Audrūnas Gruslys
Impact in
- Artificial Intelligence top 2%
- Reinforcement Learning in Robotics
- Quantum Information and Cryptography
- Quantum Computing Algorithms and Architecture
- Evolutionary Algorithms and Applications
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- Robot Manipulation and Learning
Papers in
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- Reinforcement Learning in Robotics 9
- Evolutionary Algorithms and Applications 2
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- Advanced Bandit Algorithms Research 3
- Co-authors
- Marc LanctotJoel Z. LeiboTom SchaulTodd HesterIan OsbandGabriel Dulac-ArnoldJohn AgapiouOlivier Pietquin
- Journals
- Cerebral Cortex (1 paper)IEEE Transactions on Medical Imaging (1 paper)Physical Review A (1 paper)Adaptive Agents and Multi-Agents Systems (1 paper)arXiv (Cornell University) (4 papers)
- Partner nations
- United KingdomUnited StatesGermany
In The Last Decade
Audrūnas Gruslys
13 papers receiving 1.2k citations
Hit Papers
Peers
Comparison fields: 5 of 106
- Artificial Intelligence 755
- Control and Systems Engineering 243
- Automotive Engineering 107
- Computer Networks and Communications 174
- Computer Vision and Pattern Recognition 153
Countries citing papers authored by Audrūnas Gruslys
This map shows the geographic impact of Audrūnas Gruslys'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 Audrūnas Gruslys with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Audrūnas Gruslys more than expected).
Fields of papers citing papers by Audrūnas Gruslys
This network shows the impact of papers produced by Audrūnas Gruslys. 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 Audrūnas Gruslys. The network helps show where Audrūnas Gruslys may publish in the future.
Co-authors
The 25 scholars most cited alongside Audrūnas Gruslys, 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 | Fast computation of Nash Equilibria in Imperfect Information Games | 2020 | 1 |
| 2 | 2020 | 8 | |
| 3 | Navigating the Landscape of Games. | 2020 | 2 |
| 4 | 2018 | 229 | |
| 5 | Deep Q-learning From Demonstrations Hit paper breakdown → | 2018 | 485 |
| 6 | Learning from Demonstrations for Real World Reinforcement Learning | 2017 | 43 |
| 7 | The Reactor: A Sample-Efficient Actor-Critic Architecture | 2017 | 12 |
| 8 | 2017 | 15 | |
| 9 | 2017 | 123 | |
| 10 | 2016 | 127 | |
| 11 | 2014 | 13 | |
| 12 | 2011 | 172 | |
| 13 | 2011 | 5 |
About Audrūnas Gruslys
Audrūnas Gruslys is a scholar working on Artificial Intelligence, Management Science and Operations Research, Biophysics, Computer Vision and Pattern Recognition and Radiology, Nuclear Medicine and Imaging, having authored 13 papers that have together received 1.2k indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (9 papers), Advanced Bandit Algorithms Research (3 papers), Sports Analytics and Performance (2 papers), Evolutionary Algorithms and Applications (2 papers), Medical Image Segmentation Techniques (2 papers), Cell Image Analysis Techniques (1 paper), Neural dynamics and brain function (1 paper) and Medical Imaging Techniques and Applications (1 paper). The work is most often cited by research in Artificial Intelligence (755 citations), Control and Systems Engineering (243 citations), Automotive Engineering (107 citations), Computer Networks and Communications (174 citations) and Computer Vision and Pattern Recognition (153 citations). Audrūnas Gruslys has collaborated with scholars based in United Kingdom, United States and Germany. Frequent co-authors include Marc Lanctot, Joel Z. Leibo, Tom Schaul, Todd Hester, Ian Osband, Gabriel Dulac-Arnold, John Agapiou, Olivier Pietquin, Bilal Piot and Dan Horgan. Their work appears in journals such as Cerebral Cortex, IEEE Transactions on Medical Imaging, Physical Review A, Adaptive Agents and Multi-Agents Systems and arXiv (Cornell University).
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.