André Barreto

Publication Activity (10 Years)

Years Active: 2016-2024
Publications (10 Years): 61

Top Topics

Kullback Leibler

Reinforcement Learning

Eligibility Traces

Policy Evaluation

Top Venues

Publications

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 (2024)
Sherry Yang, Jacob 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 (2024)
Evgenii Nikishin, Junhyuk Oh, Georg Ostrovski, Clare Lyle, Razvan Pascanu, Will Dabney, André Barreto
Deep Reinforcement Learning with Plasticity Injection. CoRR (2023)
David Abel, André Barreto, Hado van Hasselt, Benjamin Van Roy, Doina Precup, Satinder Singh
On the Convergence of Bounded Agents. CoRR (2023)
David Abel, André Barreto, Benjamin Van Roy, Doina Precup, Hado Philip van Hasselt, Satinder Singh
A Definition of Continual Reinforcement Learning. NeurIPS (2023)
Marlos C. Machado, André Barreto, Doina Precup, Michael Bowling
Temporal Abstraction in Reinforcement Learning with the Successor Representation. J. Mach. Learn. Res. 24 (2023)
Evgenii Nikishin, Junhyuk Oh, Georg Ostrovski, Clare Lyle, Razvan Pascanu, Will Dabney, André Barreto
Deep Reinforcement Learning with Plasticity Injection. NeurIPS (2023)
David Abel, André Barreto, Benjamin Van Roy, Doina Precup, Hado van Hasselt, Satinder Singh
A Definition of Continual Reinforcement Learning. CoRR (2023)
Shantanu Thakoor, Mark Rowland, Diana Borsa, Will Dabney, Rémi Munos, André Barreto
Generalised Policy Improvement with Geometric Policy Composition. ICML (2022)
Shantanu Thakoor, Mark Rowland, Diana Borsa, Will Dabney, Rémi Munos, André Barreto
Generalised Policy Improvement with Geometric Policy Composition. CoRR (2022)
Tom Schaul, André Barreto, John Quan, Georg Ostrovski
The Phenomenon of Policy Churn. CoRR (2022)
Angelos Filos, Eszter Vértes, Zita Marinho, Gregory Farquhar, Diana Borsa, Abram L. Friesen, Feryal Behbahani, Tom Schaul, André Barreto, Simon Osindero
Model-Value Inconsistency as a Signal for Epistemic Uncertainty. ICML (2022)
Tom Schaul, André Barreto, John Quan, Georg Ostrovski
The Phenomenon of Policy Churn. NeurIPS (2022)
Christopher Grimm, André Barreto, Satinder Singh
Approximate Value Equivalence. NeurIPS (2022)
Tom Zahavy, Brendan O'Donoghue, André Barreto, Volodymyr Mnih, Sebastian Flennerhag, Satinder Singh
Discovering Diverse Nearly Optimal Policies withSuccessor Features. CoRR (2021)
Michael Gimelfarb, André Barreto, Scott Sanner, Chi-Guhn Lee
Risk-Aware Transfer in Reinforcement Learning using Successor Features. NeurIPS (2021)
Christopher Grimm, André Barreto, Gregory Farquhar, David Silver, Satinder Singh
Proper Value Equivalence. NeurIPS (2021)
Hado van Hasselt, Sephora Madjiheurem, Matteo Hessel, David Silver, André Barreto, Diana Borsa
Expected Eligibility Traces. AAAI (2021)
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)
Marlos C. Machado, André Barreto, Doina Precup
Temporal Abstraction in Reinforcement Learning with the Successor Representation. CoRR (2021)
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 (2021)
Michael Gimelfarb, André Barreto, Scott Sanner, Chi-Guhn Lee
Risk-Aware Transfer in Reinforcement Learning using Successor Features. CoRR (2021)
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) (2021)
Angelos Filos, Eszter Vértes, Zita Marinho, Gregory Farquhar, Diana Borsa, Abram L. Friesen, Feryal Behbahani, Tom Schaul, André Barreto, Simon Osindero
Model-Value Inconsistency as a Signal for Epistemic Uncertainty. CoRR (2021)
Will Dabney, Georg Ostrovski, André Barreto
Temporally-Extended ε-Greedy Exploration. ICLR (2021)
Tom Zahavy, André Barreto, Daniel J. Mankowitz, Shaobo Hou, Brendan O'Donoghue, Iurii Kemaev, Satinder Baveja Singh
