| Human-level control through deep reinforcement learning V Mnih, K Kavukcuoglu, D Silver, AA Rusu, J Veness, MG Bellemare, ... nature 518 (7540), 529-533, 2015 | 11405 | 2015 |
| 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 | 8257 | 2016 |
| Playing atari with deep reinforcement learning V Mnih, K Kavukcuoglu, D Silver, A Graves, I Antonoglou, D Wierstra, ... arXiv preprint arXiv:1312.5602, 2013 | 4651 | 2013 |
| Continuous control with deep reinforcement learning TP Lillicrap, JJ Hunt, A Pritzel, N Heess, T Erez, Y Tassa, D Silver, ... arXiv preprint arXiv:1509.02971, 2015 | 4119 | 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 | 4089 | 2017 |
| Asynchronous methods for deep reinforcement learning V Mnih, AP Badia, M Mirza, A Graves, T Lillicrap, T Harley, D Silver, ... International conference on machine learning, 1928-1937, 2016 | 3721 | 2016 |
| Deep reinforcement learning with double q-learning H Van Hasselt, A Guez, D Silver arXiv preprint arXiv:1509.06461, 2015 | 2426 | 2015 |
| Deterministic policy gradient algorithms D Silver, G Lever, N Heess, T Degris, D Wierstra, M Riedmiller | 1567 | 2014 |
| Prioritized experience replay T Schaul, J Quan, I Antonoglou, D Silver arXiv preprint arXiv:1511.05952, 2015 | 1466 | 2015 |
| 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 | 823 | 2018 |
| Monte-Carlo planning in large POMDPs D Silver, J Veness Advances in neural information processing systems, 2164-2172, 2010 | 800 | 2010 |
| 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 | 731 | 2017 |
| Rainbow: Combining improvements in deep reinforcement learning M Hessel, J Modayil, H Van Hasselt, T Schaul, G Ostrovski, W Dabney, ... arXiv preprint arXiv:1710.02298, 2017 | 656 | 2017 |
| Combining online and offline knowledge in UCT S Gelly, D Silver Proceedings of the 24th international conference on Machine learning, 273-280, 2007 | 644 | 2007 |
| Reinforcement learning with unsupervised auxiliary tasks M Jaderberg, V Mnih, WM Czarnecki, T Schaul, JZ Leibo, D Silver, ... arXiv preprint arXiv:1611.05397, 2016 | 609 | 2016 |
| Fast gradient-descent methods for temporal-difference learning with linear function approximation RS Sutton, HR Maei, D Precup, S Bhatnagar, D Silver, C Szepesvári, ... Proceedings of the 26th Annual International Conference on Machine Learning …, 2009 | 466 | 2009 |
| Cooperative Pathfinding. D Silver AIIDE 1, 117-122, 2005 | 466 | 2005 |
| Universal value function approximators T Schaul, D Horgan, K Gregor, D Silver International conference on machine learning, 1312-1320, 2015 | 413 | 2015 |
| Emergence of locomotion behaviours in rich environments N Heess, D TB, S Sriram, J Lemmon, J Merel, G Wayne, Y Tassa, T Erez, ... arXiv preprint arXiv:1707.02286, 2017 | 408 | 2017 |
| 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 | 370 | 2017 |
