A Convergent Reinforcement Learning Algorithm in the Continuous Case: The Finite-Element Reinforcement Learning. - Rémi Munos (ICML, 1996)

Finite-Element Methods with Local Triangulation Refinement for Continuous Reimforcement Learning Problems. - Rémi Munos (ECML, 1997)

A Convergent Reinforcement Learning Algorithm in the Continuous Case Based on a Finite Difference Method. - Rémi Munos (IJCAI (2), 1997)

Reinforcement Learning for Continuous Stochastic Control Problems. - Rémi Munos; Paul Bourgine (NIPS, 1997)

A General Convergence Method for Reinforcement Learning in the Continuous Case. - Rémi Munos (ECML, 1998)

Barycentric Interpolators for Continuous Space and Time Reinforcement Learning. - Rémi Munos; Andrew W. Moore 001 (NIPS, 1998)

Variable Resolution Discretization for High-Accuracy Solutions of Optimal Control Problems. - Rémi Munos; Andrew W. Moore 001 (IJCAI, 1999)

Gradient descent approaches to neural-net-based solutions of the Hamilton-Jacobi-Bellman equation. - Rémi Munos; Leemon C. Baird III; Andrew W. Moore 001 (IJCNN, 1999)

Rates of Convergence for Variable Resolution Schemes in Optimal Control. - Rémi Munos; Andrew W. Moore 001 (ICML, 2000)

A Study of Reinforcement Learning in the Continuous Case by the Means of Viscosity Solutions. - Rémi Munos (Mach. Learn., 2000)

Efficient Resources Allocation for Markov Decision Processes. - Rémi Munos (NIPS, 2001)

Variable Resolution Discretization in Optimal Control. - Rémi Munos; Andrew W. Moore 001 (Mach. Learn., 2002)

Error Bounds for Approximate Policy Iteration. - Rémi Munos (ICML, 2003)

Error Bounds for Approximate Value Iteration. - Rémi Munos (AAAI, 2005)

Finite time bounds for sampling based fitted value iteration. - Csaba Szepesvári; Rémi Munos (ICML, 2005)

Sensitivity Analysis Using It[o-circumflex]–Malliavin Calculus and Martingales, and Application to Stochastic Optimal Control. - Emmanuel Gobet; Rémi Munos (SIAM J. Control. Optim., 2005)

Geometric Variance Reduction in Markov Chains: Application to Value Function and Gradient Estimation. - Rémi Munos (J. Mach. Learn. Res., 2006)

Policy Gradient in Continuous Time. - Rémi Munos (J. Mach. Learn. Res., 2006)

Tuning Bandit Algorithms in Stochastic Environments. - Jean-Yves Audibert; Rémi Munos; Csaba Szepesvári (ALT, 2007)

Fitted Q-iteration in continuous action-space MDPs. - András Antos; Rémi Munos; Csaba Szepesvári (NIPS, 2007)

Bandit Algorithms for Tree Search. - Pierre-Arnaud Coquelin; Rémi Munos (UAI, 2007)

Analyse en norme Lp de l'algorithme d'itérations sur les valeurs avec approximations. - Rémi Munos (Rev. d'Intelligence Artif., 2007)

Performance Bounds in Lp-norm for Approximate Value Iteration. - Rémi Munos (SIAM J. Control. Optim., 2007)

Adaptive play in Texas Hold'em Poker. - Raphaël Maîtrepierre; Jérémie Mary; Rémi Munos (ECAI, 2008)

Optimistic Planning of Deterministic Systems. - Jean-François Hren; Rémi Munos (EWRL, 2008)

Online Optimization in X-Armed Bandits. - Sébastien Bubeck; Rémi Munos; Gilles Stoltz; Csaba Szepesvári (NIPS, 2008)

Particle Filter-based Policy Gradient in POMDPs. - Pierre-Arnaud Coquelin; Romain Deguest; Rémi Munos (NIPS, 2008)

Algorithms for Infinitely Many-Armed Bandits. - Yizao Wang; Jean-Yves Audibert; Rémi Munos (NIPS, 2008)

Pure Exploration for Multi-Armed Bandit Problems - Sébastien Bubeck; Rémi Munos; Gilles Stoltz (CoRR, 2008)

Finite-Time Bounds for Fitted Value Iteration. - Rémi Munos; Csaba Szepesvári (J. Mach. Learn. Res., 2008)

Learning near-optimal policies with Bellman-residual minimization based fitted policy iteration and a single sample path. - András Antos; Csaba Szepesvári; Rémi Munos (Mach. Learn., 2008)

Pure Exploration in Multi-armed Bandits Problems. - Sébastien Bubeck; Rémi Munos; Gilles Stoltz (ALT, 2009)

Hybrid Stochastic-Adversarial On-line Learning. - Alessandro Lazaric; Rémi Munos (COLT, 2009)

Workshop summary: On-line learning with limited feedback. - Jean-Yves Audibert; Peter Auer; Alessandro Lazaric; Rémi Munos; Daniil Ryabko; Csaba Szepesvári (ICML, 2009)

Sensitivity analysis in HMMs with application to likelihood maximization. - Pierre-Arnaud Coquelin; Romain Deguest; Rémi Munos (NIPS, 2009)

Compressed Least-Squares Regression. - Odalric-Ambrym Maillard; Rémi Munos (NIPS, 2009)

Exploration-exploitation tradeoff using variance estimates in multi-armed bandits. - Jean-Yves Audibert; Rémi Munos; Csaba Szepesvári (Theor. Comput. Sci., 2009)

Best Arm Identification in Multi-Armed Bandits. - Jean-Yves Audibert; Sébastien Bubeck; Rémi Munos (COLT, 2010)

Open Loop Optimistic Planning. - Sébastien Bubeck; Rémi Munos (COLT, 2010)