Discovering a set of policies for the worst case reward. CoRR (2021)
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 (2021)
Christopher Grimm, André Barreto, Gregory Farquhar, David Silver, Satinder Singh
Proper Value Equivalence. CoRR (2021)
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)
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 (2020)
Zheng Wen, Doina Precup, Morteza Ibrahimi, André Barreto, Benjamin Van Roy, Satinder Singh
On Efficiency in Hierarchical Reinforcement Learning. NeurIPS (2020)
Christopher Grimm, André Barreto, Satinder Singh, David Silver
The Value Equivalence Principle for Model-Based Reinforcement Learning. CoRR (2020)
Will Dabney, Georg Ostrovski, André Barreto
Temporally-Extended ε-Greedy Exploration. CoRR (2020)
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 (2020)
André Barreto, Shaobo Hou, Diana Borsa, David Silver, Doina Precup
Fast reinforcement learning with generalized policy updates. Proc. Natl. Acad. Sci. USA 117 (48) (2020)
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)
Hado van Hasselt, Sephora Madjiheurem, Matteo Hessel, David Silver, André Barreto, Diana Borsa
Expected Eligibility Traces. CoRR (2020)
Christopher Grimm, André Barreto, Satinder Singh, David Silver
The Value Equivalence Principle for Model-Based Reinforcement Learning. NeurIPS (2020)
Steven Hansen, Will Dabney, André Barreto, Tom Van de Wiele, David Warde-Farley, Volodymyr Mnih
Fast Task Inference with Variational Intrinsic Successor Features. CoRR (2019)
Hado van Hasselt, John Quan, Matteo Hessel, Zhongwen Xu, Diana Borsa, André Barreto
General non-linear Bellman equations. CoRR (2019)
Jonathan J. Hunt, André Barreto, Timothy P. Lillicrap, Nicolas Heess
Composing Entropic Policies using Divergence Correction. ICML (2019)
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)
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)
Matheus R. F. Mendonça, Artur Ziviani, André Barreto
Laplacian using Abstract State Transition Graphs: A Framework for Skill Acquisition. BRACIS (2019)
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 (2019)
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 (2019)
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 (2019)
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)
Steven Hansen, Alexander Pritzel, Pablo Sprechmann, André Barreto, Charles Blundell
Fast deep reinforcement learning using online adjustments from the past. NeurIPS (2018)
Diana Borsa, André Barreto, John Quan, Daniel J. Mankowitz, Rémi Munos, Hado van Hasselt, David Silver, Tom Schaul
Universal Successor Features Approximators. CoRR (2018)
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 (2018)
Steven Hansen, Pablo Sprechmann, Alexander Pritzel, André Barreto, Charles Blundell
Fast deep reinforcement learning using online adjustments from the past. CoRR (2018)
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 (2018)
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)
Jonathan J. Hunt, André Barreto, Timothy P. Lillicrap, Nicolas Heess
Entropic Policy Composition with Generalized Policy Improvement and Divergence Correction. CoRR (2018)
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)
Zhongwen Xu, Joseph Modayil, Hado van Hasselt, André Barreto, David Silver, Tom Schaul
Natural Value Approximators: Learning when to Trust Past Estimates. NIPS (2017)
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)
Amir Massoud Farahmand, André Barreto, Daniel Nikovski
Value-Aware Loss Function for Model-based Reinforcement Learning. AISTATS (2017)
André Barreto, Rémi Munos, Tom Schaul, David Silver
Successor Features for Transfer in Reinforcement Learning. CoRR (2016)
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 (2016)