David Silver
DeepMind, UCL
http://www.cs.ucl.ac.uk/staff/D.Silver
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 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=-8DNE4UAAAAJ&citationforview=-8DNE4UAAAAJ:e5wmG9Sq2KIC
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 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=-8DNE4UAAAAJ&citationforview=-8DNE4UAAAAJ:xtoqd-5pKcoC
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 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=-8DNE4UAAAAJ&citationforview=-8DNE4UAAAAJ:JV2RwH3_ST0C
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 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=-8DNE4UAAAAJ&citationforview=-8DNE4UAAAAJ:r0BpntZqJG4C
Playing atari with deep reinforcement learning V Mnih, K Kavukcuoglu, D Silver, A Graves, I Antonoglou, D Wierstra, … arXiv preprint arXiv:1312.5602, 2013 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=-8DNE4UAAAAJ&citationforview=-8DNE4UAAAAJ:9ZlFYXVOiuMC
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 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=-8DNE4UAAAAJ&citationforview=-8DNE4UAAAAJ:blknAaTinKkC
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 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=-8DNE4UAAAAJ&citationforview=-8DNE4UAAAAJ:dshw04ExmUIC
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 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=-8DNE4UAAAAJ&citationforview=-8DNE4UAAAAJ:iH-uZ7U-co4C
Deterministic policy gradient algorithms D Silver, G Lever, N Heess, T Degris, D Wierstra, M Riedmiller International conference on machine learning, 387-395, 2014 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=-8DNE4UAAAAJ&citationforview=-8DNE4UAAAAJ:dhFuZR0502QC
Prioritized experience replay T Schaul, J Quan, I Antonoglou, D Silver arXiv preprint arXiv:1511.05952, 2015 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=-8DNE4UAAAAJ&citationforview=-8DNE4UAAAAJ:TFP_iSt0sucC
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 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=-8DNE4UAAAAJ&citationforview=-8DNE4UAAAAJ:q3oQSFYPqjQC
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 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=-8DNE4UAAAAJ&citationforview=-8DNE4UAAAAJ:N5tVd3kTz84C
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 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=-8DNE4UAAAAJ&citationforview=-8DNE4UAAAAJ:tzM49s52ZIMC
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 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=-8DNE4UAAAAJ&citationforview=-8DNE4UAAAAJ:KxtntwgDAa4C
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 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=-8DNE4UAAAAJ&citationforview=-8DNE4UAAAAJ:Fu2w8maKXqMC
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 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=-8DNE4UAAAAJ&citationforview=-8DNE4UAAAAJ:p2g8aNsByqUC
Monte-Carlo planning in large POMDPs D Silver, J Veness Advances in neural information processing systems 23, 2010 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=-8DNE4UAAAAJ&citationforview=-8DNE4UAAAAJ:Y0pCki6q_DkC
Reinforcement learning with unsupervised auxiliary tasks M Jaderberg, V Mnih, WM Czarnecki, T Schaul, JZ Leibo, D Silver, … arXiv preprint arXiv:1611.05397, 2016 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=-8DNE4UAAAAJ&citationforview=-8DNE4UAAAAJ:yD5IFk8b50cC
Universal value function approximators T Schaul, D Horgan, K Gregor, D Silver International conference on machine learning, 1312-1320, 2015 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=-8DNE4UAAAAJ&citationforview=-8DNE4UAAAAJ:_Qo2XoVZTnwC
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 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=-8DNE4UAAAAJ&citationforview=-8DNE4UAAAAJ:1sJd4Hv_s6UC