Barreto, Andre

38 publications

NeurIPS 2025 Capturing Individual Human Preferences with Reward Features Andre Barreto, Vincent Dumoulin, Yiran Mao, Mark Rowland, Nicolas Perez-Nieves, Bobak Shahriari, Yann Dauphin, Doina Precup, Hugo Larochelle

NeurIPS 2025 Constructing an Optimal Behavior Basis for the Option Keyboard Lucas N. Alegre, Ana L. C. Bazzan, Andre Barreto, Bruno Castro da Silva

JMLR 2025 Optimizing Return Distributions with Distributional Dynamic Programming Bernardo Ávila Pires, Mark Rowland, Diana Borsa, Zhaohan Daniel Guo, Khimya Khetarpal, André Barreto, David Abel, Rémi Munos, Will Dabney

NeurIPS 2025 Plasticity as the Mirror of Empowerment David Abel, Michael Bowling, Andre Barreto, Will Dabney, Shi Dong, Steven Stenberg Hansen, Anna Harutyunyan, Khimya Khetarpal, Clare Lyle, Razvan Pascanu, Georgios Piliouras, Doina Precup, Jonathan Richens, Mark Rowland, Tom Schaul, Satinder Singh

ICML 2024 A Distributional Analogue to the Successor Representation Harley Wiltzer, Jesse Farebrother, Arthur Gretton, Yunhao Tang, Andre Barreto, Will Dabney, Marc G Bellemare, Mark Rowland

ICML 2024 Position: Video as the New Language for Real-World Decision Making Sherry Yang, Jacob C Walker, Jack Parker-Holder, Yilun Du, Jake Bruce, Andre Barreto, Pieter Abbeel, Dale Schuurmans

NeurIPS 2023 A Definition of Continual Reinforcement Learning David Abel, Andre Barreto, Benjamin Van Roy, Doina Precup, Hado P van Hasselt, Satinder P. Singh

NeurIPS 2023 Deep Reinforcement Learning with Plasticity Injection Evgenii Nikishin, Junhyuk Oh, Georg Ostrovski, Clare Lyle, Razvan Pascanu, Will Dabney, Andre Barreto

ICLRW 2023 Deep Reinforcement Learning with Plasticity Injection Evgenii Nikishin, Junhyuk Oh, Georg Ostrovski, Clare Lyle, Razvan Pascanu, Will Dabney, Andre Barreto

JMLR 2023 Temporal Abstraction in Reinforcement Learning with the Successor Representation Marlos C. Machado, Andre Barreto, Doina Precup, Michael Bowling

NeurIPS 2022 Approximate Value Equivalence Christopher Grimm, Andre Barreto, Satinder P. Singh

ICML 2022 Generalised Policy Improvement with Geometric Policy Composition Shantanu Thakoor, Mark Rowland, Diana Borsa, Will Dabney, Remi Munos, Andre Barreto

ICML 2022 Model-Value Inconsistency as a Signal for Epistemic Uncertainty Angelos Filos, Eszter Vértes, Zita Marinho, Gregory Farquhar, Diana Borsa, Abram Friesen, Feryal Behbahani, Tom Schaul, Andre Barreto, Simon Osindero

ICLRW 2022 Model-Value Inconsistency as a Signal for Epistemic Uncertainty Angelos Filos, Eszter Vértes, Zita Marinho, Gregory Farquhar, Diana L Borsa, Abram L. Friesen, Feryal Behbahani, Tom Schaul, Andre Barreto, Simon Osindero

NeurIPS 2022 The Phenomenon of Policy Churn Tom Schaul, Andre Barreto, John Quan, Georg Ostrovski

ICMLW 2021 Beyond Fine-Tuning: Transferring Behavior in Reinforcement Learning Víctor Campos, Pablo Sprechmann, Steven Stenberg Hansen, Andre Barreto, Steven Kapturowski, Alex Vitvitskyi, Adria Puigdomenech Badia, Charles Blundell

ICMLW 2021 Discovering Diverse Nearly Optimal Policies with Successor Features Tom Zahavy, Brendan O'Donoghue, Andre Barreto, Sebastian Flennerhag, Volodymyr Mnih, Satinder Singh

