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ORCIDAndré Barreto 0001
André da Motta Salles Barreto
Person information
- affiliation: Google DeepMind
- affiliation (former): National Laboratory for Scientific Computing (LNCC)
- affiliation (former): Federal University of Rio de Janeiro, Brazil
Other persons with the same name
- André Noll Barreto
![[0000-0002-0260-4137]](https://dblp.org/img/orcid-mark.12x12.png "0000-0002-0260-4137")
(aka: André Barreto 0002) — Barkhausen Institut, Dresden, Germany (and 1 more)
Other persons with a similar name
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2020 – today
- 2025
[i37]Bernardo Ávila Pires, Mark Rowland, Diana Borsa, Zhaohan Daniel Guo, Khimya Khetarpal, André Barreto, David Abel, Rémi Munos, Will Dabney:
Optimizing Return Distributions with Distributional Dynamic Programming. CoRR abs/2501.13028 (2025)- 2024
- [c45]Harley Wiltzer, Jesse Farebrother, Arthur Gretton, Yunhao Tang, André Barreto, Will Dabney, Marc G. Bellemare, Mark Rowland:
A Distributional Analogue to the Successor Representation. ICML 2024 - [c44]Sherry Yang, Jacob C. Walker, Jack Parker-Holder, Yilun Du, Jake Bruce, André Barreto, Pieter Abbeel, Dale Schuurmans:
Position: Video as the New Language for Real-World Decision Making. ICML 2024 - [i36]Harley Wiltzer, Jesse Farebrother, Arthur Gretton, Yunhao Tang, André Barreto, Will Dabney, Marc G. Bellemare, Mark Rowland:
A Distributional Analogue to the Successor Representation. CoRR abs/2402.08530 (2024) - [i35]Sherry Yang, Jacob C. Walker, Jack Parker-Holder, Yilun Du, Jake Bruce, André Barreto, Pieter Abbeel, Dale Schuurmans:
Video as the New Language for Real-World Decision Making. CoRR abs/2402.17139 (2024) - 2023
- [j13]Marlos C. Machado, André Barreto, Doina Precup, Michael Bowling:
Temporal Abstraction in Reinforcement Learning with the Successor Representation. J. Mach. Learn. Res. 24: 80:1-80:69 (2023) - [c43]David Abel, André Barreto, Benjamin Van Roy, Doina Precup, Hado Philip van Hasselt, Satinder Singh:
A Definition of Continual Reinforcement Learning. NeurIPS 2023 - [c42]Evgenii Nikishin, Junhyuk Oh, Georg Ostrovski, Clare Lyle, Razvan Pascanu, Will Dabney, André Barreto:
Deep Reinforcement Learning with Plasticity Injection. NeurIPS 2023 - [i34]Evgenii Nikishin, Junhyuk Oh, Georg Ostrovski, Clare Lyle, Razvan Pascanu, Will Dabney, André Barreto:
Deep Reinforcement Learning with Plasticity Injection. CoRR abs/2305.15555 (2023) - [i33]David Abel, André Barreto, Hado van Hasselt, Benjamin Van Roy, Doina Precup, Satinder Singh:
On the Convergence of Bounded Agents. CoRR abs/2307.11044 (2023) - [i32]David Abel, André Barreto, Benjamin Van Roy, Doina Precup, Hado van Hasselt, Satinder Singh:
A Definition of Continual Reinforcement Learning. CoRR abs/2307.11046 (2023) - 2022
- [j12]Matheus R. F. Mendonça, André da Motta Salles Barreto, Artur Ziviani:
Efficient information diffusion in time-varying graphs through deep reinforcement learning. World Wide Web 25(6): 2535-2560 (2022) - [c41]Angelos Filos, Eszter Vértes, Zita Marinho, Gregory Farquhar, Diana Borsa, Abram L. Friesen, Feryal M. P. Behbahani, Tom Schaul, André Barreto, Simon Osindero:
