David Silver

According to our database, David Silver authored at least 112 papers between 2005 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2025

DataRater: Meta-Learned Dataset Curation.

Dan A. Calian, Gregory Farquhar, Iurii Kemaev, Luisa M. Zintgraf, Matteo Hessel, Jeremy Shar, Junhyuk Oh, András György, Tom Schaul, Jeffrey Dean, Hado van Hasselt, David Silver

CoRR, May, 2025

2023

Faster sorting algorithms discovered using deep reinforcement learning.

Daniel J. Mankowitz, Andrea Michi, Anton Zhernov, Marco Gelmi, Marco Selvi, Cosmin Paduraru, Edouard Leurent, Shariq Iqbal, Jean-Baptiste Lespiau, Alex Ahern, Thomas Köppe, Kevin Millikin, Stephen Gaffney, Sophie Elster, Jackson Broshear, Chris Gamble, Kieran Milan, Robert Tung, Minjae Hwang, A. Taylan Cemgil, Mohammadamin Barekatain, Yujia Li, Amol Mandhane, Thomas Hubert, Julian Schrittwieser, Demis Hassabis, Pushmeet Kohli, Martin A. Riedmiller, Oriol Vinyals, David Silver

Nat., 2023

Gemini: A Family of Highly Capable Multimodal Models.

Rohan Anil, Sebastian Borgeaud, Yonghui Wu, Jean-Baptiste Alayrac, Jiahui Yu, Radu Soricut, Johan Schalkwyk, Andrew M. Dai, Anja Hauth, Katie Millican, David Silver, Slav Petrov, Melvin Johnson, Ioannis Antonoglou, Julian Schrittwieser, Amelia Glaese, Jilin Chen, Emily Pitler, Timothy P. Lillicrap, Angeliki Lazaridou, Orhan Firat, James Molloy, Michael Isard, Paul Ronald Barham, Tom Hennigan, Benjamin Lee, Fabio Viola, Malcolm Reynolds, Yuanzhong Xu, Ryan Doherty, Eli Collins, Clemens Meyer, Eliza Rutherford, Erica Moreira, Kareem Ayoub, Megha Goel, George Tucker, Enrique Piqueras, Maxim Krikun, Iain Barr, Nikolay Savinov, Ivo Danihelka, Becca Roelofs, Anaïs White, Anders Andreassen, Tamara von Glehn, Lakshman Yagati, Mehran Kazemi, Lucas Gonzalez, Misha Khalman, Jakub Sygnowski, et al.

CoRR, 2023

2022

Figure Data for the paper "Mastering the Game of Stratego with Model-Free Multiagent Reinforcement Learning".

Julien Pérolat, Bart De Vylder, Daniel Hennes, Eugene Tarassov, Florian Strub, Vincent de Boer, Paul Muller, Jerome T. Connor, Neil Burch, Thomas Anthony, Stephen McAleer, Romuald Elie, Sarah H. Cen, Zhe Wang, Audrunas Gruslys, Aleksandra Malysheva, Mina Khan, Sherjil Ozair, Finbarr Timbers, Toby Pohlen, Tom Eccles, Mark Rowland, Marc Lanctot, Jean-Baptiste Lespiau, Bilal Piot, Shayegan Omidshafiei, Edward Lockhart, Laurent Sifre, Nathalie Beauguerlange, Rémi Munos, David Silver, Satinder Singh, Demis Hassabis, Karl Tuyls

Dataset, October, 2022

Deep learning, reinforcement learning, and world models.

Yutaka Matsuo, Yann LeCun, Maneesh Sahani, Doina Precup, David Silver, Masashi Sugiyama, Eiji Uchibe, Jun Morimoto

Neural Networks, 2022

Discovering faster matrix multiplication algorithms with reinforcement learning.

