David Silver

Universal Successor Features Approximators

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

Bayesian Optimization in AlphaGo

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

Learning to Search with MCTSnets

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

Comments ICML 2018 (camera-ready version)

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

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

Implicit Quantile Networks for Distributional Reinforcement Learning

Will Dabney, Georg Ostrovski, David Silver, Rémi Munos

Comments ICML 2018

Meta-Gradient Reinforcement Learning

Zhongwen Xu, Hado van Hasselt, David Silver

Successor Features for Transfer in Reinforcement Learning

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

Comments Published at NIPS 2017

Unsupervised Predictive Memory in a Goal-Directed Agent

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

Distributed Prioritized Experience Replay

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

Comments Accepted to International Conference on Learning Representations 2018

Imagination-Augmented Agents for Deep Reinforcement Learning

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

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 Lillicrap, Karen Simonyan, Demis Hassabis

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

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

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 Azar, David Silver

Comments Under review as a conference paper at AAAI 2018

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 Agapiou, Julian Schrittwieser, John Quan, Stephen Gaffney, Stig Petersen, Karen Simonyan, Tom Schaul, Hado van Hasselt, David Silver, Timothy Lillicrap, Kevin Calderone, Paul Keet, Anthony Brunasso, David Lawrence, Anders Ekermo, Jacob Repp, Rodney Tsing

Comments Collaboration between DeepMind & Blizzard. 20 pages, 9 figures, 2 tables

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 Reichert, Neil Rabinowitz, Andre Barreto, Thomas Degris

Comments Camera-ready version, ICML 2017, with supplement

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 Riedmiller, David Silver

Decoupled Neural Interfaces using Synthetic Gradients

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

FeUdal Networks for Hierarchical Reinforcement Learning

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

Reinforcement Learning with Unsupervised Auxiliary Tasks

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

Learning and Transfer of Modulated Locomotor Controllers

Nicolas Heess, Greg Wayne, Yuval Tassa, Timothy Lillicrap, Martin Riedmiller, David Silver

Comments Supplemental video available at https://youtu.be/sboPYvhpraQ

Learning values across many orders of magnitude

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

Comments Paper accepted for publication at NIPS 2016. This version includes the appendix

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

Johannes Heinrich, David Silver

Comments updated version, incorporating conference feedback

Asynchronous Methods for Deep Reinforcement Learning

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

Journal ref ICML 2016

Prioritized Experience Replay

Tom Schaul, John Quan, Ioannis Antonoglou, David Silver

Comments Published at ICLR 2016

Memory-based control with recurrent neural networks

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

Comments NIPS Deep Reinforcement Learning Workshop 2015

Deep Reinforcement Learning with Double Q-learning

Hado van Hasselt, Arthur Guez, David Silver

Comments AAAI 2016

Learning Continuous Control Policies by Stochastic Value Gradients

Nicolas Heess, Greg Wayne, David Silver, Timothy Lillicrap, Yuval Tassa, Tom Erez

Comments 13 pages, NIPS 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

Comments Presented at the Deep Learning Workshop, International Conference on Machine Learning, Lille, France, 2015

Move Evaluation in Go Using Deep Convolutional Neural Networks

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

Comments Minor edits and included captures in Figure 2

Efficient Bayes-Adaptive Reinforcement Learning using Sample-Based Search

Arthur Guez, David Silver, Peter Dayan

Comments 14 pages, 7 figures, includes supplementary material. Advances in Neural Information Processing Systems (NIPS) 2012

Journal ref (2012) Advances in Neural Information Processing Systems 25, pages 1034-1042