David Silver
Impact in
- Artificial Intelligence top 0.01%
- Reinforcement Learning in Robotics
- Artificial Intelligence in Games
- Evolutionary Algorithms and Applications
- Adversarial Robustness in Machine Learning
- Computer Vision and Pattern Recognition top 0.05%
- Robotic Path Planning Algorithms
Papers in
-
- Reinforcement Learning in Robotics 50
- Artificial Intelligence in Games 26
- Evolutionary Algorithms and Applications 12
- Machine Learning and Algorithms 7
-
- Robotic Path Planning Algorithms 13
- Co-authors
- Demis Hassabis (9 shared papers)Ioannis Antonoglou (5 shared papers)Arthur Guez (10 shared papers)Daan Wierstra (3 shared papers)Koray Kavukcuoglu (7 shared papers)Timothy Lillicrap (5 shared papers)Joel Veness (4 shared papers)Dharshan Kumaran (2 shared papers)
- Journals
- Nature (6 papers)Artificial Intelligence (2 papers)Science (2 papers)Communications of the ACM (2 papers)Journal of Field Robotics (2 papers)
- Partner nations
- United StatesUnited KingdomCanada
In The Last Decade
David Silver
98 papers receiving 46.6k citations
David Silver's Hit Papers
Peers
Comparison fields: 5 of 225
- Artificial Intelligence 22.8k
- Computer Vision and Pattern Recognition 7.8k
- Automotive Engineering 4.1k
- Control and Systems Engineering 7.8k
- Computer Networks and Communications 6.9k
Countries citing papers authored by David Silver
This map shows the geographic impact of David Silver's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by David Silver with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David Silver more than expected).
Fields of papers citing papers by David Silver
This network shows the impact of papers produced by David Silver. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by David Silver. The network helps show where David Silver may publish in the future.
Co-authors
The 25 scholars most cited alongside David Silver, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 99 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Human-level control through deep reinforcement learning Hit paper breakdown → | 2015 | 17153 |
| 2 | Mastering the game of Go with deep neural networks and tree search Hit paper breakdown → | 2016 | 8793 |
| 3 | Mastering the game of Go without human knowledge Hit paper breakdown → | 2017 | 5038 |
| 4 | Continuous control with deep reinforcement learning Hit paper breakdown → | 2016 | 4888 |
| 5 | Deep Reinforcement Learning with Double Q-Learning Hit paper breakdown → | 2016 | 2158 |
| 6 | Improved protein structure prediction using potentials from deep learning Hit paper breakdown → | 2020 | 2026 |
| 7 | A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play Hit paper breakdown → | 2018 | 1827 |
| 8 | Rainbow: Combining Improvements in Deep Reinforcement Learning Hit paper breakdown → | 2018 | 995 |
| 9 | Monte-Carlo Planning in Large POMDPs Hit paper breakdown → | 2010 | 484 |
| 10 | Cooperative Pathfinding Hit paper breakdown → | 2005 | 426 |
| 11 | Human-level performance in 3D multiplayer games with population-based reinforcement learning Hit paper breakdown → | 2019 | 349 |
| 12 | 2007 | 267 | |
| 13 | Deep learning, reinforcement learning, and world models Hit paper breakdown → | 2022 | 264 |
| 14 | 2009 | 258 | |
| 15 | Discovering faster matrix multiplication algorithms with reinforcement learning Hit paper breakdown → | 2022 | 244 |
| 16 | Reward is enough Hit paper breakdown → | 2021 | 219 |
| 17 | Protein structure prediction using multiple deep neural networks in the 13th Critical Assessment of Protein Structure Prediction (CASP13) Hit paper breakdown → | 2019 | 208 |
| 18 | 2011 | 206 | |
| 19 | Universal Value Function Approximators | 2015 | 203 |
| 20 | 2017 | 146 |
About David Silver
David Silver is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Economics and Econometrics, Aerospace Engineering and Control and Systems Engineering, having authored 99 papers that have together received 48.4k indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (50 papers), Artificial Intelligence in Games (26 papers), Sports Analytics and Performance (15 papers), Robotic Path Planning Algorithms (13 papers), Robotics and Sensor-Based Localization (13 papers), Evolutionary Algorithms and Applications (12 papers), Digital Games and Media (7 papers) and Machine Learning and Algorithms (7 papers). The work is most often cited by research in Artificial Intelligence (22.8k citations), Computer Vision and Pattern Recognition (7.8k citations), Automotive Engineering (4.1k citations), Control and Systems Engineering (7.8k citations) and Computer Networks and Communications (6.9k citations). David Silver has collaborated with scholars based in United States, United Kingdom and Canada. Frequent co-authors include Demis Hassabis, Ioannis Antonoglou, Arthur Guez, Daan Wierstra, Koray Kavukcuoglu, Timothy Lillicrap, Joel Veness, Dharshan Kumaran, Georg Ostrovski and Stig Petersen. Their work appears in journals such as Nature, Artificial Intelligence, Science, Communications of the ACM and Journal of Field Robotics.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.