Graph Layout Using a Genetic Algorithm. - André da Motta Salles Barreto; Helio J. C. Barbosa (SBRN, 2000)

Growing Compact RBF Networks Using a Genetic Algorithm. - André da Motta Salles Barreto; Helio J. C. Barbosa; Nelson F. F. Ebecken (SBRN, 2002)

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 (GECCO, 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)

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)

Restricted gradient-descent algorithm for value-function approximation in reinforcement learning. - André da Motta Salles Barreto; Charles W. Anderson (Artif. Intell., 2008)

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 (Artificial Evolution, 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 (GECCO, 2009)

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 (IEEE Congress on Evolutionary Computation, 2010)

Probabilistic performance profiles for the experimental evaluation of stochastic algorithms. - André da Motta Salles Barreto; Heder S. Bernardino; Helio J. C. Barbosa (GECCO, 2010)

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 (EPIA, 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 (GECCO (Companion), 2011)

Reinforcement Learning using Kernel-Based Stochastic Factorization. - André da Motta Salles Barreto; Doina Precup; Joelle Pineau (NIPS, 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)

On-line Reinforcement Learning Using Incremental Kernel-Based Stochastic Factorization. - André da Motta Salles Barreto; Doina Precup; Joelle Pineau (NIPS, 2012)

Tree-Based On-Line Reinforcement Learning. - André da Motta Salles Barreto (AAAI, 2014)

Classification-based Approximate Policy Iteration: Experiments and Extended Discussions. - Amir-massoud Farahmand; Doina Precup; André da Motta Salles Barreto; Mohammad Ghavamzadeh (CoRR, 2014)

Policy Iteration Based on Stochastic Factorization. - André da Motta Salles Barreto; Joelle Pineau; Doina Precup (J. Artif. Intell. Res., 2014)

An Expectation-Maximization Algorithm to Compute a Stochastic Factorization From Data. - André da Motta Salles Barreto; Rafael L. Beirigo; Joelle Pineau; Doina Precup (IJCAI, 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; et al. (AI Mag., 2015)

Classification-Based Approximate Policy Iteration. - Amir-massoud Farahmand; Doina Precup; André da Motta Salles Barreto; Mohammad Ghavamzadeh (IEEE Trans. Autom. Control., 2015)

Incremental Stochastic Factorization for Online Reinforcement Learning. - André da Motta Salles Barreto; Rafael L. Beirigo; Joelle Pineau; Doina Precup (AAAI, 2016)

Successor Features for Transfer in Reinforcement Learning. - André Barreto 001; Rémi Munos; Tom Schaul; David Silver (CoRR, 2016)

Practical Kernel-Based Reinforcement Learning. - André da Motta Salles Barreto; Doina Precup; Joelle Pineau (J. Mach. Learn. Res., 2016)

Value-Aware Loss Function for Model-based Reinforcement Learning. - Amir Massoud Farahmand; André Barreto 001; Daniel Nikovski (AISTATS, 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 (CDC, 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 001; Thomas Degris (ICML, 2017)

Successor Features for Transfer in Reinforcement Learning. - André Barreto 001; Will Dabney; Rémi Munos; Jonathan J. Hunt; Tom Schaul; David Silver; Hado van Hasselt (NIPS, 2017)

Natural Value Approximators: Learning when to Trust Past Estimates. - Zhongwen Xu; Joseph Modayil; Hado van Hasselt; André Barreto 001; David Silver; Tom Schaul (NIPS, 2017)

Abstract State Transition Graphs for Model-Based Reinforcement Learning. - Matheus Ribeiro Furtado de Mendonca; Artur Ziviani; André da Motta Salles Barreto (BRACIS, 2018)

Online TD(A) for discrete-time Markov jump linear systems. - Rafael L. Beirigo; Marcos Garcia Todorov; André da Motta Salles Barreto (CDC, 2018)

Transfer in Deep Reinforcement Learning Using Successor Features and Generalised Policy Improvement. - André Barreto 001; Diana Borsa; John Quan; Tom Schaul; David Silver; Matteo Hessel; Daniel J. Mankowitz; Augustin Zídek; Rémi Munos (ICML, 2018)

Fast deep reinforcement learning using online adjustments from the past. - Steven Hansen 001; Alexander Pritzel; Pablo Sprechmann; André Barreto 001; Charles Blundell (NeurIPS, 2018)

