André Barreto

According to our database, André Barreto authored at least 71 papers between 2000 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Video as the New Language for Real-World Decision Making.

Sherry Yang, Jacob Walker, Jack Parker-Holder, Yilun Du, Jake Bruce, André Barreto, Pieter Abbeel, Dale Schuurmans

CoRR, 2024

A Distributional Analogue to the Successor Representation.

Harley Wiltzer, Jesse Farebrother, Arthur Gretton, Yunhao Tang, André Barreto, Will Dabney, Marc G. Bellemare, Mark Rowland

CoRR, 2024

2023

Temporal Abstraction in Reinforcement Learning with the Successor Representation.

Marlos C. Machado, André Barreto, Doina Precup, Michael Bowling

J. Mach. Learn. Res., 2023

A Definition of Continual Reinforcement Learning.

David Abel, André Barreto, Benjamin Van Roy, Doina Precup, Hado van Hasselt, Satinder Singh

CoRR, 2023

On the Convergence of Bounded Agents.

David Abel, André Barreto, Hado van Hasselt, Benjamin Van Roy, Doina Precup, Satinder Singh

CoRR, 2023

Deep Reinforcement Learning with Plasticity Injection.

Evgenii Nikishin, Junhyuk Oh, Georg Ostrovski, Clare Lyle, Razvan Pascanu, Will Dabney, André Barreto

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

A Definition of Continual Reinforcement Learning.

David Abel, André Barreto, Benjamin Van Roy, Doina Precup, Hado Philip van Hasselt, Satinder Singh

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022

Efficient information diffusion in time-varying graphs through deep reinforcement learning.

Matheus R. F. Mendonça, André da Motta Salles Barreto, Artur Ziviani

World Wide Web, 2022

The Phenomenon of Policy Churn.

Tom Schaul, André Barreto, John Quan, Georg Ostrovski

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Approximate Value Equivalence.

Christopher Grimm, André Barreto, Satinder Singh

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Generalised Policy Improvement with Geometric Policy Composition.

Shantanu Thakoor, Mark Rowland, Diana Borsa, Will Dabney, Rémi Munos, André Barreto

Proceedings of the International Conference on Machine Learning, 2022

Model-Value Inconsistency as a Signal for Epistemic Uncertainty.

Angelos Filos, Eszter Vértes, Zita Marinho, Gregory Farquhar, Diana Borsa, Abram L. Friesen, Feryal M. P. Behbahani, Tom Schaul, André Barreto, Simon Osindero

Proceedings of the International Conference on Machine Learning, 2022

2021

Approximating Network Centrality Measures Using Node Embedding and Machine Learning.

Matheus R. F. Mendonça, André Barreto, Artur Ziviani

IEEE Trans. Netw. Sci. Eng., 2021

Temporal Abstraction in Reinforcement Learning with the Successor Representation.

Marlos C. Machado, André Barreto, Doina Precup

CoRR, 2021

Discovering Diverse Nearly Optimal Policies withSuccessor Features.

Tom Zahavy, Brendan O'Donoghue, André Barreto, Volodymyr Mnih, Sebastian Flennerhag, Satinder Singh

CoRR, 2021

Coverage as a Principle for Discovering Transferable Behavior in Reinforcement Learning.

Víctor Campos, Pablo Sprechmann, Steven Hansen, André Barreto, Steven Kapturowski, Alex Vitvitskyi, Adrià Puigdomènech Badia, Charles Blundell

CoRR, 2021

Proper Value Equivalence.

Christopher Grimm, André Barreto, Gregory Farquhar, David Silver, Satinder Singh

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Risk-Aware Transfer in Reinforcement Learning using Successor Features.

Michael Gimelfarb, André Barreto, Scott Sanner, Chi-Guhn Lee

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Discovering a set of policies for the worst case reward.

Tom Zahavy, André Barreto, Daniel J. Mankowitz, Shaobo Hou, Brendan O'Donoghue, Iurii Kemaev, Satinder Singh

Proceedings of the 9th International Conference on Learning Representations, 2021

Temporally-Extended ε-Greedy Exploration.

