World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
54
Citations
11138
World Ranking
4593
National Ranking
278

Karl Tuyls 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 Karl Tuyls 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: 250 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: 560 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: 424 scientists 242–251 publications: 408 scientists 252–261 publications: 378 scientists 262–271 publications: 300 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: 335 publications — 80th percentile

80% of scientists in this discipline score the same or lower.

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

Karl Tuyls 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 Karl Tuyls sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 984 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 969 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 765 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 517 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: 54 D-Index — 69th percentile

69% of scientists in this discipline score the same or lower.

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

Overview

Karl Tuyls is affiliated with DeepMind in the United Kingdom and has contributed extensively to research in computer science and decision sciences. Their work covers a range of topics within artificial intelligence and operations research, with a significant focus on reinforcement learning, game theory, and applications in robotics and sports analytics.

Their main fields of study include:

  • Computer Science
  • Decision Sciences

Tuyls has focused on the following subfields:

  • Artificial Intelligence
  • Management Science and Operations Research
  • Economics and Econometrics
  • Safety Research
  • Sociology and Political Science

Significant topics covered in their research include:

  • Reinforcement Learning in Robotics
  • Advanced Bandit Algorithms Research
  • Game Theory and Applications
  • Artificial Intelligence in Games
  • Sports Analytics and Performance
  • Experimental Behavioral Economics Studies
  • Anomaly Detection Techniques and Applications

They have published numerous research papers, with notable recent contributions such as:

  • 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
  • From motor control to team play in simulated humanoid football, 2022, Science Robotics
  • TacticAI: an AI assistant for football tactics, 2024, Nature Communications
  • Human-Timescale Adaptation in an Open-Ended Task Space, 2023, arXiv (Cornell University)

Tuyls frequently collaborates with several researchers in their field. Among the most frequent co-authors are:

  • Romuald Élie
  • Daniel Hennes
  • Shayegan Omidshafiei
  • Marc Lanctot
  • Julien Pérolat

Their research has appeared predominantly in venues such as:

  • arXiv (Cornell University)
  • Zenodo (CERN European Organization for Nuclear Research)
  • Journal of Artificial Intelligence Research
  • Nature Communications
  • Proceedings of the National Academy of Sciences

Best Publications

  • Value-Decomposition Networks For Cooperative Multi-Agent Learning

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

  • Multi-Agent Systems

    Unknown

  • Credit Card Fraud Detection Using Bayesian and Neural Networks

    Sam Maes;Karl Tuyls;Bram Vanschoenwinkel;Bernard Manderick

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

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

  • Evolutionary dynamics of multi-agent learning: a survey

    Daan Bloembergen;Karl Tuyls;Daniel Hennes;Michael Kaisers

  • A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning

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

  • Minimal navigation solution for a swarm of tiny flying robots to explore an unknown environment

    K. N. McGuire;C. De Wagter;K. Tuyls;H. J. Kappen

  • Multiagent Learning: Basics, Challenges, and Prospects

    Karl Tuyls;Gerhard Weiss

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

    Unknown

  • Relational Deep Reinforcement Learning.

    Vinícius Flores Zambaldi;David Raposo;Adam Santoro;Victor Bapst

  • Efficient Optical Flow and Stereo Vision for Velocity Estimation and Obstacle Avoidance on an Autonomous Pocket Drone

    Kimberly McGuire;Guido de Croon;Christophe De Wagter;Karl Tuyls

  • The Mechanics of n-Player Differentiable Games

    David Balduzzi;Sébastien Racanière;James Martens;Jakob N. Foerster

  • A selection-mutation model for q-learning in multi-agent systems

    Karl Tuyls;Katja Verbeeck;Tom Lenaerts

  • What evolutionary game theory tells us about multiagent learning

    Karl Tuyls;Simon Parsons

  • Inference of concise DTDs from XML data

    Geert Jan Bex;Frank Neven;Thomas Schwentick;Karl Tuyls

  • Inequity aversion improves cooperation in intertemporal social dilemmas

    Edward Hughes;Joel Z. Leibo;Matthew G. Phillips;Karl Tuyls

  • An Evolutionary Dynamical Analysis of Multi-Agent Learning in Iterated Games

    Karl Tuyls;Pieter Jan Hoen;Bram Vanschoenwinkel

  • Evolutionary game theory and multi-agent reinforcement learning

    Karl Tuyls;Ann Nowé

  • Lenient Multi-Agent Deep Reinforcement Learning

    Gregory Palmer;Karl Tuyls;Daan Bloembergen;Rahul Savani

  • OpenSpiel: A Framework for Reinforcement Learning in Games.

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

  • Deep reinforcement learning with relational inductive biases

    Vinícius Flores Zambaldi;David Raposo;Adam Santoro;Victor Bapst

  • Emergent Communication through Negotiation

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

  • Proceedings of the 2016 International Conference on Autonomous Agents & Multiagent Systems

    Catholijn M. Jonker;Stacy Marsella;John Thangarajah;Karl Tuyls

  • Introduction to Game Theory

    Karl Tuyls;Ann Nowe

Frequent Co-Authors

Ann Nowé
Ann Nowé Vrije Universiteit Brussel
Marc Lanctot
Marc Lanctot DeepMind (United Kingdom)
Thore Graepel
Thore Graepel University College London
Rémi Munos
Rémi Munos French Institute for Research in Computer Science and Automation - INRIA
Joel Z. Leibo
Joel Z. Leibo DeepMind (United Kingdom)
Simon Parsons
Simon Parsons University of Lincoln
Toon Calders
Toon Calders University of Antwerp
Jan Ramon
Jan Ramon French Institute for Research in Computer Science and Automation - INRIA
Sandip Sen
Sandip Sen University of Tulsa

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