Analysis of a Classification-based Policy Iteration Algorithm. - Alessandro Lazaric; Mohammad Ghavamzadeh; Rémi Munos (ICML, 2010)

Finite-Sample Analysis of LSTD. - Alessandro Lazaric; Mohammad Ghavamzadeh; Rémi Munos (ICML, 2010)

Error Propagation for Approximate Policy and Value Iteration. - Amir Massoud Farahmand; Rémi Munos; Csaba Szepesvári (NIPS, 2010)

LSTD with Random Projections. - Mohammad Ghavamzadeh; Alessandro Lazaric; Odalric-Ambrym Maillard; Rémi Munos (NIPS, 2010)

Scrambled Objects for Least-Squares Regression. - Odalric-Ambrym Maillard; Rémi Munos (NIPS, 2010)

Online Learning in Adversarial Lipschitz Environments. - Odalric-Ambrym Maillard; Rémi Munos (ECML/PKDD (2), 2010)

X-Armed Bandits - Sébastien Bubeck; Rémi Munos; Gilles Stoltz; Csaba Szepesvári (CoRR, 2010)

Finite-sample Analysis of Bellman Residual Minimization. - Odalric-Ambrym Maillard; Rémi Munos; Alessandro Lazaric; Mohammad Ghavamzadeh (ACML, 2010)

Optimistic planning for sparsely stochastic systems. - Lucian Busoniu; Rémi Munos; Bart De Schutter; Robert Babuska (ADPRL, 2011)

Upper-Confidence-Bound Algorithms for Active Learning in Multi-armed Bandits. - Alexandra Carpentier; Alessandro Lazaric; Mohammad Ghavamzadeh; Rémi Munos; Peter Auer (ALT, 2011)

Regularized Least Squares Temporal Difference Learning with Nested ℓ2 and ℓ1 Penalization. - Matthew W. Hoffman; Alessandro Lazaric; Mohammad Ghavamzadeh; Rémi Munos (EWRL, 2011)

Finite-Sample Analysis of Lasso-TD. - Mohammad Ghavamzadeh; Alessandro Lazaric; Rémi Munos; Matthew W. Hoffman (ICML, 2011)

Speedy Q-Learning. - Mohammad Gheshlaghi Azar; Rémi Munos; Mohammad Ghavamzadeh; Hilbert J. Kappen (NIPS, 2011)

Finite Time Analysis of Stratified Sampling for Monte Carlo. - Alexandra Carpentier; Rémi Munos (NIPS, 2011)

Sparse Recovery with Brownian Sensing. - Alexandra Carpentier; Odalric-Ambrym Maillard; Rémi Munos (NIPS, 2011)

Selecting the State-Representation in Reinforcement Learning. - Odalric-Ambrym Maillard; Rémi Munos; Daniil Ryabko (NIPS, 2011)

Optimistic Optimization of a Deterministic Function without the Knowledge of its Smoothness. - Rémi Munos (NIPS, 2011)

X-Armed Bandits. - Sébastien Bubeck; Rémi Munos; Gilles Stoltz; Csaba Szepesvári (J. Mach. Learn. Res., 2011)

Adaptive Bandits: Towards the best history-dependent strategy. - Odalric-Ambrym Maillard; Rémi Munos (AISTATS, 2011)

A Finite-Time Analysis of Multi-armed Bandits Problems with Kullback-Leibler Divergences. - Odalric-Ambrym Maillard; Rémi Munos; Gilles Stoltz (COLT, 2011)

Pure exploration in finitely-armed and continuous-armed bandits. - Sébastien Bubeck; Rémi Munos; Gilles Stoltz (Theor. Comput. Sci., 2011)

Least-Squares Methods for Policy Iteration. - Lucian Busoniu; Alessandro Lazaric; Mohammad Ghavamzadeh; Rémi Munos; Robert Babuska; Bart De Schutter (Reinforcement Learning, 2012)

Minimax Number of Strata for Online Stratified Sampling Given Noisy Samples. - Alexandra Carpentier; Rémi Munos (ALT, 2012)

Thompson Sampling: An Asymptotically Optimal Finite-Time Analysis. - Emilie Kaufmann; Nathaniel Korda; Rémi Munos (ALT, 2012)

On the Sample Complexity of Reinforcement Learning with a Generative Model . - Mohammad Gheshlaghi Azar; Rémi Munos; Bert Kappen (ICML, 2012)

Adaptive Stratified Sampling for Monte-Carlo integration of Differentiable functions. - Alexandra Carpentier; Rémi Munos (NIPS, 2012)

Bandit Algorithms boost Brain Computer Interfaces for motor-task selection of a brain-controlled button. - Joan Fruitet; Alexandra Carpentier; Rémi Munos; Maureen Clerc (NIPS, 2012)

Risk-Aversion in Multi-armed Bandits. - Amir Sani; Alessandro Lazaric; Rémi Munos (NIPS, 2012)

Thompson Sampling: An Optimal Finite Time Analysis - Emilie Kaufmann; Nathaniel Korda; Rémi Munos (CoRR, 2012)

Learning with stochastic inputs and adversarial outputs. - Alessandro Lazaric; Rémi Munos (J. Comput. Syst. Sci., 2012)

Optimistic planning for Markov decision processes. - Lucian Busoniu; Rémi Munos (AISTATS, 2012)

Bandit Theory meets Compressed Sensing for high dimensional Stochastic Linear Bandit. - Alexandra Carpentier; Rémi Munos (AISTATS, 2012)

Finite-sample analysis of least-squares policy iteration. - Alessandro Lazaric; Mohammad Ghavamzadeh; Rémi Munos (J. Mach. Learn. Res., 2012)

Linear regression with random projections. - Odalric-Ambrym Maillard; Rémi Munos (J. Mach. Learn. Res., 2012)

