World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

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

Rémi Munos publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Rémi Munos sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 242 publications — 60th percentile

60% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Rémi Munos D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Rémi Munos sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 82 D-Index — 94th percentile

94% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

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

Marc G. Bellemare
Marc G. Bellemare Google (United States)
Csaba Szepesvári
Csaba Szepesvári University of Alberta
Alessandro Lazaric
Alessandro Lazaric Facebook (United States)
Karl Tuyls
Karl Tuyls DeepMind (United Kingdom)
Marc Lanctot
Marc Lanctot DeepMind (United Kingdom)
Nicolas Heess
Nicolas Heess DeepMind (United Kingdom)
Sébastien Bubeck
Sébastien Bubeck Microsoft (United States)
Mohammad Ghavamzadeh
Mohammad Ghavamzadeh Amazon (United States)
David Silver
David Silver DeepMind (United Kingdom)
Tom Schaul
Tom Schaul DeepMind (United Kingdom)

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