Silver, David

72 publications

NeurIPS 2025 DataRater: Meta-Learned Dataset Curation Dan A. Calian, Gregory Farquhar, Iurii Kemaev, Luisa Zintgraf, Matteo Hessel, Jeremy Shar, Junhyuk Oh, András György, Tom Schaul, Jeff Dean, Hado van Hasselt, David Silver

ICLR 2022 Bootstrapped Meta-Learning Sebastian Flennerhag, Yannick Schroecker, Tom Zahavy, Hado van Hasselt, David Silver, Satinder Singh

ICLR 2022 Learning by Directional Gradient Descent David Silver, Anirudh Goyal, Ivo Danihelka, Matteo Hessel, Hado van Hasselt

ICLR 2022 Planning in Stochastic Environments with a Learned Model Ioannis Antonoglou, Julian Schrittwieser, Sherjil Ozair, Thomas K Hubert, David Silver

ICLR 2022 Policy Improvement by Planning with Gumbel Ivo Danihelka, Arthur Guez, Julian Schrittwieser, David Silver

NeurIPSW 2021 Bootstrapped Meta-Learning Sebastian Flennerhag, Yannick Schroecker, Tom Zahavy, Hado van Hasselt, David Silver, Satinder Singh

NeurIPS 2021 Discovery of Options via Meta-Learned Subgoals Vivek Veeriah, Tom Zahavy, Matteo Hessel, Zhongwen Xu, Junhyuk Oh, Iurii Kemaev, Hado P van Hasselt, David Silver, Satinder P. Singh

AAAI 2021 Expected Eligibility Traces Hado van Hasselt, Sephora Madjiheurem, Matteo Hessel, David Silver, André Barreto, Diana Borsa

ICML 2021 Learning and Planning in Complex Action Spaces Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Mohammadamin Barekatain, Simon Schmitt, David Silver

ICML 2021 Muesli: Combining Improvements in Policy Optimization Matteo Hessel, Ivo Danihelka, Fabio Viola, Arthur Guez, Simon Schmitt, Laurent Sifre, Theophane Weber, David Silver, Hado Van Hasselt

NeurIPS 2021 Online and Offline Reinforcement Learning by Planning with a Learned Model Julian Schrittwieser, Thomas Hubert, Amol Mandhane, Mohammadamin Barekatain, Ioannis Antonoglou, David Silver

NeurIPS 2021 Proper Value Equivalence Christopher Grimm, Andre Barreto, Greg Farquhar, David Silver, Satinder P. Singh

NeurIPS 2021 Self-Consistent Models and Values Greg Farquhar, Kate Baumli, Zita Marinho, Angelos Filos, Matteo Hessel, Hado P van Hasselt, David Silver

AAAI 2021 The Value-Improvement Path: Towards Better Representations for Reinforcement Learning Will Dabney, André Barreto, Mark Rowland, Robert Dadashi, John Quan, Marc G. Bellemare, David Silver

NeurIPS 2020 A Self-Tuning Actor-Critic Algorithm Tom Zahavy, Zhongwen Xu, Vivek Veeriah, Matteo Hessel, Junhyuk Oh, Hado P van Hasselt, David Silver, Satinder P. Singh

ICLR 2020 Behaviour Suite for Reinforcement Learning Ian Osband, Yotam Doron, Matteo Hessel, John Aslanides, Eren Sezener, Andre Saraiva, Katrina McKinney, Tor Lattimore, Csaba Szepesvari, Satinder Singh, Benjamin Van Roy, Richard Sutton, David Silver, Hado Van Hasselt

NeurIPS 2020 Discovering Reinforcement Learning Algorithms Junhyuk Oh, Matteo Hessel, Wojciech M. Czarnecki, Zhongwen Xu, Hado P van Hasselt, Satinder P. Singh, David Silver

NeurIPS 2020 Meta-Gradient Reinforcement Learning with an Objective Discovered Online Zhongwen Xu, Hado P van Hasselt, Matteo Hessel, Junhyuk Oh, Satinder P. Singh, David Silver

NeurIPS 2020 The Value Equivalence Principle for Model-Based Reinforcement Learning Christopher Grimm, Andre Barreto, Satinder P. Singh, David Silver

NeurIPS 2020 Value-Driven Hindsight Modelling Arthur Guez, Fabio Viola, Theophane Weber, Lars Buesing, Steven Kapturowski, Doina Precup, David Silver, Nicolas Heess

ICML 2020 What Can Learned Intrinsic Rewards Capture? Zeyu Zheng, Junhyuk Oh, Matteo Hessel, Zhongwen Xu, Manuel Kroiss, Hado Van Hasselt, David Silver, Satinder Singh

