Andre Barreto
Andre Barreto
Research Scientist, Google DeepMind
Email verificata su - Home page
Citata da
Citata da
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
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
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
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
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
Universal successor features approximators
D Borsa, A Barreto, J Quan, D Mankowitz, R Munos, H Van Hasselt, ...
arXiv preprint arXiv:1812.07626, 2018
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
Value-aware loss function for model-based reinforcement learning
A Farahmand, A Barreto, D Nikovski
Artificial Intelligence and Statistics, 1486-1494, 2017
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
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
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
Reinforcement learning using kernel-based stochastic factorization
A Barreto, D Precup, J Pineau
Advances in Neural Information Processing Systems 24, 2011
Practical kernel-based reinforcement learning
AMS Barreto, D Precup, J Pineau
The Journal of Machine Learning Research 17 (1), 2372-2441, 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
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
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
GOLS—Genetic orthogonal least squares algorithm for training RBF networks
AMS Barreto, HJC Barbosa, NFF Ebecken
Neurocomputing 69 (16-18), 2041-2064, 2006
Graph layout using a genetic algorithm
AMS Barreto, HJC Barbosa
Proceedings. Vol. 1. Sixth Brazilian Symposium on Neural Networks, 179-184, 2000
A note on the variance of rank-based selection strategies for genetic algorithms and genetic programming
A Sokolov, D Whitley, A da Motta Salles Barreto
Genetic Programming and Evolvable Machines 8 (3), 221-237, 2007
Temporally-extended {\epsilon}-greedy exploration
W Dabney, G Ostrovski, A Barreto
arXiv preprint arXiv:2006.01782, 2020
Il sistema al momento non può eseguire l'operazione. Riprova più tardi.
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