Rémi Munos - RankMe

316 papers A* 79A 17B 12C 2Journal 149Unranked 55

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Expanding the Capabilities of Reinforcement Learning via Text Feedback. CoRR Yuda Song, Lili Chen, Fahim Tajwar, Rémi Munos, Deepak Pathak, J. Andrew Bagnell, Aarti Singh, Andrea Zanette
2025	J	`jnl`	Aligning LLMs Toward Multi-Turn Conversational Outcomes Using Iterative PPO. CoRR Daniel R. Jiang, Jalaj Bhandari, Yukai Yang, Rémi Munos, Tyler Lu
2025	J	`jnl`	Asymmetric REINFORCE for off-Policy Reinforcement Learning: Balancing positive and negative rewards. CoRR Charles Arnal, Gaëtan Narozniak, Vivien Cabannes, Yunhao Tang, Julia Kempe, Rémi Munos
2025	J	`jnl`	Learning to chain-of-thought with Jensen's evidence lower bound. CoRR Yunhao Tang, Sid Wang, Rémi Munos
2025	J	`jnl`	On a few pitfalls in KL divergence gradient estimation for RL. CoRR Yunhao Tang, Rémi Munos
2025	A*	`conf`	Optimizing Language Models for Inference Time Objectives using Reinforcement Learning. ICML Yunhao Tang, Kunhao Zheng, Gabriel Synnaeve, Rémi Munos
2025	J	`jnl`	Optimizing Language Models for Inference Time Objectives using Reinforcement Learning. CoRR Yunhao Tang, Kunhao Zheng, Gabriel Synnaeve, Rémi Munos
2025	J	`jnl`	Optimizing Return Distributions with Distributional Dynamic Programming. CoRR Bernardo Ávila Pires, Mark Rowland, Diana Borsa, Zhaohan Daniel Guo, Khimya Khetarpal, André Barreto, David Abel, Rémi Munos, Will Dabney
2025	J	`jnl`	Optimizing Return Distributions with Distributional Dynamic Programming. J. Mach. Learn. Res. Bernardo Ávila Pires, Mark Rowland, Diana Borsa, Zhaohan Daniel Guo, Khimya Khetarpal, André Barreto, David Abel, Rémi Munos, Will Dabney
2025	J	`jnl`	Outcome-based Exploration for LLM Reasoning. CoRR Yuda Song, Julia Kempe, Rémi Munos
2025	J	`jnl`	Positional Encoding via Token-Aware Phase Attention. CoRR Yu Wang, Sheng Shen, Rémi Munos, Hongyuan Zhan, Yuandong Tian
2025	J	`jnl`	RL-finetuning LLMs from on- and off-policy data with a single algorithm. CoRR Yunhao Tang, Taco Cohen, David W. Zhang, Michal Valko, Rémi Munos
2025	J	`jnl`	Safety Alignment of LMs via Non-cooperative Games. CoRR Anselm Paulus, Ilia Kulikov, Brandon Amos, Rémi Munos, Ivan Evtimov, Kamalika Chaudhuri, Arman Zharmagambetov
2025	J	`jnl`	Soft Policy Optimization: Online Off-Policy RL for Sequence Models. CoRR Taco Cohen, David W. Zhang, Kunhao Zheng, Yunhao Tang, Rémi Munos, Gabriel Synnaeve
2025	A*	`conf`	Temporal Difference Flows. ICML Jesse Farebrother, Matteo Pirotta, Andrea Tirinzoni, Rémi Munos, Alessandro Lazaric, Ahmed Touati
2025	J	`jnl`	Temporal Difference Flows. CoRR Jesse Farebrother, Matteo Pirotta, Andrea Tirinzoni, Rémi Munos, Alessandro Lazaric, Ahmed Touati
2024	A	`conf`	A General Theoretical Paradigm to Understand Learning from Human Preferences. AISTATS Mohammad Gheshlaghi Azar, Zhaohan Daniel Guo, Bilal Piot, Rémi Munos, Mark Rowland, Michal Valko, Daniele Calandriello
2024	J	`jnl`	An Analysis of Quantile Temporal-Difference Learning. J. Mach. Learn. Res. Mark Rowland, Rémi Munos, Mohammad Gheshlaghi Azar, Yunhao Tang, Georg Ostrovski, Anna Harutyunyan, Karl Tuyls, Marc G. Bellemare, Will Dabney
2024	A*	`conf`	Generalized Preference Optimization: A Unified Approach to Offline Alignment. ICML Yunhao Tang, Zhaohan Daniel Guo, Zeyu Zheng, Daniele Calandriello, Rémi Munos, Mark Rowland, Pierre Harvey Richemond, Michal Valko, Bernardo Ávila Pires, Bilal Piot
2024	J	`jnl`	Generalized Preference Optimization: A Unified Approach to Offline Alignment. CoRR Yunhao Tang, Zhaohan Daniel Guo, Zeyu Zheng, Daniele Calandriello, Rémi Munos, Mark Rowland, Pierre Harvey Richemond, Michal Valko, Bernardo Ávila Pires, Bilal Piot
2024	A*	`conf`	Human Alignment of Large Language Models through Online Preference Optimisation. ICML Daniele Calandriello, Zhaohan 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
2024	J	`jnl`	Human Alignment of Large Language Models through Online Preference Optimisation. CoRR 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
2024	A*	`conf`	Local and Adaptive Mirror Descents in Extensive-Form Games. NeurIPS Côme Fiegel, Pierre Ménard, Tadashi Kozuno, Rémi Munos, Vianney Perchet, Michal Valko
2024	J	`jnl`	Multi-turn Reinforcement Learning from Preference Human Feedback. CoRR Lior Shani, Aviv Rosenberg, Asaf B. Cassel, Oran Lang, Daniele Calandriello, Avital Zipori, Hila Noga, Orgad Keller, Bilal Piot, Idan Szpektor, Avinatan Hassidim, Yossi Matias, Rémi Munos
2024	A*	`conf`	Multi-turn Reinforcement Learning with Preference Human Feedback. NeurIPS 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
2024	A*	`conf`	Nash Learning from Human Feedback. ICML Rémi Munos, Michal Valko, Daniele Calandriello, Mohammad Gheshlaghi Azar, Mark Rowland, Daniel Guo, Yunhao Tang, Matthieu Geist, Thomas Mesnard, Côme Fiegel, Andrea Michi, Marco Selvi, Sertan Girgin, Nikola Momchev, Olivier Bachem, Daniel J. Mankowitz, Doina Precup, Bilal Piot
2024	A*	`conf`	Near-Minimax-Optimal Distributional Reinforcement Learning with a Generative Model. NeurIPS Mark Rowland, Kevin Kevin Li, Rémi Munos, Clare Lyle, Yunhao Tang, Will Dabney
2024	J	`jnl`	Near-Minimax-Optimal Distributional Reinforcement Learning with a Generative Model. CoRR Mark Rowland, Li Kevin Wenliang, Rémi Munos, Clare Lyle, Yunhao Tang, Will Dabney
2024	J	`jnl`	Off-policy Distributional Q(λ): Distributional RL without Importance Sampling. CoRR Yunhao Tang, Mark Rowland, Rémi Munos, Bernardo Ávila Pires, Will Dabney
2024	J	`jnl`	Offline Regularised Reinforcement Learning for Large Language Models Alignment. CoRR 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
2024	J	`jnl`	Super-Exponential Regret for UCT, AlphaGo and Variants. CoRR Laurent Orseau, Rémi Munos
2024	J	`jnl`	Understanding the performance gap between online and offline alignment algorithms. CoRR Yunhao Tang, Zhaohan Daniel Guo, Zeyu Zheng, Daniele Calandriello, Yuan Cao, Eugene Tarassov, Rémi Munos, Bernardo Ávila Pires, Michal Valko, Yong Cheng, Will Dabney