ICML 2019 An Investigation of Model-Free Planning Arthur Guez, Mehdi Mirza, Karol Gregor, Rishabh Kabra, Sebastien Racaniere, Theophane Weber, David Raposo, Adam Santoro, Laurent Orseau, Tom Eccles, Greg Wayne, David Silver, Timothy Lillicrap

AISTATS 2019 Credit Assignment Techniques in Stochastic Computation Graphs Théophane Weber, Nicolas Heess, Lars Buesing, David Silver

NeurIPS 2019 Discovery of Useful Questions as Auxiliary Tasks Vivek Veeriah, Matteo Hessel, Zhongwen Xu, Janarthanan Rajendran, Richard L. Lewis, Junhyuk Oh, Hado P van Hasselt, David Silver, Satinder Singh

NeurIPS 2019 The Option Keyboard: Combining Skills in Reinforcement Learning Andre Barreto, Diana Borsa, Shaobo Hou, Gheorghe Comanici, Eser Aygün, Philippe Hamel, Daniel Toyama, Jonathan Hunt, Shibl Mourad, David Silver, Doina Precup

ICLR 2019 Universal Successor Features Approximators Diana Borsa, Andre Barreto, John Quan, Daniel J. Mankowitz, Hado van Hasselt, Remi Munos, David Silver, Tom Schaul

ICLR 2018 Distributed Prioritized Experience Replay Dan Horgan, John Quan, David Budden, Gabriel Barth-Maron, Matteo Hessel, Hado van Hasselt, David Silver

ICML 2018 Implicit Quantile Networks for Distributional Reinforcement Learning Will Dabney, Georg Ostrovski, David Silver, Remi Munos

ICML 2018 Learning to Search with MCTSnets Arthur Guez, Theophane Weber, Ioannis Antonoglou, Karen Simonyan, Oriol Vinyals, Daan Wierstra, Remi Munos, David Silver

NeurIPS 2018 Meta-Gradient Reinforcement Learning Zhongwen Xu, Hado P van Hasselt, David Silver

AAAI 2018 Rainbow: Combining Improvements in Deep Reinforcement Learning Matteo Hessel, Joseph Modayil, Hado van Hasselt, Tom Schaul, Georg Ostrovski, Will Dabney, Dan Horgan, Bilal Piot, Mohammad Gheshlaghi Azar, David Silver

ICML 2018 Transfer in Deep Reinforcement Learning Using Successor Features and Generalised Policy Improvement Andre Barreto, Diana Borsa, John Quan, Tom Schaul, David Silver, Matteo Hessel, Daniel Mankowitz, Augustin Zidek, Remi Munos

NeurIPS 2017 A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning Marc Lanctot, Vinicius Zambaldi, Audrunas Gruslys, Angeliki Lazaridou, Karl Tuyls, Julien Perolat, David Silver, Thore Graepel

ICML 2017 Decoupled Neural Interfaces Using Synthetic Gradients Max Jaderberg, Wojciech Marian Czarnecki, Simon Osindero, Oriol Vinyals, Alex Graves, David Silver, Koray Kavukcuoglu

ICML 2017 FeUdal Networks for Hierarchical Reinforcement Learning Alexander Sasha Vezhnevets, Simon Osindero, Tom Schaul, Nicolas Heess, Max Jaderberg, David Silver, Koray Kavukcuoglu

NeurIPS 2017 Imagination-Augmented Agents for Deep Reinforcement Learning Sébastien Racanière, Theophane Weber, David Reichert, Lars Buesing, Arthur Guez, Danilo Jimenez Rezende, Adrià Puigdomènech Badia, Oriol Vinyals, Nicolas Heess, Yujia Li, Razvan Pascanu, Peter Battaglia, Demis Hassabis, David Silver, Daan Wierstra

NeurIPS 2017 Natural Value Approximators: Learning When to Trust past Estimates Zhongwen Xu, Joseph Modayil, Hado P van Hasselt, Andre Barreto, David Silver, Tom Schaul

ICLR 2017 Reinforcement Learning with Unsupervised Auxiliary Tasks Max Jaderberg, Volodymyr Mnih, Wojciech Marian Czarnecki, Tom Schaul, Joel Z. Leibo, David Silver, Koray Kavukcuoglu

NeurIPS 2017 Successor Features for Transfer in Reinforcement Learning Andre Barreto, Will Dabney, Remi Munos, Jonathan J Hunt, Tom Schaul, Hado P van Hasselt, David Silver

ICML 2017 The Predictron: End-to-End Learning and Planning David Silver, Hado Hasselt, Matteo Hessel, Tom Schaul, Arthur Guez, Tim Harley, Gabriel Dulac-Arnold, David Reichert, Neil Rabinowitz, Andre Barreto, Thomas Degris

