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 | 32721 | 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 | 20388 | 2016 |
Playing atari with deep reinforcement learning V Mnih arXiv preprint arXiv:1312.5602, 2013 | 16291 | 2013 |
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 | 11601 | 2017 |
Prioritized Experience Replay T Schaul arXiv preprint arXiv:1511.05952, 2015 | 5209 | 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 | 4909 | 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 | 2611 | 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 | 2411 | 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 | 2070 | 2023 |
Playing atari with deep reinforcement learning. arXiv 2013 V Mnih, K Kavukcuoglu, D Silver, A Graves, I Antonoglou, D Wierstra, ... arXiv preprint arXiv:1312.5602, 2013 | 1060 | 2013 |
Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context M Reid, N Savinov, D Teplyashin, D Lepikhin, T Lillicrap, J Alayrac, ... arXiv preprint arXiv:2403.05530, 2024 | 614 | 2024 |
Gemini: A family of highly capable multimodal models R Anil, S Borgeaud, Y Wu, JB Alayrac, J Yu, R Soricut, J Schalkwyk, ... arXiv preprint arXiv:2312.11805 1, 2023 | 217 | 2023 |
Mastering chess and shogi by self-play with a general reinforcement learning algorithm. arXiv 2017 D Silver, T Hubert, J Schrittwieser, I Antonoglou, M Lai, A Guez, M Lanctot, ... arXiv preprint arXiv:1712.01815, 2017 | 162 | 2017 |
Bayesian optimization in alphago Y Chen, A Huang, Z Wang, I Antonoglou, J Schrittwieser, D Silver, ... arXiv preprint arXiv:1812.06855, 2018 | 160 | 2018 |
Prioritized experience replay. arXiv 2015 T Schaul, J Quan, I Antonoglou, D Silver arXiv preprint arXiv:1511.05952 5952, 2016 | 154 | 2016 |
Unit tests for stochastic optimization T Schaul, I Antonoglou, D Silver arXiv preprint arXiv:1312.6055, 2013 | 140 | 2013 |
Online and offline reinforcement learning by planning with a learned model J Schrittwieser, T Hubert, A Mandhane, M Barekatain, I Antonoglou, ... Advances in Neural Information Processing Systems 34, 27580-27591, 2021 | 124 | 2021 |
Learning to search with mctsnets A Guez, T Weber, I Antonoglou, K Simonyan, O Vinyals, D Wierstra, ... International conference on machine learning, 1822-1831, 2018 | 97 | 2018 |
Learning and planning in complex action spaces T Hubert, J Schrittwieser, I Antonoglou, M Barekatain, S Schmitt, D Silver International Conference on Machine Learning, 4476-4486, 2021 | 91 | 2021 |
A test of relative similarity for model selection in generative models W Bounliphone, E Belilovsky, MB Blaschko, I Antonoglou, A Gretton arXiv preprint arXiv:1511.04581, 2015 | 87 | 2015 |