Selected papers are grouped by project and listed in a (non-chronological) order that makes for a coherent narrative.

Successor Features and Generalised Policy Improvement


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), 2017 - selected as a spotlight
[ pdf ] [ slides ] [poster ] [ workshop version ] [ more info ]

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), 2018
[ pdf ] [ video ] [ slides 1 ] [ slides 2 ] [ poster ] [ more info ]

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), 2019
[ 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), 2019
[ 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 ]

Stochastic Factorization


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, 2011
[ 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), 2011
[ pdf ] [ more info ]

Policy Iteration Based on Stochastic Factorization

André Barreto, Joelle Pineau, Doina Precup

Journal of Artificial Intelligence Research, v. 50, pp. 763−803, 2014
[ pdf ] [ more info ]

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), 2015
[ 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), 2016
[ 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, 2011
[ pdf ] [ more info ]

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

André Barreto, Doina Precup, Joelle Pineau

In Advances in Neural Information Processing Systems (NIPS), 2012
[ pdf ] [ more info ]

Practical Kernel-Based Reinforcement Learning

André Barreto, Doina Precup, Joelle Pineau

Journal of Machine Learning Research, v. 17 (67), pp. 1−70, 2016
[ pdf ] [ more info ]

Tree-Based On-Line Reinforcement Learning

André Barreto

In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), 2014
[ pdf ] [ more info ]


Updated: 11/2019