Anton Raichuk
Impact in
- Artificial Intelligence top 10%
- Reinforcement Learning in Robotics
- Artificial Intelligence in Games
- Evolutionary Algorithms and Applications
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- Video Analysis and Summarization
- Human Pose and Action Recognition
Papers in
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- Reinforcement Learning in Robotics 3
- Machine Learning and Data Classification 1
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- Educational Games and Gamification 1
- Child and Animal Learning Development 1
- Co-authors
- Sylvain Gelly (3 shared papers)Olivier Bachem (3 shared papers)Marcin Michalski (2 shared papers)Damien Vincent (3 shared papers)Piotr Stańczyk (2 shared papers)Karol Kurach (1 shared paper)Michał Zając (1 shared paper)Olivier Bousquet (1 shared paper)
- Journals
- International Conference on Learning Representations (1 paper)arXiv (Cornell University) (2 papers)
- Partner nations
- United StatesSwitzerlandGermany
In The Last Decade
Anton Raichuk
3 papers receiving 178 citations
Peers
Comparison fields: 5 of 45
- Artificial Intelligence 133
- Computer Vision and Pattern Recognition 34
- Health Informatics 2
- Computational Theory and Mathematics 17
- Computer Science Applications 5
Countries citing papers authored by Anton Raichuk
This map shows the geographic impact of Anton Raichuk'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 Anton Raichuk with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Anton Raichuk more than expected).
Fields of papers citing papers by Anton Raichuk
This network shows the impact of papers produced by Anton Raichuk. 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 Anton Raichuk. The network helps show where Anton Raichuk may publish in the future.
Co-authors
The 22 scholars most cited alongside Anton Raichuk, 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 | 2020 | 144 | |
| 2 | What Matters for On-Policy Deep Actor-Critic Methods? A Large-Scale Study | 2021 | 27 |
| 3 | Episodic Curiosity through Reachability | 2018 | 10 |
| 4 | 2021 | 0 |
About Anton Raichuk
Anton Raichuk is a scholar working on Artificial Intelligence, Developmental and Educational Psychology, Control and Systems Engineering, Computer Vision and Pattern Recognition and Experimental and Cognitive Psychology, having authored 4 papers that have together received 181 indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (3 papers), Sports Analytics and Performance (1 paper), Educational Games and Gamification (1 paper), Human Motion and Animation (1 paper), Psychological and Educational Research Studies (1 paper), Human Pose and Action Recognition (1 paper), Child and Animal Learning Development (1 paper) and Machine Learning and Data Classification (1 paper). The work is most often cited by research in Artificial Intelligence (133 citations), Computer Vision and Pattern Recognition (34 citations), Health Informatics (2 citations), Computational Theory and Mathematics (17 citations) and Computer Science Applications (5 citations). Anton Raichuk has collaborated with scholars based in United States, Switzerland and Germany. Frequent co-authors include Sylvain Gelly, Olivier Bachem, Marcin Michalski, Damien Vincent, Piotr Stańczyk, Karol Kurach, Michał Zając, Olivier Bousquet, Lasse Espeholt and Carlos Riquelme. Their work appears in journals such as International Conference on Learning Representations 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.