David Silver

发表

The Grand Challenge of Computer Go: Monte Carlo Tree Search and Extensions

S. Gelly, O. Teytaud, M. Sebag, 2015 .

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

Sarah H. Cen, Eugene V. Tarassov, Jerome T. Connor, 2022, ArXiv.

Playing Games with Language in a Monte-Carlo framework

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

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

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

Combining Online and Offline Learning in UCT

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

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

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

Reinforcement Learning and Simulation Based Search in the Game of Go

David Silver, David Silver, 2009 .

TRAINING ACTION SELECTION NEURAL NETWORKS USING LOOK-AHEAD SEARCH

Karen Simonyan, David Silver, Julian Schrittwieser, 2018 .

Technical perspective: Solving imperfect information games

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

Planning in Stochastic Environments with a Learned Model

Ioannis Antonoglou, Sherjil Ozair, Julian Schrittwieser, 2022, ICLR.

Temporal-difference search in computer Go

R. Sutton, Martin Müller, David Silver, 2012, Machine Learning.

Learning by Directional Gradient Descent

Ivo Danihelka, Matteo Hessel, H. V. Hasselt, 2022, ICLR.

Discovering faster matrix multiplication algorithms with reinforcement learning

Aja Huang, D. Hassabis, Pushmeet Kohli, 2022, Nature.

A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning

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

Mastering the game of Go without human knowledge

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

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

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

Concurrent Reinforcement Learning from Customer Interactions

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

Highly accurate protein structure prediction with AlphaFold

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

The Value-Improvement Path: Towards Better Representations for Reinforcement Learning pdf

Marc G. Bellemare, David Silver, Mark Rowland, 2020, AAAI.

A Monte-Carlo AIXI Approximation pdf

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

Reinforcement Learning via AIXI Approximation pdf

Joel Veness, Marcus Hutter, David Silver, 2010, AAAI.

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

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

Muesli: Combining Improvements in Policy Optimization

Ivo Danihelka, L. Sifre, A. Guez, 2021, ICML.

Memory-based control with recurrent neural networks pdf

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

Learning Continuous Control Policies by Stochastic Value Gradients

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

Human-level control through deep reinforcement learning

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

Deep learning, reinforcement learning, and world models

Yann LeCun, Doina Precup, M. Sahani, 2022, Neural Networks.

Bootstrapped Meta-Learning

Satinder Singh, Hado Philip van Hasselt, Tom Zahavy, 2021, ICLR.

Improved protein structure prediction using potentials from deep learning

Demis Hassabis, Karen Simonyan, David T. Jones, 2020, Nature.

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

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

Continuous control with deep reinforcement learning pdf

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

Asynchronous Methods for Deep Reinforcement Learning pdf

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

Applying and improving AlphaFold at CASP14

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

Deep Reinforcement Learning with Double Q-Learning pdf

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

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

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

Improved protein structure prediction using potentials from deep learning

David T. Jones, L. Sifre, K. Kavukcuoglu, 2020, Nature.

Rainbow: Combining Improvements in Deep Reinforcement Learning

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

Emergence of Locomotion Behaviours in Rich Environments pdf

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

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.

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

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

Grandmaster level in StarCraft II using multi-agent reinforcement learning

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

Implicit Quantile Networks for Distributional Reinforcement Learning pdf

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

Distributed Prioritized Experience Replay pdf

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

Playing Atari with Deep Reinforcement Learning pdf

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

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.

Prioritized Experience Replay pdf

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

Imagination-Augmented Agents for Deep Reinforcement Learning

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

P OLICY IMPROVEMENT BY PLANNING WITH G UMBEL

Ivo Danihelka, A. Guez, Julian Schrittwieser, 2022 .

Deterministic Policy Gradient Algorithms

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

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

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

Smooth UCT Search in Computer Poker

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

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

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

Universal Value Function Approximators

Tom Schaul, David Silver, Karol Gregor, 2015, ICML.

Bayesian Optimization in AlphaGo pdf

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

Reward is enough

Doina Precup, David Silver, Satinder Singh, 2021, Artif. Intell..

Learning and Transfer of Modulated Locomotor Controllers pdf

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

Unsupervised Predictive Memory in a Goal-Directed Agent pdf

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

What Can Learned Intrinsic Rewards Capture? pdf

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

Successor Features for Transfer in Reinforcement Learning

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

Reinforcement Learning with Unsupervised Auxiliary Tasks pdf

Tom Schaul, Joel Z. Leibo, Max Jaderberg, 2016, ICLR.

Mastering the game of Stratego with model-free multiagent reinforcement learning

Sarah H. Cen, Jerome T. Connor, L. Sifre, 2022, Science.

Behaviour Suite for Reinforcement Learning pdf

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

Efficient Bayes-Adaptive Reinforcement Learning using Sample-Based Search

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

Bayes-Adaptive Simulation-based Search with Value Function Approximation

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

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

David Silver, Julian Schrittwieser, Ioannis Antonoglou, 2021, NeurIPS.

