| 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 | 901 | 2018 |
| A distributional perspective on reinforcement learning MG Bellemare*, W Dabney*, R Munos arXiv preprint arXiv:1707.06887, 2017 | 599 | 2017 |
| 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 | 255 | 2016 |
| Distributed distributional deterministic policy gradients G Barth-Maron, MW Hoffman, D Budden, W Dabney, D Horgan, D Tb, ... arXiv preprint arXiv:1804.08617, 2018 | 231 | 2018 |
| Distributional reinforcement learning with quantile regression W Dabney, M Rowland, M Bellemare, R Munos Proceedings of the AAAI Conference on Artificial Intelligence 32 (1), 2018 | 188 | 2018 |
| The cramer distance as a solution to biased wasserstein gradients MG Bellemare, I Danihelka, W Dabney, S Mohamed, ... arXiv preprint arXiv:1705.10743, 2017 | 185 | 2017 |
| Implicit quantile networks for distributional reinforcement learning W Dabney, G Ostrovski, D Silver, R Munos International conference on machine learning, 1096-1105, 2018 | 139 | 2018 |
| Recurrent experience replay in distributed reinforcement learning S Kapturowski, G Ostrovski, J Quan, R Munos, W Dabney International conference on learning representations, 2018 | 136 | 2018 |
| A distributional code for value in dopamine-based reinforcement learning W Dabney, Z Kurth-Nelson, N Uchida, CK Starkweather, D Hassabis, ... Nature 577 (7792), 671-675, 2020 | 92 | 2020 |
| Adaptive step-size for online temporal difference learning W Dabney, A Barto Proceedings of the AAAI Conference on Artificial Intelligence 26 (1), 2012 | 51 | 2012 |
| An analysis of categorical distributional reinforcement learning M Rowland, M Bellemare, W Dabney, R Munos, YW Teh International Conference on Artificial Intelligence and Statistics, 29-37, 2018 | 50 | 2018 |
| RLPy: a value-function-based reinforcement learning framework for education and research. A Geramifard, C Dann, RH Klein, W Dabney, JP How J. Mach. Learn. Res. 16 (1), 1573-1578, 2015 | 43 | 2015 |
| The reactor: A fast and sample-efficient actor-critic agent for reinforcement learning A Gruslys, W Dabney, MG Azar, B Piot, M Bellemare, R Munos arXiv preprint arXiv:1704.04651, 2017 | 42 | 2017 |
| Autoregressive quantile networks for generative modeling G Ostrovski, W Dabney, R Munos International Conference on Machine Learning, 3936-3945, 2018 | 41 | 2018 |
| Proximal reinforcement learning: A new theory of sequential decision making in primal-dual spaces S Mahadevan, B Liu, P Thomas, W Dabney, S Giguere, N Jacek, I Gemp, ... arXiv preprint arXiv:1405.6757, 2014 | 37 | 2014 |
| A geometric perspective on optimal representations for reinforcement learning MG Bellemare, W Dabney, R Dadashi, AA Taiga, PS Castro, NL Roux, ... arXiv preprint arXiv:1901.11530, 2019 | 34 | 2019 |
| Projected Natural Actor-Critic. PS Thomas, W Dabney, S Giguere, S Mahadevan NIPS, 2337-2345, 2013 | 26 | 2013 |
| Utile Distinctions for Relational Reinforcement Learning. W Dabney, A McGovern IJCAI 7, 738-743, 2007 | 21 | 2007 |
| Deep reinforcement learning and its neuroscientific implications M Botvinick, JX Wang, W Dabney, KJ Miller, Z Kurth-Nelson Neuron, 2020 | 20 | 2020 |
| Statistics and samples in distributional reinforcement learning M Rowland, R Dadashi, S Kumar, R Munos, MG Bellemare, W Dabney International Conference on Machine Learning, 5528-5536, 2019 | 20 | 2019 |
Rémi MunosDeepMindVerified email at inria.fr