ICLR 2021 Discovering a Set of Policies for the Worst Case Reward Tom Zahavy, Andre Barreto, Daniel J Mankowitz, Shaobo Hou, Brendan O'Donoghue, Iurii Kemaev, Satinder Singh

AAAI 2021 Expected Eligibility Traces Hado van Hasselt, Sephora Madjiheurem, Matteo Hessel, David Silver, André Barreto, Diana Borsa

NeurIPS 2021 Proper Value Equivalence Christopher Grimm, Andre Barreto, Greg Farquhar, David Silver, Satinder P. Singh

NeurIPS 2021 Risk-Aware Transfer in Reinforcement Learning Using Successor Features Michael Gimelfarb, Andre Barreto, Scott Sanner, Chi-Guhn Lee

ICLR 2021 Temporally-Extended Ε-Greedy Exploration Will Dabney, Georg Ostrovski, Andre Barreto

AAAI 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

ICLR 2020 Fast Task Inference with Variational Intrinsic Successor Features Steven Hansen, Will Dabney, Andre Barreto, Tom Van de Wiele, David Warde-Farley, Volodymyr Mnih

NeurIPS 2020 On Efficiency in Hierarchical Reinforcement Learning Zheng Wen, Doina Precup, Morteza Ibrahimi, Andre Barreto, Benjamin Van Roy, Satinder P. Singh

NeurIPS 2020 The Value Equivalence Principle for Model-Based Reinforcement Learning Christopher Grimm, Andre Barreto, Satinder P. Singh, David Silver

NeurIPS 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, Andre Barreto, Gergely Neu

ICML 2019 Composing Entropic Policies Using Divergence Correction Jonathan Hunt, Andre Barreto, Timothy Lillicrap, Nicolas Heess

NeurIPS 2019 The Option Keyboard: Combining Skills in Reinforcement Learning Andre Barreto, Diana Borsa, Shaobo Hou, Gheorghe Comanici, Eser Aygün, Philippe Hamel, Daniel Toyama, Jonathan Hunt, Shibl Mourad, David Silver, Doina Precup

ICLR 2019 Universal Successor Features Approximators Diana Borsa, Andre Barreto, John Quan, Daniel J. Mankowitz, Hado van Hasselt, Remi Munos, David Silver, Tom Schaul

NeurIPS 2018 Fast Deep Reinforcement Learning Using Online Adjustments from the past Steven Hansen, Alexander Pritzel, Pablo Sprechmann, Andre Barreto, Charles Blundell

ICML 2018 Transfer in Deep Reinforcement Learning Using Successor Features and Generalised Policy Improvement Andre Barreto, Diana Borsa, John Quan, Tom Schaul, David Silver, Matteo Hessel, Daniel Mankowitz, Augustin Zidek, Remi Munos

NeurIPS 2017 Natural Value Approximators: Learning When to Trust past Estimates Zhongwen Xu, Joseph Modayil, Hado P van Hasselt, Andre Barreto, David Silver, Tom Schaul

NeurIPS 2017 Successor Features for Transfer in Reinforcement Learning Andre Barreto, Will Dabney, Remi Munos, Jonathan J Hunt, Tom Schaul, Hado P van Hasselt, David Silver

ICML 2017 The Predictron: End-to-End Learning and Planning David Silver, Hado Hasselt, Matteo Hessel, Tom Schaul, Arthur Guez, Tim Harley, Gabriel Dulac-Arnold, David Reichert, Neil Rabinowitz, Andre Barreto, Thomas Degris

AISTATS 2017 Value-Aware Loss Function for Model-Based Reinforcement Learning Amir Massoud Farahmand, André Barreto, Daniel Nikovski

NeurIPS 2012 On-Line Reinforcement Learning Using Incremental Kernel-Based Stochastic Factorization Andre Barreto, Doina Precup, Joelle Pineau

NeurIPS 2011 Reinforcement Learning Using Kernel-Based Stochastic Factorization Andre Barreto, Doina Precup, Joelle Pineau