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Revisiting Peng's Q($λ$) for Modern Reinforcement Learning


Feb 27, 2021
Tadashi Kozuno, Yunhao Tang, Mark Rowland, Rémi Munos, Steven Kapturowski, Will Dabney, Michal Valko, David Abel

* 26 pages, 7 figures, 2 tables 

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On The Effect of Auxiliary Tasks on Representation Dynamics


Feb 25, 2021
Clare Lyle, Mark Rowland, Georg Ostrovski, Will Dabney

* AISTATS 2021 

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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 ε-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, Rémi Munos, Will Dabney


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Adaptive Trade-Offs in Off-Policy Learning


Oct 16, 2019
Mark Rowland, Will Dabney, Rémi 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, Rémi 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, Rémi Munos

* ICML 2018 

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Implicit Quantile Networks for Distributional Reinforcement Learning


Jun 14, 2018
Will Dabney, Georg Ostrovski, David Silver, Rémi 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, Rémi Munos, Yee Whye Teh


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Distributional Reinforcement Learning with Quantile Regression


Oct 27, 2017
Will Dabney, Mark Rowland, Marc G. Bellemare, Rémi 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, Rémi 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, Rémi 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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