Alhussein Fawzi, Matej Balog, Aja Huang, Thomas Hubert, Bernardino Romera-Paredes, Mohammadamin Barekatain, Alexander Novikov, Francisco J. R. Ruiz, Julian Schrittwieser, Grzegorz Swirszcz, David Silver, Demis Hassabis, Pushmeet Kohli

Nat., 2022

Mastering the Game of Stratego with Model-Free Multiagent Reinforcement Learning.

Julien Pérolat, Bart De Vylder, Daniel Hennes, Eugene Tarassov, Florian Strub, Vincent de Boer, Paul Muller, Jerome T. Connor, Neil Burch, Thomas W. Anthony, Stephen McAleer, Romuald Elie, Sarah H. Cen, Zhe Wang, Audrunas Gruslys, Aleksandra Malysheva, Mina Khan, Sherjil Ozair, Finbarr Timbers, Toby Pohlen, Tom Eccles, Mark Rowland, Marc Lanctot, Jean-Baptiste Lespiau, Bilal Piot, Shayegan Omidshafiei, Edward Lockhart, Laurent Sifre, Nathalie Beauguerlange, Rémi Munos, David Silver, Satinder Singh, Demis Hassabis, Karl Tuyls

CoRR, 2022

Learning by Directional Gradient Descent.

David Silver, Anirudh Goyal, Ivo Danihelka, Matteo Hessel, Hado van Hasselt

Proceedings of the Tenth International Conference on Learning Representations, 2022

Bootstrapped Meta-Learning.

Sebastian Flennerhag, Yannick Schroecker, Tom Zahavy, Hado van Hasselt, David Silver, Satinder Singh

Proceedings of the Tenth International Conference on Learning Representations, 2022

Policy improvement by planning with Gumbel.

Ivo Danihelka, Arthur Guez, Julian Schrittwieser, David Silver

Proceedings of the Tenth International Conference on Learning Representations, 2022

Planning in Stochastic Environments with a Learned Model.

Ioannis Antonoglou, Julian Schrittwieser, Sherjil Ozair, Thomas K. Hubert, David Silver

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

Reward is enough.

David Silver, Satinder Singh, Doina Precup, Richard S. Sutton

Artif. Intell., 2021

Discovery of Options via Meta-Learned Subgoals.

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

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Online and Offline Reinforcement Learning by Planning with a Learned Model.

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

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Proper Value Equivalence.

Christopher Grimm, André Barreto, Gregory Farquhar, David Silver, Satinder Singh

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Self-Consistent Models and Values.

Gregory Farquhar, Kate Baumli, Zita Marinho, Angelos Filos, Matteo Hessel, Hado Philip van Hasselt, David Silver

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Learning and Planning in Complex Action Spaces.

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

Proceedings of the 38th International Conference on Machine Learning, 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

Proceedings of the 38th International Conference on Machine Learning, 2021

Expected Eligibility Traces.

Hado van Hasselt, Sephora Madjiheurem, Matteo Hessel, David Silver, André Barreto, Diana Borsa

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 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

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020

Fast reinforcement learning with generalized policy updates.

André Barreto, Shaobo Hou, Diana Borsa, David Silver, Doina Precup

Proc. Natl. Acad. Sci. USA, 2020

Improved protein structure prediction using potentials from deep learning.

Andrew W. Senior, Richard Evans, John Jumper, James Kirkpatrick, Laurent Sifre, Tim Green, Chongli Qin, Augustin Zídek, Alexander W. R. Nelson, Alex Bridgland, Hugo Penedones, Stig Petersen, Karen Simonyan, Steve Crossan, Pushmeet Kohli, David T. Jones, David Silver, Koray Kavukcuoglu, Demis Hassabis

Nat., 2020

Mastering Atari, Go, chess and shogi by planning with a learned model.

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

Nat., 2020

Self-Tuning Deep Reinforcement Learning.

Tom Zahavy, Zhongwen Xu, Vivek Veeriah, Matteo Hessel, Junhyuk Oh, Hado van Hasselt, David Silver, Satinder Singh

CoRR, 2020

A Self-Tuning Actor-Critic Algorithm.

