Todd Hester
Impact in
- Artificial Intelligence top 1%
- Reinforcement Learning in Robotics
- Advanced Graph Neural Networks
- Rehabilitation top 5%
- Stroke Rehabilitation and Recovery
Papers in
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- Reinforcement Learning in Robotics 16
- Educational Robotics and Engineering 6
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- Stroke Rehabilitation and Recovery 3
- Co-authors
- Peter Stone (18 shared papers)Gabriel Dulac-Arnold (2 shared papers)Sven Gowal (1 shared paper)Cosmin Păduraru (1 shared paper)Daniel J. Mankowitz (1 shared paper)Nir Levine (1 shared paper)Michael Quinlan (7 shared papers)Dan Horgan (1 shared paper)
- Journals
- Machine Learning (2 papers)Artificial Intelligence (1 paper)IEEE Pervasive Computing (1 paper)Proceedings of the IEEE (1 paper)Lecture notes in computer science (3 papers)
- Partner nations
- United StatesUnited KingdomCanada
In The Last Decade
Todd Hester
34 papers receiving 1.9k citations
Todd Hester's Hit Papers
Peers
Comparison fields: 5 of 102
- Artificial Intelligence 1.1k
- Rehabilitation 126
- Control and Systems Engineering 199
- Computer Vision and Pattern Recognition 186
- Automotive Engineering 58
Countries citing papers authored by Todd Hester
This map shows the geographic impact of Todd Hester'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 Todd Hester with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Todd Hester more than expected).
Fields of papers citing papers by Todd Hester
This network shows the impact of papers produced by Todd Hester. 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 Todd Hester. The network helps show where Todd Hester may publish in the future.
Co-authors
The 25 scholars most cited alongside Todd Hester, 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 34 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Deep Q-learning From Demonstrations Hit paper breakdown → | 2018 | 594 |
| 2 | Challenges of real-world reinforcement learning: definitions, benchmarks and analysis Hit paper breakdown → | 2021 | 425 |
| 3 | 2021 | 147 | |
| 4 | 2010 | 144 | |
| 5 | 2012 | 80 | |
| 6 | 2010 | 74 | |
| 7 | 2006 | 70 | |
| 8 | 2019 | 65 | |
| 9 | 2015 | 59 | |
| 10 | 2012 | 54 | |
| 11 | 2006 | 53 | |
| 12 | 2009 | 36 | |
| 13 | 2008 | 27 | |
| 14 | 2012 | 27 | |
| 15 | 2008 | 23 | |
| 16 | 2012 | 21 | |
| 17 | 2006 | 15 | |
| 18 | 2010 | 12 | |
| 19 | 2013 | 11 | |
| 20 | 2013 | 10 |
About Todd Hester
Todd Hester is a scholar working on Artificial Intelligence, Rehabilitation, Biomedical Engineering, Neurology and Control and Systems Engineering, having authored 34 papers that have together received 2k indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (16 papers), Robotic Locomotion and Control (7 papers), Educational Robotics and Engineering (6 papers), Stroke Rehabilitation and Recovery (3 papers), Context-Aware Activity Recognition Systems (3 papers), Robotics and Sensor-Based Localization (3 papers), Robotics and Automated Systems (3 papers) and Social Robot Interaction and HRI (2 papers). The work is most often cited by research in Artificial Intelligence (1.1k citations), Rehabilitation (126 citations), Control and Systems Engineering (199 citations), Computer Vision and Pattern Recognition (186 citations) and Automotive Engineering (58 citations). Todd Hester has collaborated with scholars based in United States, United Kingdom and Canada. Frequent co-authors include Peter Stone, Gabriel Dulac-Arnold, Sven Gowal, Cosmin Păduraru, Daniel J. Mankowitz, Nir Levine, Michael Quinlan, Dan Horgan, Olivier Pietquin and Bilal Piot. Their work appears in journals such as Machine Learning, Artificial Intelligence, IEEE Pervasive Computing, Proceedings of the IEEE and Lecture notes in computer science.
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.