| 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 | 708 | 2017 |
| 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 |
| Transfer in deep reinforcement learning using successor features and generalised policy improvement A Barreto, D Borsa, J Quan, T Schaul, D Silver, M Hessel, D Mankowitz, ... International Conference on Machine Learning, 501-510, 2018 | 215 | 2018 |
| New machine learning and physics-based scoring functions for drug discovery IA Guedes, AMS Barreto, D Marinho, E Krempser, MA Kuenemann, ... Scientific reports 11 (1), 3198, 2021 | 200 | 2021 |
| 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 |
| Fast reinforcement learning with generalized policy updates A Barreto, S Hou, D Borsa, D Silver, D Precup Proceedings of the National Academy of Sciences 117 (48), 30079-30087, 2020 | 159 | 2020 |
| Universal successor features approximators D Borsa, A Barreto, J Quan, D Mankowitz, R Munos, H Van Hasselt, ... arXiv preprint arXiv:1812.07626, 2018 | 156 | 2018 |
| Value-aware loss function for model-based reinforcement learning A Farahmand, A Barreto, D Nikovski Artificial Intelligence and Statistics, 1486-1494, 2017 | 144 | 2017 |
| Temporally-extended {\epsilon}-greedy exploration W Dabney, G Ostrovski, A Barreto arXiv preprint arXiv:2006.01782, 2020 | 124 | 2020 |
| The option keyboard: Combining skills in reinforcement learning A Barreto, D Borsa, S Hou, G Comanici, E Aygün, P Hamel, D Toyama, ... Advances in Neural Information Processing Systems 32, 2019 | 116 | 2019 |
| The value equivalence principle for model-based reinforcement learning C Grimm, A Barreto, S Singh, D Silver Advances in neural information processing systems 33, 5541-5552, 2020 | 94 | 2020 |
| A definition of continual reinforcement learning D Abel, A Barreto, B Van Roy, D Precup, HP van Hasselt, S Singh Advances in Neural Information Processing Systems 36, 50377-50407, 2023 | 78 | 2023 |
| Using performance profiles to analyze the results of the 2006 CEC constrained optimization competition HJC Barbosa, HS Bernardino, AMS Barreto IEEE congress on evolutionary computation, 1-8, 2010 | 75 | 2010 |
| The value-improvement path: Towards better representations for reinforcement learning W Dabney, A Barreto, M Rowland, R Dadashi, J Quan, MG Bellemare, ... Proceedings of the AAAI conference on artificial intelligence 35 (8), 7160-7168, 2021 | 73 | 2021 |
| Restricted gradient-descent algorithm for value-function approximation in reinforcement learning A da Motta Salles Barreto, CW Anderson Artificial Intelligence 172 (4-5), 454-482, 2008 | 73 | 2008 |
| Temporal abstraction in reinforcement learning with the successor representation MC Machado, A Barreto, D Precup, M Bowling Journal of machine learning research 24 (80), 1-69, 2023 | 63 | 2023 |
| An interactive genetic algorithm with co-evolution of weights for multiobjective problems HJC Barbosa, AMS Barreto Proceedings of the 3rd Annual Conference on Genetic and Evolutionary …, 2001 | 61 | 2001 |
| Practical kernel-based reinforcement learning AMS Barreto, D Precup, J Pineau Journal of Machine Learning Research 17 (67), 1-70, 2016 | 56 | 2016 |
| Deep reinforcement learning with plasticity injection E Nikishin, J Oh, G Ostrovski, C Lyle, R Pascanu, W Dabney, A Barreto Advances in Neural Information Processing Systems 36, 37142-37159, 2023 | 52 | 2023 |
| Expected eligibility traces H van Hasselt, S Madjiheurem, M Hessel, D Silver, A Barreto, D Borsa Proceedings of the AAAI conference on artificial intelligence 35 (11), 9997 …, 2021 | 52 | 2021 |
David SilverDeepMind, UCLVerified email at google.com