Will Dabney, Georg Ostrovski, André Barreto

Proceedings of the 9th International Conference on Learning Representations, 2021

Expected Eligibility Traces.

Hado van Hasselt, Sephora Madjiheurem, Matteo Hessel, David Silver, André Barreto, Diana Borsa

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

The Value-Improvement Path: Towards Better Representations for Reinforcement Learning.

Will Dabney, André Barreto, Mark Rowland, Robert Dadashi, John Quan, Marc G. Bellemare, David Silver

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020

Fast reinforcement learning with generalized policy updates.

André Barreto, Shaobo Hou, Diana Borsa, David Silver, Doina Precup

Proc. Natl. Acad. Sci. USA, 2020

Temporal Difference Uncertainties as a Signal for Exploration.

Sebastian Flennerhag, Jane X. Wang, Pablo Sprechmann, Francesco Visin, Alexandre Galashov, Steven Kapturowski, Diana L. Borsa, Nicolas Heess, André Barreto, Razvan Pascanu

CoRR, 2020

On Efficiency in Hierarchical Reinforcement Learning.

Zheng Wen, Doina Precup, Morteza Ibrahimi, André Barreto, Benjamin Van Roy, Satinder Singh

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

The Value Equivalence Principle for Model-Based Reinforcement Learning.

Christopher Grimm, André Barreto, Satinder Singh, David Silver

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Fast Task Inference with Variational Intrinsic Successor Features.

Steven Hansen, Will Dabney, André Barreto, David Warde-Farley, Tom Van de Wiele, Volodymyr Mnih

Proceedings of the 8th International Conference on Learning Representations, 2020

2019

Graph-Based Skill Acquisition For Reinforcement Learning.

Matheus R. F. Mendonça, Artur Ziviani, André da Motta Salles Barreto

ACM Comput. Surv., 2019

Disentangled Cumulants Help Successor Representations Transfer to New Tasks.

Christopher Grimm, Irina Higgins, André Barreto, Denis Teplyashin, Markus Wulfmeier, Tim Hertweck, Raia Hadsell, Satinder Singh

CoRR, 2019

General non-linear Bellman equations.

Hado van Hasselt, John Quan, Matteo Hessel, Zhongwen Xu, Diana Borsa, André Barreto

CoRR, 2019

Adaptive Temporal-Difference Learning for Policy Evaluation with Per-State Uncertainty Estimates.

Carlos Riquelme, Hugo Penedones, Damien Vincent, Hartmut Maennel, Sylvain Gelly, Timothy A. Mann, André Barreto, Gergely Neu

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

The Option Keyboard: Combining Skills in Reinforcement Learning.

André Barreto, Diana Borsa, Shaobo Hou, Gheorghe Comanici, Eser Aygün, Philippe Hamel, Daniel Toyama, Jonathan J. Hunt, Shibl Mourad, David Silver, Doina Precup

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Composing Entropic Policies using Divergence Correction.

Jonathan J. Hunt, André Barreto, Timothy P. Lillicrap, Nicolas Heess

Proceedings of the 36th International Conference on Machine Learning, 2019

Universal Successor Features Approximators.

Diana Borsa, André Barreto, John Quan, Daniel J. Mankowitz, Hado van Hasselt, Rémi Munos, David Silver, Tom Schaul

Proceedings of the 7th International Conference on Learning Representations, 2019

Laplacian using Abstract State Transition Graphs: A Framework for Skill Acquisition.

Matheus R. F. Mendonça, Artur Ziviani, André Barreto

Proceedings of the 8th Brazilian Conference on Intelligent Systems, 2019

2018

Entropic Policy Composition with Generalized Policy Improvement and Divergence Correction.

Jonathan J. Hunt, André Barreto, Timothy P. Lillicrap, Nicolas Heess

CoRR, 2018

Temporal Difference Learning with Neural Networks - Study of the Leakage Propagation Problem.

Hugo Penedones, Damien Vincent, Hartmut Maennel, Sylvain Gelly, Timothy A. Mann, André Barreto

CoRR, 2018

Unicorn: Continual Learning with a Universal, Off-policy Agent.

