| 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 | 9337 | 2016 |
| Continuous control with deep reinforcement learning TP Lillicrap, JJ Hunt, A Pritzel, N Heess, T Erez, Y Tassa, D Silver, ... ICLR 2016; arXiv preprint arXiv:1509.02971, 2015 | 5027 | 2015 |
| Mastering the game of go without human knowledge D Silver, J Schrittwieser, K Simonyan, I Antonoglou, A Huang, A Guez, ... nature 550 (7676), 354-359, 2017 | 4829 | 2017 |
| Asynchronous methods for deep reinforcement learning V Mnih, AP Badia, M Mirza, A Graves, TP Lillicrap, T Harley, D Silver, ... arXiv:1602.01783, 2016 | 4377 | 2016 |
| Matching networks for one shot learning O Vinyals, C Blundell, T Lillicrap, K Kavukcuoglu, D Wierstra arXiv preprint arXiv:1606.04080, 2016 | 2301 | 2016 |
| Meta-learning with memory-augmented neural networks A Santoro, S Bartunov, M Botvinick, D Wierstra, T Lillicrap International conference on machine learning, 1842-1850, 2016 | 1209* | 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 | 1126 | 2018 |
| A simple neural network module for relational reasoning A Santoro, D Raposo, DGT Barrett, M Malinowski, R Pascanu, P Battaglia, ... arXiv preprint arXiv:1706.01427, 2017 | 921 | 2017 |
| Mastering chess and shogi by self-play with a general reinforcement learning algorithm D Silver, T Hubert, J Schrittwieser, I Antonoglou, M Lai, A Guez, M Lanctot, ... arXiv preprint arXiv:1712.01815, 2017 | 886 | 2017 |
| Deep reinforcement learning for robotic manipulation S Gu, E Holly, T Lillicrap, S Levine arXiv:1610.00633, 2016 | 842* | 2016 |
| Why copy others? Insights from the social learning strategies tournament L Rendell, R Boyd, D Cownden, M Enquist, K Eriksson, MW Feldman, ... Science 328 (5975), 208-213, 2010 | 685 | 2010 |
| Continuous deep Q-learning with model-based acceleration S Gu, T Lillicrap, I Sutskever, S Levine ICML2016; arXiv:1603.00748 [cs.LG], 2016 | 674 | 2016 |
| Grandmaster level in StarCraft II using multi-agent reinforcement learning O Vinyals, I Babuschkin, WM Czarnecki, M Mathieu, A Dudzik, J Chung, ... Nature 575 (7782), 350-354, 2019 | 596 | 2019 |
| Starcraft ii: A new challenge for reinforcement learning O Vinyals, T Ewalds, S Bartunov, P Georgiev, AS Vezhnevets, M Yeo, ... arXiv preprint arXiv:1708.04782, 2017 | 449 | 2017 |
| Random synaptic feedback weights support error backpropagation for deep learning TP Lillicrap, D Cownden, DB Tweed, CJ Akerman Nature communications 7 (1), 1-10, 2016 | 380 | 2016 |
| Learning continuous control policies by stochastic value gradients N Heess, G Wayne, D Silver, T Lillicrap, Y Tassa, T Erez arXiv preprint arXiv:1510.09142, 2015 | 362 | 2015 |
| Learning latent dynamics for planning from pixels D Hafner, T Lillicrap, I Fischer, R Villegas, D Ha, H Lee, J Davidson International Conference on Machine Learning, 2555-2565, 2019 | 308 | 2019 |
| Vector-based navigation using grid-like representations in artificial agents A Banino, C Barry, B Uria, C Blundell, T Lillicrap, P Mirowski, A Pritzel, ... Nature 557 (7705), 429-433, 2018 | 299 | 2018 |
| Alphastar: Mastering the real-time strategy game starcraft ii O Vinyals, I Babuschkin, J Chung, M Mathieu, M Jaderberg, ... DeepMind blog 2, 2019 | 267 | 2019 |
| Q-prop: Sample-efficient policy gradient with an off-policy critic S Gu, T Lillicrap, Z Ghahramani, RE Turner, S Levine arXiv preprint arXiv:1611.02247, 2016 | 244 | 2016 |
David SilverGoogle DeepMindVerified email at google.com