2023	J	`jnl`	A General Theoretical Paradigm to Understand Learning from Human Preferences. CoRR Mohammad Gheshlaghi Azar, Mark Rowland, Bilal Piot, Daniel Guo, Daniele Calandriello, Michal Valko, Rémi Munos
2023	A*	`conf`	Adapting to game trees in zero-sum imperfect information games. ICML Côme Fiegel, Pierre Ménard, Tadashi Kozuno, Rémi Munos, Vianney Perchet, Michal Valko
2023	J	`jnl`	An Analysis of Quantile Temporal-Difference Learning. CoRR Mark Rowland, Rémi Munos, Mohammad Gheshlaghi Azar, Yunhao Tang, Georg Ostrovski, Anna Harutyunyan, Karl Tuyls, Marc G. Bellemare, Will Dabney
2023	A*	`conf`	Curiosity in Hindsight: Intrinsic Exploration in Stochastic Environments. ICML Daniel Jarrett, Corentin Tallec, Florent Altché, Thomas Mesnard, Rémi Munos, Michal Valko
2023	A*	`conf`	DoMo-AC: Doubly Multi-step Off-policy Actor-Critic Algorithm. ICML Yunhao Tang, Tadashi Kozuno, Mark Rowland, Anna Harutyunyan, Rémi Munos, Bernardo Ávila Pires, Michal Valko
2023	J	`jnl`	DoMo-AC: Doubly Multi-step Off-policy Actor-Critic Algorithm. CoRR Yunhao Tang, Tadashi Kozuno, Mark Rowland, Anna Harutyunyan, Rémi Munos, Bernardo Ávila Pires, Michal Valko
2023	A*	`conf`	Fast Rates for Maximum Entropy Exploration. ICML Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Rémi Munos, Alexey Naumov, Pierre Perrault, Yunhao Tang, Michal Valko, Pierre Ménard
2023	J	`jnl`	Fast Rates for Maximum Entropy Exploration. CoRR Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Rémi Munos, Alexey Naumov, Pierre Perrault, Yunhao Tang, Michal Valko, Pierre Ménard
2023	J	`jnl`	Local and adaptive mirror descents in extensive-form games. CoRR Côme Fiegel, Pierre Ménard, Tadashi Kozuno, Rémi Munos, Vianney Perchet, Michal Valko
2023	A*	`conf`	Model-free Posterior Sampling via Learning Rate Randomization. NeurIPS Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Rémi Munos, Alexey Naumov, Pierre Perrault, Michal Valko, Pierre Ménard
2023	J	`jnl`	Model-free Posterior Sampling via Learning Rate Randomization. CoRR Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Rémi Munos, Alexey Naumov, Pierre Perrault, Michal Valko, Pierre Ménard
2023	J	`jnl`	Nash Learning from Human Feedback. CoRR 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
2023	A*	`conf`	Quantile Credit Assignment. ICML 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
2023	A*	`conf`	Regularization and Variance-Weighted Regression Achieves Minimax Optimality in Linear MDPs: Theory and Practice. ICML 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
2023	J	`jnl`	Regularization and Variance-Weighted Regression Achieves Minimax Optimality in Linear MDPs: Theory and Practice. CoRR 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
2023	A*	`conf`	Representations and Exploration for Deep Reinforcement Learning using Singular Value Decomposition. ICML Yash Chandak, Shantanu Thakoor, Zhaohan Daniel Guo, Yunhao Tang, Rémi Munos, Will Dabney, Diana L. Borsa
2023	J	`jnl`	Representations and Exploration for Deep Reinforcement Learning using Singular Value Decomposition. CoRR Yash Chandak, Shantanu Thakoor, Zhaohan Daniel Guo, Yunhao Tang, Rémi Munos, Will Dabney, Diana L. Borsa
2023	A*	`conf`	The Statistical Benefits of Quantile Temporal-Difference Learning for Value Estimation. ICML Mark Rowland, Yunhao Tang, Clare Lyle, Rémi Munos, Marc G. Bellemare, Will Dabney
2023	J	`jnl`	The Statistical Benefits of Quantile Temporal-Difference Learning for Value Estimation. CoRR Mark Rowland, Yunhao Tang, Clare Lyle, Rémi Munos, Marc G. Bellemare, Will Dabney
2023	J	`jnl`	Towards a Better Understanding of Representation Dynamics under TD-learning. CoRR Yunhao Tang, Rémi Munos
2023	A*	`conf`	Towards a better understanding of representation dynamics under TD-learning. ICML Yunhao Tang, Rémi Munos
2023	A*	`conf`	Understanding Self-Predictive Learning for Reinforcement Learning. ICML 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
2023	A*	`conf`	VA-learning as a more efficient alternative to Q-learning. ICML Yunhao Tang, Rémi Munos, Mark Rowland, Michal Valko
2023	J	`jnl`	VA-learning as a more efficient alternative to Q-learning. CoRR Yunhao Tang, Rémi Munos, Mark Rowland, Michal Valko
2022	J	`jnl`	Adapting to game trees in zero-sum imperfect information games. CoRR Côme Fiegel, Pierre Ménard, Tadashi Kozuno, Rémi Munos, Vianney Perchet, Michal Valko
2022	A*	`conf`	BYOL-Explore: Exploration by Bootstrapped Prediction. NeurIPS 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
2022	J	`jnl`	BYOL-Explore: Exploration by Bootstrapped Prediction. CoRR 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
2022	A	`conf`	Concave Utility Reinforcement Learning: The Mean-field Game Viewpoint. AAMAS Matthieu Geist, Julien Pérolat, Mathieu Laurière, Romuald Elie, Sarah Perrin, Olivier Bachem, Rémi Munos, Olivier Pietquin
2022	J	`jnl`	Curiosity in hindsight. CoRR Daniel Jarrett, Corentin Tallec, Florent Altché, Thomas Mesnard, Rémi Munos, Michal Valko
2022	A*	`conf`	Generalised Policy Improvement with Geometric Policy Composition. ICML Shantanu Thakoor, Mark Rowland, Diana Borsa, Will Dabney, Rémi Munos, André Barreto
2022	J	`jnl`	Generalised Policy Improvement with Geometric Policy Composition. CoRR Shantanu Thakoor, Mark Rowland, Diana Borsa, Will Dabney, Rémi Munos, André Barreto
2022	J	`jnl`	KL-Entropy-Regularized RL with a Generative Model is Minimax Optimal. CoRR 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
2022	A*	`conf`	Large-Scale Representation Learning on Graphs via Bootstrapping. ICLR Shantanu Thakoor, Corentin Tallec, Mohammad Gheshlaghi Azar, Mehdi Azabou, Eva L. Dyer, Rémi Munos, Petar Velickovic, Michal Valko
2022	A	`conf`	Marginalized Operators for Off-policy Reinforcement Learning. AISTATS Yunhao Tang, Mark Rowland, Rémi Munos, Michal Valko
2022	J	`jnl`	Marginalized Operators for Off-policy Reinforcement Learning. CoRR Yunhao Tang, Mark Rowland, Rémi Munos, Michal Valko
2022	J	`jnl`	Mastering the Game of Stratego with Model-Free Multiagent Reinforcement Learning. CoRR 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
