2019


The Option Keyboard: Combining Skills in Reinforcement Learning

André Barreto, Diana Borsa,  Shaobo Hou, Gheorghe Comanici, Eser Aygün, Philippe Hamel, Daniel Toyama, Jonathan J. Hunt, Shibl Mourad, David Silver, Doina Precup

In Advances in Neural Information Processing Systems (NeurIPS)
[ pdf ] [ video ] [ slides ] [ poster ] [ more info ]

Universal Successor Features Approximators

Diana Borsa, André Barreto, John Quan, Daniel Mankowitz, Rémi Munos, Hado van Hasselt, David Silver, Tom Schaul

In Proceedings of the International Conference on Learning Representations (ICLR)
[ pdf ] [ video 1 ] [ video 2 ] [ more info ]

Composing Entropic Policies Using Divergence Correction

Jonathan Hunt, André Barreto, Timothy Lillicrap, Nicolas Heess

In Proceedings of  International Conference on Machine Learning (ICML)
[ pdf ] [ videos ] [ more info ]

Fast Task Inference with Variational Intrinsic Successor Features

Steven Hansen, Will Dabney, André Barreto, Tom Van de Wiele, David Warde-Farley, Volodymyr Mnih

arXiv
[ pdf ] [ more info ]

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

In Advances in Neural Information Processing Systems (NeurIPS)
[ pdf ] [ poster ] [ more info ]

General Non-linear Bellman equations

Hado van Hasselt, John Quan, Matteo Hessel, Zhongwen Xu, Diana Borsa, Andre Barreto

The Multi-disciplinary Conference on Reinforcement Learning and Decision Making (RLDM)
[ pdf ] [ more info ]

Graph-Based Skill Acquisition for Reinforcement Learning

Matheus Mendonça, Artur Ziviani, André Barreto

ACM Computing Surveys, 52 (1)
[ more info ]

2018


Transfer in Deep Reinforcement Learning Using Successor Features and Generalised Policy Improvement

André Barreto, Diana Borsa, John Quan, Tom Schaul, David Silver, Matteo Hessel, Daniel Mankowitz, Augustin Žídek, Rémi Munos

In Proceedings of the International Conference on Machine Learning (ICML)
[ pdf ] [ video ] [ slides 1 ] [ slides 2 ] [ poster ] [ more info ]

Fast Deep Reinforcement Learning Using Online Adjustments from the Past

Steven Hansen, Alexander Pritzel, Pablo Sprechmann, André Barreto, Charles Blundell

In Advances in Neural Information Processing Systems (NeurIPS)
[ pdf ] [ more info ]

Online TD(lambda) for discrete-time Markov jump linear systems

Rafael Beirigo, Marcos Todorov, André Barreto

In Proceedings of the IEEE Annual Conference on Decision and Control (CDC)
[ more info ]

Temporal Difference Learning with Neural Networks - Study of the Leakage Propagation Problem

Hugo Penedones, Damien Vincent, Hartmut Maennel, Sylvain Gelly, Timothy Mann, André Barreto

arXiv
[ pdf ] [ more info ]

Unicorn: Continual Learning with a Universal, Off-Policy, Agent

Daniel J Mankowitz, Augustin Žídek, André Barreto, Dan Horgan, Matteo Hessel, John Quan, Junhyuk Oh, Hado van Hasselt, David Silver, Tom Schaul

The Multi-disciplinary Conference on Reinforcement Learning and Decision Making (RLDM)
[ pdf ] [ more info ]

Abstract State Transition Graphs for Model-Based Reinforcement Learning

Matheus Mendonça, Artur Ziviani, André Barreto

In Proceedings of the Brazilian Conference on Intelligent Systems (BRACIS)
[ more info ]

2017


Successor Features for Transfer in Reinforcement Learning

André Barreto, Will Dabney, Rémi Munos, Jonathan J. Hunt, Tom Schaul, Hado van Hasselt, David Silver

In Advances in Neural Information Processing Systems (NIPS) - selected as a spotlight
[ pdf ] [ slides ] [poster ] [ workshop version ] [ more info ]