ICML 2016 Asynchronous Methods for Deep Reinforcement Learning Volodymyr Mnih, Adria Puigdomenech Badia, Mehdi Mirza, Alex Graves, Timothy Lillicrap, Tim Harley, David Silver, Koray Kavukcuoglu

ICLR 2016 Continuous Control with Deep Reinforcement Learning Timothy P. Lillicrap, Jonathan J. Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, Daan Wierstra

AAAI 2016 Deep Reinforcement Learning with Double Q-Learning Hado van Hasselt, Arthur Guez, David Silver

NeurIPS 2016 Learning Values Across Many Orders of Magnitude Hado P van Hasselt, Arthur Guez, Arthur Guez, Matteo Hessel, Volodymyr Mnih, David Silver

ICLR 2016 Prioritized Experience Replay Tom Schaul, John Quan, Ioannis Antonoglou, David Silver

ICML 2015 Fictitious Self-Play in Extensive-Form Games Johannes Heinrich, Marc Lanctot, David Silver

NeurIPS 2015 Learning Continuous Control Policies by Stochastic Value Gradients Nicolas Heess, Gregory Wayne, David Silver, Timothy Lillicrap, Tom Erez, Yuval Tassa

ICLR 2015 Move Evaluation in Go Using Deep Convolutional Neural Networks Chris J. Maddison, Aja Huang, Ilya Sutskever, David Silver

IJCAI 2015 Smooth UCT Search in Computer Poker Johannes Heinrich, David Silver

ICML 2015 Universal Value Function Approximators Tom Schaul, Daniel Horgan, Karol Gregor, David Silver

NeurIPS 2014 Bayes-Adaptive Simulation-Based Search with Value Function Approximation Arthur Guez, Nicolas Heess, David Silver, Peter Dayan

ICML 2014 Deterministic Policy Gradient Algorithms David Silver, Guy Lever, Nicolas Heess, Thomas Degris, Daan Wierstra, Martin Riedmiller

ICLR 2014 Unit Tests for Stochastic Optimization Tom Schaul, Ioannis Antonoglou, David Silver

ICML 2013 Concurrent Reinforcement Learning from Customer Interactions David Silver, Leonard Newnham, David Barker, Suzanne Weller, Jason McFall

JAIR 2013 Scalable and Efficient Bayes-Adaptive Reinforcement Learning Based on Monte-Carlo Tree Search Arthur Guez, David Silver, Peter Dayan

ICML 2012 Compositional Planning Using Optimal Option Models David Silver, Kamil Ciosek

NeurIPS 2012 Efficient Bayes-Adaptive Reinforcement Learning Using Sample-Based Search Arthur Guez, David Silver, Peter Dayan

JAIR 2012 Learning to Win by Reading Manuals in a Monte-Carlo Framework S. R. K. Branavan, David Silver, Regina Barzilay

MLJ 2012 Temporal-Difference Search in Computer Go David Silver, Richard S. Sutton, Martin Müller

JAIR 2011 A Monte-Carlo AIXI Approximation Joel Veness, Kee Siong Ng, Marcus Hutter, William T. B. Uther, David Silver

IJCAI 2011 Non-Linear Monte-Carlo Search in Civilization II S. R. K. Branavan, David Silver, Regina Barzilay

NeurIPS 2010 Monte-Carlo Planning in Large POMDPs David Silver, Joel Veness

AAAI 2010 Reinforcement Learning via AIXI Approximation Joel Veness, Kee Siong Ng, Marcus Hutter, David Silver

NeurIPS 2009 Bootstrapping from Game Tree Search Joel Veness, David Silver, Alan Blair, William Uther

NeurIPS 2009 Convergent Temporal-Difference Learning with Arbitrary Smooth Function Approximation Hamid R. Maei, Csaba Szepesvári, Shalabh Bhatnagar, Doina Precup, David Silver, Richard S. Sutton

ICML 2009 Fast Gradient-Descent Methods for Temporal-Difference Learning with Linear Function Approximation Richard S. Sutton, Hamid Reza Maei, Doina Precup, Shalabh Bhatnagar, David Silver, Csaba Szepesvári, Eric Wiewiora

ICML 2009 Monte-Carlo Simulation Balancing David Silver, Gerald Tesauro

AAAI 2008 Achieving Master Level Play in 9 X 9 Computer Go Sylvain Gelly, David Silver

ICML 2008 Sample-Based Learning and Search with Permanent and Transient Memories David Silver, Richard S. Sutton, Martin Müller

ICML 2007 Combining Online and Offline Knowledge in UCT Sylvain Gelly, David Silver

ICML 2007 On the Role of Tracking in Stationary Environments Richard S. Sutton, Anna Koop, David Silver

IJCAI 2007 Reinforcement Learning of Local Shape in the Game of Go David Silver, Richard S. Sutton, Martin Müller