Book Review: Life Online: Researching Real Experience in Virtual Space. - David Silver (New Media Soc., 2000)

Hierarchical simultaneous localization and mapping. - Brad Lisien; Deryck Morales; David Silver; George Kantor; Ioannis M. Rekleitis; Howie Choset (IROS, 2003)

Arc Carving: Obtaining Accurate, Low Latency Maps from Ultrasonic Range Sensors. - David Silver; Deryck Morales; Ioannis M. Rekleitis; Brad Lisien; Howie Choset (ICRA, 2004)

Feature extraction for topological mine maps. - David Silver; Dave Ferguson 001; Aaron Morris; Scott Thayer (IROS, 2004)

A regional point descriptor for global topological localization in flooded subterranean environments. - David M. Bradley; David Silver; Scott Thayer (RAM, 2004)

Scan matching for flooded subterranean voids. - David Silver; David M. Bradley; Scott Thayer (RAM, 2004)

Internet/Cyberculture/ Digital Culture/New Media/ Fill-in-the-Blank Studies. - David Silver (New Media Soc., 2004)

Cooperative Pathfinding. - David Silver (AIIDE, 2005)

Topological Global Localization for Subterranean Voids. - David Silver; Joseph Carsten; Scott Thayer (FSR, 2005)

Towards Topological Exploration of Abandoned Mines. - Aaron Morris; David Silver; David I. Ferguson; Scott Thayer (ICRA, 2005)

The hierarchical atlas. - Brad Lisien; Deryck Morales; David Silver; George Kantor; Ioannis M. Rekleitis; Howie Choset (IEEE Trans. Robotics, 2005)

Experimental Analysis of Overhead Data Processing To Support Long Range Navigation. - David Silver; Boris Sofman; Nicolas Vandapel; J. Andrew Bagnell; Anthony Stentz (IROS, 2006)

Recent developments in subterranean robotics. - Aaron Morris; Dave Ferguson 001; Zachary Omohundro; David M. Bradley; David Silver; Christopher R. Baker; Scott Thayer; Chuck Whittaker; William Whittaker (J. Field Robotics, 2006)

Topological exploration of subterranean environments. - David Silver; Dave Ferguson 001; Aaron Morris; Scott Thayer (J. Field Robotics, 2006)

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 (ICML, 2007)

Reinforcement Learning of Local Shape in the Game of Go. - David Silver; Richard S. Sutton; Martin Müller 003 (IJCAI, 2007)

Achieving Master Level Play in 9 x 9 Computer Go. - Sylvain Gelly; David Silver (AAAI, 2008)

Sample-based learning and search with permanent and transient memories. - David Silver; Richard S. Sutton; Martin Müller 003 (ICML, 2008)

High Performance Outdoor Navigation from Overhead Data using Imitation Learning. - David Silver; James A. Bagnell; Anthony Stentz (Robotics: Science and Systems, 2008)

History, Hype, and Hope: An Afterward. - David Silver (First Monday, 2008)

Applied Imitation Learning for Autonomous Navigation in Complex Natural Terrain. - David Silver; J. Andrew Bagnell; Anthony Stentz (FSR, 2009)

Monte-Carlo simulation balancing. - David Silver; Gerald Tesauro (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)

Perceptual Interpretation for Autonomous Navigation through Dynamic Imitation Learning. - David Silver; J. Andrew Bagnell; Anthony Stentz (ISRR, 2009)

Convergent Temporal-Difference Learning with Arbitrary Smooth Function Approximation. - Hamid Reza Maei; Csaba Szepesvári; Shalabh Bhatnagar; Doina Precup; David Silver; Richard S. Sutton (NIPS, 2009)

Bootstrapping from Game Tree Search. - Joel Veness; David Silver; William T. B. Uther; Alan Blair 001 (NIPS, 2009)

Learning to search: Functional gradient techniques for imitation learning. - Nathan D. Ratliff; David Silver; J. Andrew Bagnell (Auton. Robots, 2009)

A Monte Carlo AIXI Approximation - Joel Veness; Kee Siong Ng; Marcus Hutter; David Silver (CoRR, 2009)

Reinforcement Learning via AIXI Approximation. - Joel Veness; Kee Siong Ng; Marcus Hutter; David Silver (AAAI, 2010)

Monte-Carlo Planning in Large POMDPs. - David Silver; Joel Veness (NIPS, 2010)

Learning from Demonstration for Autonomous Navigation in Complex Unstructured Terrain. - David Silver; J. Andrew Bagnell; Anthony Stentz (Int. J. Robotics Res., 2010)

Learning for Autonomous Navigation. - J. Andrew Bagnell; David M. Bradley; David Silver; Boris Sofman; Anthony Stentz (IEEE Robotics Autom. Mag., 2010)

