Nir Levine
Impact in
- Artificial Intelligence top 10%
- Reinforcement Learning in Robotics
- Control and Systems Engineering top 10%
- Robot Manipulation and Learning
- Advanced Control Systems Optimization
- Traffic control and management
Papers in
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- Reinforcement Learning in Robotics 3
- Data Stream Mining Techniques 1
- Semantic Web and Ontologies 1
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- Optimization and Search Problems 1
- Co-authors
- Daniel J. Mankowitz (3 shared papers)Gabriel Dulac-Arnold (1 shared paper)Cosmin Păduraru (1 shared paper)Jerry Li (1 shared paper)Todd Hester (2 shared papers)Sven Gowal (1 shared paper)WALTER F. DENHAM (1 shared paper)Shie Mannor (2 shared papers)
- Journals
- Machine Learning (1 paper)Automatica (1 paper)Bell System Technical Journal (1 paper)arXiv (Cornell University) (1 paper)Neural Information Processing Systems (2 papers)
- Partner nations
- IsraelUnited StatesUnited Kingdom
In The Last Decade
Nir Levine
7 papers receiving 345 citations
Nir Levine's Hit Papers
Peers
Comparison fields: 5 of 71
- Artificial Intelligence 156
- Control and Systems Engineering 99
- Automotive Engineering 35
- Management Science and Operations Research 30
- Industrial and Manufacturing Engineering 23
Countries citing papers authored by Nir Levine
This map shows the geographic impact of Nir Levine'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 Nir Levine with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nir Levine more than expected).
Fields of papers citing papers by Nir Levine
This network shows the impact of papers produced by Nir Levine. 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 Nir Levine. The network helps show where Nir Levine may publish in the future.
Co-authors
The 21 scholars most cited alongside Nir Levine, 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 | Challenges of real-world reinforcement learning: definitions, benchmarks and analysis Hit paper breakdown → | 2021 | 320 |
| 2 | 1961 | 14 | |
| 3 | 1971 | 12 | |
| 4 | 2017 | 9 | |
| 5 | Rotting Bandits | 2017 | 6 |
| 6 | Shallow Updates for Deep Reinforcement Learning | 2017 | 5 |
| 7 | Robust Reinforcement Learning for Continuous Control with Model Misspecification | 2020 | 1 |
About Nir Levine
Nir Levine is a scholar working on Artificial Intelligence, Computer Networks and Communications, Control and Systems Engineering, Information Systems and Computational Theory and Mathematics, having authored 7 papers that have together received 367 indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (3 papers), Data Stream Mining Techniques (1 paper), Semantic Web and Ontologies (1 paper), Optimization and Search Problems (1 paper), Aerospace Engineering and Control Systems (1 paper), Rocket and propulsion systems research (1 paper), Adaptive Dynamic Programming Control (1 paper) and Spacecraft Dynamics and Control (1 paper). The work is most often cited by research in Artificial Intelligence (156 citations), Control and Systems Engineering (99 citations), Automotive Engineering (35 citations), Management Science and Operations Research (30 citations) and Industrial and Manufacturing Engineering (23 citations). Nir Levine has collaborated with scholars based in Israel, United States and United Kingdom. Frequent co-authors include Daniel J. Mankowitz, Gabriel Dulac-Arnold, Cosmin Păduraru, Jerry Li, Todd Hester, Sven Gowal, WALTER F. DENHAM, Shie Mannor, Jason L. Speyer and Henry J. Kelley. Their work appears in journals such as Machine Learning, Automatica, Bell System Technical Journal, arXiv (Cornell University) and Neural Information Processing 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.