D. Silver

发表

Grandmaster level in StarCraft II using multi-agent reinforcement learning

Wojciech M. Czarnecki, Oriol Vinyals, I. Babuschkin, 2019, Nature.

Learning Continuous Control Policies by Stochastic Value Gradients

Yuval Tassa, David Silver, Nicolas Heess, 2015, NIPS.

Convergent Temporal-Difference Learning with Arbitrary Smooth Function Approximation

Shalabh Bhatnagar, Doina Precup, Richard S. Sutton, 2009, NIPS.

Bayes-Adaptive Simulation-based Search with Value Function Approximation

Peter Dayan, David Silver, Nicolas Heess, 2014, NIPS.

Human-level control through deep reinforcement learning

Shane Legg, Alex Graves, Marc G. Bellemare, 2015, Nature.

Credit Assignment Techniques in Stochastic Computation Graphs

David Silver, Nicolas Heess, Lars Buesing, 2019, AISTATS.

Rainbow: Combining Improvements in Deep Reinforcement Learning

Tom Schaul, David Silver, Matteo Hessel, 2017, AAAI.

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

Tom Schaul, Junhyuk Oh, David Silver, 2018, ArXiv.

Proceedings of the Twenty-Third AAAI Conference on Artificial Intelligence (2008) Achieving Master Level Play in 9 × 9 Computer Go

David Silver, Sylvain Gelly, D. Silver, 2022 .

A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning

David Silver, Thore Graepel, Karl Tuyls, 2017, NIPS.

Continuous control with deep reinforcement learning pdf

Yuval Tassa, David Silver, Daan Wierstra, 2015, ICLR.

Deterministic Policy Gradient Algorithms

Guy Lever, Martin A. Riedmiller, David Silver, 2014, ICML.

Playing Atari with Deep Reinforcement Learning pdf

Alex Graves, Koray Kavukcuoglu, Martin A. Riedmiller, 2013, ArXiv.

Fast gradient-descent methods for temporal-difference learning with linear function approximation

Shalabh Bhatnagar, Doina Precup, Richard S. Sutton, 2009, ICML '09.

Asynchronous Methods for Deep Reinforcement Learning pdf

Alex Graves, Koray Kavukcuoglu, David Silver, 2016, ICML.

Emergence of Locomotion Behaviours in Rich Environments pdf

Yuval Tassa, Martin A. Riedmiller, David Silver, 2017, ArXiv.

Memory-based control with recurrent neural networks pdf

David Silver, Nicolas Heess, Jonathan J. Hunt, 2015, ArXiv.

Reinforcement learning and simulation-based search in computer go

David Silver, D. Silver, David Silver, 2009 .

Unsupervised Predictive Memory in a Goal-Directed Agent pdf

Joel Z. Leibo, Demis Hassabis, Koray Kavukcuoglu, 2018, ArXiv.

Learning to Search with MCTSnets

Rémi Munos, Oriol Vinyals, Karen Simonyan, 2018, ICML.

Move Evaluation in Go Using Deep Convolutional Neural Networks pdf

David Silver, Ilya Sutskever, Chris J. Maddison, 2014, ICLR.

Successor Features for Transfer in Reinforcement Learning

Tom Schaul, David Silver, Jonathan J. Hunt, 2016, NIPS.

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

Demis Hassabis, Karen Simonyan, David Silver, 2017, ArXiv.

What Can Learned Intrinsic Rewards Capture? pdf

Junhyuk Oh, Satinder Singh, David Silver, 2019, ICML.

Compositional Planning Using Optimal Option Models

David Silver, Kamil Ciosek, D. Silver, 2012, ICML.

Better Optimism By Bayes: Adaptive Planning with Rich Models pdf

Peter Dayan, David Silver, Arthur Guez, 2014, ArXiv.

Combining online and offline knowledge in UCT

David Silver, Sylvain Gelly, D. Silver, 2007, ICML '07.

Efficient Bayes-Adaptive Reinforcement Learning using Sample-Based Search

Peter Dayan, David Silver, Arthur Guez, 2012, NIPS.

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

David Silver, Sylvain Gelly, D. Silver, 2011, Artif. Intell..

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

Peter Dayan, David Silver, Arthur Guez, 2013, J. Artif. Intell. Res..

Unit Tests for Stochastic Optimization pdf

Tom Schaul, David Silver, Ioannis Antonoglou, 2013, ICLR.

Bayesian Optimization in AlphaGo pdf

Nando de Freitas, David Silver, Ziyu Wang, 2018, ArXiv.

Meta-Gradient Reinforcement Learning

David Silver, Zhongwen Xu, Hado van Hasselt, 2018, NeurIPS.

Non-Linear Monte-Carlo Search in Civilization II

Regina Barzilay, David Silver, S. R. K. Branavan, 2011, IJCAI.

Learning and Transfer of Modulated Locomotor Controllers pdf

Yuval Tassa, Martin A. Riedmiller, David Silver, 2016, ArXiv.

Reinforcement Learning of Local Shape in the Game of Go

Richard S. Sutton, David Silver, Martin Müller, 2007, IJCAI.