Model-Value Inconsistency as a Signal for Epistemic Uncertainty. ICML 2022: 6474-6498 - [c40]Shantanu Thakoor, Mark Rowland, Diana Borsa, Will Dabney, Rémi Munos, André Barreto:
Generalised Policy Improvement with Geometric Policy Composition. ICML 2022: 21272-21307 - [c39]Christopher Grimm, André Barreto, Satinder Singh:
Approximate Value Equivalence. NeurIPS 2022 - [c38]Tom Schaul, André Barreto, John Quan, Georg Ostrovski:
The Phenomenon of Policy Churn. NeurIPS 2022 - [i31]Tom Schaul, André Barreto, John Quan, Georg Ostrovski:
The Phenomenon of Policy Churn. CoRR abs/2206.00730 (2022) - [i30]Shantanu Thakoor, Mark Rowland, Diana Borsa, Will Dabney, Rémi Munos, André Barreto:
Generalised Policy Improvement with Geometric Policy Composition. CoRR abs/2206.08736 (2022) - 2021
- [j11]Matheus R. F. Mendonça, André Barreto, Artur Ziviani:
Approximating Network Centrality Measures Using Node Embedding and Machine Learning. IEEE Trans. Netw. Sci. Eng. 8(1): 220-230 (2021) - [c37]Will Dabney, André Barreto, Mark Rowland, Robert Dadashi, John Quan, Marc G. Bellemare, David Silver:
The Value-Improvement Path: Towards Better Representations for Reinforcement Learning. AAAI 2021: 7160-7168 - [c36]Hado van Hasselt, Sephora Madjiheurem, Matteo Hessel, David Silver, André Barreto, Diana Borsa:
Expected Eligibility Traces. AAAI 2021: 9997-10005 - [c35]Will Dabney, Georg Ostrovski, André Barreto:
Temporally-Extended ε-Greedy Exploration. ICLR 2021 - [c34]Tom Zahavy, André Barreto, Daniel J. Mankowitz, Shaobo Hou, Brendan O'Donoghue, Iurii Kemaev, Satinder Singh:
Discovering a set of policies for the worst case reward. ICLR 2021 - [c33]Christopher Grimm, André Barreto, Gregory Farquhar, David Silver, Satinder Singh:
Proper Value Equivalence. NeurIPS 2021: 7773-7786 - [c32]Michael Gimelfarb, André Barreto, Scott Sanner, Chi-Guhn Lee:
Risk-Aware Transfer in Reinforcement Learning using Successor Features. NeurIPS 2021: 17298-17310 - [i29]Tom Zahavy, André Barreto, Daniel J. Mankowitz, Shaobo Hou, Brendan O'Donoghue, Iurii Kemaev, Satinder Singh:
Discovering a set of policies for the worst case reward. CoRR abs/2102.04323 (2021) - [i28]Víctor Campos, Pablo Sprechmann, Steven Hansen, André Barreto, Steven Kapturowski, Alex Vitvitskyi, Adrià Puigdomènech Badia, Charles Blundell:
Coverage as a Principle for Discovering Transferable Behavior in Reinforcement Learning. CoRR abs/2102.13515 (2021) - [i27]Michael Gimelfarb, André Barreto, Scott Sanner, Chi-Guhn Lee:
Risk-Aware Transfer in Reinforcement Learning using Successor Features. CoRR abs/2105.14127 (2021) - [i26]Tom Zahavy, Brendan O'Donoghue, André Barreto, Volodymyr Mnih, Sebastian Flennerhag, Satinder Singh:
Discovering Diverse Nearly Optimal Policies withSuccessor Features. CoRR abs/2106.00669 (2021) - [i25]Christopher Grimm, André Barreto, Gregory Farquhar, David Silver, Satinder Singh:
Proper Value Equivalence. CoRR abs/2106.10316 (2021) - [i24]André Barreto, Diana Borsa, Shaobo Hou, Gheorghe Comanici, Eser Aygün, Philippe Hamel, Daniel Toyama, Jonathan J. Hunt, Shibl Mourad, David Silver, Doina Precup:
