World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
82
Citations
28015
World Ranking
958
National Ranking
11

Overview

Rémi Munos is affiliated with Facebook in the United States and has a primary research focus in Computer Science. Their work predominantly spans the subfields of Artificial Intelligence, Management Science and Operations Research, Computer Networks and Communications, Electrical and Electronic Engineering, and Cognitive Neuroscience.

The scientist's research encompasses several main topics, including Reinforcement Learning in Robotics, Advanced Bandit Algorithms Research, Evolutionary Algorithms and Applications, Artificial Intelligence in Games, Neural Dynamics and Brain Function, Optimization and Search Problems, and Auction Theory and Applications.

Rémi Munos has published extensively, with 74 publications in Computer Science. Frequent publication venues include:

  • arXiv (Cornell University)
  • Zenodo (CERN European Organization for Nuclear Research)
  • Nature
  • Science
  • Journal of Artificial Intelligence Research

The scientist has collaborated with several researchers repeatedly. Frequent co-authors include:

  • Yunhao Tang
  • Michal Vaľko
  • Mark Rowland
  • Will Dabney
  • Bilal Piot

Some of their recent papers are:

  • Bootstrap your own latent: A new approach to self-supervised Learning, 2020, arXiv (Cornell University)
  • A distributional code for value in dopamine-based reinforcement learning, 2020, Nature
  • Mastering the game of Stratego with model-free multiagent reinforcement learning, 2022, Science
  • Bootstrap Latent-Predictive Representations for Multitask Reinforcement Learning, 2020, arXiv (Cornell University)
  • Monte-Carlo Tree Search as Regularized Policy Optimization, 2020, arXiv (Cornell University)

Best Publications

  • Bootstrap Your Own Latent: A New Approach to Self-Supervised Learning

    Jean-Bastien Grill;Florian Strub;Florent Altché;Corentin Tallec

  • Unifying count-based exploration and intrinsic motivation

    Marc G. Bellemare;Sriram Srinivasan;Georg Ostrovski;Tom Schaul

  • A Distributional Perspective on Reinforcement Learning

    Marc G. Bellemare;Will Dabney;Rémi Munos

  • IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures

    Lasse Espeholt;Hubert Soyer;Remi Munos;Karen Simonyan

  • Exploration-exploitation tradeoff using variance estimates in multi-armed bandits

    Jean-Yves Audibert;Rémi Munos;Csaba Szepesvári

  • Noisy Networks For Exploration

    Meire Fortunato;Mohammad Gheshlaghi Azar;Bilal Piot;Jacob Menick

  • Thompson sampling: an asymptotically optimal finite-time analysis

    Emilie Kaufmann;Nathaniel Korda;Rémi Munos

  • Learning to reinforcement learn

    Jane X. Wang;Zeb Kurth-Nelson;Dhruva Tirumala;Hubert Soyer

  • Distributional Reinforcement Learning With Quantile Regression

    Will Dabney;Mark Rowland;Marc G. Bellemare;Rémi Munos

  • X -Armed Bandits

    Sébastien Bubeck;Rémi Munos;Gilles Stoltz;Csaba Szepesvári

  • Modification of UCT with Patterns in Monte-Carlo Go

    Sylvain Gelly;Yizao Wang;Rémi Munos;Olivier Teytaud

  • Pure exploration in multi-armed bandits problems

    Sébastien Bubeck;Rémi Munos;Gilles Stoltz

  • A distributional code for value in dopamine-based reinforcement learning

    Will Dabney;Zeb Kurth-Nelson;Naoshige Uchida;Clara Kwon Starkweather

  • Sample Efficient Actor-Critic with Experience Replay.

    Ziyu Wang;Victor Bapst;Nicolas Heess;Volodymyr Mnih

  • Count-based exploration with neural density models

    Georg Ostrovski;Marc G. Bellemare;Aäron van den Oord;Rémi Munos

  • Variable Resolution Discretization in Optimal Control

    Rémi Munos;Andrew Moore

  • Finite-Time Bounds for Fitted Value Iteration

    Rémi Munos;Csaba Szepesvári

  • Learning near-optimal policies with Bellman-residual minimization based fitted policy iteration and a single sample path

    András Antos;Csaba Szepesvári;Rémi Munos

  • Kullback–Leibler upper confidence bounds for optimal sequential allocation

    Olivier Cappé;Aurélien Garivier;Odalric-Ambrym Maillard;Rémi Munos

  • Successor Features for Transfer in Reinforcement Learning

    Andre Barreto;Will Dabney;Remi Munos;Jonathan J. Hunt

  • Minimax regret bounds for reinforcement learning

    Mohammad Gheshlaghi Azar;Ian Osband;Rémi Munos

  • Maximum a Posteriori Policy Optimisation

    Abbas Abdolmaleki;Jost Tobias Springenberg;Yuval Tassa;Rémi Munos

  • Recurrent Experience Replay in Distributed Reinforcement Learning.

    Steven Kapturowski;Georg Ostrovski;John Quan;Remi Munos

Frequent Co-Authors

Peter Auer
Peter Auer University of Leoben
Hilbert J. Kappen
Hilbert J. Kappen Radboud University
Robert Babuska
Robert Babuska Delft University of Technology
Bart De Schutter
Bart De Schutter Delft University of Technology
Matthew Botvinick
Matthew Botvinick Yale University

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