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Counterfactual Credit Assignment in Model-Free Reinforcement Learning

Nov 18, 2020
Thomas Mesnard, Théophane Weber, Fabio Viola, Shantanu Thakoor, Alaa Saade, Anna Harutyunyan, Will Dabney, Tom Stepleton, Nicolas Heess, Arthur Guez, Marcus Hutter, Lars Buesing, Rémi Munos


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Revisiting Fundamentals of Experience Replay

Jul 13, 2020
William Fedus, Prajit Ramachandran, Rishabh Agarwal, Yoshua Bengio, Hugo Larochelle, Mark Rowland, Will Dabney

* Published at ICML 2020. First two authors contributed equally and code available at https://github.com/google-research/google-research/tree/master/experience_replay 

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Deep Reinforcement Learning and its Neuroscientific Implications

Jul 07, 2020
Matthew Botvinick, Jane X. Wang, Will Dabney, Kevin J. Miller, Zeb Kurth-Nelson

* 22 pages, 5 figures 

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The Value-Improvement Path: Towards Better Representations for Reinforcement Learning

Jun 03, 2020
Will Dabney, André Barreto, Mark Rowland, Robert Dadashi, John Quan, Marc G. Bellemare, David Silver


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Temporally-Extended Δ-Greedy Exploration

Jun 02, 2020
Will Dabney, Georg Ostrovski, André Barreto


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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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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, RĂ©mi Munos, Will Dabney


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Adaptive Trade-Offs in Off-Policy Learning

Oct 16, 2019
Mark Rowland, Will Dabney, RĂ©mi Munos


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Fast Task Inference with Variational Intrinsic Successor Features

Jun 12, 2019
Steven Hansen, Will Dabney, Andre Barreto, Tom Van de Wiele, David Warde-Farley, Volodymyr Mnih


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The Termination Critic

Feb 26, 2019
Anna Harutyunyan, Will Dabney, Diana Borsa, Nicolas Heess, Remi Munos, Doina Precup

* AISTATS 2019 

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Statistics and Samples in Distributional Reinforcement Learning

Feb 21, 2019
Mark Rowland, Robert Dadashi, Saurabh Kumar, RĂ©mi Munos, Marc G. Bellemare, Will Dabney


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A Geometric Perspective on Optimal Representations for Reinforcement Learning

Jan 31, 2019
Marc G. Bellemare, Will Dabney, Robert Dadashi, Adrien Ali Taiga, Pablo Samuel Castro, Nicolas Le Roux, Dale Schuurmans, Tor Lattimore, Clare Lyle


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Autoregressive Quantile Networks for Generative Modeling

Jun 14, 2018
Georg Ostrovski, Will Dabney, RĂ©mi Munos

* ICML 2018 

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Implicit Quantile Networks for Distributional Reinforcement Learning

Jun 14, 2018
Will Dabney, Georg Ostrovski, David Silver, RĂ©mi Munos

* ICML 2018 

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Low-pass Recurrent Neural Networks - A memory architecture for longer-term correlation discovery

May 13, 2018
Thomas Stepleton, Razvan Pascanu, Will Dabney, Siddhant M. Jayakumar, Hubert Soyer, Remi Munos


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Distributed Distributional Deterministic Policy Gradients

Apr 23, 2018
Gabriel Barth-Maron, Matthew W. Hoffman, David Budden, Will Dabney, Dan Horgan, Dhruva TB, Alistair Muldal, Nicolas Heess, Timothy Lillicrap


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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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An Analysis of Categorical Distributional Reinforcement Learning

Feb 22, 2018
Mark Rowland, Marc G. Bellemare, Will Dabney, RĂ©mi Munos, Yee Whye Teh


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Distributional Reinforcement Learning with Quantile Regression

Oct 27, 2017
Will Dabney, Mark Rowland, Marc G. Bellemare, RĂ©mi Munos


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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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A Distributional Perspective on Reinforcement Learning

Jul 21, 2017
Marc G. Bellemare, Will Dabney, RĂ©mi Munos

* ICML 2017 

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The Cramer Distance as a Solution to Biased Wasserstein Gradients

May 30, 2017
Marc G. Bellemare, Ivo Danihelka, Will Dabney, Shakir Mohamed, Balaji Lakshminarayanan, Stephan Hoyer, RĂ©mi Munos


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Proximal Reinforcement Learning: A New Theory of Sequential Decision Making in Primal-Dual Spaces

May 26, 2014
Sridhar Mahadevan, Bo Liu, Philip Thomas, Will Dabney, Steve Giguere, Nicholas Jacek, Ian Gemp, Ji Liu

* 121 pages 

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