Learning Preference Models for Autonomous Mobile Robots in Complex Domains. - David Silver (2010)

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

Monte Carlo Localization and registration to prior data for outdoor navigation. - David Silver; Anthony Stentz (IROS, 2011)

Monte-Carlo tree search and rapid action value estimation in computer Go. - Sylvain Gelly; David Silver (Artif. Intell., 2011)

A Monte-Carlo AIXI Approximation. - Joel Veness; Kee Siong Ng; Marcus Hutter; William T. B. Uther; David Silver (J. Artif. Intell. Res., 2011)

Actor-Critic Reinforcement Learning with Energy-Based Policies. - Nicolas Heess; David Silver; Yee Whye Teh (EWRL, 2012)

Gradient Temporal Difference Networks. - David Silver (EWRL, 2012)

Compositional Planning Using Optimal Option Models. - David Silver; Kamil Ciosek (ICML, 2012)

Active learning from demonstration for robust autonomous navigation. - David Silver; J. Andrew Bagnell; Anthony Stentz (ICRA, 2012)

Learning Autonomous Driving Styles and Maneuvers from Expert Demonstration. - David Silver; J. Andrew Bagnell; Anthony Stentz (ISER, 2012)

Efficient Bayes-Adaptive Reinforcement Learning using Sample-Based Search. - Arthur Guez; David Silver; Peter Dayan (NIPS, 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)

Digital natives on a media fast. - David Silver (Inf. Serv. Use, 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)

Temporal-difference search in computer Go. - David Silver; Richard S. Sutton; Martin Müller 003 (Mach. Learn., 2012)

Concurrent Reinforcement Learning from Customer Interactions. - David Silver; Leonard Newnham; David Barker; Suzanne Weller; Jason McFall (ICML (3), 2013)

Playing Atari with Deep Reinforcement Learning. - Volodymyr Mnih; Koray Kavukcuoglu; David Silver; Alex Graves; Ioannis Antonoglou; Daan Wierstra; Martin A. Riedmiller (CoRR, 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)

Deterministic Policy Gradient Algorithms. - David Silver; Guy Lever; Nicolas Heess; Thomas Degris; Daan Wierstra; Martin A. Riedmiller (ICML, 2014)

Bayes-Adaptive Simulation-based Search with Value Function Approximation. - Arthur Guez; Nicolas Heess; David Silver; Peter Dayan (NIPS, 2014)

Password Managers: Attacks and Defenses. - David Silver; Suman Jana; Dan Boneh; Eric Yawei Chen; Collin Jackson (USENIX Security Symposium, 2014)

Better Optimism By Bayes: Adaptive Planning with Rich Models. - Arthur Guez; David Silver; Peter Dayan (CoRR, 2014)

Unit Tests for Stochastic Optimization. - Tom Schaul; Ioannis Antonoglou; David Silver (ICLR, 2014)

Authenticity, Relatability and Collaborative Approaches to Sharing Knowledge about Assistive Living Technology. - John Vines; Peter C. Wright; David Silver; Maggie Winchcombe; Patrick Olivier (CSCW, 2015)

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

Universal Value Function Approximators. - Tom Schaul; Daniel Horgan; Karol Gregor; David Silver (ICML, 2015)

Smooth UCT Search in Computer Poker. - Johannes Heinrich; David Silver (IJCAI, 2015)

Scene understanding for a high-mobility walking robot. - David M. Bradley; Jonathan K. Chang; David Silver; Matthew Powers; Herman Herman; Peter Rander; Anthony Stentz (IROS, 2015)

Learning Continuous Control Policies by Stochastic Value Gradients. - Nicolas Heess; Gregory Wayne; David Silver; Timothy P. Lillicrap; Tom Erez; Yuval Tassa (NIPS, 2015)

Value Iteration with Options and State Aggregation. - Kamil Ciosek; David Silver (CoRR, 2015)

Memory-based control with recurrent neural networks. - Nicolas Heess; Jonathan J. Hunt; Timothy P. Lillicrap; David Silver (CoRR, 2015)

Move Evaluation in Go Using Deep Convolutional Neural Networks. - Chris J. Maddison; Aja Huang; Ilya Sutskever; David Silver (ICLR (Poster), 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; et al. (CoRR, 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; et al. (Nat., 2015)

Deep Reinforcement Learning with Double Q-Learning. - Hado van Hasselt; Arthur Guez; David Silver (AAAI, 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 (ICML, 2016)

Learning values across many orders of magnitude. - Hado van Hasselt; Arthur Guez; Matteo Hessel; Volodymyr Mnih; David Silver (NIPS, 2016)

Successor Features for Transfer in Reinforcement Learning. - André Barreto 001; Rémi Munos; Tom Schaul; David Silver (CoRR, 2016)