2022	A*	`conf`	Optimistic Posterior Sampling for Reinforcement Learning with Few Samples and Tight Guarantees. NeurIPS Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Rémi Munos, Alexey Naumov, Mark Rowland, Michal Valko, Pierre Ménard
2022	J	`jnl`	Optimistic Posterior Sampling for Reinforcement Learning with Few Samples and Tight Guarantees. CoRR Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Rémi Munos, Alexey Naumov, Mark Rowland, Michal Valko, Pierre Ménard
2022	A*	`conf`	The Nature of Temporal Difference Errors in Multi-step Distributional Reinforcement Learning. NeurIPS Yunhao Tang, Rémi Munos, Mark Rowland, Bernardo Ávila Pires, Will Dabney, Marc G. Bellemare
2022	J	`jnl`	The Nature of Temporal Difference Errors in Multi-step Distributional Reinforcement Learning. CoRR Yunhao Tang, Mark Rowland, Rémi Munos, Bernardo Ávila Pires, Will Dabney, Marc G. Bellemare
2022	J	`jnl`	Understanding Self-Predictive Learning for Reinforcement Learning. CoRR 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
2021	J	`jnl`	Bootstrapped Representation Learning on Graphs. CoRR Shantanu Thakoor, Corentin Tallec, Mohammad Gheshlaghi Azar, Rémi Munos, Petar Velickovic, Michal Valko
2021	J	`jnl`	Concave Utility Reinforcement Learning: the Mean-field Game viewpoint. CoRR Matthieu Geist, Julien Pérolat, Mathieu Laurière, Romuald Elie, Sarah Perrin, Olivier Bachem, Rémi Munos, Olivier Pietquin
2021	J	`jnl`	Concentration bounds for temporal difference learning with linear function approximation: the case of batch data and uniform sampling. Mach. Learn. Prashanth L. A., Nathaniel Korda, Rémi Munos
2021	A*	`conf`	Counterfactual Credit Assignment in Model-Free Reinforcement Learning. ICML 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
2021	A*	`conf`	From Poincaré Recurrence to Convergence in Imperfect Information Games: Finding Equilibrium via Regularization. ICML 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
2021	J	`jnl`	Game Plan: What AI can do for Football, and What Football can do for AI. J. Artif. Intell. Res. 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
2021	J	`jnl`	Geometric Entropic Exploration. CoRR Zhaohan Daniel Guo, Mohammad Gheshlaghi Azar, Alaa Saade, Shantanu Thakoor, Bilal Piot, Bernardo Ávila Pires, Michal Valko, Thomas Mesnard, Tor Lattimore, Rémi Munos
2021	A*	`conf`	Learning in two-player zero-sum partially observable Markov games with perfect recall. NeurIPS Tadashi Kozuno, Pierre Ménard, Rémi Munos, Michal Valko
2021	J	`jnl`	Model-Free Learning for Two-Player Zero-Sum Partially Observable Markov Games with Perfect Recall. CoRR Tadashi Kozuno, Pierre Ménard, Rémi Munos, Michal Valko
2021	A*	`conf`	Revisiting Peng's Q(λ) for Modern Reinforcement Learning. ICML Tadashi Kozuno, Yunhao Tang, Mark Rowland, Rémi Munos, Steven Kapturowski, Will Dabney, Michal Valko, David Abel
2021	J	`jnl`	Revisiting Peng's Q(λ) for Modern Reinforcement Learning. CoRR Tadashi Kozuno, Yunhao Tang, Mark Rowland, Rémi Munos, Steven Kapturowski, Will Dabney, Michal Valko, David Abel
2021	A*	`conf`	Taylor Expansion of Discount Factors. ICML Yunhao Tang, Mark Rowland, Rémi Munos, Michal Valko
2021	J	`jnl`	Taylor Expansion of Discount Factors. CoRR Yunhao Tang, Mark Rowland, Rémi Munos, Michal Valko
2021	A*	`conf`	Unifying Gradient Estimators for Meta-Reinforcement Learning via Off-Policy Evaluation. NeurIPS Yunhao Tang, Tadashi Kozuno, Mark Rowland, Rémi Munos, Michal Valko
2021	J	`jnl`	Unifying Gradient Estimators for Meta-Reinforcement Learning via Off-Policy Evaluation. CoRR Yunhao Tang, Tadashi Kozuno, Mark Rowland, Rémi Munos, Michal Valko
2020	A*	`conf`	A Generalized Training Approach for Multiagent Learning. ICLR 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
2020	J	`jnl`	A distributional code for value in dopamine-based reinforcement learning. Nat. Will Dabney, Zeb Kurth-Nelson, Naoshige Uchida, Clara Kwon Starkweather, Demis Hassabis, Rémi Munos, Matthew M. Botvinick
2020	A	`conf`	Adaptive Trade-Offs in Off-Policy Learning. AISTATS Mark Rowland, Will Dabney, Rémi Munos
2020	A*	`conf`	Bootstrap Latent-Predictive Representations for Multitask Reinforcement Learning. ICML Zhaohan Daniel Guo, Bernardo Ávila Pires, Bilal Piot, Jean-Bastien Grill, Florent Altché, Rémi Munos, Mohammad Gheshlaghi Azar
2020	J	`jnl`	Bootstrap Latent-Predictive Representations for Multitask Reinforcement Learning. CoRR Zhaohan Daniel Guo, Bernardo Ávila Pires, Bilal Piot, Jean-Bastien Grill, Florent Altché, Rémi Munos, Mohammad Gheshlaghi Azar
2020	A*	`conf`	Bootstrap Your Own Latent - A New Approach to Self-Supervised Learning. NeurIPS 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
2020	J	`jnl`	Bootstrap Your Own Latent: A New Approach to Self-Supervised Learning. CoRR 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
2020	A	`conf`	Conditional Importance Sampling for Off-Policy Learning. AISTATS Mark Rowland, Anna Harutyunyan, Hado van Hasselt, Diana Borsa, Tom Schaul, Rémi Munos, Will Dabney
2020	J	`jnl`	Counterfactual Credit Assignment in Model-Free Reinforcement Learning. CoRR 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
2020	A*	`conf`	Fast computation of Nash Equilibria in Imperfect Information Games. ICML 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
2020	J	`jnl`	From Poincaré Recurrence to Convergence in Imperfect Information Games: Finding Equilibrium via Regularization. CoRR 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
2020	J	`jnl`	Game Plan: What AI can do for Football, and What Football can do for AI. CoRR 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
2020	A*	`conf`	Leverage the Average: an Analysis of KL Regularization in Reinforcement Learning. NeurIPS Nino Vieillard, Tadashi Kozuno, Bruno Scherrer, Olivier Pietquin, Rémi Munos, Matthieu Geist
2020	J	`jnl`	Leverage the Average: an Analysis of Regularization in RL. CoRR Nino Vieillard, Tadashi Kozuno, Bruno Scherrer, Olivier Pietquin, Rémi Munos, Matthieu Geist
2020	A*	`conf`	Monte-Carlo Tree Search as Regularized Policy Optimization. ICML Jean-Bastien Grill, Florent Altché, Yunhao Tang, Thomas Hubert, Michal Valko, Ioannis Antonoglou, Rémi Munos