Tom Zahavy, Zhongwen Xu, Vivek Veeriah, Matteo Hessel, Junhyuk Oh, Hado van Hasselt, David Silver, Satinder Singh

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Meta-Gradient Reinforcement Learning with an Objective Discovered Online.

Zhongwen Xu, Hado Philip van Hasselt, Matteo Hessel, Junhyuk Oh, Satinder Singh, David Silver

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Discovering Reinforcement Learning Algorithms.

Junhyuk Oh, Matteo Hessel, Wojciech M. Czarnecki, Zhongwen Xu, Hado van Hasselt, Satinder Singh, David Silver

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Value-driven Hindsight Modelling.

Arthur Guez, Fabio Viola, Theophane Weber, Lars Buesing, Steven Kapturowski, Doina Precup, David Silver, Nicolas Heess

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

The Value Equivalence Principle for Model-Based Reinforcement Learning.

Christopher Grimm, André Barreto, Satinder Singh, David Silver

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

What Can Learned Intrinsic Rewards Capture?

Zeyu Zheng, Junhyuk Oh, Matteo Hessel, Zhongwen Xu, Manuel Kroiss, Hado van Hasselt, David Silver, Satinder Singh

Proceedings of the 37th International Conference on Machine Learning, 2020

Behaviour Suite for Reinforcement Learning.

Ian Osband, Yotam Doron, Matteo Hessel, John Aslanides, Eren Sezener, Andre Saraiva, Katrina McKinney, Tor Lattimore, Csaba Szepesvári, Satinder Singh, Benjamin Van Roy, Richard S. Sutton, David Silver, Hado van Hasselt

Proceedings of the 8th International Conference on Learning Representations, 2020

2019

Grandmaster level in StarCraft II using multi-agent reinforcement learning.

Oriol Vinyals, Igor Babuschkin, Wojciech M. Czarnecki, Michaël Mathieu, Andrew Dudzik, Junyoung Chung, David H. Choi, Richard Powell, Timo Ewalds, Petko Georgiev, Junhyuk Oh, Dan Horgan, Manuel Kroiss, Ivo Danihelka, Aja Huang, Laurent Sifre, Trevor Cai, John P. Agapiou, Max Jaderberg, Alexander Sasha Vezhnevets, Rémi Leblond, Tobias Pohlen, Valentin Dalibard, David Budden, Yury Sulsky, James Molloy, Tom Le Paine, Çaglar Gülçehre, Ziyu Wang, Tobias Pfaff, Yuhuai Wu, Roman Ring, Dani Yogatama, Dario Wünsch, Katrina McKinney, Oliver Smith, Tom Schaul, Timothy P. Lillicrap, Koray Kavukcuoglu, Demis Hassabis, Chris Apps, David Silver

Nat., 2019

On Inductive Biases in Deep Reinforcement Learning.

Matteo Hessel, Hado van Hasselt, Joseph Modayil, David Silver

CoRR, 2019

Discovery of Useful Questions as Auxiliary Tasks.

Vivek Veeriah, Matteo Hessel, Zhongwen Xu, Janarthanan Rajendran, Richard L. Lewis, Junhyuk Oh, Hado van Hasselt, David Silver, Satinder Singh

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

The Option Keyboard: Combining Skills in Reinforcement Learning.

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

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

An Investigation of Model-Free Planning.

Arthur Guez, Mehdi Mirza, Karol Gregor, Rishabh Kabra, Sébastien Racanière, Theophane Weber, David Raposo, Adam Santoro, Laurent Orseau, Tom Eccles, Greg Wayne, David Silver, Timothy P. Lillicrap

Proceedings of the 36th International Conference on Machine Learning, 2019

Universal Successor Features Approximators.

