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André 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
(aka: André Barreto 0002) — Barkhausen Institut, Dresden, Germany (and 1 more)
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2020 – today
- 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
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
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
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
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
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
Graph-Based Skill Acquisition For Reinforcement Learning. ACM Comput. Surv. 52(1): 6:1-6:26 (2019) - [c28]Matheus R. F. Mendonça
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
Abstract State Transition Graphs for Model-Based Reinforcement Learning. BRACIS 2018: 115-120 - [c22]Rafael L. Beirigo, Marcos Garcia Todorov
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
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
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
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
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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