John Agapiou
- Artificial Intelligence top 1%
- Reinforcement Learning in Robotics 5
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- Industrial Vision Systems and Defect Detection 3
- Automotive Engineering top 10%
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- Magnetic Properties and Applications 5
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- Advanced Measurement and Metrology Techniques 4
- Non-Destructive Testing Techniques 2
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- Electric Motor Design and Analysis 4
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- Neural dynamics and brain function 2
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- Sports Analytics and Performance 2
John Agapiou
21 papers receiving 1.6k citations
Hit Papers
Peers
Comparison fields: 5 of 130
- Artificial Intelligence 890
- Computer Vision and Pattern Recognition 288
- Control and Systems Engineering 272
- Industrial and Manufacturing Engineering 89
- Automotive Engineering 102
Countries citing papers authored by John Agapiou
This map shows the geographic impact of John Agapiou'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 John Agapiou with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites John Agapiou more than expected).
Fields of papers citing papers by John Agapiou
This network shows the impact of papers produced by John Agapiou. 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 John Agapiou. The network helps show where John Agapiou may publish in the future.
Co-authorship network
The 25 scholars most cited alongside John Agapiou, 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 | 2024 | 0 | |
| 2 | 2024 | 1 | |
| 3 | 2024 | 1 | |
| 4 | 2023 | 6 | |
| 5 | 2022 | 1 | |
| 6 | 2021 | 16 | |
| 7 | 2019 | 10 | |
| 8 | 2019 | 10 | |
| 9 | Learning from Demonstrations for Real World Reinforcement Learning | 2017 | 43 |
| 10 | 2017 | 3 | |
| 11 | 2017 | 123 | |
| 12 | Strategic Attentive Writer for Learning Macro-Actions | 2016 | 17 |
| 13 | Hybrid computing using a neural network with dynamic external memorybreakdown → | 2016 | 683 |
| 14 | 2016 | 130 | |
| 15 | 2012 | 8 | |
| 16 | 2009 | 25 | |
| 17 | 2009 | 16 | |
| 18 | 2008 | 14 | |
| 19 | 2000 | 63 | |
| 20 | 1995 | 36 |
About John Agapiou
John Agapiou is a scholar working on Industrial and Manufacturing Engineering, Electronic, Optical and Magnetic Materials and Mechanical Engineering, having authored 22 papers that have together received 1.7k indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (5 papers), Magnetic Properties and Applications (5 papers), Advanced Measurement and Metrology Techniques (4 papers), Electric Motor Design and Analysis (4 papers), Industrial Vision Systems and Defect Detection (3 papers), Neural dynamics and brain function (2 papers), Non-Destructive Testing Techniques (2 papers) and Sports Analytics and Performance (2 papers). The work is most often cited by research in Artificial Intelligence (890 citations), Computer Vision and Pattern Recognition (288 citations) and Control and Systems Engineering (272 citations). John Agapiou has collaborated with scholars based in United States, United Kingdom and Australia. Frequent co-authors include David A. Stephenson, Joel Z. Leibo, Tom Schaul, Ian Osband, Marc Lanctot, Gabriel Dulac-Arnold, Audrūnas Gruslys, Todd Hester, Olivier Pietquin and Dan Horgan. Their work appears in journals such as CIRP Annals, Energies, Journal of Neuroscience, Journal of Neurophysiology and Behavioral and Brain Sciences.
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.