Diana Borsa, André Barreto, John Quan, Daniel J. Mankowitz, Hado van Hasselt, Rémi Munos, David Silver, Tom Schaul

Proceedings of the 7th International Conference on Learning Representations, 2019

Credit Assignment Techniques in Stochastic Computation Graphs.

Théophane Weber, Nicolas Heess, Lars Buesing, David Silver

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018

Introduction to the special issue on deep reinforcement learning: An editorial.

Ron Sun, David Silver, Gerald Tesauro, Guang-Bin Huang

Neural Networks, 2018

Bayesian Optimization in AlphaGo.

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

CoRR, 2018

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

Max Jaderberg, Wojciech M. Czarnecki, Iain Dunning, Luke Marris, Guy Lever, Antonio García Castañeda, 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

CoRR, 2018

Unsupervised Predictive Memory in a Goal-Directed Agent.

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

CoRR, 2018

Unicorn: Continual Learning with a Universal, Off-policy Agent.

Daniel J. Mankowitz, Augustin Zídek, André Barreto, Dan Horgan, Matteo Hessel, John Quan, Junhyuk Oh, Hado van Hasselt, David Silver, Tom Schaul

CoRR, 2018

Meta-Gradient Reinforcement Learning.

Zhongwen Xu, Hado van Hasselt, David Silver

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Learning to Search with MCTSnets.

Arthur Guez, Theophane Weber, Ioannis Antonoglou, Karen Simonyan, Oriol Vinyals, Daan Wierstra, Rémi Munos, David Silver

Proceedings of the 35th International Conference on Machine Learning, 2018

Implicit Quantile Networks for Distributional Reinforcement Learning.

Will Dabney, Georg Ostrovski, David Silver, Rémi Munos

Proceedings of the 35th International Conference on Machine Learning, 2018

Transfer in Deep Reinforcement Learning Using Successor Features and Generalised Policy Improvement.

André Barreto, Diana Borsa, John Quan, Tom Schaul, David Silver, Matteo Hessel, Daniel J. Mankowitz, Augustin Zídek, Rémi Munos

Proceedings of the 35th International Conference on Machine Learning, 2018

Distributed Prioritized Experience Replay.

Dan Horgan, John Quan, David Budden, Gabriel Barth-Maron, Matteo Hessel, Hado van Hasselt, David Silver

Proceedings of the 6th International Conference on Learning Representations, 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

Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018

2017

Mastering the game of Go without human knowledge.

David Silver, Julian Schrittwieser, Karen Simonyan, Ioannis Antonoglou, Aja Huang, Arthur Guez, Thomas Hubert, Lucas Baker, Matthew Lai, Adrian Bolton, Yutian Chen, Timothy P. Lillicrap, Fan Hui, Laurent Sifre, George van den Driessche, Thore Graepel, Demis Hassabis

Nat., 2017

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

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

CoRR, 2017

StarCraft II: A New Challenge for Reinforcement Learning.

Oriol Vinyals, Timo Ewalds, Sergey Bartunov, Petko Georgiev, Alexander Sasha Vezhnevets, Michelle Yeo, Alireza Makhzani, Heinrich Küttler, John P. Agapiou, Julian Schrittwieser, John Quan, Stephen Gaffney, Stig Petersen, Karen Simonyan, Tom Schaul, Hado van Hasselt, David Silver, Timothy P. Lillicrap, Kevin Calderone, Paul Keet, Anthony Brunasso, David Lawrence, Anders Ekermo, Jacob Repp, Rodney Tsing

CoRR, 2017

Imagination-Augmented Agents for Deep Reinforcement Learning.

Theophane Weber, Sébastien Racanière, David P. Reichert, Lars Buesing, Arthur Guez, Danilo Jimenez Rezende, Adrià Puigdomènech Badia, Oriol Vinyals, Nicolas Heess, Yujia Li, Razvan Pascanu, Peter W. Battaglia, David Silver, Daan Wierstra

CoRR, 2017

Emergence of Locomotion Behaviours in Rich Environments.

