| 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 | 502 | 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 | 277 | 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 | 160 | 2018 |
| 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 | 116 | 2019 |
| Value-aware loss function for model-based reinforcement learning A Farahmand, A Barreto, D Nikovski Artificial Intelligence and Statistics, 1486-1494, 2017 | 100 | 2017 |
| Universal successor features approximators D Borsa, A Barreto, J Quan, D Mankowitz, R Munos, H Van Hasselt, ... arXiv preprint arXiv:1812.07626, 2018 | 96 | 2018 |
| 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 | 93 | 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 | 75 | 2019 |
| 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 | 68 | 2008 |
| Temporally-extended {\epsilon}-greedy exploration W Dabney, G Ostrovski, A Barreto arXiv preprint arXiv:2006.01782, 2020 | 65 | 2020 |
| 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 | 64 | 2010 |
| 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 | 60 | 2001 |
| 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 | 58 | 2020 |
| 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 | 51 | 2021 |
| Practical kernel-based reinforcement learning AMS Barreto, D Precup, J Pineau The Journal of Machine Learning Research 17 (1), 2372-2441, 2016 | 50 | 2016 |
| Reinforcement learning using kernel-based stochastic factorization A Barreto, D Precup, J Pineau Advances in Neural Information Processing Systems 24, 2011 | 48 | 2011 |
| Growing compact RBF networks using a genetic algorithm AMS Barreto, HJC Barbosa, NFF Ebecken VII Brazilian Symposium on Neural Networks, 2002. SBRN 2002. Proceedings., 61-66, 2002 | 45 | 2002 |
| Fast deep reinforcement learning using online adjustments from the past S Hansen, A Pritzel, P Sprechmann, A Barreto, C Blundell Advances in Neural Information Processing Systems 31, 2018 | 44 | 2018 |
| Unicorn: Continual learning with a universal, off-policy agent DJ Mankowitz, A Žídek, A Barreto, D Horgan, M Hessel, J Quan, J Oh, ... arXiv preprint arXiv:1802.08294, 2018 | 43 | 2018 |
| 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 | 35 | 2021 |
David SilverDeepMind, UCLVerified email at google.com