Learning functions across many orders of magnitudes. - Hado van Hasselt; Arthur Guez; Matteo Hessel; 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)

Deep Reinforcement Learning from Self-Play in Imperfect-Information Games. - Johannes Heinrich; David Silver (CoRR, 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 (ICLR (Poster), 2016)

Prioritized Experience Replay. - Tom Schaul; John Quan; Ioannis Antonoglou; David Silver (ICLR (Poster), 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 002; Julian Schrittwieser; Ioannis Antonoglou; Vedavyas Panneershelvam; Marc Lanctot; Sander Dieleman; Dominik Grewe; et al. (Nat., 2016)

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

Decoupled Neural Interfaces using Synthetic Gradients. - Max Jaderberg; Wojciech Marian Czarnecki; Simon Osindero; Oriol Vinyals; Alex Graves; David Silver; Koray Kavukcuoglu (ICML, 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 001; Thomas Degris (ICML, 2017)

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

Successor Features for Transfer in Reinforcement Learning. - André Barreto 001; Will Dabney; Rémi Munos; Jonathan J. Hunt; Tom Schaul; David Silver; Hado van Hasselt (NIPS, 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 (NIPS, 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 001; Razvan Pascanu; Peter W. Battaglia; et al. (NIPS, 2017)

Natural Value Approximators: Learning when to Trust Past Estimates. - Zhongwen Xu; Joseph Modayil; Hado van Hasselt; André Barreto 001; David Silver; Tom Schaul (NIPS, 2017)

Technical perspective: Solving imperfect information games. - David Silver (Commun. ACM, 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 001; S. M. Ali Eslami; Martin A. Riedmiller; David Silver (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 001; Razvan Pascanu; Peter W. Battaglia; et al. (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; et al. (CoRR, 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; et al. (CoRR, 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 001; Timothy P. Lillicrap; et al. (Nat., 2017)

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 (AAAI, 2018)

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

Transfer in Deep Reinforcement Learning Using Successor Features and Generalised Policy Improvement. - André Barreto 001; Diana Borsa; John Quan; Tom Schaul; David Silver; Matteo Hessel; Daniel J. Mankowitz; Augustin Zídek; Rémi Munos (ICML, 2018)

Implicit Quantile Networks for Distributional Reinforcement Learning. - Will Dabney; Georg Ostrovski; David Silver; Rémi Munos (ICML, 2018)

Learning to Search with MCTSnets. - Arthur Guez; Theophane Weber; Ioannis Antonoglou; Karen Simonyan; Oriol Vinyals; Daan Wierstra; Rémi Munos; David Silver (ICML, 2018)

Meta-Gradient Reinforcement Learning. - Zhongwen Xu; Hado van Hasselt; David Silver (NeurIPS, 2018)

Unicorn: Continual Learning with a Universal, Off-policy Agent. - Daniel J. Mankowitz; Augustin Zídek; André Barreto 001; Dan Horgan; Matteo Hessel; John Quan; Junhyuk Oh; Hado van Hasselt; David Silver; Tom Schaul (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; et al. (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; et al. (CoRR, 2018)

Bayesian Optimization in AlphaGo. - Yutian Chen 001; Aja Huang; Ziyu Wang 001; Ioannis Antonoglou; Julian Schrittwieser; David Silver; Nando de Freitas (CoRR, 2018)

Introduction to the special issue on deep reinforcement learning: An editorial. - Ron Sun; David Silver; Gerald Tesauro; Guang-Bin Huang (Neural Networks, 2018)

Credit Assignment Techniques in Stochastic Computation Graphs. - Théophane Weber; Nicolas Heess; Lars Buesing; David Silver (AISTATS, 2019)

Universal Successor Features Approximators. - Diana Borsa; André Barreto 001; John Quan; Daniel J. Mankowitz; Hado van Hasselt; Rémi Munos; David Silver; Tom Schaul (ICLR (Poster), 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; et al. (ICML, 2019)

The Option Keyboard: Combining Skills in Reinforcement Learning. - André Barreto 001; Diana Borsa; Shaobo Hou; Gheorghe Comanici; Eser Aygün; Philippe Hamel; Daniel Toyama; Jonathan J. Hunt; Shibl Mourad; David Silver; Doina Precup (NeurIPS, 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 001 (NeurIPS, 2019)

On Inductive Biases in Deep Reinforcement Learning. - Matteo Hessel; Hado van Hasselt; Joseph Modayil; David Silver (CoRR, 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; et al. (Nat., 2019)

Behaviour Suite for Reinforcement Learning. - Ian Osband; Yotam Doron; Matteo Hessel; John Aslanides; Eren Sezener; Andre Saraiva 001; Katrina McKinney; Tor Lattimore; Csaba Szepesvári; Satinder Singh 001; Benjamin Van Roy; Richard S. Sutton; et al. (ICLR, 2020)

