Stebėti
Karl Tuyls
Karl Tuyls
Research Scientist, Google DeepMind and Professor of computer science, University of Liverpool
Patvirtintas el. paštas google.com - Pagrindinis puslapis
Pavadinimas
Cituota
Cituota
Metai
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
9802017
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 30, 2017
5312017
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
5092002
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, 2019
373*2019
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
2792015
The mechanics of n-player differentiable games
D Balduzzi, S Racaniere, J Martens, J Foerster, K Tuyls, T Graepel
International Conference on Machine Learning, 354-363, 2018
2622018
Multiagent learning: Basics, challenges, and prospects
K Tuyls, G Weiss
Ai Magazine 33 (3), 41-41, 2012
2542012
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
1852017
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
1832018
A multi-agent reinforcement learning model of common-pool resource appropriation
J Perolat, JZ Leibo, V Zambaldi, C Beattie, K Tuyls, T Graepel
Advances in neural information processing systems 30, 2017
1752017
Inference of concise DTDs from XML data
GJ Bex, F Neven, T Schwentick, K Tuyls
ACM Press, 2006
1722006
Lenient multi-agent deep reinforcement learning
G Palmer, K Tuyls, D Bloembergen, R Savani
arXiv preprint arXiv:1707.04402, 2017
1622017
What evolutionary game theory tells us about multiagent learning
K Tuyls, S Parsons
Artificial Intelligence 171 (7), 406-416, 2007
1622007
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
1612003
Inequity aversion improves cooperation in intertemporal social dilemmas
E Hughes, JZ Leibo, M Phillips, K Tuyls, E Dueñez-Guzman, ...
Advances in neural information processing systems 31, 2018
1602018
OpenSpiel: A framework for reinforcement learning in games
M Lanctot, E Lockhart, JB Lespiau, V Zambaldi, S Upadhyay, J Pérolat, ...
arXiv preprint arXiv:1908.09453, 2019
1572019
An evolutionary dynamical analysis of multi-agent learning in iterated games
K Tuyls, PJT Hoen, B Vanschoenwinkel
Autonomous Agents and Multi-Agent Systems 12, 115-153, 2006
1522006
Multi-robot collision avoidance with localization uncertainty
D Hennes, D Claes, W Meeussen, K Tuyls
Proceedings of the 11th International Conference on Autonomous Agents and …, 2012
1492012
Emergent communication through negotiation
K Cao, A Lazaridou, M Lanctot, JZ Leibo, K Tuyls, S Clark
arXiv preprint arXiv:1804.03980, 2018
1482018
Actor-critic policy optimization in partially observable multiagent environments
S Srinivasan, M Lanctot, V Zambaldi, J Pérolat, K Tuyls, R Munos, ...
Advances in neural information processing systems 31, 2018
1372018
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Straipsniai 1–20