfect Information Games. ICML (2020)
Nino Vieillard, Tadashi Kozuno, Bruno Scherrer, Olivier Pietquin, Rémi Munos, Matthieu Geist
Leverage the Average: an Analysis of Regularization in RL. CoRR (2020)
Paul Muller, Shayegan Omidshafiei, Mark Rowland, Karl Tuyls, Julien Pérolat, Siqi Liu, Daniel Hennes, Luke Marris, Marc Lanctot, Edward Hughes, Zhe Wang, Guy Lever, Nicolas Heess, Thore Graepel, Rémi Munos
A Generalized Training Approach for Multiagent Learning. ICLR (2020)
Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre H. Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Ávila Pires, Zhaohan Daniel Guo, Mohammad Gheshlaghi Azar, Bilal Piot, Koray Kavukcuoglu, Rémi Munos, Michal Valko
Bootstrap Your Own Latent: A New Approach to Self-Supervised Learning. CoRR (2020)
Will Dabney, Zeb Kurth-Nelson, Naoshige Uchida, Clara Kwon Starkweather, Demis Hassabis, Rémi Munos, Matthew Botvinick
A distributional code for value in dopamine-based reinforcement learning. Nat. 577 (7792) (2020)
Mark Rowland, Anna Harutyunyan, Hado van Hasselt, Diana Borsa, Tom Schaul, Rémi Munos, Will Dabney
Conditional Importance Sampling for Off-Policy Learning. AISTATS (2020)
Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre H. Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Ávila Pires, Zhaohan Guo, Mohammad Gheshlaghi Azar, Bilal Piot, Koray Kavukcuoglu, Rémi Munos, Michal Valko
Bootstrap Your Own Latent - A New Approach to Self-Supervised Learning. NeurIPS (2020)
Nino Vieillard, Tadashi Kozuno, Bruno Scherrer, Olivier Pietquin, Rémi Munos, Matthieu Geist
Leverage the Average: an Analysis of KL Regularization in Reinforcement Learning. NeurIPS (2020)
Karl Tuyls, Shayegan Omidshafiei, Paul Muller, Zhe Wang, Jerome T. Connor, Daniel Hennes, Ian Graham, William Spearman, Tim Waskett, Dafydd Steele, Pauline Luc, Adrià Recasens, Alexandre Galashov, Gregory Thornton, Romuald Elie, Pablo Sprechmann, Pol Moreno, Kris Cao, Marta Garnelo, Praneet Dutta, Michal Valko, Nicolas Heess, Alex Bridgland, Julien Pérolat, Bart De Vylder, S. M. Ali Eslami, Mark Rowland, Andrew Jaegle, Rémi Munos, Trevor Back, Razia Ahamed, Simon Bouton, Nathalie Beauguerlange, Jackson Broshear, Thore Graepel, Demis Hassabis
Game Plan: What AI can do for Football, and What Football can do for AI. CoRR (2020)
Shayegan Omidshafiei, Karl Tuyls, Wojciech M. Czarnecki, Francisco C. Santos, Mark Rowland, Jerome T. Connor, Daniel Hennes, Paul Muller, Julien Pérolat, Bart De Vylder, Audrunas Gruslys, Rémi Munos
Navigating the Landscape of Games. CoRR (2020)
Julien Pérolat, Rémi Munos, Jean-Baptiste Lespiau, Shayegan Omidshafiei, Mark Rowland, Pedro A. Ortega, Neil Burch, Thomas W. Anthony, David Balduzzi, Bart De Vylder, Georgios Piliouras, Marc Lanctot, Karl Tuyls
From Poincaré Recurrence to Convergence in Imperfect Information Games: Finding Equilibrium via Regularization. CoRR (2020)
Yunhao Tang, Michal Valko, Rémi Munos
Taylor Expansion Policy Optimization. ICML (2020)
Jean-Bastien Grill, Florent Altché, Yunhao Tang, Thomas Hubert, Michal Valko, Ioannis Antonoglou, Rémi Munos
