World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
33
Citations
44108
World Ranking
12332
National Ranking
4988

Marc G. Bellemare 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 Marc G. Bellemare 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: 81 publications — 3rd percentile

3% of scientists in this discipline score the same or lower.

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

Marc G. Bellemare 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 Marc G. Bellemare 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: 33 D-Index — 13th percentile

13% of scientists in this discipline score the same or lower.

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

Overview

Marc G. Bellemare is affiliated with Google in the United States and has contributed extensively to the field of computer science, with a primary focus on artificial intelligence. Their work spans significant subfields such as artificial intelligence, management science and operations research, structural biology, surfaces, coatings and films, and computational theory and mathematics.

The research topics covered by Marc G. Bellemare include:

  • Reinforcement Learning in Robotics
  • Evolutionary Algorithms and Applications
  • Advanced Bandit Algorithms Research
  • Artificial Intelligence in Games
  • Advanced Electron Microscopy Techniques and Applications
  • Electron and X-Ray Spectroscopy Techniques
  • Digital Games and Media

Notable published papers by Marc G. Bellemare are:

  • Autonomous navigation of stratospheric balloons using reinforcement learning (2020, Nature)
  • Investigating Contingency Awareness Using Atari 2600 Games (2021, Proceedings of the AAAI Conference on Artificial Intelligence)

Frequent co-authors include:

  • Pablo Samuel Castro (13 joint publications)
  • Rishabh Agarwal (10 joint publications)
  • Will Dabney (9 joint publications)
  • Aaron Courville (7 joint publications)
  • Joshua Greaves (7 joint publications)

Marc G. Bellemare has published predominantly in venues such as:

  • arXiv (Cornell University) with 28 publications
  • Proceedings of the AAAI Conference on Artificial Intelligence with 5 publications
  • Microscopy and Microanalysis with 2 publications
  • Nature with 1 publication
  • Advanced Materials Interfaces with 1 publication

In addition to journal and conference papers, Marc G. Bellemare has contributed to book publications, including a title published by The MIT Press:

  • Distributional Reinforcement Learning (2023)

Best Publications

  • Human-level control through deep reinforcement learning

    Volodymyr Mnih;Koray Kavukcuoglu;David Silver;Andrei A. Rusu

  • The arcade learning environment: an evaluation platform for general agents

    Marc G. Bellemare;Yavar Naddaf;Joel Veness;Michael Bowling

  • An Introduction to Deep Reinforcement Learning

    Vincent François-Lavet;Peter Henderson;Riashat Islam;Marc G. Bellemare

  • 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

  • Distributional Reinforcement Learning With Quantile Regression

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

  • Count-based exploration with neural density models

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

  • Revisiting the Arcade Learning Environment: Evaluation Protocols and Open Problems for General Agents

    Marlos C. Machado;Marc G. Bellemare;Erik Talvitie;Joel Veness

  • Safe and Efficient Off-Policy Reinforcement Learning

    Rémi Munos;Tom Stepleton;Anna Harutyunyan;Marc G. Bellemare

  • Automated Curriculum Learning for Neural Networks

    Alex Graves;Marc G. Bellemare;Jacob Menick;Rémi Munos

  • The Cramer Distance as a Solution to Biased Wasserstein Gradients

    Marc G. Bellemare;Ivo Danihelka;Will Dabney;Shakir Mohamed

  • Autonomous navigation of stratospheric balloons using reinforcement learning.

    Marc G. Bellemare;Salvatore Candido;Pablo Samuel Castro;Jun Gong

  • The Hanabi Challenge: A New Frontier for AI Research

    Nolan Bard;Jakob N. Foerster;Sarath Chandar;Neil Burch

  • Dopamine: A Research Framework for Deep Reinforcement Learning

    Pablo Samuel Castro;Subhodeep Moitra;Carles Gelada;Saurabh Kumar

  • A Laplacian Framework for option discovery in reinforcement learning

    Marlos C. Machado;Marc G. Bellemare;Michael Bowling

  • Increasing the action gap: new operators for reinforcement learning

    Marc G. Bellemare;Georg Ostrovski;Arthur Guez;Philip S. Thomas

  • DeepMDP: Learning Continuous Latent Space Models for Representation Learning

    Carles Gelada;Saurabh Kumar;Jacob Buckman;Ofir Nachum

  • Count-Based Exploration with the Successor Representation

    Marlos C. Machado;Marc G. Bellemare;Michael Bowling

  • Investigating contingency awareness using Atari 2600 games

    Marc G. Bellemare;Joel Veness;Michael Bowling

  • The Reactor: A Sample-Efficient Actor-Critic Architecture

    Audrunas Gruslys;Mohammad Gheshlaghi Azar;Marc G. Bellemare;Remi Munos

  • Hyperbolic Discounting and Learning over Multiple Horizons

    William Fedus;Carles Gelada;Yoshua Bengio;Marc G. Bellemare

Frequent Co-Authors

Rémi Munos
Rémi Munos French Institute for Research in Computer Science and Automation - INRIA
Michael Bowling
Michael Bowling University of Alberta
Doina Precup
Doina Precup McGill University
Aaron Courville
Aaron Courville University of Montreal
Hugo Larochelle
Hugo Larochelle Google (United States)
Dale Schuurmans
Dale Schuurmans University of Alberta
Koray Kavukcuoglu
Koray Kavukcuoglu DeepMind (United Kingdom)
Marcus Hutter
Marcus Hutter DeepMind (United Kingdom)
Joelle Pineau
Joelle Pineau McGill University
Tom Schaul
Tom Schaul DeepMind (United Kingdom)

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