Optimistic planning for continuous-action deterministic systems. - Lucian Busoniu; Alexander Daniels; Rémi Munos; Robert Babuska (ADPRL, 2013)

Optimistic planning for belief-augmented Markov Decision Processes. - Raphaël Fonteneau; Lucian Busoniu; Rémi Munos (ADPRL, 2013)

Editors' Introduction. - Sanjay Jain 001; Rémi Munos; Frank Stephan 001; Thomas Zeugmann (ALT, 2013)

Toward Optimal Stratification for Stratified Monte-Carlo Integration. - Alexandra Carpentier; Rémi Munos (ICML (2), 2013)

Stochastic Simultaneous Optimistic Optimization. - Michal Valko; Alexandra Carpentier; Rémi Munos (ICML (2), 2013)

Aggregating Optimistic Planning Trees for Solving Markov Decision Processes. - Gunnar Kedenburg; Raphaël Fonteneau; Rémi Munos (NIPS, 2013)

Thompson Sampling for 1-Dimensional Exponential Family Bandits. - Nathaniel Korda; Emilie Kaufmann; Rémi Munos (NIPS, 2013)

Finite-Time Analysis of Kernelised Contextual Bandits. - Michal Valko; Nathaniel Korda; Rémi Munos; Ilias N. Flaounas; Nello Cristianini (UAI, 2013)

Online gradient descent for least squares regression: Non-asymptotic bounds and application to bandits. - Nathaniel Korda; Prashanth L. A.; Rémi Munos (CoRR, 2013)

Analysis of stochastic approximation for efficient least squares regression and LSTD. - Prashanth L. A.; Nathaniel Korda; Rémi Munos (CoRR, 2013)

Minimax PAC bounds on the sample complexity of reinforcement learning with a generative model. - Mohammad Gheshlaghi Azar; Rémi Munos; Hilbert J. Kappen (Mach. Learn., 2013)

Spectral Thompson Sampling. - Tomás Kocák; Michal Valko; Rémi Munos; Shipra Agrawal 001 (AAAI, 2014)

An analysis of optimistic, best-first search for minimax sequential decision making. - Lucian Busoniu; Rémi Munos; Elod Páll (ADPRL, 2014)

Optimistic planning with a limited number of action switches for near-optimal nonlinear control. - Koppány Máthé; Lucian Busoniu; Rémi Munos; Bart De Schutter (CDC, 2014)

Bandits attack function optimization. - Philippe Preux; Rémi Munos; Michal Valko (IEEE Congress on Evolutionary Computation, 2014)

Spectral Bandits for Smooth Graph Functions. - Michal Valko; Rémi Munos; Branislav Kveton; Tomás Kocák (ICML, 2014)

Relative Upper Confidence Bound for the K-Armed Dueling Bandit Problem. - Masrour Zoghi; Shimon Whiteson; Rémi Munos; Maarten de Rijke (ICML, 2014)

Efficient learning by implicit exploration in bandit problems with side observations. - Tomás Kocák; Gergely Neu; Michal Valko; Rémi Munos (NIPS, 2014)

Bounded Regret for Finite-Armed Structured Bandits. - Tor Lattimore; Rémi Munos (NIPS, 2014)

Active Regression by Stratification. - Sivan Sabato; Rémi Munos (NIPS, 2014)

Best-Arm Identification in Linear Bandits. - Marta Soare; Alessandro Lazaric; Rémi Munos (NIPS, 2014)

Optimistic Planning in Markov Decision Processes Using a Generative Model. - Balázs Szörényi; Gunnar Kedenburg; Rémi Munos (NIPS, 2014)

Fast LSTD Using Stochastic Approximation: Finite Time Analysis and Application to Traffic Control. - Prashanth L. A.; Nathaniel Korda; Rémi Munos (ECML/PKDD (2), 2014)

Relative confidence sampling for efficient on-line ranker evaluation. - Masrour Zoghi; Shimon Whiteson; Maarten de Rijke; Rémi Munos (WSDM, 2014)

On Minimax Optimal Offline Policy Evaluation. - Lihong Li 001; Rémi Munos; Csaba Szepesvári (CoRR, 2014)

From Bandits to Monte-Carlo Tree Search: The Optimistic Principle Applied to Optimization and Planning. - Rémi Munos (Found. Trends Mach. Learn., 2014)

Minimax number of strata for online stratified sampling: The case of noisy samples. - Alexandra Carpentier; Rémi Munos (Theor. Comput. Sci., 2014)

Regret bounds for restless Markov bandits. - Ronald Ortner; Daniil Ryabko; Peter Auer; Rémi Munos (Theor. Comput. Sci., 2014)

Fast Gradient Descent for Drifting Least Squares Regression, with Application to Bandits. - Nathaniel Korda; Prashanth L. A.; Rémi Munos (AAAI, 2015)

Toward Minimax Off-policy Value Estimation. - Lihong Li 001; Rémi Munos; Csaba Szepesvári (AISTATS, 2015)

Cheap Bandits. - Manjesh Kumar Hanawal; Venkatesh Saligrama; Michal Valko; Rémi Munos (ICML, 2015)

Black-box optimization of noisy functions with unknown smoothness. - Jean-Bastien Grill; Michal Valko; Rémi Munos (NIPS, 2015)

Upper-Confidence-Bound Algorithms for Active Learning in Multi-Armed Bandits. - Alexandra Carpentier; Alessandro Lazaric; Mohammad Ghavamzadeh; Rémi Munos; Peter Auer; András Antos (CoRR, 2015)

Adaptive strategy for stratified Monte Carlo sampling. - Alexandra Carpentier; Rémi Munos; András Antos (J. Mach. Learn. Res., 2015)

