Andre Barreto
Andre Barreto
Research Scientist, Google DeepMind
Verified email at google.com - Homepage
Title
Cited by
Cited by
Year
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
2432016
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
1892017
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
592008
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
592001
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
562018
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
482010
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
392002
Reinforcement learning using kernel-based stochastic factorization
A Barreto, D Precup, J Pineau
Advances in Neural Information Processing Systems 24, 720-728, 2011
332011
GOLS—Genetic orthogonal least squares algorithm for training RBF networks
AMS Barreto, HJC Barbosa, NFF Ebecken
Neurocomputing 69 (16-18), 2041-2064, 2006
312006
Value-aware loss function for model-based reinforcement learning
A Farahmand, A Barreto, D Nikovski
Artificial Intelligence and Statistics, 1486-1494, 2017
292017
Practical kernel-based reinforcement learning
AMS Barreto, D Precup, J Pineau
The Journal of Machine Learning Research 17 (1), 2372-2441, 2016
272016
Graph layout using a genetic algorithm
AMS Barreto, HJC Barbosa
Proceedings. Vol. 1. Sixth Brazilian Symposium on Neural Networks, 179-184, 2000
272000
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
262018
Universal successor features approximators
D Borsa, A Barreto, J Quan, D Mankowitz, R Munos, H van Hasselt, ...
arXiv preprint arXiv:1812.07626, 2018
222018
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
202018
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
202007
Policy iteration based on stochastic factorization
AMS Barreto, J Pineau, D Precup
Journal of Artificial Intelligence Research 50, 763-803, 2014
162014
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
152019
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
142011
Using performance profiles for the analysis and design of benchmark experiments
HJC Barbosa, HS Bernardino, AMS Barreto
Advances in Metaheuristics, 21-36, 2013
132013
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