Deep Reinforcement Learning with Double Q-Learning pdf

David Silver, Arthur Guez, Hado van Hasselt, 2015, AAAI.

Actor-Critic Reinforcement Learning with Energy-Based Policies

Yee Whye Teh, David Silver, Nicolas Heess, 2012, EWRL.

A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play

Demis Hassabis, Karen Simonyan, David Silver, 2018, Science.

Monte-Carlo Planning in Large POMDPs

Joel Veness, David Silver, D. Silver, 2010, NIPS.

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

Demis Hassabis, Koray Kavukcuoglu, David Silver, 2016, Nature.

Implicit Quantile Networks for Distributional Reinforcement Learning pdf

Rémi Munos, David Silver, Georg Ostrovski, 2018, ICML.

Massively Parallel Methods for Deep Reinforcement Learning pdf

Shane Legg, Praveen Srinivasan, Koray Kavukcuoglu, 2015, ArXiv.

Imagination-Augmented Agents for Deep Reinforcement Learning

Razvan Pascanu, Demis Hassabis, David Silver, 2017, NIPS.

Prioritized Experience Replay pdf

Tom Schaul, David Silver, Ioannis Antonoglou, 2015, ICLR.

Distributed Prioritized Experience Replay pdf

David Budden, David Silver, Matteo Hessel, 2018, ICLR.

Decoupled Neural Interfaces using Synthetic Gradients pdf

Alex Graves, Max Jaderberg, Simon Osindero, 2016, ICML.

Temporal-difference search in computer Go

Richard S. Sutton, David Silver, Martin Müller, 2012, Machine Learning.

Mastering the game of Go without human knowledge

Demis Hassabis, Karen Simonyan, Fan Hui, 2017, Nature.

On the role of tracking in stationary environments

Richard S. Sutton, David Silver, Anna Koop, 2007, ICML '07.

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

Demis Hassabis, Karen Simonyan, David Silver, 2019, Nature.

The grand challenge of computer Go

Michèle Sebag, Olivier Teytaud, Marc Schoenauer, 2012, Commun. ACM.

StarCraft II: A New Challenge for Reinforcement Learning pdf

Tom Schaul, Oriol Vinyals, Karen Simonyan, 2017, ArXiv.

Concurrent Reinforcement Learning from Customer Interactions

David Silver, David Barker, Leonard Newnham, 2013, ICML.

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

Tom Schaul, Rémi Munos, David Silver, 2018, ICML.

Fictitious Self-Play in Extensive-Form Games

David Silver, Marc Lanctot, Johannes Heinrich, 2015, ICML.

Smooth UCT Search in Computer Poker

David Silver, Johannes Heinrich, D. Silver, 2015, IJCAI.

Learning values across many orders of magnitude

David Silver, Matteo Hessel, Arthur Guez, 2016, NIPS.

Behaviour Suite for Reinforcement Learning pdf

Tor Lattimore, Richard S. Sutton, Csaba Szepesvári, 2019, ICLR.

Human-level performance in 3D multiplayer games with population-based reinforcement learning

Guy Lever, Joel Z. Leibo, Demis Hassabis, 2018, Science.

Bootstrapping from Game Tree Search

Joel Veness, David Silver, William T. B. Uther, 2009, NIPS.

Combining Online and Offline Learning in UCT

David Silver, S Gelly, D. Silver, 2007 .

A Monte-Carlo AIXI Approximation pdf

Joel Veness, Marcus Hutter, David Silver, 2009, J. Artif. Intell. Res..

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

Regina Barzilay, David Silver, S. R. K. Branavan, 2011, ACL.

Protein structure prediction using multiple deep neural networks in the 13th Critical Assessment of Protein Structure Prediction (CASP13)

Pushmeet Kohli, Demis Hassabis, Karen Simonyan, 2019, Proteins.

Value Iteration with Options and State Aggregation pdf

David Silver, Kamil Ciosek, D. Silver, 2015, ArXiv.

Fast reinforcement learning with generalized policy updates

Doina Precup, Shaobo Hou, André Barreto, 2020, Proceedings of the National Academy of Sciences.

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

David Silver, Johannes Heinrich, D. Silver, 2016, ArXiv.

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

Joel Z. Leibo, Ari S. Morcos, Neil C. Rabinowitz, 2018, ArXiv.

A Self-Tuning Actor-Critic Algorithm

Junhyuk Oh, D. Silver, Satinder Singh, 2020, NeurIPS.

Self-Tuning Deep Reinforcement Learning

Junhyuk Oh, D. Silver, Satinder Singh, 2020, ArXiv.

Highly accurate protein structure prediction with AlphaFold

Oriol Vinyals, K. Kavukcuoglu, D. Hassabis, 2021, Nature.

Recurrent Gradient Temporal Difference Networks

D. Silver, 2012 .

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

Gerald Tesauro, David Silver, Guang-Bin Huang, 2018, Neural Networks.

Playing Games with Language in a Monte-Carlo framework

Regina Barzilay, David Silver, Srk Branavan, 2011, Annual Meeting of the Association for Computational Linguistics.

Technical perspective: Solving imperfect information games

David Silver, D. Silver, David Silver, 2017, Commun. ACM.