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

Generalized Emphatic Temporal Difference Learning: Bias-Variance Analysis. - Assaf Hallak; Aviv Tamar; Rémi Munos; Shie Mannor (AAAI, 2016)

Q(λ) with Off-Policy Corrections. - Anna Harutyunyan; Marc G. Bellemare; Tom Stepleton; Rémi Munos (ALT, 2016)

Discounted near-optimal control of general continuous-action nonlinear systems using optimistic planning. - Lucian Busoniu; Elod Páll; Rémi Munos (ACC, 2016)

Unifying Count-Based Exploration and Intrinsic Motivation. - Marc G. Bellemare; Sriram Srinivasan 005; Georg Ostrovski; Tom Schaul; David Saxton; Rémi Munos (NIPS, 2016)

Blazing the trails before beating the path: Sample-efficient Monte-Carlo planning. - Jean-Bastien Grill; Michal Valko; Rémi Munos (NIPS, 2016)

Memory-Efficient Backpropagation Through Time. - Audrunas Gruslys; Rémi Munos; Ivo Danihelka; Marc Lanctot; Alex Graves (NIPS, 2016)

Safe and Efficient Off-Policy Reinforcement Learning. - Rémi Munos; Tom Stepleton; Anna Harutyunyan; Marc G. Bellemare (NIPS, 2016)

Successor Features for Transfer in Reinforcement Learning. - André Barreto 001; Rémi Munos; Tom Schaul; David Silver (CoRR, 2016)

PGQ: Combining policy gradient and Q-learning. - Brendan O'Donoghue; Rémi Munos; Koray Kavukcuoglu; Volodymyr Mnih (CoRR, 2016)

Analysis of Classification-based Policy Iteration Algorithms. - Alessandro Lazaric; Mohammad Ghavamzadeh; Rémi Munos (J. Mach. Learn. Res., 2016)

Guest Editors' foreword. - Sanjay Jain 001; Rémi Munos; Frank Stephan 001; Thomas Zeugmann (Theor. Comput. Sci., 2016)

Learning to reinforcement learn. - Jane Wang 001; Zeb Kurth-Nelson; Hubert Soyer; Joel Z. Leibo; Dhruva Tirumala; Rémi Munos; Charles Blundell; Dharshan Kumaran; Matt M. Botvinick (CogSci, 2017)

Sample Efficient Actor-Critic with Experience Replay. - Ziyu Wang 001; Victor Bapst; Nicolas Heess; Volodymyr Mnih; Rémi Munos; Koray Kavukcuoglu; Nando de Freitas (ICLR (Poster), 2017)

Combining policy gradient and Q-learning. - Brendan O'Donoghue; Rémi Munos; Koray Kavukcuoglu; Volodymyr Mnih (ICLR (Poster), 2017)

Minimax Regret Bounds for Reinforcement Learning. - Mohammad Gheshlaghi Azar; Ian Osband; Rémi Munos (ICML, 2017)

A Distributional Perspective on Reinforcement Learning. - Marc G. Bellemare; Will Dabney; Rémi Munos (ICML, 2017)

Automated Curriculum Learning for Neural Networks. - Alex Graves; Marc G. Bellemare; Jacob Menick; Rémi Munos; Koray Kavukcuoglu (ICML, 2017)

Count-Based Exploration with Neural Density Models. - Georg Ostrovski; Marc G. Bellemare; Aäron van den Oord; Rémi Munos (ICML, 2017)

Successor Features for Transfer in Reinforcement Learning. - André Barreto 001; Will Dabney; Rémi Munos; Jonathan J. Hunt; Tom Schaul; David Silver; Hado van Hasselt (NIPS, 2017)

The Cramer Distance as a Solution to Biased Wasserstein Gradients. - Marc G. Bellemare; Ivo Danihelka; Will Dabney; Shakir Mohamed; Balaji Lakshminarayanan; Stephan Hoyer; Rémi Munos (CoRR, 2017)

Observational Learning by Reinforcement Learning. - Diana Borsa; Bilal Piot; Rémi Munos; Olivier Pietquin (CoRR, 2017)

Noisy Networks for Exploration. - 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 (CoRR, 2017)

The Reactor: A Sample-Efficient Actor-Critic Architecture. - Audrunas Gruslys; Mohammad Gheshlaghi Azar; Marc G. Bellemare; Rémi Munos (CoRR, 2017)

Distributional Reinforcement Learning With Quantile Regression. - Will Dabney; Mark Rowland 001; Marc G. Bellemare; Rémi Munos (AAAI, 2018)

An Analysis of Categorical Distributional Reinforcement Learning. - Mark Rowland 001; Marc G. Bellemare; Will Dabney; Rémi Munos; Yee Whye Teh (AISTATS, 2018)

Maximum a Posteriori Policy Optimisation. - Abbas Abdolmaleki; Jost Tobias Springenberg; Yuval Tassa; Rémi Munos; Nicolas Heess; Martin A. Riedmiller (ICLR (Poster), 2018)

Noisy Networks For Exploration. - 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; et al. (ICLR (Poster), 2018)

The Reactor: A fast and sample-efficient Actor-Critic agent for Reinforcement Learning. - Audrunas Gruslys; Will Dabney; Mohammad Gheshlaghi Azar; Bilal Piot; Marc G. Bellemare; Rémi Munos (ICLR (Poster), 2018)

Transfer in Deep Reinforcement Learning Using Successor Features and Generalised Policy Improvement. - André Barreto 001; Diana Borsa; John Quan; Tom Schaul; David Silver; Matteo Hessel; Daniel J. Mankowitz; Augustin Zídek; Rémi Munos (ICML, 2018)

Implicit Quantile Networks for Distributional Reinforcement Learning. - Will Dabney; Georg Ostrovski; David Silver; Rémi Munos (ICML, 2018)

IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures. - Lasse Espeholt; Hubert Soyer; Rémi Munos; Karen Simonyan; Volodymyr Mnih; Tom Ward; Yotam Doron; Vlad Firoiu; Tim Harley; Iain Dunning; Shane Legg; Koray Kavukcuoglu (ICML, 2018)

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

The Uncertainty Bellman Equation and Exploration. - Brendan O'Donoghue; Ian Osband; Rémi Munos; Volodymyr Mnih (ICML, 2018)

Autoregressive Quantile Networks for Generative Modeling. - Georg Ostrovski; Will Dabney; Rémi Munos (ICML, 2018)

Optimistic optimization of a Brownian. - Jean-Bastien Grill; Michal Valko; Rémi Munos (NeurIPS, 2018)

Actor-Critic Policy Optimization in Partially Observable Multiagent Environments. - Sriram Srinivasan 005; Marc Lanctot; Vinícius Flores Zambaldi; Julien Pérolat; Karl Tuyls; Rémi Munos; Michael Bowling (NeurIPS, 2018)

Continuous-action planning for discounted infinite-horizon nonlinear optimal control with Lipschitz values. - Lucian Busoniu; Elod Páll; Rémi Munos (Autom., 2018)

A Study on Overfitting in Deep Reinforcement Learning. - Chiyuan Zhang; Oriol Vinyals; Rémi Munos; Samy Bengio (CoRR, 2018)

Low-pass Recurrent Neural Networks - A memory architecture for longer-term correlation discovery. - Thomas S. Stepleton; Razvan Pascanu; Will Dabney; Siddhant M. Jayakumar; Hubert Soyer; Rémi Munos (CoRR, 2018)

Observe and Look Further: Achieving Consistent Performance on Atari. - 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; et al. (CoRR, 2018)

Neural Predictive Belief Representations. - Zhaohan Daniel Guo; Mohammad Gheshlaghi Azar; Bilal Piot; Bernardo A. Pires; Toby Pohlen; Rémi Munos (CoRR, 2018)

Optimistic planning with an adaptive number of action switches for near-optimal nonlinear control. - Koppány Máthé; Lucian Busoniu; Rémi Munos; Bart De Schutter (Eng. Appl. Artif. Intell., 2018)

The Termination Critic. - Anna Harutyunyan; Will Dabney; Diana Borsa; Nicolas Heess; Rémi Munos; Doina Precup (AISTATS, 2019)

Observational Learning by Reinforcement Learning. - Diana Borsa; Nicolas Heess; Bilal Piot; Siqi Liu 002; Leonard Hasenclever; Rémi Munos; Olivier Pietquin (AAMAS, 2019)

Universal Successor Features Approximators. - Diana Borsa; André Barreto 001; John Quan; Daniel J. Mankowitz; Hado van Hasselt; Rémi Munos; David Silver; Tom Schaul (ICLR (Poster), 2019)

Recurrent Experience Replay in Distributed Reinforcement Learning. - Steven Kapturowski; Georg Ostrovski; John Quan; Rémi Munos; Will Dabney (ICLR (Poster), 2019)

Statistics and Samples in Distributional Reinforcement Learning. - Mark Rowland 001; Robert Dadashi; Saurabh Kumar 004; Rémi Munos; Marc G. Bellemare; Will Dabney (ICML, 2019)

Planning in entropy-regularized Markov decision processes and games. - Jean-Bastien Grill; Omar Darwiche Domingues; Pierre Ménard; Rémi Munos; Michal Valko (NeurIPS, 2019)

Hindsight Credit Assignment. - Anna Harutyunyan; Will Dabney; Thomas Mesnard; Mohammad Gheshlaghi Azar; Bilal Piot; Nicolas Heess; Hado van Hasselt; Gregory Wayne; Satinder Singh 001; Doina Precup; Rémi Munos (NeurIPS, 2019)

Multiagent Evaluation under Incomplete Information. - Mark Rowland 001; Shayegan Omidshafiei; Karl Tuyls; Julien Pérolat; Michal Valko; Georgios Piliouras; Rémi Munos (NeurIPS, 2019)

World Discovery Models. - Mohammad Gheshlaghi Azar; Bilal Piot; Bernardo A. Pires; Jean-Bastien Grill; Florent Altché; Rémi Munos (CoRR, 2019)

α-Rank: Multi-Agent Evaluation by Evolution. - Shayegan Omidshafiei; Christos H. Papadimitriou; Georgios Piliouras; Karl Tuyls; Mark Rowland 001; Jean-Baptiste Lespiau; Wojciech M. Czarnecki; Marc Lanctot; Julien Pérolat; Rémi Munos (CoRR, 2019)

Neural Replicator Dynamics. - Shayegan Omidshafiei; Daniel Hennes; Dustin Morrill; Rémi Munos; Julien Pérolat; Marc Lanctot; Audrunas Gruslys; Jean-Baptiste Lespiau; Karl Tuyls (CoRR, 2019)

Adaptive Trade-Offs in Off-Policy Learning. - Mark Rowland 001; Will Dabney; Rémi Munos (AISTATS, 2020)

Conditional Importance Sampling for Off-Policy Learning. - Mark Rowland 001; Anna Harutyunyan; Hado van Hasselt; Diana Borsa; Tom Schaul; Rémi Munos; Will Dabney (AISTATS, 2020)

Neural Replicator Dynamics: Multiagent Learning via Hedging Policy Gradients. - 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 (AAMAS, 2020)

A Generalized Training Approach for Multiagent Learning. - Paul Muller; Shayegan Omidshafiei; Mark Rowland 001; Karl Tuyls; Julien Pérolat; Siqi Liu 002; Daniel Hennes; Luke Marris; Marc Lanctot; Edward Hughes 001; Zhe Wang 055; Guy Lever; et al. (ICLR, 2020)