Nicolas Heess, Dhruva TB, Srinivasan Sriram, Jay Lemmon, Josh Merel, Greg Wayne, Yuval Tassa, Tom Erez, Ziyu Wang, S. M. Ali Eslami, Martin A. Riedmiller, David Silver

CoRR, 2017

Technical perspective: Solving imperfect information games.

David Silver

Commun. ACM, 2017

Natural Value Approximators: Learning when to Trust Past Estimates.

Zhongwen Xu, Joseph Modayil, Hado van Hasselt, André Barreto, David Silver, Tom Schaul

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Imagination-Augmented Agents for Deep Reinforcement Learning.

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

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning.

Marc Lanctot, Vinícius Flores Zambaldi, Audrunas Gruslys, Angeliki Lazaridou, Karl Tuyls, Julien Pérolat, David Silver, Thore Graepel

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Successor Features for Transfer in Reinforcement Learning.

André Barreto, Will Dabney, Rémi Munos, Jonathan J. Hunt, Tom Schaul, David Silver, Hado van Hasselt

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

FeUdal Networks for Hierarchical Reinforcement Learning.

Alexander Sasha Vezhnevets, Simon Osindero, Tom Schaul, Nicolas Heess, Max Jaderberg, David Silver, Koray Kavukcuoglu

Proceedings of the 34th International Conference on Machine Learning, 2017

The Predictron: End-To-End Learning and Planning.

David Silver, Hado van Hasselt, Matteo Hessel, Tom Schaul, Arthur Guez, Tim Harley, Gabriel Dulac-Arnold, David P. Reichert, Neil C. Rabinowitz, André Barreto, Thomas Degris

Proceedings of the 34th International Conference on Machine Learning, 2017

Decoupled Neural Interfaces using Synthetic Gradients.

Max Jaderberg, Wojciech Marian Czarnecki, Simon Osindero, Oriol Vinyals, Alex Graves, David Silver, Koray Kavukcuoglu

Proceedings of the 34th International Conference on Machine Learning, 2017

Reinforcement Learning with Unsupervised Auxiliary Tasks.

Max Jaderberg, Volodymyr Mnih, Wojciech Marian Czarnecki, Tom Schaul, Joel Z. Leibo, David Silver, Koray Kavukcuoglu

Proceedings of the 5th International Conference on Learning Representations, 2017

2016

Mastering the game of Go with deep neural networks and tree search.

David Silver, Aja Huang, Chris J. Maddison, Arthur Guez, Laurent Sifre, George van den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Vedavyas Panneershelvam, Marc Lanctot, Sander Dieleman, Dominik Grewe, John Nham, Nal Kalchbrenner, Ilya Sutskever, Timothy P. Lillicrap, Madeleine Leach, Koray Kavukcuoglu, Thore Graepel, Demis Hassabis

Nat., 2016

Prioritized Experience Replay.

Tom Schaul, John Quan, Ioannis Antonoglou, David Silver

Proceedings of the 4th International Conference on Learning Representations, 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

Proceedings of the 4th International Conference on Learning Representations, 2016

Deep Reinforcement Learning from Self-Play in Imperfect-Information Games.

Johannes Heinrich, David Silver

CoRR, 2016

Learning and Transfer of Modulated Locomotor Controllers.

Nicolas Heess, Gregory Wayne, Yuval Tassa, Timothy P. Lillicrap, Martin A. Riedmiller, David Silver

CoRR, 2016

Learning functions across many orders of magnitudes.

Hado van Hasselt, Arthur Guez, Matteo Hessel, David Silver

CoRR, 2016

Successor Features for Transfer in Reinforcement Learning.

André Barreto, Rémi Munos, Tom Schaul, David Silver

CoRR, 2016

Learning values across many orders of magnitude.

Hado van Hasselt, Arthur Guez, Matteo Hessel, Volodymyr Mnih, David Silver

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Asynchronous Methods for Deep Reinforcement Learning.