The Option Keyboard: Combining Skills in Reinforcement Learning. CoRR abs/2106.13105 (2021) - [i23]Marlos C. Machado, André Barreto, Doina Precup:
Temporal Abstraction in Reinforcement Learning with the Successor Representation. CoRR abs/2110.05740 (2021) - [i22]Angelos Filos, Eszter Vértes, Zita Marinho, Gregory Farquhar, Diana Borsa, Abram L. Friesen, Feryal M. P. Behbahani, Tom Schaul, André Barreto, Simon Osindero:
Model-Value Inconsistency as a Signal for Epistemic Uncertainty. CoRR abs/2112.04153 (2021) - 2020
- [j10]André Barreto, Shaobo Hou, Diana Borsa, David Silver, Doina Precup:
Fast reinforcement learning with generalized policy updates. Proc. Natl. Acad. Sci. USA 117(48): 30079-30087 (2020) - [c31]Steven Hansen, Will Dabney, André Barreto, David Warde-Farley, Tom Van de Wiele, Volodymyr Mnih:
Fast Task Inference with Variational Intrinsic Successor Features. ICLR 2020 - [c30]Christopher Grimm, André Barreto, Satinder Singh, David Silver:
The Value Equivalence Principle for Model-Based Reinforcement Learning. NeurIPS 2020 - [c29]Zheng Wen, Doina Precup, Morteza Ibrahimi, André Barreto, Benjamin Van Roy, Satinder Singh:
On Efficiency in Hierarchical Reinforcement Learning. NeurIPS 2020 - [i21]Will Dabney, Georg Ostrovski, André Barreto:
Temporally-Extended ε-Greedy Exploration. CoRR abs/2006.01782 (2020) - [i20]Will Dabney, André Barreto, Mark Rowland, Robert Dadashi, John Quan, Marc G. Bellemare, David Silver:
The Value-Improvement Path: Towards Better Representations for Reinforcement Learning. CoRR abs/2006.02243 (2020) - [i19]Matheus R. F. Mendonça, André da Motta Salles Barreto, Artur Ziviani:
Approximating Network Centrality Measures Using Node Embedding and Machine Learning. CoRR abs/2006.16392 (2020) - [i18]Hado van Hasselt, Sephora Madjiheurem, Matteo Hessel, David Silver, André Barreto, Diana Borsa:
Expected Eligibility Traces. CoRR abs/2007.01839 (2020) - [i17]Sebastian Flennerhag, Jane X. Wang, Pablo Sprechmann, Francesco Visin, Alexandre Galashov, Steven Kapturowski, Diana L. Borsa, Nicolas Heess, André Barreto, Razvan Pascanu:
Temporal Difference Uncertainties as a Signal for Exploration. CoRR abs/2010.02255 (2020) - [i16]Christopher Grimm, André Barreto, Satinder Singh, David Silver:
The Value Equivalence Principle for Model-Based Reinforcement Learning. CoRR abs/2011.03506 (2020) - [i15]Matheus R. F. Mendonça, André da Motta Salles Barreto, Artur Ziviani:
Efficient Information Diffusion in Time-Varying Graphs through Deep Reinforcement Learning. CoRR abs/2011.13518 (2020)
2010 – 2019
- 2019
- [j9]Matheus R. F. Mendonça, Artur Ziviani, André da Motta Salles Barreto:
Graph-Based Skill Acquisition For Reinforcement Learning. ACM Comput. Surv. 52(1): 6:1-6:26 (2019) - [c28]Matheus R. F. Mendonça, Artur Ziviani, André Barreto:
Laplacian using Abstract State Transition Graphs: A Framework for Skill Acquisition. BRACIS 2019: 263-268 - [c27]Diana Borsa, André Barreto, John Quan, Daniel J. Mankowitz, Hado van Hasselt, Rémi Munos, David Silver, Tom Schaul:
Universal Successor Features Approximators. ICLR (Poster) 2019 - [c26]Jonathan J. Hunt, André Barreto, Timothy P. Lillicrap, Nicolas Heess:
Composing Entropic Policies using Divergence Correction. ICML 2019: 2911-2920 - [c25]Carlos Riquelme, Hugo Penedones, Damien Vincent, Hartmut Maennel, Sylvain Gelly, Timothy A. Mann, André Barreto, Gergely Neu:
Adaptive Temporal-Difference Learning for Policy Evaluation with Per-State Uncertainty Estimates. NeurIPS 2019: 11872-11882 - [c24]André Barreto, Diana Borsa, Shaobo Hou, Gheorghe Comanici, Eser Aygün, Philippe Hamel, Daniel Toyama, Jonathan J. Hunt, Shibl Mourad, David Silver, Doina Precup:
The Option Keyboard: Combining Skills in Reinforcement Learning. NeurIPS 2019: 13031-13041 - [i14]André Barreto, Diana Borsa, John Quan, Tom Schaul, David Silver, Matteo Hessel, Daniel J. Mankowitz, Augustin Zídek, Rémi Munos:
Transfer in Deep Reinforcement Learning Using Successor Features and Generalised Policy Improvement. CoRR abs/1901.10964 (2019) - [i13]Steven Hansen, Will Dabney, André Barreto, Tom Van de Wiele, David Warde-Farley, Volodymyr Mnih:
Fast Task Inference with Variational Intrinsic Successor Features. CoRR abs/1906.05030 (2019) - [i12]Hugo Penedones, Carlos Riquelme, Damien Vincent, Hartmut Maennel, Timothy A. Mann, André Barreto, Sylvain Gelly, Gergely Neu:
Adaptive Temporal-Difference Learning for Policy Evaluation with Per-State Uncertainty Estimates. CoRR abs/1906.07987 (2019) - [i11]Hado van Hasselt, John Quan, Matteo Hessel, Zhongwen Xu, Diana Borsa, André Barreto:
General non-linear Bellman equations. CoRR abs/1907.03687 (2019) - [i10]Christopher Grimm, Irina Higgins, André Barreto, Denis Teplyashin, Markus Wulfmeier, Tim Hertweck, Raia Hadsell, Satinder Singh:
Disentangled Cumulants Help Successor Representations Transfer to New Tasks. CoRR abs/1911.10866 (2019) - 2018
- [c23]Matheus Ribeiro Furtado de Mendonca, Artur Ziviani, André da Motta Salles Barreto:
Abstract State Transition Graphs for Model-Based Reinforcement Learning. BRACIS 2018: 115-120 - [c22]Rafael L. Beirigo, Marcos Garcia Todorov, André da Motta Salles Barreto:
Online TD(A) for discrete-time Markov jump linear systems. CDC 2018: 2229-2234 - [c21]André Barreto, Diana Borsa, John Quan, Tom Schaul, David Silver, Matteo Hessel, Daniel J. Mankowitz, Augustin Zídek, Rémi Munos:
Transfer in Deep Reinforcement Learning Using Successor Features and Generalised Policy Improvement. ICML 2018: 510-519 - [c20]Steven Hansen, Alexander Pritzel, Pablo Sprechmann, André Barreto, Charles Blundell:
Fast deep reinforcement learning using online adjustments from the past. NeurIPS 2018: 10590-10600 - [i9]Daniel J. Mankowitz, Augustin Zídek, André Barreto, Dan Horgan, Matteo Hessel, John Quan, Junhyuk Oh, Hado van Hasselt, David Silver, Tom Schaul:
Unicorn: Continual Learning with a Universal, Off-policy Agent. CoRR abs/1802.08294 (2018) - [i8]Hugo Penedones, Damien Vincent, Hartmut Maennel, Sylvain Gelly, Timothy A. Mann, André Barreto:
Temporal Difference Learning with Neural Networks - Study of the Leakage Propagation Problem. CoRR abs/1807.03064 (2018) - [i7]Steven Hansen, Pablo Sprechmann, Alexander Pritzel, André Barreto, Charles Blundell:
Fast deep reinforcement learning using online adjustments from the past. CoRR abs/1810.08163 (2018) - [i6]Jonathan J. Hunt, André Barreto, Timothy P. Lillicrap, Nicolas Heess:
