Dan Horgan
Impact in
- Artificial Intelligence top 1%
- Reinforcement Learning in Robotics
- Evolutionary Algorithms and Applications
- Adversarial Robustness in Machine Learning
- Automotive Engineering top 5%
- Autonomous Vehicle Technology and Safety
Papers in
-
- Reinforcement Learning in Robotics 5
- Evolutionary Algorithms and Applications 2
- Explainable Artificial Intelligence (XAI) 1
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- Sports Analytics and Performance 2
- Co-authors
- Tom SchaulBilal PiotMatteo HesselHado van HasseltDavid SilverWill DabneyMohammad Gheshlaghi AzarJoseph Modayil
- Journals
- International Conference on Learning Representations (1 paper)Proceedings of the AAAI Conference on Artificial Intelligence (2 papers)arXiv (Cornell University) (2 papers)
- Partner nations
- United KingdomUnited States
In The Last Decade
Dan Horgan
5 papers receiving 1.6k citations
Hit Papers
Peers
Comparison fields: 5 of 102
- Artificial Intelligence 983
- Automotive Engineering 219
- Control and Systems Engineering 392
- Computer Vision and Pattern Recognition 269
- Computer Networks and Communications 291
Countries citing papers authored by Dan Horgan
This map shows the geographic impact of Dan Horgan'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 Dan Horgan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dan Horgan more than expected).
Fields of papers citing papers by Dan Horgan
This network shows the impact of papers produced by Dan Horgan. 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 Dan Horgan. The network helps show where Dan Horgan may publish in the future.
Co-authors
The 24 scholars most cited alongside Dan Horgan, 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 | Distributed Prioritized Experience Replay | 2018 | 56 |
| 2 | Distributed Distributional Deterministic Policy Gradients | 2018 | 34 |
| 3 | Deep Q-learning From Demonstrations Hit paper breakdown → | 2018 | 485 |
| 4 | Rainbow: Combining Improvements in Deep Reinforcement Learning Hit paper breakdown → | 2018 | 995 |
| 5 | 2017 | 123 |
About Dan Horgan
Dan Horgan is a scholar working on Artificial Intelligence, Economics and Econometrics, Cell Biology, Cognitive Neuroscience and Information Systems, having authored 5 papers that have together received 1.7k indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (5 papers), Evolutionary Algorithms and Applications (2 papers), Sports Analytics and Performance (2 papers), Software Engineering Research (1 paper), Modular Robots and Swarm Intelligence (1 paper), Explainable Artificial Intelligence (XAI) (1 paper), Zebrafish Biomedical Research Applications (1 paper) and Robotic Locomotion and Control (1 paper). The work is most often cited by research in Artificial Intelligence (983 citations), Automotive Engineering (219 citations), Control and Systems Engineering (392 citations), Computer Vision and Pattern Recognition (269 citations) and Computer Networks and Communications (291 citations). Dan Horgan has collaborated with scholars based in United Kingdom and United States. Frequent co-authors include Tom Schaul, Bilal Piot, Matteo Hessel, Hado van Hasselt, David Silver, Will Dabney, Mohammad Gheshlaghi Azar, Joseph Modayil, Georg Ostrovski and John Quan. Their work appears in journals such as International Conference on Learning Representations, Proceedings of the AAAI Conference on Artificial Intelligence 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.