World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
32
Citations
9305
World Ranking
12870
National Ranking
816

Jakob Foerster 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 Jakob Foerster 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: 110 publications — 11th percentile

11% of scientists in this discipline score the same or lower.

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

Jakob Foerster 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 Jakob Foerster 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: 32 D-Index — 10th percentile

10% of scientists in this discipline score the same or lower.

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

Overview

Jakob Foerster is affiliated with the University of Oxford in the United Kingdom and specializes in computer science with a focus on artificial intelligence and related subfields. Their research portfolio consists of 138 publications primarily in computer science, including 108 specifically on artificial intelligence, 20 on management science and operations research, and others in computer vision and pattern recognition, computational theory and mathematics, as well as economics and econometrics.

Their work covers a range of topics with notable emphasis on reinforcement learning in robotics, topic modeling, adversarial robustness in machine learning, explainable artificial intelligence (XAI), artificial intelligence in games, advanced bandit algorithms research, and machine learning and data classification.

Jakob Foerster has a significant number of frequent co-authors including Chris Xiaoxuan Lu, Shimon Whiteson, Christian Schroeder de Witt, Tim Rocktäschel, and Timon Willi. These collaborations span many papers and research projects.

The scientist frequently publishes in venues including arXiv (Cornell University) with 119 publications, the Proceedings of the AAAI Conference on Artificial Intelligence with 2 publications, SSRN Electronic Journal, Machine Learning Science and Technology, and Frontiers in Robotics and AI.

Among recent papers published, the following stand out:

  • Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning (2020), arXiv (Cornell University)
  • Exploratory Combinatorial Optimization with Reinforcement Learning (2020), Proceedings of the AAAI Conference on Artificial Intelligence
  • The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery (2024), arXiv (Cornell University)
  • "Other-Play" for Zero-Shot Coordination (2020), arXiv (Cornell University)
  • On the interaction between supervision and self-play in emergent communication (2020), arXiv (Cornell University)

Best Publications

  • Counterfactual Multi-Agent Policy Gradients

    Jakob Foerster;Gregory Farquhar;Triantafyllos Afouras;Nantas Nardelli

  • Learning to Communicate with Deep Multi-Agent Reinforcement Learning

    Jakob N. Foerster;Yannis M. Assael;Nando de Freitas;Shimon Whiteson

  • Learning to Communicate with Deep Multi−Agent Reinforcement Learning

    Jakob Foerster;Ioannis Alexandros Assael;Nando de Freitas;Shimon Whiteson

  • Counterfactual Multi−Agent Policy Gradients

    Jakob N. Foerster;Gregory Farquhar;Triantafyllos Afouras;Nantas Nardelli

  • QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning

    Tabish Rashid;Mikayel Samvelyan;Christian Schroeder;Gregory Farquhar

  • Stabilising experience replay for deep multi-agent reinforcement learning

    Jakob Foerster;Nantas Nardelli;Gregory Farquhar;Triantafyllos Afouras

  • QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning

    Tabish Rashid;Mikayel Samvelyan;Christian Schroeder de Witt;Gregory Farquhar

  • Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning

    Tabish Rashid;Mikayel Samvelyan;Christian Schröder de Witt;Gregory Farquhar

  • Learning with Opponent-Learning Awareness

    Jakob Foerster;Richard Y. Chen;Maruan Al-Shedivat;Shimon Whiteson

  • Three-dimensional head-direction coding in the bat brain

    Arseny Finkelstein;Dori Derdikman;Alon Rubin;Jakob N. Foerster;Jakob N. Foerster

  • The Hanabi Challenge: A New Frontier for AI Research

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

  • The Mechanics of n-Player Differentiable Games

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

  • The StarCraft Multi-Agent Challenge

    Mikayel Samvelyan;Tabish Rashid;Christian Schroeder de Witt;Gregory Farquhar

  • A Survey of Reinforcement Learning Informed by Natural Language

    Jelena Luketina;Nantas Nardelli;Nantas Nardelli;Gregory Farquhar;Gregory Farquhar;Jakob N. Foerster

  • Exploratory Combinatorial Optimization with Reinforcement Learning

    Thomas D. Barrett;William R. Clements;Jakob N. Foerster;A. I. Lvovsky

  • Learning to Communicate to Solve Riddles with Deep Distributed Recurrent Q-Networks

    Jakob N. Foerster;Yannis M. Assael;Nando de Freitas;Shimon Whiteson

  • On the Pitfalls of Measuring Emergent Communication

    Ryan Lowe;Jakob Foerster;Y-Lan Boureau;Joelle Pineau

  • Multi-Agent Common Knowledge Reinforcement Learning

    Christian A. Schroeder de Witt;Jakob N. Foerster;Gregory Farquhar;Philip H. S. Torr

  • Stable Opponent Shaping in Differentiable Games

    Alistair Letcher;Jakob N. Foerster;David Balduzzi;Tim Rocktäschel

  • Bayesian Action Decoder for Deep Multi-Agent Reinforcement Learning

    Jakob N. Foerster;H. Francis Song;Edward Hughes;Neil Burch

  • Differentiable Game Mechanics

    Alistair Letcher;David Balduzzi;Sébastien Racanière;James Martens

Frequent Co-Authors

Shimon Whiteson
Shimon Whiteson University of Oxford
Tim Rocktäschel
Tim Rocktäschel University College London
Philip H. S. Torr
Philip H. S. Torr University of Oxford
Joelle Pineau
Joelle Pineau McGill University
Edward Grefenstette
Edward Grefenstette University College London
Kyunghyun Cho
Kyunghyun Cho New York University
Douwe Kiela
Douwe Kiela Stanford University
Thore Graepel
Thore Graepel University College London
Karl Tuyls
Karl Tuyls DeepMind (United Kingdom)
Francoise Beaufays
Francoise Beaufays Google (United States)

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