Unicorn: Continual Learning with a Universal, Off-policy Agent. - Daniel J. Mankowitz; Augustin Zídek; André Barreto 001; Dan Horgan; Matteo Hessel; John Quan; Junhyuk Oh; Hado van Hasselt; David Silver; Tom Schaul (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 001 (CoRR, 2018)

Entropic Policy Composition with Generalized Policy Improvement and Divergence Correction. - Jonathan J. Hunt; André Barreto 001; Timothy P. Lillicrap; Nicolas Heess (CoRR, 2018)

Laplacian using Abstract State Transition Graphs: A Framework for Skill Acquisition. - Matheus R. F. Mendonça; Artur Ziviani; André Barreto 001 (BRACIS, 2019)

Universal Successor Features Approximators. - Diana Borsa; André Barreto 001; John Quan; Daniel J. Mankowitz; Hado van Hasselt; Rémi Munos; David Silver; Tom Schaul (ICLR (Poster), 2019)

Composing Entropic Policies using Divergence Correction. - Jonathan J. Hunt; André Barreto 001; Timothy P. Lillicrap; Nicolas Heess (ICML, 2019)

The Option Keyboard: Combining Skills in Reinforcement Learning. - André Barreto 001; Diana Borsa; Shaobo Hou; Gheorghe Comanici; Eser Aygün; Philippe Hamel; Daniel Toyama; Jonathan J. Hunt; Shibl Mourad; David Silver; Doina Precup (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; André Barreto 001; Gergely Neu (NeurIPS, 2019)

General non-linear Bellman equations. - Hado van Hasselt; John Quan; Matteo Hessel; Zhongwen Xu; Diana Borsa; André Barreto 001 (CoRR, 2019)

Disentangled Cumulants Help Successor Representations Transfer to New Tasks. - Christopher Grimm; Irina Higgins; André Barreto 001; Denis Teplyashin; Markus Wulfmeier; Tim Hertweck; Raia Hadsell; Satinder Singh 001 (CoRR, 2019)

Graph-Based Skill Acquisition For Reinforcement Learning. - Matheus R. F. Mendonça; Artur Ziviani; André da Motta Salles Barreto (ACM Comput. Surv., 2019)

Fast Task Inference with Variational Intrinsic Successor Features. - Steven Hansen 001; Will Dabney; André Barreto 001; David Warde-Farley; Tom Van de Wiele; Volodymyr Mnih (ICLR, 2020)

The Value Equivalence Principle for Model-Based Reinforcement Learning. - Christopher Grimm; André Barreto 001; Satinder Singh 001; David Silver (NeurIPS, 2020)

On Efficiency in Hierarchical Reinforcement Learning. - Zheng Wen 002; Doina Precup; Morteza Ibrahimi; André Barreto 001; Benjamin Van Roy; Satinder Singh 001 (NeurIPS, 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 001; Razvan Pascanu (CoRR, 2020)

Fast reinforcement learning with generalized policy updates. - André Barreto 001; Shaobo Hou; Diana Borsa; David Silver; Doina Precup (Proc. Natl. Acad. Sci. USA, 2020)

The Value-Improvement Path: Towards Better Representations for Reinforcement Learning. - Will Dabney; André Barreto 001; Mark Rowland 001; Robert Dadashi; John Quan; Marc G. Bellemare; David Silver (AAAI, 2021)

Expected Eligibility Traces. - Hado van Hasselt; Sephora Madjiheurem; Matteo Hessel; David Silver; André Barreto 001; Diana Borsa (AAAI, 2021)

Temporally-Extended ε-Greedy Exploration. - Will Dabney; Georg Ostrovski; André Barreto 001 (ICLR, 2021)

Discovering a set of policies for the worst case reward. - Tom Zahavy; André Barreto 001; Daniel J. Mankowitz; Shaobo Hou; Brendan O'Donoghue; Iurii Kemaev; Satinder Singh 001 (ICLR, 2021)

Risk-Aware Transfer in Reinforcement Learning using Successor Features. - Michael Gimelfarb; André Barreto 001; Scott Sanner; Chi-Guhn Lee (NeurIPS, 2021)

Proper Value Equivalence. - Christopher Grimm; André Barreto 001; Gregory Farquhar; David Silver; Satinder Singh 001 (NeurIPS, 2021)

Coverage as a Principle for Discovering Transferable Behavior in Reinforcement Learning. - Víctor Campos 001; Pablo Sprechmann; Steven Hansen 001; André Barreto 001; Steven Kapturowski; Alex Vitvitskyi; Adrià Puigdomènech Badia; Charles Blundell (CoRR, 2021)

