| 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 | 270 | 2016 |
| 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 | 201 | 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 | 64 | 2018 |
| 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 | 62 | 2008 |
| 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 | 59 | 2001 |
| 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 | 49 | 2010 |
| 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 | 41 | 2002 |
| Reinforcement learning using kernel-based stochastic factorization A Barreto, D Precup, J Pineau Advances in Neural Information Processing Systems 24, 720-728, 2011 | 34 | 2011 |
| Value-aware loss function for model-based reinforcement learning A Farahmand, A Barreto, D Nikovski Artificial Intelligence and Statistics, 1486-1494, 2017 | 31 | 2017 |
| GOLS—Genetic orthogonal least squares algorithm for training RBF networks AMS Barreto, HJC Barbosa, NFF Ebecken Neurocomputing 69 (16-18), 2041-2064, 2006 | 31 | 2006 |
| Universal successor features approximators D Borsa, A Barreto, J Quan, D Mankowitz, R Munos, H van Hasselt, ... arXiv preprint arXiv:1812.07626, 2018 | 30 | 2018 |
| Practical kernel-based reinforcement learning AMS Barreto, D Precup, J Pineau The Journal of Machine Learning Research 17 (1), 2372-2441, 2016 | 30 | 2016 |
| 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 | 29 | 2018 |
| Graph layout using a genetic algorithm AMS Barreto, HJC Barbosa Proceedings. Vol. 1. Sixth Brazilian Symposium on Neural Networks, 179-184, 2000 | 28 | 2000 |
| 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 | 24 | 2019 |
| Fast deep reinforcement learning using online adjustments from the past S Hansen, P Sprechmann, A Pritzel, A Barreto, C Blundell arXiv preprint arXiv:1810.08163, 2018 | 20 | 2018 |
| A note on the variance of rank-based selection strategies for genetic algorithms and genetic programming A Sokolov, D Whitley Genetic Programming and Evolvable Machines 8 (3), 221-237, 2007 | 20 | 2007 |
| Policy iteration based on stochastic factorization AMS Barreto, J Pineau, D Precup Journal of Artificial Intelligence Research 50, 763-803, 2014 | 16 | 2014 |
| Computing the stationary distribution of a finite Markov chain through stochastic factorization AMS Barreto, MD Fragoso SIAM Journal on Matrix Analysis and Applications 32 (4), 1513-1523, 2011 | 14 | 2011 |
| The option keyboard: Combining skills in reinforcement learning A Barreto, D Borsa, S Hou, G Comanici, E Aygün, P Hamel, DK Toyama, ... | 13 | 2019 |
David SilverGoogle DeepMindVerified email at google.com
