| Mastering the game of Go with deep neural networks and tree search D Silver, A Huang, CJ Maddison, A Guez, L Sifre, G Van Den Driessche, ... Nature 529 (7587), 484-489, 2016 | 9201 | 2016 |
| A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play D Silver, T Hubert, J Schrittwieser, I Antonoglou, M Lai, A Guez, M Lanctot, ... Science 362 (6419), 1140-1144, 2018 | 1917* | 2018 |
| Dueling Network Architectures for Deep Reinforcement Learning Z Wang, T Schaul, M Hessel, H van Hasselt, M Lanctot, N de Freitas arXiv preprint arXiv:1511.06581, 2016 | 1541 | 2016 |
| Deep Q-learning from Demonstrations T Hester, M Vecerik, O Pietquin, M Lanctot, T Schaul, B Piot, D Horgan, ... Association for the Advancement of Artificial Intelligence (AAAI), 2018 | 403 | 2018 |
| Multi-agent Reinforcement Learning in Sequential Social Dilemmas JZ Leibo, V Zambaldi, M Lanctot, J Marecki, T Graepel AAMAS, 2017 | 327 | 2017 |
| A unified game-theoretic approach to multiagent reinforcement learning M Lanctot, V Zambaldi, A Gruslys, A Lazaridou, K Tuyls, J Pérolat, D Silver, ... arXiv preprint arXiv:1711.00832, 2017 | 256 | 2017 |
| Value-decomposition networks for cooperative multi-agent learning based on team reward P Sunehag, G Lever, A Gruslys, WM Czarnecki, V Zambaldi, M Jaderberg, ... Proceedings of the 17th international conference on autonomous agents and …, 2018 | 239* | 2018 |
| Monte Carlo sampling for regret minimization in extensive games M Lanctot, K Waugh, M Zinkevich, M Bowling Advances in neural information processing systems 22, 1078-1086, 2009 | 214 | 2009 |
| Memory-efficient backpropagation through time A Gruslys, R Munos, I Danihelka, M Lanctot, A Graves Advances In Neural Information Processing Systems, 4125-4133, 2016 | 122* | 2016 |
| Fictitious Self-Play in Extensive-Form Games J Heinrich, M Lanctot, D Silver International Conference on Machine Learning, 2015 | 122 | 2015 |
| Convolution by evolution: Differentiable pattern producing networks C Fernando, D Banarse, M Reynolds, F Besse, D Pfau, M Jaderberg, ... Proceedings of the Genetic and Evolutionary Computation Conference 2016, 109-116, 2016 | 89 | 2016 |
| Adversarial planning through strategy simulation F Sailer, M Buro, M Lanctot 2007 IEEE Symposium on Computational Intelligence and Games, 80-87, 2007 | 88 | 2007 |
| Real-Time Monte-Carlo Tree Search in Ms Pac-Man T Pepels, MHM Winands, M Lanctot Transactions on Computation Intelligence and AI in Games, 2014 | 85 | 2014 |
| The hanabi challenge: A new frontier for ai research N Bard, JN Foerster, S Chandar, N Burch, M Lanctot, HF Song, E Parisotto, ... Artificial Intelligence 280, 103216, 2020 | 75 | 2020 |
| 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, 3422-3435, 2018 | 75 | 2018 |
| Efficient Nash equilibrium approximation through Monte Carlo counterfactual regret minimization. M Johanson, N Bard, M Lanctot, RG Gibson, M Bowling AAMAS, 837-846, 2012 | 72 | 2012 |
| No-Regret Learning in Extensive-Form Games with Imperfect Recall M Lanctot, R Gibson, N Burch, M Zinkevich, M Bowling International Conference on Machine Learning, 2012 | 71 | 2012 |
| Emergent Communication through Negotiation K Cao, A Lazaridou, M Lanctot, JZ Leibo, K Tuyls, S Clark arXiv preprint arXiv:1804.03980, 2018 | 69 | 2018 |
| Online Monte Carlo Counterfactual Regret Minimization for Search in Imperfect Information Games V Lisý, M Lanctot, M Bowling International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2015 | 45* | 2015 |
| Computing approximate Nash equilibria and robust best-responses using sampling M Ponsen, S De Jong, M Lanctot Journal of Artificial Intelligence Research, 575-605, 2011 | 45 | 2011 |
Thore GraepelResearch Scientist, Google DeepMind, and Professor of Computer Science, UCLVerified email at ucl.ac.uk