Volodymyr Mnih, Adrià Puigdomènech Badia, Mehdi Mirza, Alex Graves, Timothy P. Lillicrap, Tim Harley, David Silver, Koray Kavukcuoglu

Proceedings of the 33nd International Conference on Machine Learning, 2016

Deep Reinforcement Learning with Double Q-Learning.

Hado van Hasselt, Arthur Guez, David Silver

Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, 2016

2015

Human-level control through deep reinforcement learning.

Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A. Rusu, Joel Veness, Marc G. Bellemare, Alex Graves, Martin A. Riedmiller, Andreas Fidjeland, Georg Ostrovski, Stig Petersen, Charles Beattie, Amir Sadik, Ioannis Antonoglou, Helen King, Dharshan Kumaran, Daan Wierstra, Shane Legg, Demis Hassabis

Nat., 2015

Massively Parallel Methods for Deep Reinforcement Learning.

Arun Nair, Praveen Srinivasan, Sam Blackwell, Cagdas Alcicek, Rory Fearon, Alessandro De Maria, Vedavyas Panneershelvam, Mustafa Suleyman, Charles Beattie, Stig Petersen, Shane Legg, Volodymyr Mnih, Koray Kavukcuoglu, David Silver

CoRR, 2015

Move Evaluation in Go Using Deep Convolutional Neural Networks.

Chris J. Maddison, Aja Huang, Ilya Sutskever, David Silver

Proceedings of the 3rd International Conference on Learning Representations, 2015

Memory-based control with recurrent neural networks.

Nicolas Heess, Jonathan J. Hunt, Timothy P. Lillicrap, David Silver

CoRR, 2015

Value Iteration with Options and State Aggregation.

Kamil Ciosek, David Silver

CoRR, 2015

Learning Continuous Control Policies by Stochastic Value Gradients.

Nicolas Heess, Gregory Wayne, David Silver, Timothy P. Lillicrap, Tom Erez, Yuval Tassa

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Smooth UCT Search in Computer Poker.

Johannes Heinrich, David Silver

Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, 2015

Universal Value Function Approximators.

Tom Schaul, Daniel Horgan, Karol Gregor, David Silver

Proceedings of the 32nd International Conference on Machine Learning, 2015

Fictitious Self-Play in Extensive-Form Games.

Johannes Heinrich, Marc Lanctot, David Silver

Proceedings of the 32nd International Conference on Machine Learning, 2015

2014

Unit Tests for Stochastic Optimization.

Tom Schaul, Ioannis Antonoglou, David Silver

Proceedings of the 2nd International Conference on Learning Representations, 2014

Better Optimism By Bayes: Adaptive Planning with Rich Models.

Arthur Guez, David Silver, Peter Dayan

CoRR, 2014

Bayes-Adaptive Simulation-based Search with Value Function Approximation.

Arthur Guez, Nicolas Heess, David Silver, Peter Dayan

Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

Deterministic Policy Gradient Algorithms.

David Silver, Guy Lever, Nicolas Heess, Thomas Degris, Daan Wierstra, Martin A. Riedmiller

Proceedings of the 31th International Conference on Machine Learning, 2014

2013

Scalable and Efficient Bayes-Adaptive Reinforcement Learning Based on Monte-Carlo Tree Search.

Arthur Guez, David Silver, Peter Dayan

J. Artif. Intell. Res., 2013

Playing Atari with Deep Reinforcement Learning.

Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, Martin A. Riedmiller

CoRR, 2013

Concurrent Reinforcement Learning from Customer Interactions.

David Silver, Leonard Newnham, David Barker, Suzanne Weller, Jason McFall

Proceedings of the 30th International Conference on Machine Learning, 2013

2012

Temporal-difference search in computer Go.

David Silver, Richard S. Sutton, Martin Müller

Mach. Learn., 2012

Learning to Win by Reading Manuals in a Monte-Carlo Framework.