Expected Eligibility Traces

André Barreto, Matteo Hessel, H. V. Hasselt, 2020, AAAI.

Monte-Carlo Planning in Large POMDPs

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

Learning values across many orders of magnitude

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

Discovering Reinforcement Learning Algorithms pdf

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

Meta-Gradient Reinforcement Learning with an Objective Discovered Online pdf

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

A Self-Tuning Actor-Critic Algorithm

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

Meta-Gradient Reinforcement Learning

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

Discovery of Options via Meta-Learned Subgoals pdf

Junhyuk Oh, David Silver, Matteo Hessel, 2021, NeurIPS.

On the role of tracking in stationary environments

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

FeUdal Networks for Hierarchical Reinforcement Learning

Tom Schaul, Max Jaderberg, Simon Osindero, 2017, ICML.

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.

Universal Successor Features Approximators pdf

Tom Schaul, David Silver, Hado van Hasselt, 2018, ICLR.

StarCraft II: A New Challenge for Reinforcement Learning pdf

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

Fast reinforcement learning with generalized policy updates

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

The Option Keyboard: Combining Skills in Reinforcement Learning

Doina Precup, Shaobo Hou, Gheorghe Comanici, 2021, NeurIPS.

Decoupled Neural Interfaces using Synthetic Gradients pdf

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

Compositional Planning Using Optimal Option Models

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

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

Tom Schaul, David Silver, Matteo Hessel, 2016, ICML.

Massively Parallel Methods for Deep Reinforcement Learning pdf

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

Discovery of Useful Questions as Auxiliary Tasks

Richard L. Lewis, Junhyuk Oh, Satinder Singh, 2019, NeurIPS.

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

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

Credit Assignment Techniques in Stochastic Computation Graphs

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

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

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

Self-Consistent Models and Values pdf

Zita Marinho, David Silver, Matteo Hessel, 2021, NeurIPS.

The Value Equivalence Principle for Model-Based Reinforcement Learning pdf

Satinder Singh, David Silver, Andr'e Barreto, 2020, NeurIPS.

An investigation of model-free planning pdf

Tom Eccles, Laurent Orseau, David Silver, 2019, ICML.

Sample-based learning and search with permanent and transient memories

Richard S. Sutton, David Silver, Martin Müller, 2008, ICML '08.

Actor-Critic Reinforcement Learning with Energy-Based Policies

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

Self-play Monte-Carlo tree search in computer poker

J Heinrich, D Silver, David Silver, 2014, AAAI 2014.

Temporal-difference search in computer Go

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

Bootstrapping from Game Tree Search

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

Monte-Carlo simulation balancing

Gerald Tesauro, David Silver, G. Tesauro, 2009, ICML '09.

Combining online and offline knowledge in UCT

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

Reinforcement Learning of Local Shape in the Game of Go

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

The grand challenge of computer Go

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

Fictitious Self-Play in Extensive-Form Games

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

Value Iteration with Options and State Aggregation pdf

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

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

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

AlphaFold: Improved protein structure prediction using 1 potentials from deep learning

David T. Jones, L. Sifre, K. Kavukcuoglu, 2019 .

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

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

Cooperative Pathfinding

David Silver, David Silver, 2005, AIIDE.

Learning to Search with MCTSnets

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

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 .

On Inductive Biases in Deep Reinforcement Learning pdf

David Silver, Matteo Hessel, Joseph Modayil, 2019, ArXiv.

Proper Value Equivalence pdf

Christopher Grimm, David Silver, Gregory Farquhar, 2021, NeurIPS.

Distributed Prioritized Experience Replay

Dan Horgan, D. Budden, Gabriel Barth-Maron, 2018, ICLR.

Learning and Planning in Complex Action Spaces pdf

David Silver, Julian Schrittwieser, Ioannis Antonoglou, 2021, ICML.

Convergent Temporal-Difference Learning with Arbitrary Smooth Function Approximation

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

Reinforcement learning and simulation-based search in computer go

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

Self-Tuning Deep Reinforcement Learning

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

Gradient Temporal Difference Networks

David Silver, David Silver, 2012, EWRL.

Policy improvement by planning with Gumbel

Ivo Danihelka, A. Guez, Julian Schrittwieser, 2022, ICLR.

Natural Value Approximators: Learning when to Trust Past Estimates

Tom Schaul, David Silver, Zhongwen Xu, 2017, NIPS.

Intrinsic Reward Episode 1 Episode 2 Lifetime with task

Junhyuk Oh, M. Kroiss, Satinder Singh, 2019 .

Faster sorting algorithms discovered using deep reinforcement learning

Martin A. Riedmiller, Oriol Vinyals, D. Hassabis, 2023, Nature.

Move Evaluation in Go Using Deep Convolutional Neural Networks pdf

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

Human-level control through deep reinforcement learning

Marc G. Bellemare, Martin A. Riedmiller, Andrei A. Rusu, 2015, Nature.

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

Chris J. Maddison, George van den Driessche, Aja Huang, 2016, Nature.

Unit Tests for Stochastic Optimization pdf

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