Entropic Policy Composition with Generalized Policy Improvement and Divergence Correction. CoRR abs/1812.02216 (2018) - [i5]Diana Borsa, André Barreto, John Quan, Daniel J. Mankowitz, Rémi Munos, Hado van Hasselt, David Silver, Tom Schaul:
Universal Successor Features Approximators. CoRR abs/1812.07626 (2018) - 2017
- [c19]Amir Massoud Farahmand, André Barreto, Daniel Nikovski:
Value-Aware Loss Function for Model-based Reinforcement Learning. AISTATS 2017: 1486-1494 - [c18]Rafael L. Beirigo, Marcos G. Todorov, André da Motta Salles Barreto:
Count-based quadratic control of Markov jump linear systems with unknown transition probabilities. CDC 2017: 4315-4320 - [c17]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:
The Predictron: End-To-End Learning and Planning. ICML 2017: 3191-3199 - [c16]Zhongwen Xu, Joseph Modayil, Hado van Hasselt, André Barreto, David Silver, Tom Schaul:
Natural Value Approximators: Learning when to Trust Past Estimates. NIPS 2017: 2120-2128 - [c15]André Barreto, Will Dabney, Rémi Munos, Jonathan J. Hunt, Tom Schaul, David Silver, Hado van Hasselt:
Successor Features for Transfer in Reinforcement Learning. NIPS 2017: 4055-4065 - 2016
- [j8]André da Motta Salles Barreto, Doina Precup, Joelle Pineau:
Practical Kernel-Based Reinforcement Learning. J. Mach. Learn. Res. 17: 67:1-67:70 (2016) - [c14]André da Motta Salles Barreto, Rafael L. Beirigo, Joelle Pineau, Doina Precup:
Incremental Stochastic Factorization for Online Reinforcement Learning. AAAI 2016: 1468-1475 - [i4]André Barreto, Rémi Munos, Tom Schaul, David Silver:
Successor Features for Transfer in Reinforcement Learning. CoRR abs/1606.05312 (2016) - [i3]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:
The Predictron: End-To-End Learning and Planning. CoRR abs/1612.08810 (2016) - 2015
- [j7]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:
Reports of the AAAI 2014 Conference Workshops. AI Mag. 36(1): 87-98 (2015) - [j6]Amir-massoud Farahmand, Doina Precup, André da Motta Salles Barreto, Mohammad Ghavamzadeh:
Classification-Based Approximate Policy Iteration. IEEE Trans. Autom. Control. 60(11): 2989-2993 (2015) - [c13]André da Motta Salles Barreto, Rafael L. Beirigo, Joelle Pineau, Doina Precup:
An Expectation-Maximization Algorithm to Compute a Stochastic Factorization From Data. IJCAI 2015: 3329-3336 - 2014
- [j5]André da Motta Salles Barreto, Joelle Pineau, Doina Precup:
Policy Iteration Based on Stochastic Factorization. J. Artif. Intell. Res. 50: 763-803 (2014) - [c12]André da Motta Salles Barreto:
Tree-Based On-Line Reinforcement Learning. AAAI 2014: 2417-2423 - [i2]Amir-massoud Farahmand, Doina Precup, André da Motta Salles Barreto, Mohammad Ghavamzadeh:
Classification-based Approximate Policy Iteration: Experiments and Extended Discussions. CoRR abs/1407.0449 (2014) - [i1]André da Motta Salles Barreto, Doina Precup, Joelle Pineau:
Practical Kernel-Based Reinforcement Learning. CoRR abs/1407.5358 (2014) - 2012
- [c11]André da Motta Salles Barreto, Doina Precup, Joelle Pineau:
On-line Reinforcement Learning Using Incremental Kernel-Based Stochastic Factorization. NIPS 2012: 1493-1501 - 2011