Daniel J. Mankowitz, Augustin Zídek, André Barreto, Dan Horgan, Matteo Hessel, John Quan, Junhyuk Oh, Hado van Hasselt, David Silver, Tom Schaul

CoRR, 2018

Fast deep reinforcement learning using online adjustments from the past.

Steven Hansen, Alexander Pritzel, Pablo Sprechmann, André Barreto, Charles Blundell

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Transfer in Deep Reinforcement Learning Using Successor Features and Generalised Policy Improvement.

André Barreto, Diana Borsa, John Quan, Tom Schaul, David Silver, Matteo Hessel, Daniel J. Mankowitz, Augustin Zídek, Rémi Munos

Proceedings of the 35th International Conference on Machine Learning, 2018

Online TD(A) for discrete-time Markov jump linear systems.

Rafael L. Beirigo, Marcos Garcia Todorov, André da Motta Salles Barreto

Proceedings of the 57th IEEE Conference on Decision and Control, 2018

Abstract State Transition Graphs for Model-Based Reinforcement Learning.

Matheus Ribeiro Furtado de Mendonca, Artur Ziviani, André da Motta Salles Barreto

Proceedings of the 7th Brazilian Conference on Intelligent Systems, 2018

2017

Natural Value Approximators: Learning when to Trust Past Estimates.

Zhongwen Xu, Joseph Modayil, Hado van Hasselt, André Barreto, David Silver, Tom Schaul

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Successor Features for Transfer in Reinforcement Learning.

André Barreto, Will Dabney, Rémi Munos, Jonathan J. Hunt, Tom Schaul, David Silver, Hado van Hasselt

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

The Predictron: End-To-End Learning and Planning.

David Silver, Hado van Hasselt, Matteo Hessel, Tom Schaul, Arthur Guez, Tim Harley, Gabriel Dulac-Arnold, David P. Reichert, Neil C. Rabinowitz, André Barreto, Thomas Degris

Proceedings of the 34th International Conference on Machine Learning, 2017

Count-based quadratic control of Markov jump linear systems with unknown transition probabilities.

Rafael L. Beirigo, Marcos G. Todorov, André da Motta Salles Barreto

Proceedings of the 56th IEEE Annual Conference on Decision and Control, 2017

Value-Aware Loss Function for Model-based Reinforcement Learning.

Amir Massoud Farahmand, André Barreto, Daniel Nikovski

Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017

2016

Practical Kernel-Based Reinforcement Learning.

André da Motta Salles Barreto, Doina Precup, Joelle Pineau

J. Mach. Learn. Res., 2016

Successor Features for Transfer in Reinforcement Learning.

André Barreto, Rémi Munos, Tom Schaul, David Silver

CoRR, 2016

Incremental Stochastic Factorization for Online Reinforcement Learning.

André da Motta Salles Barreto, Rafael L. Beirigo, Joelle Pineau, Doina Precup

Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, 2016

2015

Classification-Based Approximate Policy Iteration.

Amir-massoud Farahmand, Doina Precup, André da Motta Salles Barreto, Mohammad Ghavamzadeh

IEEE Trans. Autom. Control., 2015

Reports of the AAAI 2014 Conference Workshops.

Stefano V. Albrecht, André da Motta Salles Barreto, Darius Braziunas, David L. Buckeridge, Heriberto Cuayáhuitl, Nina Dethlefs, Markus Endres, Amir-massoud Farahmand, Mark Fox, Lutz Frommberger, Sam Ganzfried, Yolanda Gil, Sébastien Guillet, Lawrence E. Hunter, Arnav Jhala, Kristian Kersting, George Dimitri Konidaris, Freddy Lécué, Sheila A. McIlraith, Sriraam Natarajan, Zeinab Noorian, David Poole, Rémi Ronfard, Alessandro Saffiotti, Arash Shaban-Nejad, Biplav Srivastava, Gerald Tesauro, Rosario Uceda-Sosa, Guy Van den Broeck, Martijn van Otterlo, Byron C. Wallace, Paul Weng, Jenna Wiens, Jie Zhang

AI Mag., 2015

An Expectation-Maximization Algorithm to Compute a Stochastic Factorization From Data.

