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Policy Evaluation Networks

Feb 26, 2020
Jean Harb, Tom Schaul, Doina Precup, Pierre-Luc Bacon

* 12 pages, 11 figures 

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Adapting Behaviour for Learning Progress

Dec 14, 2019
Tom Schaul, Diana Borsa, David Ding, David Szepesvari, Georg Ostrovski, Will Dabney, Simon Osindero


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Conditional Importance Sampling for Off-Policy Learning

Oct 16, 2019
Mark Rowland, Anna Harutyunyan, Hado van Hasselt, Diana Borsa, Tom Schaul, Rémi Munos, Will Dabney


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Non-Differentiable Supervised Learning with Evolution Strategies and Hybrid Methods

Jun 07, 2019
Karel Lenc, Erich Elsen, Tom Schaul, Karen Simonyan


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Ray Interference: a Source of Plateaus in Deep Reinforcement Learning

Apr 25, 2019
Tom Schaul, Diana Borsa, Joseph Modayil, Razvan Pascanu

* Full version of RLDM abstract 

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

Jan 30, 2019
André Barreto, Diana Borsa, John Quan, Tom Schaul, David Silver, Matteo Hessel, Daniel Mankowitz, Augustin Žídek, Rémi Munos

* Published at ICML 2018 

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Universal Successor Features Approximators

Dec 18, 2018
Diana Borsa, André Barreto, John Quan, Daniel Mankowitz, Rémi Munos, Hado van Hasselt, David Silver, Tom Schaul


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The Barbados 2018 List of Open Issues in Continual Learning

Nov 16, 2018
Tom Schaul, Hado van Hasselt, Joseph Modayil, Martha White, Adam White, Pierre-Luc Bacon, Jean Harb, Shibl Mourad, Marc Bellemare, Doina Precup

* NIPS Continual Learning Workshop 2018 

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

Jul 03, 2018
Daniel J. Mankowitz, Augustin Žídek, André Barreto, Dan Horgan, Matteo Hessel, John Quan, Junhyuk Oh, Hado van Hasselt, David Silver, Tom Schaul


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Meta-Learning by the Baldwin Effect

Jun 22, 2018
Chrisantha Thomas Fernando, Jakub Sygnowski, Simon Osindero, Jane Wang, Tom Schaul, Denis Teplyashin, Pablo Sprechmann, Alexander Pritzel, Andrei A. Rusu


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Successor Features for Transfer in Reinforcement Learning

Apr 12, 2018
André Barreto, Will Dabney, Rémi Munos, Jonathan J. Hunt, Tom Schaul, Hado van Hasselt, David Silver

* Published at NIPS 2017 

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Deep Q-learning from Demonstrations

Nov 22, 2017
Todd Hester, Matej Vecerik, Olivier Pietquin, Marc Lanctot, Tom Schaul, Bilal Piot, Dan Horgan, John Quan, Andrew Sendonaris, Gabriel Dulac-Arnold, Ian Osband, John Agapiou, Joel Z. Leibo, Audrunas Gruslys

* Published at AAAI 2018. Previously on arxiv as "Learning from Demonstrations for Real World Reinforcement Learning" 

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Rainbow: Combining Improvements in Deep Reinforcement Learning

Oct 06, 2017
Matteo Hessel, Joseph Modayil, Hado van Hasselt, Tom Schaul, Georg Ostrovski, Will Dabney, Dan Horgan, Bilal Piot, Mohammad Azar, David Silver

* Under review as a conference paper at AAAI 2018 

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StarCraft II: A New Challenge for Reinforcement Learning

Aug 16, 2017
Oriol Vinyals, Timo Ewalds, Sergey Bartunov, Petko Georgiev, Alexander Sasha Vezhnevets, Michelle Yeo, Alireza Makhzani, Heinrich Küttler, John Agapiou, Julian Schrittwieser, John Quan, Stephen Gaffney, Stig Petersen, Karen Simonyan, Tom Schaul, Hado van Hasselt, David Silver, Timothy Lillicrap, Kevin Calderone, Paul Keet, Anthony Brunasso, David Lawrence, Anders Ekermo, Jacob Repp, Rodney Tsing

* Collaboration between DeepMind & Blizzard. 20 pages, 9 figures, 2 tables 

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The Predictron: End-To-End Learning and Planning

Jul 20, 2017
David Silver, Hado van Hasselt, Matteo Hessel, Tom Schaul, Arthur Guez, Tim Harley, Gabriel Dulac-Arnold, David Reichert, Neil Rabinowitz, Andre Barreto, Thomas Degris

* Camera-ready version, ICML 2017, with supplement 

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FeUdal Networks for Hierarchical Reinforcement Learning

Mar 06, 2017
Alexander Sasha Vezhnevets, Simon Osindero, Tom Schaul, Nicolas Heess, Max Jaderberg, David Silver, Koray Kavukcuoglu


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Learning to learn by gradient descent by gradient descent

Nov 30, 2016
Marcin Andrychowicz, Misha Denil, Sergio Gomez, Matthew W. Hoffman, David Pfau, Tom Schaul, Brendan Shillingford, Nando de Freitas


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Reinforcement Learning with Unsupervised Auxiliary Tasks

Nov 16, 2016
Max Jaderberg, Volodymyr Mnih, Wojciech Marian Czarnecki, Tom Schaul, Joel Z Leibo, David Silver, Koray Kavukcuoglu


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Unifying Count-Based Exploration and Intrinsic Motivation

Nov 07, 2016
Marc G. Bellemare, Sriram Srinivasan, Georg Ostrovski, Tom Schaul, David Saxton, Remi Munos


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Dueling Network Architectures for Deep Reinforcement Learning

Apr 05, 2016
Ziyu Wang, Tom Schaul, Matteo Hessel, Hado van Hasselt, Marc Lanctot, Nando de Freitas

* 15 pages, 5 figures, and 5 tables 

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Prioritized Experience Replay

Feb 25, 2016
Tom Schaul, John Quan, Ioannis Antonoglou, David Silver

* Published at ICLR 2016 

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Unit Tests for Stochastic Optimization

Feb 25, 2014
Tom Schaul, Ioannis Antonoglou, David Silver

* Final submission to ICLR 2014 (revised according to reviews, additional results added) 

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Adaptive learning rates and parallelization for stochastic, sparse, non-smooth gradients

Mar 27, 2013
Tom Schaul, Yann LeCun

* Published at the First International Conference on Learning Representations (ICLR-2013). Public reviews are available at http://openreview.net/document/c14f2204-fd66-4d91-bed4-153523694041#c14f2204-fd66-4d91-bed4-153523694041 

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No More Pesky Learning Rates

Feb 18, 2013
Tom Schaul, Sixin Zhang, Yann LeCun


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Efficient Natural Evolution Strategies

Sep 26, 2012
Yi Sun, Daan Wierstra, Tom Schaul, Juergen Schmidhuber

* Puslished in GECCO'2009 

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Measuring Intelligence through Games

Sep 06, 2011
Tom Schaul, Julian Togelius, Jürgen Schmidhuber


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Natural Evolution Strategies

Jun 22, 2011
Daan Wierstra, Tom Schaul, Tobias Glasmachers, Yi Sun, Jürgen Schmidhuber


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A Linear Time Natural Evolution Strategy for Non-Separable Functions

Jun 13, 2011
Yi Sun, Faustino Gomez, Tom Schaul, Juergen Schmidhuber


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