| Deep reinforcement learning with double Q-learning H van Hasselt, A Guez, D Silver AAAI Conference on Artificial Intelligence, 2094-2100, 2016 | 10598 | 2016 |
| 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 | 5611 | 2016 |
| 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 | 2951 | 2018 |
| Double Q-learning H van Hasselt Advances in Neural Information Processing Systems, 2613-2621, 2010 | 2356* | 2010 |
| 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 | 1164 | 2017 |
| Advances in neural information processing systems G Papamakarios, I Murray, T Pavlakou Inc. Curran Associates, Long Beach, California, USA, 2338, 2017 | 904 | 2017 |
| Deep Reinforcement Learning in Large Discrete Action Spaces G Dulac-Arnold, R Evans, H van Hasselt, P Sunehag, T Lillicrap, J Hunt | 794 | 2015 |
| Successor features for transfer in reinforcement learning A Barreto, W Dabney, R Munos, JJ Hunt, T Schaul, HP van Hasselt, ... Advances in neural information processing systems 30, 2017 | 704 | 2017 |
| Meta-gradient reinforcement learning Z Xu, HP van Hasselt, D Silver Advances in neural information processing systems 31, 2018 | 380 | 2018 |
| Reinforcement learning in continuous action spaces H van Hasselt, MA Wiering Approximate Dynamic Programming and Reinforcement Learning, 2007. ADPRL 2007 …, 2007 | 343 | 2007 |
| Multi-task deep reinforcement learning with popart M Hessel, H Soyer, L Espeholt, W Czarnecki, S Schmitt, H Van Hasselt Proceedings of the AAAI Conference on Artificial Intelligence 33 (01), 3796-3803, 2019 | 336 | 2019 |
| Reinforcement Learning in Continuous State and Action Spaces H van Hasselt Reinforcement Learning: State of the Art, 207-251, 2012 | 318 | 2012 |
| 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 | 316 | 2017 |
| A theoretical and empirical analysis of Expected Sarsa H van Seijen, H van Hasselt, S Whiteson, M Wiering Adaptive Dynamic Programming and Reinforcement Learning, 2009. ADPRL'09 …, 2009 | 303 | 2009 |
| Deep reinforcement learning and the deadly triad H van Hasselt, Y Doron, F Strub, M Hessel, N Sonnerat, J Modayil arXiv preprint arXiv:1812.02648, 2018 | 284 | 2018 |
| Ensemble algorithms in reinforcement learning MA Wiering, H van Hasselt IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 38 …, 2008 | 268 | 2008 |
| When to use parametric models in reinforcement learning? HP Van Hasselt, M Hessel, J Aslanides Advances in Neural Information Processing Systems 32, 2019 | 237 | 2019 |
| Learning values across many orders of magnitude H van Hasselt, A Guez, M Hessel, V Mnih, D Silver Advances in Neural Information Processing Systems 29 (NIPS 2016), 2016 | 215 | 2016 |
| Behaviour suite for reinforcement learning I Osband, Y Doron, M Hessel, J Aslanides, E Sezener, A Saraiva, ... arXiv preprint arXiv:1908.03568, 2019 | 204 | 2019 |
| Weighted importance sampling for off-policy learning with linear function approximation AR Mahmood, H van Hasselt, RS Sutton Advances in Neural Information Processing Systems 27, 2014 | 189 | 2014 |
Tom SchaulSenior Staff Scientist, DeepMindยืนยันอีเมลแล้วที่ nyu.edu