Discovering Diverse Nearly Optimal Policies withSuccessor Features. - Tom Zahavy; Brendan O'Donoghue; André Barreto 001; Volodymyr Mnih; Sebastian Flennerhag; Satinder Singh 001 (CoRR, 2021)

Temporal Abstraction in Reinforcement Learning with the Successor Representation. - Marlos C. Machado; André Barreto 001; Doina Precup (CoRR, 2021)

Approximating Network Centrality Measures Using Node Embedding and Machine Learning. - Matheus R. F. Mendonça; André Barreto 001; Artur Ziviani (IEEE Trans. Netw. Sci. Eng., 2021)

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 001; Simon Osindero (ICML, 2022)

Generalised Policy Improvement with Geometric Policy Composition. - Shantanu Thakoor; Mark Rowland 001; Diana Borsa; Will Dabney; Rémi Munos; André Barreto 001 (ICML, 2022)

Approximate Value Equivalence. - Christopher Grimm; André Barreto 001; Satinder Singh 001 (NeurIPS, 2022)

The Phenomenon of Policy Churn. - Tom Schaul; André Barreto 001; John Quan; Georg Ostrovski (NeurIPS, 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)

A Definition of Continual Reinforcement Learning. - David Abel; André Barreto 001; Benjamin Van Roy; Doina Precup; Hado Philip van Hasselt; Satinder Singh 001 (NeurIPS, 2023)

Deep Reinforcement Learning with Plasticity Injection. - Evgenii Nikishin; Junhyuk Oh; Georg Ostrovski; Clare Lyle; Razvan Pascanu; Will Dabney; André Barreto 001 (NeurIPS, 2023)

On the Convergence of Bounded Agents. - David Abel; André Barreto 001; Hado van Hasselt; Benjamin Van Roy; Doina Precup; Satinder Singh 001 (CoRR, 2023)

Temporal Abstraction in Reinforcement Learning with the Successor Representation. - Marlos C. Machado; André Barreto 001; Doina Precup; Michael Bowling (J. Mach. Learn. Res., 2023)

A Distributional Analogue to the Successor Representation. - Harley Wiltzer; Jesse Farebrother; Arthur Gretton; Yunhao Tang; André Barreto 001; Will Dabney; Marc G. Bellemare; Mark Rowland 001 (ICML, 2024)

Position: Video as the New Language for Real-World Decision Making. - Sherry Yang 001; Jacob C. Walker; Jack Parker-Holder; Yilun Du; Jake Bruce; André Barreto 001; Pieter Abbeel; Dale Schuurmans (ICML, 2024)

Video as the New Language for Real-World Decision Making. - Sherry Yang 001; Jacob C. Walker; Jack Parker-Holder; Yilun Du; Jake Bruce; André Barreto 001; Pieter Abbeel; Dale Schuurmans (CoRR, 2024)

Optimizing Return Distributions with Distributional Dynamic Programming. - Bernardo Ávila Pires; Mark Rowland 001; Diana Borsa; Zhaohan Daniel Guo; Khimya Khetarpal; André Barreto 001; David Abel; Rémi Munos; Will Dabney (CoRR, 2025)

Agency Is Frame-Dependent. - David Abel; André Barreto 001; Michael Bowling; Will Dabney; Shi Dong; Steven Hansen 001; Anna Harutyunyan; Khimya Khetarpal; Clare Lyle; Razvan Pascanu; Georgios Piliouras; Doina Precup; et al. (CoRR, 2025)

Capturing Individual Human Preferences with Reward Features. - André Barreto 001; Vincent Dumoulin; Yiran Mao; Nicolas Perez Nieves; Bobak Shahriari; Yann N. Dauphin; Doina Precup; Hugo Larochelle (CoRR, 2025)

Constructing an Optimal Behavior Basis for the Option Keyboard. - Lucas N. Alegre; Ana L. C. Bazzan; André Barreto 001; Bruno C. da Silva 001 (CoRR, 2025)

Plasticity as the Mirror of Empowerment. - David Abel; Michael Bowling; André Barreto 001; Will Dabney; Shi Dong; Steven Hansen 001; Anna Harutyunyan; Khimya Khetarpal; Clare Lyle; Razvan Pascanu; Georgios Piliouras; Doina Precup; et al. (CoRR, 2025)