Nicolas Sonnerat
Impact in
- Artificial Intelligence top 5%
- Reinforcement Learning in Robotics
- Artificial Intelligence in Games
- Evolutionary Algorithms and Applications
- Adversarial Robustness in Machine Learning
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- Adaptive Dynamic Programming Control
Papers in
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- Evolutionary Algorithms and Applications 2
- Reinforcement Learning in Robotics 2
- Artificial Intelligence in Games 1
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- Genomics and Phylogenetic Studies 1
- Co-authors
- Joel Z. Leibo (2 shared papers)Max Jaderberg (2 shared papers)Guy Lever (2 shared papers)Thore Graepel (2 shared papers)Wojciech Marian Czarnecki (2 shared papers)Audrūnas Gruslys (1 shared paper)Marc Lanctot (1 shared paper)Karl Tuyls (1 shared paper)
- Journals
- Science (1 paper)Journal of Combinatorial Optimization (1 paper)UCL Discovery (University College London) (1 paper)Adaptive Agents and Multi-Agents Systems (1 paper)
- Partner nations
- United KingdomCanadaUnited States
In The Last Decade
Nicolas Sonnerat
5 papers receiving 602 citations
Nicolas Sonnerat's Hit Papers
Peers
Comparison fields: 5 of 76
- Artificial Intelligence 420
- Computational Theory and Mathematics 86
- Computer Networks and Communications 109
- Management Science and Operations Research 55
- Computer Vision and Pattern Recognition 84
Countries citing papers authored by Nicolas Sonnerat
This map shows the geographic impact of Nicolas Sonnerat'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 Nicolas Sonnerat with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nicolas Sonnerat more than expected).
Fields of papers citing papers by Nicolas Sonnerat
This network shows the impact of papers produced by Nicolas Sonnerat. 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 Nicolas Sonnerat. The network helps show where Nicolas Sonnerat may publish in the future.
Co-authors
The 25 scholars most cited alongside Nicolas Sonnerat, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | Human-level performance in 3D multiplayer games with population-based reinforcement learning Hit paper breakdown → | 2019 | 358 |
| 2 | 2018 | 240 | |
| 3 | SCAN: Learning Hierarchical Compositional Visual Concepts | 2018 | 17 |
| 4 | 2024 | 8 | |
| 5 | 2009 | 1 |
About Nicolas Sonnerat
Nicolas Sonnerat is a scholar working on Artificial Intelligence, Molecular Biology, Virology, Statistical and Nonlinear Physics and Computational Theory and Mathematics, having authored 5 papers that have together received 624 indexed citations. Recurring topics across this work include Evolutionary Algorithms and Applications (2 papers), Reinforcement Learning in Robotics (2 papers), Genomics and Phylogenetic Studies (1 paper), Artificial Intelligence in Games (1 paper), HIV Research and Treatment (1 paper), Language and cultural evolution (1 paper), Complex Network Analysis Techniques (1 paper) and Advanced Graph Theory Research (1 paper). The work is most often cited by research in Artificial Intelligence (420 citations), Computational Theory and Mathematics (86 citations), Computer Networks and Communications (109 citations), Management Science and Operations Research (55 citations) and Computer Vision and Pattern Recognition (84 citations). Nicolas Sonnerat has collaborated with scholars based in United Kingdom, Canada and United States. Frequent co-authors include Joel Z. Leibo, Max Jaderberg, Guy Lever, Thore Graepel, Wojciech Marian Czarnecki, Audrūnas Gruslys, Marc Lanctot, Karl Tuyls, Vinícius Zambaldi and Peter Sunehag. Their work appears in journals such as Science, Journal of Combinatorial Optimization, UCL Discovery (University College London) and Adaptive Agents and Multi-Agents Systems.
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.