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D-Index & Metrics

Computer Science

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

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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