- [j4]André da Motta Salles Barreto, Marcelo D. Fragoso:
Computing the Stationary Distribution of a Finite Markov Chain Through Stochastic Factorization. SIAM J. Matrix Anal. Appl. 32(4): 1513-1523 (2011) - [c10]Douglas Adriano Augusto, Helio J. C. Barbosa, André da Motta Salles Barreto, Heder S. Bernardino:
Evolving Numerical Constants in Grammatical Evolution with the Ephemeral Constant Method. EPIA 2011: 110-124 - [c9]Douglas Adriano Augusto, Helio J. C. Barbosa, André da Motta Salles Barreto, Heder S. Bernardino:
A new approach for generating numerical constants in grammatical evolution. GECCO (Companion) 2011: 193-194 - [c8]André da Motta Salles Barreto, Doina Precup, Joelle Pineau:
Reinforcement Learning using Kernel-Based Stochastic Factorization. NIPS 2011: 720-728 - 2010
- [c7]Helio J. C. Barbosa, Heder S. Bernardino, André da Motta Salles Barreto:
Using performance profiles to analyze the results of the 2006 CEC constrained optimization competition. IEEE Congress on Evolutionary Computation 2010: 1-8 - [c6]André da Motta Salles Barreto, Heder S. Bernardino, Helio J. C. Barbosa:
Probabilistic performance profiles for the experimental evaluation of stochastic algorithms. GECCO 2010: 751-758
2000 – 2009
- 2009
- [c5]André da Motta Salles Barreto, Douglas Adriano Augusto, Helio J. C. Barbosa:
On the Characteristics of Sequential Decision Problems and Their Impact on Evolutionary Computation and Reinforcement Learning. Artificial Evolution 2009: 194-205 - [c4]André da Motta Salles Barreto, Douglas Adriano Augusto, Helio J. C. Barbosa:
On the characteristics of sequential decision problems and their impact on evolutionary computation. GECCO 2009: 1767-1768 - 2008
- [j3]André da Motta Salles Barreto, Charles W. Anderson:
Restricted gradient-descent algorithm for value-function approximation in reinforcement learning. Artif. Intell. 172(4-5): 454-482 (2008) - 2007
- [j2]Artem Sokolov, L. Darrell Whitley, André da Motta Salles Barreto:
A note on the variance of rank-based selection strategies for genetic algorithms and genetic programming. Genet. Program. Evolvable Mach. 8(3): 221-237 (2007) - 2006
- [j1]André da Motta Salles Barreto, Helio J. C. Barbosa, Nelson F. F. Ebecken:
GOLS - Genetic orthogonal least squares algorithm for training RBF networks. Neurocomputing 69(16-18): 2041-2064 (2006) - [c3]L. Darrell Whitley, Marc D. Richards, J. Ross Beveridge, André da Motta Salles Barreto:
Alternative evolutionary algorithms for evolving programs: evolution strategies and steady state GP. GECCO 2006: 919-926 - 2002
- [c2]André da Motta Salles Barreto, Helio J. C. Barbosa, Nelson F. F. Ebecken:
Growing Compact RBF Networks Using a Genetic Algorithm. SBRN 2002: 61-66 - 2000
- [c1]André da Motta Salles Barreto, Helio J. C. Barbosa:
Graph Layout Using a Genetic Algorithm. SBRN 2000: 179-184
Coauthor Index
Diana Borsa
aka: Diana L. Borsa
aka: Diana L. Borsa
Hado van Hasselt
aka: Hado Philip van Hasselt
aka: Hado Philip van Hasselt
Matheus R. F. Mendonça
aka: Matheus Ribeiro Furtado de Mendonca
aka: Matheus Ribeiro Furtado de Mendonca
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