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The Option Keyboard: Combining Skills in Reinforcement Learning


Jun 24, 2021
André Barreto, Diana Borsa, Shaobo Hou, Gheorghe Comanici, Eser Aygün, Philippe Hamel, Daniel Toyama, Jonathan Hunt, Shibl Mourad, David Silver, Doina Precup

* Published at NeurIPS 2019 

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Proper Value Equivalence


Jun 18, 2021
Christopher Grimm, André Barreto, Gregory Farquhar, David Silver, Satinder Singh


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Learning and Planning in Complex Action Spaces


Apr 13, 2021
Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Mohammadamin Barekatain, Simon Schmitt, David Silver


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Online and Offline Reinforcement Learning by Planning with a Learned Model


Apr 13, 2021
Julian Schrittwieser, Thomas Hubert, Amol Mandhane, Mohammadamin Barekatain, Ioannis Antonoglou, David Silver


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Muesli: Combining Improvements in Policy Optimization


Apr 13, 2021
Matteo Hessel, Ivo Danihelka, Fabio Viola, Arthur Guez, Simon Schmitt, Laurent Sifre, Theophane Weber, David Silver, Hado van Hasselt


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Discovery of Options via Meta-Learned Subgoals


Feb 12, 2021
Vivek Veeriah, Tom Zahavy, Matteo Hessel, Zhongwen Xu, Junhyuk Oh, Iurii Kemaev, Hado van Hasselt, David Silver, Satinder Singh


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The Value Equivalence Principle for Model-Based Reinforcement Learning


Nov 06, 2020
Christopher Grimm, André Barreto, Satinder Singh, David Silver

* NeurIPS-2020 

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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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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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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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Value-driven Hindsight Modelling


Feb 19, 2020
Arthur Guez, Fabio Viola, Théophane Weber, Lars Buesing, Steven Kapturowski, Doina Precup, David Silver, Nicolas Heess

* 8 pages + reference + appendix 

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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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Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model


Nov 19, 2019
Julian Schrittwieser, Ioannis Antonoglou, Thomas Hubert, Karen Simonyan, Laurent Sifre, Simon Schmitt, Arthur Guez, Edward Lockhart, Demis Hassabis, Thore Graepel, Timothy Lillicrap, David Silver


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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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Behaviour Suite for Reinforcement Learning


Aug 13, 2019
Ian Osband, Yotam Doron, Matteo Hessel, John Aslanides, Eren Sezener, Andre Saraiva, Katrina McKinney, Tor Lattimore, Csaba Szepezvari, Satinder Singh, Benjamin Van Roy, Richard Sutton, David Silver, Hado Van Hasselt


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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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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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An investigation of model-free planning


Jan 11, 2019
Arthur Guez, Mehdi Mirza, Karol Gregor, Rishabh Kabra, Sébastien Racanière, Théophane Weber, David Raposo, Adam Santoro, Laurent Orseau, Tom Eccles, Greg Wayne, David Silver, Timothy Lillicrap


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Credit Assignment Techniques in Stochastic Computation Graphs


Jan 07, 2019
Théophane Weber, Nicolas Heess, Lars Buesing, David Silver


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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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Bayesian Optimization in AlphaGo


Dec 17, 2018
Yutian Chen, Aja Huang, Ziyu Wang, Ioannis Antonoglou, Julian Schrittwieser, David Silver, Nando de Freitas


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Learning to Search with MCTSnets


Jul 17, 2018
Arthur Guez, Théophane Weber, Ioannis Antonoglou, Karen Simonyan, Oriol Vinyals, Daan Wierstra, Rémi Munos, David Silver

* ICML 2018 (camera-ready version) 

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Human-level performance in first-person multiplayer games with population-based deep reinforcement learning


Jul 03, 2018
Max Jaderberg, Wojciech M. Czarnecki, Iain Dunning, Luke Marris, Guy Lever, Antonio Garcia Castaneda, Charles Beattie, Neil C. Rabinowitz, Ari S. Morcos, Avraham Ruderman, Nicolas Sonnerat, Tim Green, Louise Deason, Joel Z. Leibo, David Silver, Demis Hassabis, Koray Kavukcuoglu, Thore Graepel


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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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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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Meta-Gradient Reinforcement Learning


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


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