World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
41
Citations
34039
World Ranking
8550
National Ranking
514

Marc Lanctot 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 Lanctot 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: 102 publications — 9th percentile

9% of scientists in this discipline score the same or lower.

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

Marc Lanctot 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 Lanctot 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: 41 D-Index — 40th percentile

40% of scientists in this discipline score the same or lower.

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

Overview

Marc Lanctot is affiliated with DeepMind in the United Kingdom. Their research primarily spans the fields of Computer Science and Decision Sciences, with significant contributions in subfields including Artificial Intelligence, Management Science and Operations Research, Economics and Econometrics, Safety Research, and Sociology and Political Science.

Their scholarly work focuses on several main topics such as Reinforcement Learning in Robotics, Artificial Intelligence in Games, Advanced Bandit Algorithms Research, Experimental Behavioral Economics Studies, Sports Analytics and Performance, Game Theory and Applications, and Adversarial Robustness in Machine Learning.

Marc Lanctot has authored numerous publications, many of which appear in prominent venues. Frequent publication venues include:

  • arXiv (Cornell University)
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • Zenodo (CERN European Organization for Nuclear Research)
  • Science
  • Artificial Intelligence

Some recent papers authored or coauthored by Lanctot include:

  • Learning a Generic Value-Selection Heuristic Inside a Constraint Programming Solver, 2023, arXiv (Cornell University)
  • Mastering the game of Stratego with model-free multiagent reinforcement learning, 2022, Science
  • Negotiating team formation using deep reinforcement learning, 2020, Artificial Intelligence
  • Learning to Play No-Press Diplomacy with Best Response Policy Iteration, 2020, arXiv (Cornell University)
  • From Poincaré Recurrence to Convergence in Imperfect Information Games: Finding Equilibrium via Regularization, 2020, arXiv (Cornell University)

They frequently collaborate with a number of other researchers. Frequent coauthors include:

  • Michael Bowling
  • Neil Burch
  • Karl Tuyls
  • Luke Marris
  • Ian Gemp

Best Publications

  • Mastering the game of Go with deep neural networks and tree search

    David Silver;Aja Huang;Christopher J. Maddison;Arthur Guez

  • A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play.

    David Silver;Thomas Hubert;Julian Schrittwieser;Ioannis Antonoglou

  • Dueling network architectures for deep reinforcement learning

    Ziyu Wang;Tom Schaul;Matteo Hessel;Hado Van Hasselt

  • Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm

    David Silver;Thomas Hubert;Julian Schrittwieser;Ioannis Antonoglou

  • Deep Q-learning from Demonstrations

    Todd Hester;Matej Vecerik;Olivier Pietquin;Marc Lanctot

  • Deep Q-learning From Demonstrations.

    Todd Hester;Matej Vecerík;Olivier Pietquin;Marc Lanctot

  • Value-Decomposition Networks For Cooperative Multi-Agent Learning

    Peter Sunehag;Guy Lever;Audrunas Gruslys;Wojciech Marian Czarnecki

  • Multi-agent Reinforcement Learning in Sequential Social Dilemmas

    Joel Z. Leibo;Vinicius Zambaldi;Marc Lanctot;Janusz Marecki

  • Value-Decomposition Networks For Cooperative Multi-Agent Learning Based On Team Reward

    Peter Sunehag;Guy Lever;Audrunas Gruslys;Wojciech Marian Czarnecki

  • A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning

    Marc Lanctot;Vinícius Flores Zambaldi;Audrunas Gruslys;Angeliki Lazaridou

  • Monte Carlo Sampling for Regret Minimization in Extensive Games

    Marc Lanctot;Kevin Waugh;Martin Zinkevich;Michael Bowling

  • The Hanabi Challenge: A New Frontier for AI Research

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

  • Mastering the game of Stratego with model-free multiagent reinforcement learning

    Unknown

  • Fictitious Self-Play in Extensive-Form Games

    Johannes Heinrich;Marc Lanctot;David Silver

  • Learning from Demonstrations for Real World Reinforcement Learning

    Todd Hester;Matej Vecerík;Olivier Pietquin;Marc Lanctot

  • OpenSpiel: A Framework for Reinforcement Learning in Games.

    Marc Lanctot;Edward Lockhart;Jean-Baptiste Lespiau;Vinícius Flores Zambaldi

  • Memory-efficient backpropagation through time

    Marc Lanctot;Audrunas Gruslys;Ivo Danihelka;Remi Munos

  • Actor-Critic Policy Optimization in Partially Observable Multiagent Environments

    Sriram Srinivasan;Marc Lanctot;Vinícius Flores Zambaldi;Julien Pérolat

  • Real-Time Monte Carlo Tree Search in Ms Pac-Man

    Tom Pepels;Mark H. M. Winands;Marc Lanctot

  • Convolution by Evolution: Differentiable Pattern Producing Networks

    Chrisantha Fernando;Dylan Banarse;Malcolm Reynolds;Frederic Besse

  • Emergent Communication through Negotiation

    Kris Cao;Angeliki Lazaridou;Marc Lanctot;Joel Z. Leibo

  • Memory-Efficient Backpropagation Through Time

    Audrūnas Gruslys;Remi Munos;Ivo Danihelka;Marc Lanctot

  • alpha-Rank: Multi-Agent Evaluation by Evolution

    Shayegan Omidshafiei;Christos Papadimitriou;Georgios Piliouras;Karl Tuyls

Frequent Co-Authors

Karl Tuyls
Karl Tuyls DeepMind (United Kingdom)
Michael Bowling
Michael Bowling University of Alberta
Thore Graepel
Thore Graepel University College London
Joel Z. Leibo
Joel Z. Leibo DeepMind (United Kingdom)
Rémi Munos
Rémi Munos French Institute for Research in Computer Science and Automation - INRIA
Neil Burch
Neil Burch University of Alberta
Angeliki Lazaridou
Angeliki Lazaridou Google (United States)
David Silver
David Silver DeepMind (United Kingdom)
Tom Schaul
Tom Schaul DeepMind (United Kingdom)

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