Monte-Carlo Tree Search as Regularized Policy Optimization. - Jean-Bastien Grill; Florent Altché; Yunhao Tang; Thomas Hubert; Michal Valko; Ioannis Antonoglou; Rémi Munos (ICML, 2020)

Bootstrap Latent-Predictive Representations for Multitask Reinforcement Learning. - Zhaohan Daniel Guo; Bernardo Ávila Pires; Bilal Piot; Jean-Bastien Grill; Florent Altché; Rémi Munos; Mohammad Gheshlaghi Azar (ICML, 2020)

Fast computation of Nash Equilibria in Imperfect Information Games. - Rémi Munos; Julien Pérolat; Jean-Baptiste Lespiau; Mark Rowland 001; Bart De Vylder; Marc Lanctot; Finbarr Timbers; Daniel Hennes; Shayegan Omidshafiei; Audrunas Gruslys; Mohammad Gheshlaghi Azar; Edward Lockhart; et al. (ICML, 2020)

Taylor Expansion Policy Optimization. - Yunhao Tang; Michal Valko; Rémi Munos (ICML, 2020)

Bootstrap Your Own Latent - A New Approach to Self-Supervised Learning. - 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; et al. (NeurIPS, 2020)

Leverage the Average: an Analysis of KL Regularization in Reinforcement Learning. - Nino Vieillard; Tadashi Kozuno; Bruno Scherrer; Olivier Pietquin; Rémi Munos; Matthieu Geist (NeurIPS, 2020)

Leverage the Average: an Analysis of Regularization in RL. - Nino Vieillard; Tadashi Kozuno; Bruno Scherrer; Olivier Pietquin; Rémi Munos; Matthieu Geist (CoRR, 2020)

Navigating the Landscape of Games. - Shayegan Omidshafiei; Karl Tuyls; Wojciech M. Czarnecki; Francisco C. Santos; Mark Rowland 001; Jerome T. Connor; Daniel Hennes; Paul Muller; Julien Pérolat; Bart De Vylder; Audrunas Gruslys; Rémi Munos (CoRR, 2020)

The Advantage Regret-Matching Actor-Critic. - 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; et al. (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)

A distributional code for value in dopamine-based reinforcement learning. - Will Dabney; Zeb Kurth-Nelson; Naoshige Uchida; Clara Kwon Starkweather; Demis Hassabis; Rémi Munos; Matthew M. Botvinick (Nat., 2020)

Revisiting Peng's Q(λ) for Modern Reinforcement Learning. - Tadashi Kozuno; Yunhao Tang; Mark Rowland 001; Rémi Munos; Steven Kapturowski; Will Dabney; Michal Valko; David Abel (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)

From Poincaré Recurrence to Convergence in Imperfect Information Games: Finding Equilibrium via Regularization. - Julien Pérolat; Rémi Munos; Jean-Baptiste Lespiau; Shayegan Omidshafiei; Mark Rowland 001; Pedro A. Ortega; Neil Burch; Thomas W. Anthony 001; David Balduzzi; Bart De Vylder; Georgios Piliouras; Marc Lanctot; et al. (ICML, 2021)

Taylor Expansion of Discount Factors. - Yunhao Tang; Mark Rowland 001; Rémi Munos; Michal Valko (ICML, 2021)

Learning in two-player zero-sum partially observable Markov games with perfect recall. - Tadashi Kozuno; Pierre Ménard; Rémi Munos; Michal Valko (NeurIPS, 2021)

Unifying Gradient Estimators for Meta-Reinforcement Learning via Off-Policy Evaluation. - Yunhao Tang; Tadashi Kozuno; Mark Rowland 001; Rémi Munos; Michal Valko (NeurIPS, 2021)

Geometric Entropic Exploration. - Zhaohan Daniel Guo; Mohammad Gheshlaghi Azar; Alaa Saade; Shantanu Thakoor; Bilal Piot; Bernardo Ávila Pires; Michal Valko; Thomas Mesnard; Tor Lattimore; Rémi Munos (CoRR, 2021)

Bootstrapped Representation Learning on Graphs. - Shantanu Thakoor; Corentin Tallec; Mohammad Gheshlaghi Azar; Rémi Munos; Petar Velickovic; Michal Valko (CoRR, 2021)

Model-Free Learning for Two-Player Zero-Sum Partially Observable Markov Games with Perfect Recall. - Tadashi Kozuno; Pierre Ménard; Rémi Munos; Michal Valko (CoRR, 2021)

Game Plan: What AI can do for Football, and What Football can do for AI. - Karl Tuyls; Shayegan Omidshafiei; Paul Muller; Zhe Wang 055; Jerome T. Connor; Daniel Hennes; Ian Graham; William Spearman; Tim Waskett; Dafydd Steele; Pauline Luc; Adrià Recasens; et al. (J. Artif. Intell. Res., 2021)

Concentration bounds for temporal difference learning with linear function approximation: the case of batch data and uniform sampling. - Prashanth L. A.; Nathaniel Korda; Rémi Munos (Mach. Learn., 2021)

Marginalized Operators for Off-policy Reinforcement Learning. - Yunhao Tang; Mark Rowland 001; Rémi Munos; Michal Valko (AISTATS, 2022)

Concave Utility Reinforcement Learning: The Mean-field Game Viewpoint. - Matthieu Geist; Julien Pérolat; Mathieu Laurière; Romuald Elie; Sarah Perrin; Olivier Bachem; Rémi Munos; Olivier Pietquin (AAMAS, 2022)

Large-Scale Representation Learning on Graphs via Bootstrapping. - Shantanu Thakoor; Corentin Tallec; Mohammad Gheshlaghi Azar; Mehdi Azabou; Eva L. Dyer; Rémi Munos; Petar Velickovic; Michal Valko (ICLR, 2022)

