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Forethought and Hindsight in Credit Assignment

Oct 26, 2020
Veronica Chelu, Doina Precup, Hado van Hasselt


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Discovering Reinforcement Learning Algorithms

Jul 17, 2020
Junhyuk Oh, Matteo Hessel, Wojciech M. Czarnecki, Zhongwen Xu, Hado van Hasselt, Satinder Singh, David Silver


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Meta-Gradient Reinforcement Learning with an Objective Discovered Online

Jul 16, 2020
Zhongwen Xu, Hado van Hasselt, Matteo Hessel, Junhyuk Oh, Satinder Singh, David Silver


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Expected Eligibility Traces

Jul 03, 2020
Hado van Hasselt, Sephora Madjiheurem, Matteo Hessel, David Silver, André Barreto, Diana Borsa


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Self-Tuning Deep Reinforcement Learning

Mar 02, 2020
Tom Zahavy, Zhongwen Xu, Vivek Veeriah, Matteo Hessel, Junhyuk Oh, Hado van Hasselt, David Silver, Satinder Singh


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What Can Learned Intrinsic Rewards Capture?

Dec 11, 2019
Zeyu Zheng, Junhyuk Oh, Matteo Hessel, Zhongwen Xu, Manuel Kroiss, Hado van Hasselt, David Silver, Satinder Singh


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Hindsight Credit Assignment

Dec 05, 2019
Anna Harutyunyan, Will Dabney, Thomas Mesnard, Mohammad Azar, Bilal Piot, Nicolas Heess, Hado van Hasselt, Greg Wayne, Satinder Singh, Doina Precup, Remi Munos

* NeurIPS 2019 

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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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Discovery of Useful Questions as Auxiliary Tasks

Sep 10, 2019
Vivek Veeriah, Matteo Hessel, Zhongwen Xu, Richard Lewis, Janarthanan Rajendran, Junhyuk Oh, Hado van Hasselt, David Silver, Satinder Singh


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General non-linear Bellman equations

Jul 08, 2019
Hado van Hasselt, John Quan, Matteo Hessel, Zhongwen Xu, Diana Borsa, Andre Barreto


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On Inductive Biases in Deep Reinforcement Learning

Jul 05, 2019
Matteo Hessel, Hado van Hasselt, Joseph Modayil, David Silver


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When to use parametric models in reinforcement learning?

Jun 12, 2019
Hado van Hasselt, Matteo Hessel, John Aslanides


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Meta-learning of Sequential Strategies

May 08, 2019
Pedro A. Ortega, Jane X. Wang, Mark Rowland, Tim Genewein, Zeb Kurth-Nelson, Razvan Pascanu, Nicolas Heess, Joel Veness, Alex Pritzel, Pablo Sprechmann, Siddhant M. Jayakumar, Tom McGrath, Kevin Miller, Mohammad Azar, Ian Osband, Neil Rabinowitz, András György, Silvia Chiappa, Simon Osindero, Yee Whye Teh, Hado van Hasselt, Nando de Freitas, Matthew Botvinick, Shane Legg

* DeepMind Technical Report (15 pages, 6 figures) 

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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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Deep Reinforcement Learning and the Deadly Triad

Dec 06, 2018
Hado van Hasselt, Yotam Doron, Florian Strub, Matteo Hessel, Nicolas Sonnerat, Joseph Modayil


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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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Multi-task Deep Reinforcement Learning with PopArt

Sep 12, 2018
Matteo Hessel, Hubert Soyer, Lasse Espeholt, Wojciech Czarnecki, Simon Schmitt, Hado van Hasselt


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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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Observe and Look Further: Achieving Consistent Performance on Atari

May 29, 2018
Tobias Pohlen, Bilal Piot, Todd Hester, Mohammad Gheshlaghi Azar, Dan Horgan, David Budden, Gabriel Barth-Maron, Hado van Hasselt, John Quan, Mel Večerík, Matteo Hessel, Rémi Munos, Olivier Pietquin


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Meta-Gradient Reinforcement Learning

May 24, 2018
Zhongwen Xu, Hado van Hasselt, David Silver


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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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Distributed Prioritized Experience Replay

Mar 02, 2018
Dan Horgan, John Quan, David Budden, Gabriel Barth-Maron, Matteo Hessel, Hado van Hasselt, David Silver

* Accepted to International Conference on Learning Representations 2018 

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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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Learning values across many orders of magnitude

Aug 16, 2016
Hado van Hasselt, Arthur Guez, Matteo Hessel, Volodymyr Mnih, David Silver

* Paper accepted for publication at NIPS 2016. This version includes the appendix 

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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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Deep Reinforcement Learning in Large Discrete Action Spaces

Apr 04, 2016
Gabriel Dulac-Arnold, Richard Evans, Hado van Hasselt, Peter Sunehag, Timothy Lillicrap, Jonathan Hunt, Timothy Mann, Theophane Weber, Thomas Degris, Ben Coppin


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Deep Reinforcement Learning with Double Q-learning

Dec 08, 2015
Hado van Hasselt, Arthur Guez, David Silver

* AAAI 2016 

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Learning to Predict Independent of Span

Aug 19, 2015
Hado van Hasselt, Richard S. Sutton

* 32 pages 

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