2020	J	`jnl`	Monte-Carlo Tree Search as Regularized Policy Optimization. CoRR Jean-Bastien Grill, Florent Altché, Yunhao Tang, Thomas Hubert, Michal Valko, Ioannis Antonoglou, Rémi Munos
2020	J	`jnl`	Navigating the Landscape of Games. CoRR 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
2020	A	`conf`	Neural Replicator Dynamics: Multiagent Learning via Hedging Policy Gradients. AAMAS 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
2020	A*	`conf`	Taylor Expansion Policy Optimization. ICML Yunhao Tang, Michal Valko, Rémi Munos
2020	J	`jnl`	Taylor Expansion Policy Optimization. CoRR Yunhao Tang, Michal Valko, Rémi Munos
2020	J	`jnl`	The Advantage Regret-Matching Actor-Critic. CoRR 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
2019	J	`jnl`	A Generalized Training Approach for Multiagent Learning. CoRR 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
2019	J	`jnl`	Adaptive Trade-Offs in Off-Policy Learning. CoRR Mark Rowland, Will Dabney, Rémi Munos
2019	J	`jnl`	Conditional Importance Sampling for Off-Policy Learning. CoRR Mark Rowland, Anna Harutyunyan, Hado van Hasselt, Diana Borsa, Tom Schaul, Rémi Munos, Will Dabney
2019	A*	`conf`	Hindsight Credit Assignment. NeurIPS Anna Harutyunyan, Will Dabney, Thomas Mesnard, Mohammad Gheshlaghi Azar, Bilal Piot, Nicolas Heess, Hado van Hasselt, Gregory Wayne, Satinder Singh, Doina Precup, Rémi Munos
2019	J	`jnl`	Hindsight Credit Assignment. CoRR Anna Harutyunyan, Will Dabney, Thomas Mesnard, Mohammad Gheshlaghi Azar, Bilal Piot, Nicolas Heess, Hado van Hasselt, Greg Wayne, Satinder Singh, Doina Precup, Rémi Munos
2019	A*	`conf`	Multiagent Evaluation under Incomplete Information. NeurIPS Mark Rowland, Shayegan Omidshafiei, Karl Tuyls, Julien Pérolat, Michal Valko, Georgios Piliouras, Rémi Munos
2019	J	`jnl`	Multiagent Evaluation under Incomplete Information. CoRR Mark Rowland, Shayegan Omidshafiei, Karl Tuyls, Julien Pérolat, Michal Valko, Georgios Piliouras, Rémi Munos
2019	J	`jnl`	Neural Replicator Dynamics. CoRR Shayegan Omidshafiei, Daniel Hennes, Dustin Morrill, Rémi Munos, Julien Pérolat, Marc Lanctot, Audrunas Gruslys, Jean-Baptiste Lespiau, Karl Tuyls
2019	A	`conf`	Observational Learning by Reinforcement Learning. AAMAS Diana Borsa, Nicolas Heess, Bilal Piot, Siqi Liu, Leonard Hasenclever, Rémi Munos, Olivier Pietquin
2019	J	`jnl`	Optimistic optimization of a Brownian. CoRR Jean-Bastien Grill, Michal Valko, Rémi Munos
2019	A*	`conf`	Planning in entropy-regularized Markov decision processes and games. NeurIPS Jean-Bastien Grill, Omar Darwiche Domingues, Pierre Ménard, Rémi Munos, Michal Valko
2019	—	`conf`	Recurrent Experience Replay in Distributed Reinforcement Learning. ICLR (Poster) Steven Kapturowski, Georg Ostrovski, John Quan, Rémi Munos, Will Dabney
2019	A*	`conf`	Statistics and Samples in Distributional Reinforcement Learning. ICML Mark Rowland, Robert Dadashi, Saurabh Kumar, Rémi Munos, Marc G. Bellemare, Will Dabney
2019	J	`jnl`	Statistics and Samples in Distributional Reinforcement Learning. CoRR Mark Rowland, Robert Dadashi, Saurabh Kumar, Rémi Munos, Marc G. Bellemare, Will Dabney
2019	A	`conf`	The Termination Critic. AISTATS Anna Harutyunyan, Will Dabney, Diana Borsa, Nicolas Heess, Rémi Munos, Doina Precup
2019	J	`jnl`	The Termination Critic. CoRR Anna Harutyunyan, Will Dabney, Diana Borsa, Nicolas Heess, Rémi Munos, Doina Precup
2019	J	`jnl`	Transfer in Deep Reinforcement Learning Using Successor Features and Generalised Policy Improvement. CoRR André Barreto, Diana Borsa, John Quan, Tom Schaul, David Silver, Matteo Hessel, Daniel J. Mankowitz, Augustin Zídek, Rémi Munos
2019	—	`conf`	Universal Successor Features Approximators. ICLR (Poster) Diana Borsa, André Barreto, John Quan, Daniel J. Mankowitz, Hado van Hasselt, Rémi Munos, David Silver, Tom Schaul
2019	J	`jnl`	World Discovery Models. CoRR Mohammad Gheshlaghi Azar, Bilal Piot, Bernardo A. Pires, Jean-Bastien Grill, Florent Altché, Rémi Munos
2019	J	`jnl`	α-Rank: Multi-Agent Evaluation by Evolution. CoRR Shayegan Omidshafiei, Christos H. Papadimitriou, Georgios Piliouras, Karl Tuyls, Mark Rowland, Jean-Baptiste Lespiau, Wojciech M. Czarnecki, Marc Lanctot, Julien Pérolat, Rémi Munos
2018	J	`jnl`	A Study on Overfitting in Deep Reinforcement Learning. CoRR Chiyuan Zhang, Oriol Vinyals, Rémi Munos, Samy Bengio
2018	A*	`conf`	Actor-Critic Policy Optimization in Partially Observable Multiagent Environments. NeurIPS Sriram Srinivasan, Marc Lanctot, Vinícius Flores Zambaldi, Julien Pérolat, Karl Tuyls, Rémi Munos, Michael Bowling
2018	J	`jnl`	Actor-Critic Policy Optimization in Partially Observable Multiagent Environments. CoRR Sriram Srinivasan, Marc Lanctot, Vinícius Flores Zambaldi, Julien Pérolat, Karl Tuyls, Rémi Munos, Michael Bowling
2018	A	`conf`	An Analysis of Categorical Distributional Reinforcement Learning. AISTATS Mark Rowland, Marc G. Bellemare, Will Dabney, Rémi Munos, Yee Whye Teh
2018	A*	`conf`	Autoregressive Quantile Networks for Generative Modeling. ICML Georg Ostrovski, Will Dabney, Rémi Munos
2018	J	`jnl`	Autoregressive Quantile Networks for Generative Modeling. CoRR Georg Ostrovski, Will Dabney, Rémi Munos
2018	J	`jnl`	Continuous-action planning for discounted infinite-horizon nonlinear optimal control with Lipschitz values. Autom. Lucian Busoniu, Elod Páll, Rémi Munos
2018	A*	`conf`	Distributional Reinforcement Learning With Quantile Regression. AAAI Will Dabney, Mark Rowland, Marc G. Bellemare, Rémi Munos
2018	A*	`conf`	IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures. ICML Lasse Espeholt, Hubert Soyer, Rémi Munos, Karen Simonyan, Volodymyr Mnih, Tom Ward, Yotam Doron, Vlad Firoiu, Tim Harley, Iain Dunning, Shane Legg, Koray Kavukcuoglu
2018	J	`jnl`	IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures. CoRR Lasse Espeholt, Hubert Soyer, Rémi Munos, Karen Simonyan, Volodymyr Mnih, Tom Ward, Yotam Doron, Vlad Firoiu, Tim Harley, Iain Dunning, Shane Legg, Koray Kavukcuoglu
2018	A*	`conf`	Implicit Quantile Networks for Distributional Reinforcement Learning. ICML Will Dabney, Georg Ostrovski, David Silver, Rémi Munos
