David Silver
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AI researcher
David Silver 's AcademicInfluence.com Rankings
David Silver computer-science Degrees
Computer Science
#1663
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#1722
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Algorithms
#72
World Rank
#72
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Machine Learning
#331
World Rank
#335
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Artificial Intelligence
#342
World Rank
#349
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Computer Science
Why Is David Silver Influential?
(Suggest an Edit or Addition)According to Wikipedia, David Silver is a principal research scientist at Google DeepMind and a professor at University College London. He has led research on reinforcement learning with AlphaGo, AlphaZero and co-lead on AlphaStar.
David Silver 's Published Works
Number of citations in a given year to any of this author's works
Total number of citations to an author for the works they published in a given year. This highlights publication of the most important work(s) by the author
Published Works
- Human-level control through deep reinforcement learning (2015) (19924)
- Mastering the game of Go with deep neural networks and tree search (2016) (13400)
- Continuous control with deep reinforcement learning (2015) (8827)
- Highly accurate protein structure prediction with AlphaFold (2021) (8720)
- Playing Atari with Deep Reinforcement Learning (2013) (8672)
- Mastering the game of Go without human knowledge (2017) (7165)
- Asynchronous Methods for Deep Reinforcement Learning (2016) (6519)
- Deep Reinforcement Learning with Double Q-Learning (2015) (4754)
- Deterministic Policy Gradient Algorithms (2014) (2873)
- Prioritized Experience Replay (2015) (2682)
- A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play (2018) (2281)
- Grandmaster level in StarCraft II using multi-agent reinforcement learning (2019) (2227)
- Improved protein structure prediction using potentials from deep learning (2020) (1753)
- Rainbow: Combining Improvements in Deep Reinforcement Learning (2017) (1527)
- Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm (2017) (1211)
- Mastering Atari, Go, chess and shogi by planning with a learned model (2019) (1081)
- Reinforcement Learning with Unsupervised Auxiliary Tasks (2016) (1035)
- Monte-Carlo Planning in Large POMDPs (2010) (1001)
- Universal Value Function Approximators (2015) (822)
- Emergence of Locomotion Behaviours in Rich Environments (2017) (737)
- FeUdal Networks for Hierarchical Reinforcement Learning (2017) (686)
- StarCraft II: A New Challenge for Reinforcement Learning (2017) (651)
- Fast gradient-descent methods for temporal-difference learning with linear function approximation (2009) (584)
- Combining online and offline knowledge in UCT (2007) (573)
- Distributed Prioritized Experience Replay (2018) (538)
- Cooperative Pathfinding (2005) (536)
- Human-level performance in 3D multiplayer games with population-based reinforcement learning (2018) (492)
- Learning Continuous Control Policies by Stochastic Value Gradients (2015) (478)
- A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning (2017) (461)
- Imagination-Augmented Agents for Deep Reinforcement Learning (2017) (450)
- Massively Parallel Methods for Deep Reinforcement Learning (2015) (437)
- Successor Features for Transfer in Reinforcement Learning (2016) (398)
- Monte-Carlo tree search and rapid action value estimation in computer Go (2011) (356)
- Implicit Quantile Networks for Distributional Reinforcement Learning (2018) (331)
- Decoupled Neural Interfaces using Synthetic Gradients (2016) (284)
- Deep Reinforcement Learning from Self-Play in Imperfect-Information Games (2016) (260)
- Convergent Temporal-Difference Learning with Arbitrary Smooth Function Approximation (2009) (258)
- Meta-Gradient Reinforcement Learning (2018) (243)
- The Predictron: End-To-End Learning and Planning (2016) (239)
- Memory-based control with recurrent neural networks (2015) (236)
- Fictitious Self-Play in Extensive-Form Games (2015) (193)
- Reward is enough (2021) (191)
- Learning and Transfer of Modulated Locomotor Controllers (2016) (191)
- The grand challenge of computer Go (2012) (179)
- A Monte-Carlo AIXI Approximation (2009) (175)
- Learning to Win by Reading Manuals in a Monte-Carlo Framework (2011) (171)
- Unsupervised Predictive Memory in a Goal-Directed Agent (2018) (159)
- Efficient Bayes-Adaptive Reinforcement Learning using Sample-Based Search (2012) (156)
- Reinforcement Learning of Local Shape in the Game of Go (2007) (146)
- Achieving Master Level Play in 9 x 9 Computer Go (2008) (142)
- Learning values across many orders of magnitude (2016) (140)
- Protein structure prediction using multiple deep neural networks in the 13th Critical Assessment of Protein Structure Prediction (CASP13) (2019) (137)
- Move Evaluation in Go Using Deep Convolutional Neural Networks (2014) (135)
- Human-level performance in first-person multiplayer games with population-based deep reinforcement learning (2018) (134)
- Sample-based learning and search with permanent and transient memories (2008) (125)