Monte-Carlo Tree Search as Regularized Policy Optimization. CoRR (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)
Zhaohan Daniel Guo, Bernardo Ávila Pires, Bilal Piot, Jean-Bastien Grill, Florent Altché, Rémi Munos, Mohammad Gheshlaghi Azar
Bootstrap Latent-Predictive Representations for Multitask Reinforcement Learning. CoRR (2020)
Jean-Bastien Grill, Florent Altché, Yunhao Tang, Thomas Hubert, Michal Valko, Ioannis Antonoglou, Rémi Munos
Monte-Carlo Tree Search as Regularized Policy Optimization. ICML (2020)
Mark Rowland, Will Dabney, Rémi Munos
Adaptive Trade-Offs in Off-Policy Learning. AISTATS (2020)
Diana Borsa, Nicolas Heess, Bilal Piot, Siqi Liu, Leonard Hasenclever, Rémi Munos, Olivier Pietquin
Observational Learning by Reinforcement Learning. AAMAS (2019)
Mark Rowland, Anna Harutyunyan, Hado van Hasselt, Diana Borsa, Tom Schaul, Rémi Munos, Will Dabney
Conditional Importance Sampling for Off-Policy Learning. CoRR (2019)
Mark Rowland, Shayegan Omidshafiei, Karl Tuyls, Julien Pérolat, Michal Valko, Georgios Piliouras, Rémi Munos
Multiagent Evaluation under Incomplete Information. NeurIPS (2019)
Anna Harutyunyan, Will Dabney, Diana Borsa, Nicolas Heess, Rémi Munos, Doina Precup
The Termination Critic. CoRR (2019)
Shayegan Omidshafiei, Christos H. Papadimitriou, Georgios Piliouras, Karl Tuyls, Mark Rowland, Jean-Baptiste Lespiau, Wojciech M. Czarnecki, Marc Lanctot, Julien Pérolat, Rémi Munos
α-Rank: Multi-Agent Evaluation by Evolution. CoRR (2019)
Steven Kapturowski, Georg Ostrovski, John Quan, Rémi Munos, Will Dabney
Recurrent Experience Replay in Distributed Reinforcement Learning. ICLR (Poster) (2019)
Jean-Bastien Grill, Omar Darwiche Domingues, Pierre Ménard, Rémi Munos, Michal Valko
Planning in entropy-regularized Markov decision processes and games. NeurIPS (2019)
Jean-Bastien Grill, Michal Valko, Rémi Munos
Optimistic optimization of a Brownian. CoRR (2019)
Anna Harutyunyan, Will Dabney, Diana Borsa, Nicolas Heess, Rémi Munos, Doina Precup
The Termination Critic. AISTATS (2019)
Mark Rowland, Will Dabney, Rémi Munos
Adaptive Trade-Offs in Off-Policy Learning. CoRR (2019)
Mark Rowland, Robert Dadashi, Saurabh Kumar, Rémi Munos, Marc G. Bellemare, Will Dabney
Statistics and Samples in Distributional Reinforcement Learning. ICML (2019)
Mark Rowland, Robert Dadashi, Saurabh Kumar, Rémi Munos, Marc G. Bellemare, Will Dabney
Statistics and Samples in Distributional Reinforcement Learning. CoRR (2019)
Mark Rowland, Shayegan Omidshafiei, Karl Tuyls, Julien Pérolat, Michal Valko, Georgios Piliouras, Rémi Munos
Multiagent Evaluation under Incomplete Information. CoRR (2019)
Anna Harutyunyan, Will Dabney, Thomas Mesnard, Mohammad Gheshlaghi Azar, Bilal Piot, Nicolas Heess, Hado van Hasselt, Gregory Wayne, Satinder Singh, Doina Precup, Rémi Munos
Hindsight Credit Assignment. NeurIPS (2019)
André Barreto, Diana Borsa, John Quan, Tom Schaul, David Silver, Matteo Hessel, Daniel J. Mankowitz, Augustin Zídek, Rémi Munos
Transfer in Deep Reinforcement Learning Using Successor Features and Generalised Policy Improvement. CoRR (2019)

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Rémi Munos