2018	J	`jnl`	Implicit Quantile Networks for Distributional Reinforcement Learning. CoRR Will Dabney, Georg Ostrovski, David Silver, Rémi Munos
2018	A*	`conf`	Learning to Search with MCTSnets. ICML Arthur Guez, Theophane Weber, Ioannis Antonoglou, Karen Simonyan, Oriol Vinyals, Daan Wierstra, Rémi Munos, David Silver
2018	J	`jnl`	Learning to Search with MCTSnets. CoRR Arthur Guez, Théophane Weber, Ioannis Antonoglou, Karen Simonyan, Oriol Vinyals, Daan Wierstra, Rémi Munos, David Silver
2018	J	`jnl`	Low-pass Recurrent Neural Networks - A memory architecture for longer-term correlation discovery. CoRR Thomas S. Stepleton, Razvan Pascanu, Will Dabney, Siddhant M. Jayakumar, Hubert Soyer, Rémi Munos
2018	—	`conf`	Maximum a Posteriori Policy Optimisation. ICLR (Poster) Abbas Abdolmaleki, Jost Tobias Springenberg, Yuval Tassa, Rémi Munos, Nicolas Heess, Martin A. Riedmiller
2018	J	`jnl`	Maximum a Posteriori Policy Optimisation. CoRR Abbas Abdolmaleki, Jost Tobias Springenberg, Yuval Tassa, Rémi Munos, Nicolas Heess, Martin A. Riedmiller
2018	J	`jnl`	Neural Predictive Belief Representations. CoRR Zhaohan Daniel Guo, Mohammad Gheshlaghi Azar, Bilal Piot, Bernardo A. Pires, Toby Pohlen, Rémi Munos
2018	—	`conf`	Noisy Networks For Exploration. ICLR (Poster) Meire Fortunato, Mohammad Gheshlaghi Azar, Bilal Piot, Jacob Menick, Matteo Hessel, Ian Osband, Alex Graves, Volodymyr Mnih, Rémi Munos, Demis Hassabis, Olivier Pietquin, Charles Blundell, Shane Legg
2018	J	`jnl`	Observe and Look Further: Achieving Consistent Performance on Atari. CoRR Tobias Pohlen, Bilal Piot, Todd Hester, Mohammad Gheshlaghi Azar, Dan Horgan, David Budden, Gabriel Barth-Maron, Hado van Hasselt, John Quan, Mel Vecerík, Matteo Hessel, Rémi Munos, Olivier Pietquin
2018	A*	`conf`	Optimistic optimization of a Brownian. NeurIPS Jean-Bastien Grill, Michal Valko, Rémi Munos
2018	J	`jnl`	Optimistic planning with an adaptive number of action switches for near-optimal nonlinear control. Eng. Appl. Artif. Intell. Koppány Máthé, Lucian Busoniu, Rémi Munos, Bart De Schutter
2018	—	`conf`	The Reactor: A fast and sample-efficient Actor-Critic agent for Reinforcement Learning. ICLR (Poster) Audrunas Gruslys, Will Dabney, Mohammad Gheshlaghi Azar, Bilal Piot, Marc G. Bellemare, Rémi Munos
2018	A*	`conf`	The Uncertainty Bellman Equation and Exploration. ICML Brendan O'Donoghue, Ian Osband, Rémi Munos, Volodymyr Mnih
2018	A*	`conf`	Transfer in Deep Reinforcement Learning Using Successor Features and Generalised Policy Improvement. ICML André Barreto, Diana Borsa, John Quan, Tom Schaul, David Silver, Matteo Hessel, Daniel J. Mankowitz, Augustin Zídek, Rémi Munos
2018	J	`jnl`	Universal Successor Features Approximators. CoRR Diana Borsa, André Barreto, John Quan, Daniel J. Mankowitz, Rémi Munos, Hado van Hasselt, David Silver, Tom Schaul
2017	A*	`conf`	A Distributional Perspective on Reinforcement Learning. ICML Marc G. Bellemare, Will Dabney, Rémi Munos
2017	J	`jnl`	A Distributional Perspective on Reinforcement Learning. CoRR Marc G. Bellemare, Will Dabney, Rémi Munos
2017	A*	`conf`	Automated Curriculum Learning for Neural Networks. ICML Alex Graves, Marc G. Bellemare, Jacob Menick, Rémi Munos, Koray Kavukcuoglu
2017	J	`jnl`	Automated Curriculum Learning for Neural Networks. CoRR Alex Graves, Marc G. Bellemare, Jacob Menick, Rémi Munos, Koray Kavukcuoglu
2017	—	`conf`	Combining policy gradient and Q-learning. ICLR (Poster) Brendan O'Donoghue, Rémi Munos, Koray Kavukcuoglu, Volodymyr Mnih
2017	A*	`conf`	Count-Based Exploration with Neural Density Models. ICML Georg Ostrovski, Marc G. Bellemare, Aäron van den Oord, Rémi Munos
2017	J	`jnl`	Count-Based Exploration with Neural Density Models. CoRR Georg Ostrovski, Marc G. Bellemare, Aäron van den Oord, Rémi Munos
2017	J	`jnl`	Distributional Reinforcement Learning with Quantile Regression. CoRR Will Dabney, Mark Rowland, Marc G. Bellemare, Rémi Munos
2017	B	`conf`	Learning to reinforcement learn. CogSci Jane Wang, Zeb Kurth-Nelson, Hubert Soyer, Joel Z. Leibo, Dhruva Tirumala, Rémi Munos, Charles Blundell, Dharshan Kumaran, Matt M. Botvinick
2017	A*	`conf`	Minimax Regret Bounds for Reinforcement Learning. ICML Mohammad Gheshlaghi Azar, Ian Osband, Rémi Munos
2017	J	`jnl`	Minimax Regret Bounds for Reinforcement Learning. CoRR Mohammad Gheshlaghi Azar, Ian Osband, Rémi Munos
2017	J	`jnl`	Noisy Networks for Exploration. CoRR Meire Fortunato, Mohammad Gheshlaghi Azar, Bilal Piot, Jacob Menick, Ian Osband, Alex Graves, Vlad Mnih, Rémi Munos, Demis Hassabis, Olivier Pietquin, Charles Blundell, Shane Legg
2017	J	`jnl`	Observational Learning by Reinforcement Learning. CoRR Diana Borsa, Bilal Piot, Rémi Munos, Olivier Pietquin
2017	—	`conf`	Sample Efficient Actor-Critic with Experience Replay. ICLR (Poster) Ziyu Wang, Victor Bapst, Nicolas Heess, Volodymyr Mnih, Rémi Munos, Koray Kavukcuoglu, Nando de Freitas
2017	—	`conf`	Successor Features for Transfer in Reinforcement Learning. NIPS André Barreto, Will Dabney, Rémi Munos, Jonathan J. Hunt, Tom Schaul, David Silver, Hado van Hasselt
2017	J	`jnl`	The Cramer Distance as a Solution to Biased Wasserstein Gradients. CoRR Marc G. Bellemare, Ivo Danihelka, Will Dabney, Shakir Mohamed, Balaji Lakshminarayanan, Stephan Hoyer, Rémi Munos
2017	J	`jnl`	The Reactor: A Sample-Efficient Actor-Critic Architecture. CoRR Audrunas Gruslys, Mohammad Gheshlaghi Azar, Marc G. Bellemare, Rémi Munos
2017	J	`jnl`	The Uncertainty Bellman Equation and Exploration. CoRR Brendan O'Donoghue, Ian Osband, Rémi Munos, Volodymyr Mnih
2016	J	`jnl`	Analysis of Classification-based Policy Iteration Algorithms. J. Mach. Learn. Res. Alessandro Lazaric, Mohammad Ghavamzadeh, Rémi Munos
2016	—	`conf`	Blazing the trails before beating the path: Sample-efficient Monte-Carlo planning. NIPS Jean-Bastien Grill, Michal Valko, Rémi Munos
2016	C	`conf`	Discounted near-optimal control of general continuous-action nonlinear systems using optimistic planning. ACC Lucian Busoniu, Elod Páll, Rémi Munos
2016	A*	`conf`	Generalized Emphatic Temporal Difference Learning: Bias-Variance Analysis. AAAI Assaf Hallak, Aviv Tamar, Rémi Munos, Shie Mannor