André da Motta Salles Barreto, Rafael L. Beirigo, Joelle Pineau, Doina Precup

Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, 2015

2014

Policy Iteration Based on Stochastic Factorization.

André da Motta Salles Barreto, Joelle Pineau, Doina Precup

J. Artif. Intell. Res., 2014

Classification-based Approximate Policy Iteration: Experiments and Extended Discussions.

Amir-massoud Farahmand, Doina Precup, André da Motta Salles Barreto, Mohammad Ghavamzadeh

CoRR, 2014

Tree-Based On-Line Reinforcement Learning.

André da Motta Salles Barreto

Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, 2014

2012

On-line Reinforcement Learning Using Incremental Kernel-Based Stochastic Factorization.

André da Motta Salles Barreto, Doina Precup, Joelle Pineau

Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

2011

Computing the Stationary Distribution of a Finite Markov Chain Through Stochastic Factorization.

André da Motta Salles Barreto, Marcelo D. Fragoso

SIAM J. Matrix Anal. Appl., 2011

Reinforcement Learning using Kernel-Based Stochastic Factorization.

André da Motta Salles Barreto, Doina Precup, Joelle Pineau

Proceedings of the Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, 2011

A new approach for generating numerical constants in grammatical evolution.

Douglas Adriano Augusto, Helio J. C. Barbosa, André da Motta Salles Barreto, Heder S. Bernardino

Proceedings of the 13th Annual Genetic and Evolutionary Computation Conference, 2011

Evolving Numerical Constants in Grammatical Evolution with the Ephemeral Constant Method.

Douglas Adriano Augusto, Helio J. C. Barbosa, André da Motta Salles Barreto, Heder S. Bernardino

Proceedings of the Progress in Artificial Intelligence, 2011

2010

Probabilistic performance profiles for the experimental evaluation of stochastic algorithms.

André da Motta Salles Barreto, Heder S. Bernardino, Helio J. C. Barbosa

Proceedings of the Genetic and Evolutionary Computation Conference, 2010

Using performance profiles to analyze the results of the 2006 CEC constrained optimization competition.

Helio J. C. Barbosa, Heder S. Bernardino, André da Motta Salles Barreto

Proceedings of the IEEE Congress on Evolutionary Computation, 2010

2009

On the characteristics of sequential decision problems and their impact on evolutionary computation.

André da Motta Salles Barreto, Douglas Adriano Augusto, Helio J. C. Barbosa

Proceedings of the Genetic and Evolutionary Computation Conference, 2009

On the Characteristics of Sequential Decision Problems and Their Impact on Evolutionary Computation and Reinforcement Learning.

André da Motta Salles Barreto, Douglas Adriano Augusto, Helio J. C. Barbosa

Proceedings of the Artifical Evolution, 2009

2008

Restricted gradient-descent algorithm for value-function approximation in reinforcement learning.

André da Motta Salles Barreto, Charles W. Anderson

Artif. Intell., 2008

2007

A note on the variance of rank-based selection strategies for genetic algorithms and genetic programming.

Artem Sokolov, L. Darrell Whitley, André da Motta Salles Barreto

Genet. Program. Evolvable Mach., 2007

2006

GOLS - Genetic orthogonal least squares algorithm for training RBF networks.

André da Motta Salles Barreto, Helio J. C. Barbosa, Nelson F. F. Ebecken

Neurocomputing, 2006

Alternative evolutionary algorithms for evolving programs: evolution strategies and steady state GP.

L. Darrell Whitley, Marc D. Richards, J. Ross Beveridge, André da Motta Salles Barreto

Proceedings of the Genetic and Evolutionary Computation Conference, 2006

2002

Growing Compact RBF Networks Using a Genetic Algorithm.

André da Motta Salles Barreto, Helio J. C. Barbosa, Nelson F. F. Ebecken

Proceedings of the 7th Brazilian Symposium on Neural Networks (SBRN 2002), 2002

2000

Graph Layout Using a Genetic Algorithm.

André da Motta Salles Barreto, Helio J. C. Barbosa

Proceedings of the 6th Brazilian Symposium on Neural Networks (SBRN 2000), 2000