Generalised Policy Improvement with Geometric Policy Composition. - Shantanu Thakoor; Mark Rowland 001; Diana Borsa; Will Dabney; Rémi Munos; André Barreto 001 (ICML, 2022)

BYOL-Explore: Exploration by Bootstrapped Prediction. - 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; et al. (NeurIPS, 2022)

The Nature of Temporal Difference Errors in Multi-step Distributional Reinforcement Learning. - Yunhao Tang; Rémi Munos; Mark Rowland 001; Bernardo Ávila Pires; Will Dabney; Marc G. Bellemare (NeurIPS, 2022)

Optimistic Posterior Sampling for Reinforcement Learning with Few Samples and Tight Guarantees. - Daniil Tiapkin; Denis Belomestny; Daniele Calandriello; Eric Moulines; Rémi Munos; Alexey Naumov; Mark Rowland 001; Michal Valko; Pierre Ménard (NeurIPS, 2022)

KL-Entropy-Regularized RL with a Generative Model is Minimax Optimal. - 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; et al. (CoRR, 2022)

Mastering the Game of Stratego with Model-Free Multiagent Reinforcement Learning. - Julien Pérolat; Bart De Vylder; Daniel Hennes; Eugene Tarassov; Florian Strub; Vincent de Boer; Paul Muller; Jerome T. Connor; Neil Burch; Thomas W. Anthony 001; Stephen McAleer; Romuald Elie; et al. (CoRR, 2022)

Curiosity in hindsight. - Daniel Jarrett; Corentin Tallec; Florent Altché; Thomas Mesnard; Rémi Munos; Michal Valko (CoRR, 2022)

Figure Data for the paper "Mastering the Game of Stratego with Model-Free Multiagent Reinforcement Learning". - Julien Pérolat; Bart De Vylder; Daniel Hennes; Eugene Tarassov; Florian Strub; Vincent de Boer; Paul Muller; Jerome T. Connor; Neil Burch; Thomas Anthony 001; Stephen McAleer; Romuald Elie; et al. (Zenodo, 2022)

Representations and Exploration for Deep Reinforcement Learning using Singular Value Decomposition. - Yash Chandak; Shantanu Thakoor; Zhaohan Daniel Guo; Yunhao Tang; Rémi Munos; Will Dabney; Diana L. Borsa (ICML, 2023)

Adapting to game trees in zero-sum imperfect information games. - Côme Fiegel; Pierre Ménard; Tadashi Kozuno; Rémi Munos; Vianney Perchet; Michal Valko (ICML, 2023)

Curiosity in Hindsight: Intrinsic Exploration in Stochastic Environments. - Daniel Jarrett; Corentin Tallec; Florent Altché; Thomas Mesnard; Rémi Munos; Michal Valko (ICML, 2023)

Regularization and Variance-Weighted Regression Achieves Minimax Optimality in Linear MDPs: Theory and Practice. - 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; et al. (ICML, 2023)

Quantile Credit Assignment. - Thomas Mesnard; Wenqi Chen; Alaa Saade; Yunhao Tang; Mark Rowland 001; Theophane Weber; Clare Lyle; Audrunas Gruslys; Michal Valko; Will Dabney; Georg Ostrovski; Eric Moulines; et al. (ICML, 2023)

The Statistical Benefits of Quantile Temporal-Difference Learning for Value Estimation. - Mark Rowland 001; Yunhao Tang; Clare Lyle; Rémi Munos; Marc G. Bellemare; Will Dabney (ICML, 2023)

Understanding Self-Predictive Learning for Reinforcement Learning. - Yunhao Tang; Zhaohan Daniel Guo; Pierre Harvey Richemond; Bernardo Ávila Pires; Yash Chandak; Rémi Munos; Mark Rowland 001; Mohammad Gheshlaghi Azar; Charline Le Lan; Clare Lyle; András György 001; Shantanu Thakoor; et al. (ICML, 2023)

DoMo-AC: Doubly Multi-step Off-policy Actor-Critic Algorithm. - Yunhao Tang; Tadashi Kozuno; Mark Rowland 001; Anna Harutyunyan; Rémi Munos; Bernardo Ávila Pires; Michal Valko (ICML, 2023)

Towards a better understanding of representation dynamics under TD-learning. - Yunhao Tang; Rémi Munos (ICML, 2023)

VA-learning as a more efficient alternative to Q-learning. - Yunhao Tang; Rémi Munos; Mark Rowland 001; Michal Valko (ICML, 2023)

Fast Rates for Maximum Entropy Exploration. - Daniil Tiapkin; Denis Belomestny; Daniele Calandriello; Eric Moulines; Rémi Munos; Alexey Naumov; Pierre Perrault; Yunhao Tang; Michal Valko; Pierre Ménard (ICML, 2023)

Model-free Posterior Sampling via Learning Rate Randomization. - Daniil Tiapkin; Denis Belomestny; Daniele Calandriello; Eric Moulines; Rémi Munos; Alexey Naumov; Pierre Perrault; Michal Valko; Pierre Ménard (NeurIPS, 2023)

Nash Learning from Human Feedback. - Rémi Munos; Michal Valko; Daniele Calandriello; Mohammad Gheshlaghi Azar; Mark Rowland 001; Zhaohan Daniel Guo; Yunhao Tang; Matthieu Geist; Thomas Mesnard; Andrea Michi; Marco Selvi; Sertan Girgin; et al. (CoRR, 2023)

A General Theoretical Paradigm to Understand Learning from Human Preferences. - Mohammad Gheshlaghi Azar; Zhaohan Daniel Guo; Bilal Piot; Rémi Munos; Mark Rowland 001; Michal Valko; Daniele Calandriello (AISTATS, 2024)