2016	J	`jnl`	Guest Editors' foreword. Theor. Comput. Sci. Sanjay Jain, Rémi Munos, Frank Stephan, Thomas Zeugmann
2016	A*	`conf`	Increasing the Action Gap: New Operators for Reinforcement Learning. AAAI Marc G. Bellemare, Georg Ostrovski, Arthur Guez, Philip S. Thomas, Rémi Munos
2016	J	`jnl`	Learning to reinforcement learn. CoRR Jane X. Wang, Zeb Kurth-Nelson, Dhruva Tirumala, Hubert Soyer, Joel Z. Leibo, Rémi Munos, Charles Blundell, Dharshan Kumaran, Matthew M. Botvinick
2016	—	`conf`	Memory-Efficient Backpropagation Through Time. NIPS Audrunas Gruslys, Rémi Munos, Ivo Danihelka, Marc Lanctot, Alex Graves
2016	J	`jnl`	Memory-Efficient Backpropagation Through Time. CoRR Audrunas Gruslys, Rémi Munos, Ivo Danihelka, Marc Lanctot, Alex Graves
2016	J	`jnl`	PGQ: Combining policy gradient and Q-learning. CoRR Brendan O'Donoghue, Rémi Munos, Koray Kavukcuoglu, Volodymyr Mnih
2016	J	`jnl`	Q($λ$) with Off-Policy Corrections. CoRR Anna Harutyunyan, Marc G. Bellemare, Tom Stepleton, Rémi Munos
2016	B	`conf`	Q(λ) with Off-Policy Corrections. ALT Anna Harutyunyan, Marc G. Bellemare, Tom Stepleton, Rémi Munos
2016	—	`conf`	Safe and Efficient Off-Policy Reinforcement Learning. NIPS Rémi Munos, Tom Stepleton, Anna Harutyunyan, Marc G. Bellemare
2016	J	`jnl`	Safe and Efficient Off-Policy Reinforcement Learning. CoRR Rémi Munos, Tom Stepleton, Anna Harutyunyan, Marc G. Bellemare
2016	J	`jnl`	Sample Efficient Actor-Critic with Experience Replay. CoRR Ziyu Wang, Victor Bapst, Nicolas Heess, Volodymyr Mnih, Rémi Munos, Koray Kavukcuoglu, Nando de Freitas
2016	J	`jnl`	Successor Features for Transfer in Reinforcement Learning. CoRR André Barreto, Rémi Munos, Tom Schaul, David Silver
2016	—	`conf`	Unifying Count-Based Exploration and Intrinsic Motivation. NIPS Marc G. Bellemare, Sriram Srinivasan, Georg Ostrovski, Tom Schaul, David Saxton, Rémi Munos
2016	J	`jnl`	Unifying Count-Based Exploration and Intrinsic Motivation. CoRR Marc G. Bellemare, Sriram Srinivasan, Georg Ostrovski, Tom Schaul, David Saxton, Rémi Munos
2015	J	`jnl`	Adaptive strategy for stratified Monte Carlo sampling. J. Mach. Learn. Res. Alexandra Carpentier, Rémi Munos, András Antos
2015	—	`conf`	Black-box optimization of noisy functions with unknown smoothness. NIPS Jean-Bastien Grill, Michal Valko, Rémi Munos
2015	A*	`conf`	Cheap Bandits. ICML Manjesh Kumar Hanawal, Venkatesh Saligrama, Michal Valko, Rémi Munos
2015	J	`jnl`	Cheap Bandits. CoRR Manjesh Kumar Hanawal, Venkatesh Saligrama, Michal Valko, Rémi Munos
2015	A*	`conf`	Fast Gradient Descent for Drifting Least Squares Regression, with Application to Bandits. AAAI Nathaniel Korda, Prashanth L. A., Rémi Munos
2015	J	`jnl`	Generalized Emphatic Temporal Difference Learning: Bias-Variance Analysis. CoRR Assaf Hallak, Aviv Tamar, Rémi Munos, Shie Mannor
2015	J	`jnl`	Increasing the Action Gap: New Operators for Reinforcement Learning. CoRR Marc G. Bellemare, Georg Ostrovski, Arthur Guez, Philip S. Thomas, Rémi Munos
2015	A	`conf`	Toward Minimax Off-policy Value Estimation. AISTATS Lihong Li, Rémi Munos, Csaba Szepesvári
2015	J	`jnl`	Upper-Confidence-Bound Algorithms for Active Learning in Multi-Armed Bandits. CoRR Alexandra Carpentier, Alessandro Lazaric, Mohammad Ghavamzadeh, Rémi Munos, Peter Auer, András Antos
2014	—	`conf`	Active Regression by Stratification. NIPS Sivan Sabato, Rémi Munos
2014	J	`jnl`	Active Regression by Stratification. CoRR Sivan Sabato, Rémi Munos
2014	—	`conf`	An analysis of optimistic, best-first search for minimax sequential decision making. ADPRL Lucian Busoniu, Rémi Munos, Elod Páll
2014	J	`jnl`	Bandit Algorithms for Tree Search. CoRR Pierre-Arnaud Coquelin, Rémi Munos
2014	B	`conf`	Bandits attack function optimization. IEEE Congress on Evolutionary Computation Philippe Preux, Rémi Munos, Michal Valko
2014	—	`conf`	Best-Arm Identification in Linear Bandits. NIPS Marta Soare, Alessandro Lazaric, Rémi Munos
2014	J	`jnl`	Best-Arm Identification in Linear Bandits. CoRR Marta Soare, Alessandro Lazaric, Rémi Munos
2014	—	`conf`	Bounded Regret for Finite-Armed Structured Bandits. NIPS Tor Lattimore, Rémi Munos
2014	J	`jnl`	Bounded Regret for Finite-Armed Structured Bandits. CoRR Tor Lattimore, Rémi Munos
2014	—	`conf`	Efficient learning by implicit exploration in bandit problems with side observations. NIPS Tomás Kocák, Gergely Neu, Michal Valko, Rémi Munos
2014	—	`conf`	Fast LSTD Using Stochastic Approximation: Finite Time Analysis and Application to Traffic Control. ECML/PKDD (2) Prashanth L. A., Nathaniel Korda, Rémi Munos
2014	J	`jnl`	From Bandits to Monte-Carlo Tree Search: The Optimistic Principle Applied to Optimization and Planning. Found. Trends Mach. Learn. Rémi Munos
2014	J	`jnl`	Minimax number of strata for online stratified sampling: The case of noisy samples. Theor. Comput. Sci. Alexandra Carpentier, Rémi Munos
2014	J	`jnl`	On Minimax Optimal Offline Policy Evaluation. CoRR Lihong Li, Rémi Munos, Csaba Szepesvári
2014	—	`conf`	Optimistic Planning in Markov Decision Processes Using a Generative Model. NIPS Balázs Szörényi, Gunnar Kedenburg, Rémi Munos
2014	—	`conf`	Optimistic planning with a limited number of action switches for near-optimal nonlinear control. CDC Koppány Máthé, Lucian Busoniu, Rémi Munos, Bart De Schutter
2014	J	`jnl`	Regret bounds for restless Markov bandits. Theor. Comput. Sci. Ronald Ortner, Daniil Ryabko, Peter Auer, Rémi Munos
2014	A*	`conf`	Relative Upper Confidence Bound for the K-Armed Dueling Bandit Problem. ICML Masrour Zoghi, Shimon Whiteson, Rémi Munos, Maarten de Rijke
2014	A	`conf`	Relative confidence sampling for efficient on-line ranker evaluation. WSDM Masrour Zoghi, Shimon Whiteson, Maarten de Rijke, Rémi Munos
2014	A*	`conf`	Spectral Bandits for Smooth Graph Functions. ICML Michal Valko, Rémi Munos, Branislav Kveton, Tomás Kocák
2014	A*	`conf`	Spectral Thompson Sampling. AAAI Tomás Kocák, Michal Valko, Rémi Munos, Shipra Agrawal
2013	—	`conf`	Aggregating Optimistic Planning Trees for Solving Markov Decision Processes. NIPS Gunnar Kedenburg, Raphaël Fonteneau, Rémi Munos
