Karl Tuyls
Karl Tuyls
Research Scientist, Google DeepMind and Professor of computer science, University of Liverpool
Verified email at google.com - Homepage
Cited by
Cited by
Credit card fraud detection using Bayesian and neural networks
S Maes, K Tuyls, B Vanschoenwinkel, B Manderick
Proceedings of the 1st international naiso congress on neuro fuzzy …, 2002
A unified game-theoretic approach to multiagent reinforcement learning
M Lanctot, V Zambaldi, A Gruslys, A Lazaridou, K Tuyls, J Pérolat, D Silver, ...
Advances in neural information processing systems, 4190-4203, 2017
Evolutionary dynamics of multi-agent learning: A survey
D Bloembergen, K Tuyls, D Hennes, M Kaisers
Journal of Artificial Intelligence Research 53, 659-697, 2015
Deep reinforcement learning with relational inductive biases
V Zambaldi, D Raposo, A Santoro, V Bapst, Y Li, I Babuschkin, K Tuyls, ...
International Conference on Learning Representations, 2018
Inference of concise DTDs from XML data
GJ Bex, F Neven, T Schwentick, K Tuyls
Proceedings of the 32nd international conference on Very large data bases …, 2006
Multiagent learning: Basics, challenges, and prospects
K Tuyls, G Weiss
Ai Magazine 33 (3), 41-41, 2012
Cube and conquer: Guiding CDCL SAT solvers by lookaheads
MJH Heule, O Kullmann, S Wieringa, A Biere
Haifa Verification Conference, 50-65, 2011
What evolutionary game theory tells us about multiagent learning
K Tuyls, S Parsons
Artificial Intelligence 171 (7), 406-416, 2007
The mechanics of n-player differentiable games
D Balduzzi, S Racaniere, J Martens, J Foerster, K Tuyls, T Graepel
arXiv preprint arXiv:1802.05642, 2018
A selection-mutation model for q-learning in multi-agent systems
K Tuyls, K Verbeeck, T Lenaerts
Proceedings of the second international joint conference on Autonomous …, 2003
An evolutionary dynamical analysis of multi-agent learning in iterated games
K Tuyls, PJT Hoen, B Vanschoenwinkel
Autonomous Agents and Multi-Agent Systems 12 (1), 115-153, 2006
Value-Decomposition Networks For Cooperative Multi-Agent Learning Based On Team Reward.
P Sunehag, G Lever, A Gruslys, WM Czarnecki, VF Zambaldi, ...
AAMAS, 2085-2087, 2018
Multi-robot collision avoidance with localization uncertainty.
D Hennes, D Claes, W Meeussen, K Tuyls
AAMAS, 147-154, 2012
Efficient optical flow and stereo vision for velocity estimation and obstacle avoidance on an autonomous pocket drone
K McGuire, G De Croon, C De Wagter, K Tuyls, H Kappen
IEEE Robotics and Automation Letters 2 (2), 1070-1076, 2017
Theoretical advantages of lenient learners: An evolutionary game theoretic perspective
L Panait, K Tuyls, S Luke
Journal of Machine Learning Research 9 (Mar), 423-457, 2008
Evolutionary game theory and multi-agent reinforcement learning
K Tuyls11, A Nowé
Value-decomposition networks for cooperative multi-agent learning
P Sunehag, G Lever, A Gruslys, WM Czarnecki, V Zambaldi, M Jaderberg, ...
arXiv preprint arXiv:1706.05296, 2017
How to reach linguistic consensus: A proof of convergence for the naming game
B De Vylder, K Tuyls
Journal of theoretical biology 242 (4), 818-831, 2006
Lenient multi-agent deep reinforcement learning
G Palmer, K Tuyls, D Bloembergen, R Savani
arXiv preprint arXiv:1707.04402, 2017
Emergence of linguistic communication from referential games with symbolic and pixel input
A Lazaridou, KM Hermann, K Tuyls, S Clark
arXiv preprint arXiv:1804.03984, 2018
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