| 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 | 914 | 2017 |
| 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 | 507 | 2017 |
| 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 | 504 | 2002 |
| 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 | 362* | 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 | 272 | 2015 |
| 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 | 253 | 2018 |
| Multiagent learning: Basics, challenges, and prospects K Tuyls, G Weiss Ai Magazine 33 (3), 41-41, 2012 | 221 | 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 | 182 | 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 | 174 | 2018 |
| Inference of concise DTDs from XML data GJ Bex, F Neven, T Schwentick, K Tuyls ACM Press, 2006 | 171 | 2006 |
| 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 | 170 | 2017 |
| Lenient multi-agent deep reinforcement learning G Palmer, K Tuyls, D Bloembergen, R Savani arXiv preprint arXiv:1707.04402, 2017 | 162 | 2017 |
| What evolutionary game theory tells us about multiagent learning K Tuyls, S Parsons Artificial Intelligence 171 (7), 406-416, 2007 | 158 | 2007 |
| 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 | 157 | 2003 |
| 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 | 154 | 2018 |
| 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 | 148 | 2019 |
| 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 | 148 | 2006 |
| 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 | 146 | 2012 |
| Emergent communication through negotiation K Cao, A Lazaridou, M Lanctot, JZ Leibo, K Tuyls, S Clark arXiv preprint arXiv:1804.03980, 2018 | 144 | 2018 |
| 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 | 135 | 2018 |
Daniel HennesResearch Scientist, Google DeepMindVerified email at google.com