2013	B	`ed.`	Algorithmic Learning Theory - 24th International Conference, ALT 2013, Singapore, October 6-9, 2013. Proceedings ALT Sanjay Jain, Rémi Munos, Frank Stephan, Thomas Zeugmann
2013	J	`jnl`	Analysis of stochastic approximation for efficient least squares regression and LSTD. CoRR Prashanth L. A., Nathaniel Korda, Rémi Munos
2013	B	`conf`	Editors' Introduction. ALT Sanjay Jain, Rémi Munos, Frank Stephan, Thomas Zeugmann
2013	A	`conf`	Finite-Time Analysis of Kernelised Contextual Bandits. UAI Michal Valko, Nathaniel Korda, Rémi Munos, Ilias N. Flaounas, Nello Cristianini
2013	J	`jnl`	Finite-Time Analysis of Kernelised Contextual Bandits. CoRR Michal Valko, Nathaniel Korda, Rémi Munos, Ilias N. Flaounas, Nello Cristianini
2013	J	`jnl`	Minimax PAC bounds on the sample complexity of reinforcement learning with a generative model. Mach. Learn. Mohammad Gheshlaghi Azar, Rémi Munos, Hilbert J. Kappen
2013	J	`jnl`	Online gradient descent for least squares regression: Non-asymptotic bounds and application to bandits. CoRR Nathaniel Korda, Prashanth L. A., Rémi Munos
2013	—	`conf`	Optimistic planning for belief-augmented Markov Decision Processes. ADPRL Raphaël Fonteneau, Lucian Busoniu, Rémi Munos
2013	—	`conf`	Optimistic planning for continuous-action deterministic systems. ADPRL Lucian Busoniu, Alexander Daniels, Rémi Munos, Robert Babuska
2013	J	`jnl`	Relative Upper Confidence Bound for the K-Armed Dueling Bandit Problem. CoRR Masrour Zoghi, Shimon Whiteson, Rémi Munos, Maarten de Rijke
2013	J	`jnl`	Risk-Aversion in Multi-armed Bandits CoRR Amir Sani, Alessandro Lazaric, Rémi Munos
2013	J	`jnl`	Selecting the State-Representation in Reinforcement Learning CoRR Odalric-Ambrym Maillard, Rémi Munos, Daniil Ryabko
2013	—	`conf`	Stochastic Simultaneous Optimistic Optimization. ICML (2) Michal Valko, Alexandra Carpentier, Rémi Munos
2013	—	`conf`	Thompson Sampling for 1-Dimensional Exponential Family Bandits. NIPS Nathaniel Korda, Emilie Kaufmann, Rémi Munos
2013	—	`conf`	Toward Optimal Stratification for Stratified Monte-Carlo Integration. ICML (2) Alexandra Carpentier, Rémi Munos
2012	—	`conf`	Adaptive Stratified Sampling for Monte-Carlo integration of Differentiable functions. NIPS Alexandra Carpentier, Rémi Munos
2012	—	`conf`	Bandit Algorithms boost Brain Computer Interfaces for motor-task selection of a brain-controlled button. NIPS Joan Fruitet, Alexandra Carpentier, Rémi Munos, Maureen Clerc
2012	A	`conf`	Bandit Theory meets Compressed Sensing for high dimensional Stochastic Linear Bandit. AISTATS Alexandra Carpentier, Rémi Munos
2012	J	`jnl`	Finite-sample analysis of least-squares policy iteration. J. Mach. Learn. Res. Alessandro Lazaric, Mohammad Ghavamzadeh, Rémi Munos
2012	J	`jnl`	Learning with stochastic inputs and adversarial outputs. J. Comput. Syst. Sci. Alessandro Lazaric, Rémi Munos
2012	—	`ch.`	Least-Squares Methods for Policy Iteration. Reinforcement Learning Lucian Busoniu, Alessandro Lazaric, Mohammad Ghavamzadeh, Rémi Munos, Robert Babuska, Bart De Schutter
2012	J	`jnl`	Linear regression with random projections. J. Mach. Learn. Res. Odalric-Ambrym Maillard, Rémi Munos
2012	B	`conf`	Minimax Number of Strata for Online Stratified Sampling Given Noisy Samples. ALT Alexandra Carpentier, Rémi Munos
2012	A*	`conf`	On the Sample Complexity of Reinforcement Learning with a Generative Model . ICML Mohammad Gheshlaghi Azar, Rémi Munos, Bert Kappen
2012	A	`conf`	Optimistic planning for Markov decision processes. AISTATS Lucian Busoniu, Rémi Munos
2012	J	`jnl`	Regret Bounds for Restless Markov Bandits CoRR Ronald Ortner, Daniil Ryabko, Peter Auer, Rémi Munos
2012	B	`conf`	Regret Bounds for Restless Markov Bandits. ALT Ronald Ortner, Daniil Ryabko, Peter Auer, Rémi Munos
2012	—	`conf`	Risk-Aversion in Multi-armed Bandits. NIPS Amir Sani, Alessandro Lazaric, Rémi Munos
2012	B	`conf`	Thompson Sampling: An Asymptotically Optimal Finite-Time Analysis. ALT Emilie Kaufmann, Nathaniel Korda, Rémi Munos
2012	J	`jnl`	Thompson Sampling: An Optimal Finite Time Analysis CoRR Emilie Kaufmann, Nathaniel Korda, Rémi Munos
2011	A*	`conf`	A Finite-Time Analysis of Multi-armed Bandits Problems with Kullback-Leibler Divergences. COLT Odalric-Ambrym Maillard, Rémi Munos, Gilles Stoltz
2011	A	`conf`	Adaptive Bandits: Towards the best history-dependent strategy. AISTATS Odalric-Ambrym Maillard, Rémi Munos
2011	—	`conf`	Finite Time Analysis of Stratified Sampling for Monte Carlo. NIPS Alexandra Carpentier, Rémi Munos
2011	A*	`conf`	Finite-Sample Analysis of Lasso-TD. ICML Mohammad Ghavamzadeh, Alessandro Lazaric, Rémi Munos, Matthew W. Hoffman
2011	—	`conf`	Optimistic Optimization of a Deterministic Function without the Knowledge of its Smoothness. NIPS Rémi Munos
2011	—	`conf`	Optimistic planning for sparsely stochastic systems. ADPRL Lucian Busoniu, Rémi Munos, Bart De Schutter, Robert Babuska
2011	J	`jnl`	Pure exploration in finitely-armed and continuous-armed bandits. Theor. Comput. Sci. Sébastien Bubeck, Rémi Munos, Gilles Stoltz
2011	—	`conf`	Regularized Least Squares Temporal Difference Learning with Nested ℓ2 and ℓ1 Penalization. EWRL Matthew W. Hoffman, Alessandro Lazaric, Mohammad Ghavamzadeh, Rémi Munos
2011	—	`conf`	Selecting the State-Representation in Reinforcement Learning. NIPS Odalric-Ambrym Maillard, Rémi Munos, Daniil Ryabko
2011	—	`conf`	Sparse Recovery with Brownian Sensing. NIPS Alexandra Carpentier, Odalric-Ambrym Maillard, Rémi Munos
2011	—	`conf`	Speedy Q-Learning. NIPS Mohammad Gheshlaghi Azar, Rémi Munos, Mohammad Ghavamzadeh, Hilbert J. Kappen
2011	B	`conf`	Upper-Confidence-Bound Algorithms for Active Learning in Multi-armed Bandits. ALT Alexandra Carpentier, Alessandro Lazaric, Mohammad Ghavamzadeh, Rémi Munos, Peter Auer
2010	A*	`conf`	Analysis of a Classification-based Policy Iteration Algorithm. ICML Alessandro Lazaric, Mohammad Ghavamzadeh, Rémi Munos
2010	A*	`conf`	Best Arm Identification in Multi-Armed Bandits. COLT Jean-Yves Audibert, Sébastien Bubeck, Rémi Munos
