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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, Rémi 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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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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Unsupervised Predictive Memory in a Goal-Directed Agent

Mar 28, 2018
Greg Wayne, Chia-Chun Hung, David Amos, Mehdi Mirza, Arun Ahuja, Agnieszka Grabska-Barwinska, Jack Rae, Piotr Mirowski, Joel Z. Leibo, Adam Santoro, Mevlana Gemici, Malcolm Reynolds, Tim Harley, Josh Abramson, Shakir Mohamed, Danilo Rezende, David Saxton, Adam Cain, Chloe Hillier, David Silver, Koray Kavukcuoglu, Matt Botvinick, Demis Hassabis, Timothy Lillicrap


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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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Imagination-Augmented Agents for Deep Reinforcement Learning

Feb 14, 2018
Théophane Weber, Sébastien Racanière, David P. Reichert, Lars Buesing, Arthur Guez, Danilo Jimenez Rezende, Adria Puigdomènech Badia, Oriol Vinyals, Nicolas Heess, Yujia Li, Razvan Pascanu, Peter Battaglia, Demis Hassabis, David Silver, Daan Wierstra


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Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm

Dec 05, 2017
David Silver, Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Matthew Lai, Arthur Guez, Marc Lanctot, Laurent Sifre, Dharshan Kumaran, Thore Graepel, Timothy Lillicrap, Karen Simonyan, Demis Hassabis


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A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning

Nov 07, 2017
Marc Lanctot, Vinicius Zambaldi, Audrunas Gruslys, Angeliki Lazaridou, Karl Tuyls, Julien Perolat, David Silver, Thore Graepel

* Camera-ready copy of NIPS 2017 paper, including appendix 

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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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