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, Thomas Degris

In Proceedings of the International Conference on Machine Learning, (ICML)
[ pdf ] [ video ] [ more info ]

Natural Value Approximators: Learning When to Trust Past Estimates

Zhongwen Xu, Joseph Modayil, Hado van Hasselt, André Barreto, David Silver, Tom Schaul

In Advances in Neural Information Processing Systems (NIPS) - selected as a spotlight
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Value-Aware Loss Function for Model-based Reinforcement Learning

Amir-Massoud Farahmand, André Barreto, Daniel Nikovski

In Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS)
[ pdf ] [ more info ]

Count-Based Quadratic Control of Markov Jump Linear Systems with Unknown Transition Probabilities

Rafael Beirigo, Marcos Todorov, André Barreto

In Proceedings of the IEEE Annual Conference on Decision and Control (CDC)
[ more info ]

Transfer on Count-based Quadratic Control of Markov Jump Linear Systems with Unknown Transition Probabilities

Rafael Beirigo, Marcos Todorov, André Barreto

In Proceedings of Conferência Brasileira de Dinâmica, Controle e Aplicações (DINCON)
[ pdf ]

2016


Practical Kernel-Based Reinforcement Learning

André Barreto, Doina Precup, Joelle Pineau

Journal of Machine Learning Research, v. 17 (67), pp. 1−70
[ pdf ] [ more info ]

Incremental Stochastic Factorization for Online Reinforcement Learning

André Barreto, Rafael L. Beirigo, Joelle Pineau, Doina Precup

In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI)
[ pdf ] [ more info ]

2015


An Expectation-Maximization Algorithm to Compute a Stochastic Factorization From Data

André Barreto, Rafael L. Beirigo, Joelle Pineau, Doina Precup

In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI)
[ pdf ] [ more info ]

Classification-based Approximate Policy Iteration

Amir-massoud Farahmand, Doina Precup, André Barreto, Mohammad Ghavamzadeh

IEEE Transactions on Automatic Control, v. 60 (12)
[ pdf ] [ more info ]

2014


Policy Iteration Based on Stochastic Factorization

André Barreto, Joelle Pineau, Doina Precup

Journal of Artificial Intelligence Research, v. 50, pp. 763−803
[ pdf ] [ more info ]

Tree-Based On-Line Reinforcement Learning

André Barreto

In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI)
[ pdf ] [ more info ]

Starting to Uncover the Relationship Between Stochastic Factorization and Hidden Markov Models

André Barreto, Borja Pigem, Joelle Pineau, Doina Precup

In NIPS: Workshop on Novel Trends and Applications in Reinforcement Learning
[ pdf ] [ more info ]

2013


CAPI: Generalized Classification-Based Approximate Policy Iteration

Amir-massoud Farahmand,  Doina Precup, André Barreto, Mohammad Ghavamzadeh

The Multidisciplinary Conference on Reinforcement Learning and Decision Making (RLDM)
[ pdf ] [ more info ]

Reinforcement Learning Competition 2013: Controllability and Kernel-­Based Stochastic Factorization

Anwarissa Asbah, André Barreto, Clement Gehring, Joelle Pineau, Doina Precup

In ICML Workshop on the Reinforcement Learning Competition
[ pdf ] [ more info ]

2012


On-Line Reinforcement Learning Using Incremental Kernel-Based Stochastic Factorization

André Barreto, Doina Precup, Joelle Pineau

In Advances in Neural Information Processing Systems (NIPS)
[ pdf ] [ more info ]

Analysis of Composition-Based Metagenomic Classification

Susan Higashi, André Barreto, Maurício Cantão, Ana Tereza Vasconcelos

BMC Genomics, v. 13, pp. 1–11
[ pdf ] [ more info ]

2011


Computing the Stationary Distribution of a Finite Markov Chain Through Stochastic Factorization

André Barreto and Marcelo Fragoso

SIAM Journal on Matrix Analysis and Applications, v. 32, pp. 1513–1523
[ pdf ] [ more info ]

