Hado van Hasselt
Hado van Hasselt
Research Scientist, DeepMind; Honorary Professor, UCL
Verified email at google.com - Homepage
Title
Cited by
Cited by
Year
Deep reinforcement learning with double Q-learning
H van Hasselt, A Guez, D Silver
AAAI Conference on Artificial Intelligence, 2094-2100, 2016
29352016
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
29292016
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
29292016
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
29292016
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
29292016
Dueling Network Architectures for Deep Reinforcement Learning
Z Wang, T Schaul, M Hessel, H van Hasselt, M Lanctot, N de Freitas
The 33rd International Conference on Machine Learning, 1995–2003, 2016
16002016
Dueling network architectures for deep reinforcement learning
Z Wang, T Schaul, M Hessel, H Hasselt, M Lanctot, N Freitas
International conference on machine learning, 1995-2003, 2016
15852016
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
8222018
Rainbow: Combining improvements in deep reinforcement learning
M Hessel, J Modayil, H van Hasselt, T Schaul, G Ostrovski, W Dabney, ...
Thirty-Second AAAI Conference on Artificial Intelligence, 2018
8222018
Double Q-learning
H van Hasselt
Advances in Neural Information Processing Systems, 2613-2621, 2010
757*2010
Double Q-learning
H Hasselt
Advances in neural information processing systems 23, 2613-2621, 2010
7492010
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
4492017
Distributed prioritized experience replay
D Horgan, J Quan, D Budden, G Barth-Maron, M Hessel, H van Hasselt, ...
arXiv preprint arXiv:1803.00933, 2018
2782018
Reinforcement learning in continuous action spaces
H van Hasselt, MA Wiering
Approximate Dynamic Programming and Reinforcement Learning, 2007. ADPRL 2007 …, 2007
2472007
Successor features for transfer in reinforcement learning
A Barreto, W Dabney, R Munos, JJ Hunt, T Schaul, H Van Hasselt, ...
arXiv preprint arXiv:1606.05312, 2016
2442016
Reinforcement Learning in Continuous State and Action Spaces
H van Hasselt
Reinforcement Learning: State of the Art, 207-251, 2012
2052012
Deep Reinforcement Learning in Large Discrete Action Spaces
G Dulac-Arnold, R Evans, H van Hasselt, P Sunehag, T Lillicrap, J Hunt
1912015
The predictron: End-to-end learning and planning
D Silver, H Hasselt, M Hessel, T Schaul, A Guez, T Harley, ...
International Conference on Machine Learning, 3191-3199, 2017
1872017
Ensemble algorithms in reinforcement learning
MA Wiering, H van Hasselt
IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 38 …, 2008
1502008
Meta-gradient reinforcement learning
Z Xu, H van Hasselt, D Silver
arXiv preprint arXiv:1805.09801, 2018
147*2018
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Articles 1–20