- Behaviour Suite for Reinforcement Learning (2019) (123)
- Transfer in Deep Reinforcement Learning Using Successor Features and Generalised Policy Improvement (2018) (112)
- Applying and improving AlphaFold at CASP14 (2021) (111)
- Monte-Carlo simulation balancing (2009) (104)
- Temporal-difference search in computer Go (2012) (98)
- Unit Tests for Stochastic Optimization (2013) (91)
- On the role of tracking in stationary environments (2007) (88)
- Discovering faster matrix multiplication algorithms with reinforcement learning (2022) (85)
- An investigation of model-free planning (2019) (80)
- Bayesian Optimization in AlphaGo (2018) (80)
- Discovering Reinforcement Learning Algorithms (2020) (76)
- Discovery of Useful Questions as Auxiliary Tasks (2019) (76)
- Universal Successor Features Approximators (2018) (71)
- Concurrent Reinforcement Learning from Customer Interactions (2013) (71)
- Learning to Search with MCTSnets (2018) (70)
- Scalable and Efficient Bayes-Adaptive Reinforcement Learning Based on Monte-Carlo Tree Search (2013) (70)
- Actor-Critic Reinforcement Learning with Energy-Based Policies (2012) (68)
- Fast reinforcement learning with generalized policy updates (2020) (67)
- Protein structure prediction using multiple deep neural networks in CASP13. (2019) (66)
- The Option Keyboard: Combining Skills in Reinforcement Learning (2021) (65)
- Reinforcement learning and simulation-based search in computer go (2009) (64)
- A Self-Tuning Actor-Critic Algorithm (2020) (63)
- Bootstrapping from Game Tree Search (2009) (62)
- Compositional Planning Using Optimal Option Models (2012) (55)
- Online and Offline Reinforcement Learning by Planning with a Learned Model (2021) (51)
- Meta-Gradient Reinforcement Learning with an Objective Discovered Online (2020) (49)
- What Can Learned Intrinsic Rewards Capture? (2019) (48)
- Unicorn: Continual Learning with a Universal, Off-policy Agent (2018) (47)
- The Grand Challenge of Computer Go: Monte Carlo Tree Search and Extensions (2015) (46)
- The Value Equivalence Principle for Model-Based Reinforcement Learning (2020) (44)
- The Value-Improvement Path: Towards Better Representations for Reinforcement Learning (2020) (41)
- Muesli: Combining Improvements in Policy Optimization (2021) (38)
- Credit Assignment Techniques in Stochastic Computation Graphs (2019) (37)
- On Inductive Biases in Deep Reinforcement Learning (2019) (34)
- Non-Linear Monte-Carlo Search in Civilization II (2011) (34)
- Mastering the game of Stratego with model-free multiagent reinforcement learning (2022) (33)
- Deep learning, reinforcement learning, and world models (2022) (31)
- Bootstrapped Meta-Learning (2021) (29)
- Combining Online and Offline Learning in UCT (2007) (28)
- Reinforcement Learning via AIXI Approximation (2010) (28)
- Improved protein structure prediction using potentials from deep learning (2020) (27)
- Smooth UCT Search in Computer Poker (2015) (26)
- Bayes-Adaptive Simulation-based Search with Value Function Approximation (2014) (25)
- Learning and Planning in Complex Action Spaces (2021) (23)
- Learning functions across many orders of magnitudes (2016) (17)
- Discovery of Options via Meta-Learned Subgoals (2021) (17)
- Expected Eligibility Traces (2020) (15)
- Proper Value Equivalence (2021) (14)
- Self-play Monte-Carlo tree search in computer poker (2014) (14)
- Planning in Stochastic Environments with a Learned Model (2022) (13)
- Reinforced Variational Inference (2015) (13)
- Self-Tuning Deep Reinforcement Learning (2020) (11)
- Value-driven Hindsight Modelling (2020) (10)
- Value Iteration with Options and State Aggregation (2015) (9)
- Learning by Directional Gradient Descent (2022) (8)
- Natural Value Approximators: Learning when to Trust Past Estimates (2017) (8)
- Gradient Temporal Difference Networks (2012) (8)
- Better Optimism By Bayes: Adaptive Planning with Rich Models (2014) (7)
- Policy improvement by planning with Gumbel (2022) (7)
- Reinforcement Learning and Simulation Based Search in the Game of Go (2009) (5)
- Introduction to the special issue on deep reinforcement learning: An editorial (2018) (3)
- Self-Consistent Models and Values (2021) (3)
- Technical perspective: Solving imperfect information games (2017) (1)
- Playing Games with Language in a Monte-Carlo framework (2011) (1)
- Temporal-difference search in computer Go (2012) (1)
- Towards a practical Bayes-optimal agent (2013) (1)
- ICLR 2020 Intrinsic Reward Episode 1 Episode 2 Lifetime with task (2019) (0)
- learn continuous control with deep bestärkendem (2016) (0)
- P OLICY IMPROVEMENT BY PLANNING WITH G UMBEL (2022) (0)
- Recurrent Gradient Temporal Difference Networks (2012) (0)
- AlphaFold: Improved protein structure prediction using (2019) (0)
- TRAINING ACTION SELECTION NEURAL NETWORKS USING LOOK-AHEAD SEARCH (2018) (0)
- 4 Variational Inference as Reinforcement Learning (2015) (0)
- P LANNING IN S TOCHASTIC E NVIRONMENTS WITH A L EARNED M ODEL A (2022) (0)
- Convergent Temporal-Difference Learning with Arbitrary Differentiable Function Approximator (2010) (0)
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