What Can Learned Intrinsic Rewards Capture? - Zeyu Zheng; Junhyuk Oh; Matteo Hessel; Zhongwen Xu; Manuel Kroiss; Hado van Hasselt; David Silver; Satinder Singh 001 (ICML, 2020)

The Value Equivalence Principle for Model-Based Reinforcement Learning. - Christopher Grimm; André Barreto 001; Satinder Singh 001; David Silver (NeurIPS, 2020)

Value-driven Hindsight Modelling. - Arthur Guez; Fabio Viola; Theophane Weber; Lars Buesing; Steven Kapturowski; Doina Precup; David Silver; Nicolas Heess (NeurIPS, 2020)

Discovering Reinforcement Learning Algorithms. - Junhyuk Oh; Matteo Hessel; Wojciech M. Czarnecki; Zhongwen Xu; Hado van Hasselt; Satinder Singh 001; David Silver (NeurIPS, 2020)

Meta-Gradient Reinforcement Learning with an Objective Discovered Online. - Zhongwen Xu; Hado Philip van Hasselt; Matteo Hessel; Junhyuk Oh; Satinder Singh 001; David Silver (NeurIPS, 2020)

A Self-Tuning Actor-Critic Algorithm. - Tom Zahavy; Zhongwen Xu; Vivek Veeriah; Matteo Hessel; Junhyuk Oh; Hado van Hasselt; David Silver; Satinder Singh 001 (NeurIPS, 2020)

Self-Tuning Deep Reinforcement Learning. - Tom Zahavy; Zhongwen Xu; Vivek Veeriah; Matteo Hessel; Junhyuk Oh; Hado van Hasselt; David Silver; Satinder Singh 001 (CoRR, 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)

Improved protein structure prediction using potentials from deep learning. - Andrew W. Senior; Richard Evans 001; John Jumper; James Kirkpatrick; Laurent Sifre; Tim Green; Chongli Qin; Augustin Zídek; Alexander W. R. Nelson; Alex Bridgland; Hugo Penedones; Stig Petersen; et al. (Nat., 2020)

Fast reinforcement learning with generalized policy updates. - André Barreto 001; Shaobo Hou; Diana Borsa; David Silver; Doina Precup (Proc. Natl. Acad. Sci. USA, 2020)

The Value-Improvement Path: Towards Better Representations for Reinforcement Learning. - Will Dabney; André Barreto 001; Mark Rowland 001; Robert Dadashi; John Quan; Marc G. Bellemare; David Silver (AAAI, 2021)

Expected Eligibility Traces. - Hado van Hasselt; Sephora Madjiheurem; Matteo Hessel; David Silver; André Barreto 001; Diana Borsa (AAAI, 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 (ICML, 2021)

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

Self-Consistent Models and Values. - Gregory Farquhar; Kate Baumli; Zita Marinho; Angelos Filos; Matteo Hessel; Hado Philip van Hasselt; David Silver (NeurIPS, 2021)

Proper Value Equivalence. - Christopher Grimm; André Barreto 001; Gregory Farquhar; David Silver; Satinder Singh 001 (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)

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 001 (NeurIPS, 2021)

Reward is enough. - David Silver; Satinder Singh 001; Doina Precup; Richard S. Sutton (Artif. Intell., 2021)

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 (ICLR, 2022)

Bootstrapped Meta-Learning. - Sebastian Flennerhag; Yannick Schroecker; Tom Zahavy; Hado van Hasselt; David Silver; Satinder Singh 001 (ICLR, 2022)

Learning by Directional Gradient Descent. - David Silver; Anirudh Goyal; Ivo Danihelka; Matteo Hessel; Hado van Hasselt (ICLR, 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 001; Stephen McAleer; Romuald Elie; et al. (CoRR, 2022)

Discovering faster matrix multiplication algorithms with reinforcement learning. - Alhussein Fawzi; Matej Balog; Aja Huang; Thomas Hubert; Bernardino Romera-Paredes; Mohammadamin Barekatain; Alexander Novikov 001; Francisco J. R. Ruiz; Julian Schrittwieser; Grzegorz Swirszcz; David Silver; Demis Hassabis; et al. (Nat., 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)

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 001; Stephen McAleer; Romuald Elie; et al. (Zenodo, 2022)

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; et al. (CoRR, 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; et al. (Nat., 2023)

DataRater: Meta-Learned Dataset Curation. - Dan A. Calian; Gregory Farquhar; Iurii Kemaev; Luisa M. Zintgraf; Matteo Hessel; Jeremy Shar; Junhyuk Oh; András György 001; Tom Schaul; Jeffrey Dean; Hado van Hasselt; David Silver (CoRR, 2025)