Reinforcement Learning Using Kernel-Based Stochastic Factorization

André Barreto, Doina Precup, Joelle Pineau

In Advances in Neural Information Processing Systems (NIPS),  pp.720–728
[ pdf ] [ more info ]

Lumping the States of a Finite Markov Chain Through Stochastic Factorization

André Barreto and Marcelo Fragoso

In Proceedings of the World Congress of the International Federation of Automatic Control (IFAC)
[ pdf ] [ more info ]

Exploring Performance Profiles for Analyzing Benchmark Experiments

Helio Barbosa, Heder Bernardino, André Barreto

In Proceedings of the Metaheuristics International Conference (MIC), 2011
[ more info ]

Evolving Numerical Constants in Grammatical Evolution with the Ephemeral Constant Method

Douglas Augusto, Helio Barbosa, André Barreto, Heder Bernardino

In Proceedings of the Portuguese Conference on Artificial Intelligence (EPIA)
[ more info ]

A New Approach for Generating Numerical Constants in Grammatical Evolution

Douglas Augusto, Helio Barbosa, André Barreto, Heder Bernardino

In Proceedings of the Conference on Genetic and Evolutionary Computation (GECCO)
[ more info ]


2010


Probabilistic Performance Profiles for the Experimental Evaluation of Stochastic Algorithms

André Barreto, Heder Bernardino, Helio Barbosa

In Proceedings of the Conference on Genetic and Evolutionary Computation (GECCO)
[ pdf ] [ more info ]

Kernel-Based Stochastic Factorization for Batch Reinforcement Learning

André Barreto and Doina Precup

In NIPS: Learning and Planning from Batch Time Series Data Workshop
[ pdf ] [ more info ]

On the Characteristics of Sequential Decision Problems and Their Impact on Evolutionary Computation and Reinforcement Learning

André Barreto, Douglas Augusto, Helio Barbosa

In Artificial Evolution, volume 5975 of Lecture Notes in Computer Science
[ pdf ] [ more info ]

Using Performance Profiles to Analyze the Results of the 2006 CEC Constrained Optimization Competition

Helio Barbosa, Heder Bernardino, André Barreto

In Proceedings of the IEEE World Congress on Computational Intelligence (WCCI)
[ more info ]

2009


On the Characteristics of Sequential Decision Problems and Their Impact on Evolutionary Computation

André Barreto, Douglas Augusto, Helio Barbosa

In Proceedings of the Conference on Genetic and Evolutionary Computation (GECCO)
[ pdf ] [ more info ]

2008


Restricted Gradient-Descent Algorithm for Value-Function Approximation in Reinforcement Learning

André Barreto and Charles Anderson

Artificial Intelligence, v. 172(4-5), pages 454–482
[ pdf ] [ more info ]

...2007


A Note on the Variance of Rank-Based Selection Strategies for Genetic Algorithms and Genetic Programming

Artem Sokolov, Darrell Whitley, André Barreto

Genetic Programming and Evolvable Machines, v. 8(3), pp. 221–237, 2007

GOLS—Genetic Orthogonal Least Squares Algorithm for Training RBF Networks

André Barreto, Helio Barbosa, Nelson Ebecken

Neurocomputing, v. 69 (16-18), pp. 2041–2064, 2006

Alternative Evolutionary Algorithms for Evolving Programs: Evolution Strategies and Steady-State GP

Darrel Whitley, Marc Richards, Ross Beveridge, André Barreto

In Proceedings of the Conference on Genetic and Evolutionary Computation (GECCO), 2006 - winner best paper on genetic programming

An Interactive Genetic Algorithm with Coevolution of Weights for Multiobjective Problems

Helio Barbosa and André Barreto

In Proceedings of the Conference on Genetic and Evolutionary Computation (GECCO), 2001


Growing Compact RBF Networks Using a Genetic Algorithm

André Barreto, Helio Barbosa, Nelson Ebecken

In Proceedings of the Brazilian Symposium on Neural Networks (SBRN), 2002

Graph Layout Using a Genetic Algorithm

André Barreto and Helio Barbosa

In Proceedings of the Brazilian Symposium on Neural Networks (SBRN), 2000


Updated: 11/2019