| 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 | 2985 | 2018 |
| A distributional perspective on reinforcement learning MG Bellemare*, W Dabney*, R Munos arXiv preprint arXiv:1707.06887, 2017 | 2025 | 2017 |
| 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 | 964 | 2018 |
| 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 | 706 | 2017 |
| Distributed distributional deterministic policy gradients G Barth-Maron, MW Hoffman, D Budden, W Dabney, D Horgan, D Tb, ... arXiv preprint arXiv:1804.08617, 2018 | 705 | 2018 |
| Implicit quantile networks for distributional reinforcement learning W Dabney, G Ostrovski, D Silver, R Munos International conference on machine learning, 1096-1105, 2018 | 687 | 2018 |
| Recurrent experience replay in distributed reinforcement learning S Kapturowski, G Ostrovski, J Quan, R Munos, W Dabney International conference on learning representations, 2018 | 606 | 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 | 473 | 2020 |
| The cramer distance as a solution to biased wasserstein gradients MG Bellemare, I Danihelka, W Dabney, S Mohamed, ... arXiv preprint arXiv:1705.10743, 2017 | 446 | 2017 |
| Revisiting fundamentals of experience replay W Fedus, P Ramachandran, R Agarwal, Y Bengio, H Larochelle, ... International conference on machine learning, 3061-3071, 2020 | 340 | 2020 |
| Deep reinforcement learning and its neuroscientific implications M Botvinick, JX Wang, W Dabney, KJ Miller, Z Kurth-Nelson Neuron 107 (4), 603-616, 2020 | 252 | 2020 |
| Fast task inference with variational intrinsic successor features S Hansen, W Dabney, A Barreto, T Van de Wiele, D Warde-Farley, V Mnih arXiv preprint arXiv:1906.05030, 2019 | 198 | 2019 |
| Distributional reinforcement learning MG Bellemare, W Dabney, M Rowland MIT Press, 2023 | 182 | 2023 |
| 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 | 150 | 2018 |
| Temporally-extended {\epsilon}-greedy exploration W Dabney, G Ostrovski, A Barreto arXiv preprint arXiv:2006.01782, 2020 | 124 | 2020 |
| Understanding and preventing capacity loss in reinforcement learning C Lyle, M Rowland, W Dabney arXiv preprint arXiv:2204.09560, 2022 | 123 | 2022 |
| 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 | 113 | 2017 |
| On the expressivity of markov reward D Abel, W Dabney, A Harutyunyan, MK Ho, M Littman, D Precup, S Singh Advances in Neural Information Processing Systems 34, 7799-7812, 2021 | 112 | 2021 |
| 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 | 112 | 2019 |
| A geometric perspective on optimal representations for reinforcement learning M Bellemare, W Dabney, R Dadashi, A Ali Taiga, PS Castro, N Le Roux, ... Advances in neural information processing systems 32, 2019 | 108 | 2019 |
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