2010	—	`conf`	Error Propagation for Approximate Policy and Value Iteration. NIPS Amir Massoud Farahmand, Rémi Munos, Csaba Szepesvári
2010	A*	`conf`	Finite-Sample Analysis of LSTD. ICML Alessandro Lazaric, Mohammad Ghavamzadeh, Rémi Munos
2010	C	`conf`	Finite-sample Analysis of Bellman Residual Minimization. ACML Odalric-Ambrym Maillard, Rémi Munos, Alessandro Lazaric, Mohammad Ghavamzadeh
2010	—	`conf`	LSTD with Random Projections. NIPS Mohammad Ghavamzadeh, Alessandro Lazaric, Odalric-Ambrym Maillard, Rémi Munos
2010	—	`conf`	Online Learning in Adversarial Lipschitz Environments. ECML/PKDD (2) Odalric-Ambrym Maillard, Rémi Munos
2010	A*	`conf`	Open Loop Optimistic Planning. COLT Sébastien Bubeck, Rémi Munos
2010	—	`conf`	Scrambled Objects for Least-Squares Regression. NIPS Odalric-Ambrym Maillard, Rémi Munos
2010	J	`jnl`	X-Armed Bandits CoRR Sébastien Bubeck, Rémi Munos, Gilles Stoltz, Csaba Szepesvári
2009	—	`conf`	Compressed Least-Squares Regression. NIPS Odalric-Ambrym Maillard, Rémi Munos
2009	J	`jnl`	Exploration-exploitation tradeoff using variance estimates in multi-armed bandits. Theor. Comput. Sci. Jean-Yves Audibert, Rémi Munos, Csaba Szepesvári
2009	A*	`conf`	Hybrid Stochastic-Adversarial On-line Learning. COLT Alessandro Lazaric, Rémi Munos
2009	B	`conf`	Pure Exploration in Multi-armed Bandits Problems. ALT Sébastien Bubeck, Rémi Munos, Gilles Stoltz
2009	—	`conf`	Sensitivity analysis in HMMs with application to likelihood maximization. NIPS Pierre-Arnaud Coquelin, Romain Deguest, Rémi Munos
2009	A*	`conf`	Workshop summary: On-line learning with limited feedback. ICML Jean-Yves Audibert, Peter Auer, Alessandro Lazaric, Rémi Munos, Daniil Ryabko, Csaba Szepesvári
2008	A	`conf`	Adaptive play in Texas Hold'em Poker. ECAI Raphaël Maîtrepierre, Jérémie Mary, Rémi Munos
2008	—	`conf`	Algorithms for Infinitely Many-Armed Bandits. NIPS Yizao Wang, Jean-Yves Audibert, Rémi Munos
2008	J	`jnl`	Finite-Time Bounds for Fitted Value Iteration. J. Mach. Learn. Res. Rémi Munos, Csaba Szepesvári
2008	J	`jnl`	Learning near-optimal policies with Bellman-residual minimization based fitted policy iteration and a single sample path. Mach. Learn. András Antos, Csaba Szepesvári, Rémi Munos
2008	—	`conf`	Online Optimization in X-Armed Bandits. NIPS Sébastien Bubeck, Rémi Munos, Gilles Stoltz, Csaba Szepesvári
2008	—	`conf`	Optimistic Planning of Deterministic Systems. EWRL Jean-François Hren, Rémi Munos
2008	—	`conf`	Particle Filter-based Policy Gradient in POMDPs. NIPS Pierre-Arnaud Coquelin, Romain Deguest, Rémi Munos
2008	J	`jnl`	Pure Exploration for Multi-Armed Bandit Problems CoRR Sébastien Bubeck, Rémi Munos, Gilles Stoltz
2008	—	`ed.`	Recent Advances in Reinforcement Learning, 8th European Workshop, EWRL 2008, Villeneuve d'Ascq, France, June 30 - July 3, 2008, Revised and Selected Papers EWRL Sertan Girgin, Manuel Loth, Rémi Munos, Philippe Preux, Daniil Ryabko
2007	J	`jnl`	Analyse en norme Lp de l'algorithme d'itérations sur les valeurs avec approximations. Rev. d'Intelligence Artif. Rémi Munos
2007	J	`jnl`	Bandit Algorithms for Tree Search CoRR Pierre-Arnaud Coquelin, Rémi Munos
2007	A	`conf`	Bandit Algorithms for Tree Search. UAI Pierre-Arnaud Coquelin, Rémi Munos
2007	—	`conf`	Fitted Q-iteration in continuous action-space MDPs. NIPS András Antos, Rémi Munos, Csaba Szepesvári
2007	J	`jnl`	Performance Bounds in L SIAM J. Control. Optim. Rémi Munos
2007	B	`conf`	Tuning Bandit Algorithms in Stochastic Environments. ALT Jean-Yves Audibert, Rémi Munos, Csaba Szepesvári
2006	J	`jnl`	Geometric Variance Reduction in Markov Chains: Application to Value Function and Gradient Estimation. J. Mach. Learn. Res. Rémi Munos
2006	A*	`conf`	Learning Near-Optimal Policies with Bellman-Residual Minimization Based Fitted Policy Iteration and a Single Sample Path. COLT András Antos, Csaba Szepesvári, Rémi Munos
2006	J	`jnl`	Policy Gradient in Continuous Time. J. Mach. Learn. Res. Rémi Munos
2005	A*	`conf`	Error Bounds for Approximate Value Iteration. AAAI Rémi Munos
2005	A*	`conf`	Finite time bounds for sampling based fitted value iteration. ICML Csaba Szepesvári, Rémi Munos
2005	A*	`conf`	Geometric Variance Reduction in Markov Chains. Application to Value Function and Gradient Estimation. AAAI Rémi Munos
2005	—	`conf`	Policy gradient in continuous time. CAP Rémi Munos
2005	J	`jnl`	Sensitivity Analysis Using It[o-circumflex]--Malliavin Calculus and Martingales, and Application to Stochastic Optimal Control. SIAM J. Control. Optim. Emmanuel Gobet, Rémi Munos
2003	A*	`conf`	Error Bounds for Approximate Policy Iteration. ICML Rémi Munos
2002	J	`jnl`	Variable Resolution Discretization in Optimal Control. Mach. Learn. Rémi Munos, Andrew W. Moore
2001	—	`conf`	Efficient Resources Allocation for Markov Decision Processes. NIPS Rémi Munos
2000	J	`jnl`	A Study of Reinforcement Learning in the Continuous Case by the Means of Viscosity Solutions. Mach. Learn. Rémi Munos
2000	A*	`conf`	Rates of Convergence for Variable Resolution Schemes in Optimal Control. ICML Rémi Munos, Andrew W. Moore
1999	B	`conf`	Gradient descent approaches to neural-net-based solutions of the Hamilton-Jacobi-Bellman equation. IJCNN Rémi Munos, Leemon C. Baird III, Andrew W. Moore
1999	A*	`conf`	Variable Resolution Discretization for High-Accuracy Solutions of Optimal Control Problems. IJCAI Rémi Munos, Andrew W. Moore
1998	—	`conf`	A General Convergence Method for Reinforcement Learning in the Continuous Case. ECML Rémi Munos
1998	—	`conf`	Barycentric Interpolators for Continuous Space and Time Reinforcement Learning. NIPS Rémi Munos, Andrew W. Moore
1997	—	`conf`	A Convergent Reinforcement Learning Algorithm in the Continuous Case Based on a Finite Difference Method. IJCAI (2) Rémi Munos
1997	—	`conf`	Finite-Element Methods with Local Triangulation Refinement for Continuous Reimforcement Learning Problems. ECML Rémi Munos
1997	—	`conf`	Reinforcement Learning for Continuous Stochastic Control Problems. NIPS Rémi Munos, Paul Bourgine
1996	A*	`conf`	A Convergent Reinforcement Learning Algorithm in the Continuous Case: The Finite-Element Reinforcement Learning. ICML Rémi Munos