S. R. K. Branavan, David Silver, Regina Barzilay

J. Artif. Intell. Res., 2012

The grand challenge of computer Go: Monte Carlo tree search and extensions.

Sylvain Gelly, Levente Kocsis, Marc Schoenauer, Michèle Sebag, David Silver, Csaba Szepesvári, Olivier Teytaud

Commun. ACM, 2012

Efficient Bayes-Adaptive Reinforcement Learning using Sample-Based Search.

Arthur Guez, David Silver, Peter Dayan

Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

Compositional Planning Using Optimal Option Models.

David Silver, Kamil Ciosek

Proceedings of the 29th International Conference on Machine Learning, 2012

Gradient Temporal Difference Networks.

David Silver

Proceedings of the Tenth European Workshop on Reinforcement Learning, 2012

Actor-Critic Reinforcement Learning with Energy-Based Policies.

Nicolas Heess, David Silver, Yee Whye Teh

Proceedings of the Tenth European Workshop on Reinforcement Learning, 2012

2011

A Monte-Carlo AIXI Approximation.

Joel Veness, Kee Siong Ng, Marcus Hutter, William T. B. Uther, David Silver

J. Artif. Intell. Res., 2011

Monte-Carlo tree search and rapid action value estimation in computer Go.

Sylvain Gelly, David Silver

Artif. Intell., 2011

Non-Linear Monte-Carlo Search in Civilization II.

S. R. K. Branavan, David Silver, Regina Barzilay

Proceedings of the IJCAI 2011, 2011

2010

Monte-Carlo Planning in Large POMDPs.

David Silver, Joel Veness

Proceedings of the Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010. Proceedings of a meeting held 6-9 December 2010, 2010

Reinforcement Learning via AIXI Approximation.

Joel Veness, Kee Siong Ng, Marcus Hutter, David Silver

Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence, 2010

2009

A Monte Carlo AIXI Approximation

Joel Veness, Kee Siong Ng, Marcus Hutter, David Silver

CoRR, 2009

Bootstrapping from Game Tree Search.

Joel Veness, David Silver, William T. B. Uther, Alan Blair

Proceedings of the Advances in Neural Information Processing Systems 22: 23rd Annual Conference on Neural Information Processing Systems 2009. Proceedings of a meeting held 7-10 December 2009, 2009

Convergent Temporal-Difference Learning with Arbitrary Smooth Function Approximation.

Hamid Reza Maei, Csaba Szepesvári, Shalabh Bhatnagar, Doina Precup, David Silver, Richard S. Sutton

Proceedings of the Advances in Neural Information Processing Systems 22: 23rd Annual Conference on Neural Information Processing Systems 2009. Proceedings of a meeting held 7-10 December 2009, 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

Proceedings of the 26th Annual International Conference on Machine Learning, 2009

Monte-Carlo simulation balancing.

David Silver, Gerald Tesauro

Proceedings of the 26th Annual International Conference on Machine Learning, 2009

2008

Sample-based learning and search with permanent and transient memories.

David Silver, Richard S. Sutton, Martin Müller

Proceedings of the Machine Learning, 2008

Achieving Master Level Play in 9 x 9 Computer Go.

Sylvain Gelly, David Silver

Proceedings of the Twenty-Third AAAI Conference on Artificial Intelligence, 2008

2007

Reinforcement Learning of Local Shape in the Game of Go.

David Silver, Richard S. Sutton, Martin Müller

Proceedings of the IJCAI 2007, 2007

On the role of tracking in stationary environments.

Richard S. Sutton, Anna Koop, David Silver

Proceedings of the Machine Learning, 2007

Combining online and offline knowledge in UCT.

Sylvain Gelly, David Silver

Proceedings of the Machine Learning, 2007

2005

Cooperative Pathfinding.

David Silver

Proceedings of the First Artificial Intelligence and Interactive Digital Entertainment Conference, 2005