Human Alignment of Large Language Models through Online Preference Optimisation. - Daniele Calandriello; Zhaohan Daniel Guo; Rémi Munos; Mark Rowland 001; Yunhao Tang; Bernardo Ávila Pires; Pierre Harvey Richemond; Charline Le Lan; Michal Valko; Tianqi Liu 002; Rishabh Joshi; Zeyu Zheng; et al. (ICML, 2024)

Nash Learning from Human Feedback. - Rémi Munos; Michal Valko; Daniele Calandriello; Mohammad Gheshlaghi Azar; Mark Rowland 001; Daniel Guo 001; Yunhao Tang; Matthieu Geist; Thomas Mesnard; Côme Fiegel; Andrea Michi; Marco Selvi; et al. (ICML, 2024)

Generalized Preference Optimization: A Unified Approach to Offline Alignment. - Yunhao Tang; Zhaohan Daniel Guo; Zeyu Zheng; Daniele Calandriello; Rémi Munos; Mark Rowland 001; Pierre Harvey Richemond; Michal Valko; Bernardo Ávila Pires; Bilal Piot (ICML, 2024)

Near-Minimax-Optimal Distributional Reinforcement Learning with a Generative Model. - Mark Rowland 001; Kevin Kevin Li; Rémi Munos; Clare Lyle; Yunhao Tang; Will Dabney (NeurIPS, 2024)

Local and Adaptive Mirror Descents in Extensive-Form Games. - Côme Fiegel; Pierre Ménard; Tadashi Kozuno; Rémi Munos; Vianney Perchet; Michal Valko (NeurIPS, 2024)

Multi-turn Reinforcement Learning with Preference Human Feedback. - Lior Shani; Aviv Rosenberg 002; Asaf Cassel; Oran Lang; Daniele Calandriello; Avital Zipori; Hila Noga; Orgad Keller; Bilal Piot; Idan Szpektor; Avinatan Hassidim; Yossi Matias; et al. (NeurIPS, 2024)

Off-policy Distributional Q(λ): Distributional RL without Importance Sampling. - Yunhao Tang; Mark Rowland 001; Rémi Munos; Bernardo Ávila Pires; Will Dabney (CoRR, 2024)

Near-Minimax-Optimal Distributional Reinforcement Learning with a Generative Model. - Mark Rowland 001; Li Kevin Wenliang; Rémi Munos; Clare Lyle; Yunhao Tang; Will Dabney (CoRR, 2024)

Super-Exponential Regret for UCT, AlphaGo and Variants. - Laurent Orseau; Rémi Munos (CoRR, 2024)

Understanding the performance gap between online and offline alignment algorithms. - Yunhao Tang; Zhaohan Daniel Guo; Zeyu Zheng; Daniele Calandriello; Yuan Cao; Eugene Tarassov; Rémi Munos; Bernardo Ávila Pires; Michal Valko; Yong Cheng; Will Dabney (CoRR, 2024)

Multi-turn Reinforcement Learning from Preference Human Feedback. - Lior Shani; Aviv Rosenberg 002; Asaf B. Cassel; Oran Lang; Daniele Calandriello; Avital Zipori; Hila Noga; Orgad Keller; Bilal Piot; Idan Szpektor; Avinatan Hassidim; Yossi Matias; et al. (CoRR, 2024)

Offline Regularised Reinforcement Learning for Large Language Models Alignment. - Pierre Harvey Richemond; Yunhao Tang; Daniel Guo 001; Daniele Calandriello; Mohammad Gheshlaghi Azar; Rafael Rafailov; Bernardo Ávila Pires; Eugene Tarassov; Lucas Spangher; Will Ellsworth; Aliaksei Severyn; Jonathan Mallinson; et al. (CoRR, 2024)

An Analysis of Quantile Temporal-Difference Learning. - Mark Rowland 001; Rémi Munos; Mohammad Gheshlaghi Azar; Yunhao Tang; Georg Ostrovski; Anna Harutyunyan; Karl Tuyls; Marc G. Bellemare; Will Dabney (J. Mach. Learn. Res., 2024)

Optimizing Return Distributions with Distributional Dynamic Programming. - Bernardo Ávila Pires; Mark Rowland 001; Diana Borsa; Zhaohan Daniel Guo; Khimya Khetarpal; André Barreto 001; David Abel; Rémi Munos; Will Dabney (CoRR, 2025)

Soft Policy Optimization: Online Off-Policy RL for Sequence Models. - Taco Cohen; David W. Zhang; Kunhao Zheng; Yunhao Tang; Rémi Munos; Gabriel Synnaeve (CoRR, 2025)

Temporal Difference Flows. - Jesse Farebrother; Matteo Pirotta; Andrea Tirinzoni; Rémi Munos; Alessandro Lazaric; Ahmed Touati (CoRR, 2025)

Optimizing Language Models for Inference Time Objectives using Reinforcement Learning. - Yunhao Tang; Kunhao Zheng; Gabriel Synnaeve; Rémi Munos (CoRR, 2025)

RL-finetuning LLMs from on- and off-policy data with a single algorithm. - Yunhao Tang; Taco Cohen; David W. Zhang; Michal Valko; Rémi Munos (CoRR, 2025)

Learning to chain-of-thought with Jensen's evidence lower bound. - Yunhao Tang; Sid Wang; Rémi Munos (CoRR, 2025)

On a few pitfalls in KL divergence gradient estimation for RL. - Yunhao Tang; Rémi Munos (CoRR, 2025)

Asymmetric REINFORCE for off-Policy Reinforcement Learning: Balancing positive and negative rewards. - Charles Arnal; Gaëtan Narozniak; Vivien Cabannes; Yunhao Tang; Julia Kempe; Rémi Munos (CoRR, 2025)