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 | 32998 | 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 | 29676 | 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 | 20519 | 2016 |
Continuous control with deep reinforcement learning TP Lillicrap arXiv preprint arXiv:1509.02971, 2015 | 17859 | 2015 |
Playing atari with deep reinforcement learning V Mnih arXiv preprint arXiv:1312.5602, 2013 | 16426 | 2013 |
Asynchronous Methods for Deep Reinforcement Learning V Mnih arXiv preprint arXiv:1602.01783, 2016 | 12081 | 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 | 11681 | 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 | 10054 | 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 | 5518 | 2014 |
Prioritized Experience Replay T Schaul arXiv preprint arXiv:1511.05952, 2015 | 5252 | 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 | 4946 | 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 | 4913 | 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 | 3422 | 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 | 2850 | 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 | 2630 | 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 | 2429 | 2017 |
Gemini: a family of highly capable multimodal models G Team, R Anil, S Borgeaud, JB Alayrac, J Yu, R Soricut, J Schalkwyk, ... arXiv preprint arXiv:2312.11805, 2023 | 2209 | 2023 |
Monte-Carlo planning in large POMDPs D Silver, J Veness Advances in neural information processing systems 23, 2010 | 1521 | 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 | 1453 | 2016 |
Universal value function approximators T Schaul, D Horgan, K Gregor, D Silver International conference on machine learning, 1312-1320, 2015 | 1281 | 2015 |