David Silver
Title
Cited by
Cited by
Year
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
114052015
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
82572016
Playing atari with deep reinforcement learning
V Mnih, K Kavukcuoglu, D Silver, A Graves, I Antonoglou, D Wierstra, ...
arXiv preprint arXiv:1312.5602, 2013
46512013
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
41192015
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
40892017
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
37212016
Deep reinforcement learning with double q-learning
H Van Hasselt, A Guez, D Silver
arXiv preprint arXiv:1509.06461, 2015
24262015
Deterministic policy gradient algorithms
D Silver, G Lever, N Heess, T Degris, D Wierstra, M Riedmiller
15672014
Prioritized experience replay
T Schaul, J Quan, I Antonoglou, D Silver
arXiv preprint arXiv:1511.05952, 2015
14662015
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
8232018
Monte-Carlo planning in large POMDPs
D Silver, J Veness
Advances in neural information processing systems, 2164-2172, 2010
8002010
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
7312017
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
6562017
Combining online and offline knowledge in UCT
S Gelly, D Silver
Proceedings of the 24th international conference on Machine learning, 273-280, 2007
6442007
Reinforcement learning with unsupervised auxiliary tasks
M Jaderberg, V Mnih, WM Czarnecki, T Schaul, JZ Leibo, D Silver, ...
arXiv preprint arXiv:1611.05397, 2016
6092016
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
4662009
Cooperative Pathfinding.
D Silver
AIIDE 1, 117-122, 2005
4662005
Universal value function approximators
T Schaul, D Horgan, K Gregor, D Silver
International conference on machine learning, 1312-1320, 2015
4132015
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
4082017
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
3702017
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