| 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 | 29441 | 2015 |
| Highly accurate protein structure prediction with AlphaFold J Jumper, R Evans, A Pritzel, T Green, M Figurnov, O Ronneberger, ... Nature 596 (7873), 583-589, 2021 | 21434 | 2021 |
| 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 | 18448 | 2016 |
| 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 | 15500 | 2015 |
| Playing atari with deep reinforcement learning V Mnih, K Kavukcuoglu, D Silver, A Graves, I Antonoglou, D Wierstra, ... arXiv preprint arXiv:1312.5602, 2013 | 14366 | 2013 |
| 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 | 10766 | 2016 |
| 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 | 10478 | 2017 |
| Deep reinforcement learning with double q-learning H Van Hasselt, A Guez, D Silver Proceedings of the AAAI conference on artificial intelligence 30 (1), 2016 | 8734 | 2016 |
| Deterministic policy gradient algorithms D Silver, G Lever, N Heess, T Degris, D Wierstra, M Riedmiller International conference on machine learning, 387-395, 2014 | 4838 | 2014 |
| Prioritized experience replay T Schaul, J Quan, I Antonoglou, D Silver arXiv preprint arXiv:1511.05952, 2015 | 4637 | 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 | 4201 | 2018 |
| 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 | 4115 | 2019 |
| Improved protein structure prediction using potentials from deep learning AW Senior, R Evans, J Jumper, J Kirkpatrick, L Sifre, T Green, C Qin, ... Nature 577 (7792), 706-710, 2020 | 3008 | 2020 |
| Rainbow: Combining improvements in deep reinforcement learning M Hessel, J Modayil, H Van Hasselt, T Schaul, G Ostrovski, W Dabney, ... Proceedings of the AAAI conference on artificial intelligence 32 (1), 2018 | 2487 | 2018 |
| Mastering atari, go, chess and shogi by planning with a learned model J Schrittwieser, I Antonoglou, T Hubert, K Simonyan, L Sifre, S Schmitt, ... Nature 588 (7839), 604-609, 2020 | 2157 | 2020 |
| 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 | 2122 | 2017 |
| Monte-Carlo planning in large POMDPs D Silver, J Veness Advances in neural information processing systems 23, 2010 | 1410 | 2010 |
| Reinforcement learning with unsupervised auxiliary tasks M Jaderberg, V Mnih, WM Czarnecki, T Schaul, JZ Leibo, D Silver, ... arXiv preprint arXiv:1611.05397, 2016 | 1348 | 2016 |
| Universal value function approximators T Schaul, D Horgan, K Gregor, D Silver International conference on machine learning, 1312-1320, 2015 | 1163 | 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 | 1068 | 2017 |
