Steven Kapturowski
Impact in
- Artificial Intelligence top 10%
- Reinforcement Learning in Robotics
- Evolutionary Algorithms and Applications
- Artificial Intelligence in Games
- Adversarial Robustness in Machine Learning
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- Robotic Path Planning Algorithms
Papers in
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- Reinforcement Learning in Robotics 4
- Artificial Intelligence in Games 1
- Data Stream Mining Techniques 1
- Explainable Artificial Intelligence (XAI) 1
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- Human Pose and Action Recognition 1
- Co-authors
- John Quan (1 shared paper)Rémi Munos (1 shared paper)Will Dabney (1 shared paper)Georg Ostrovski (1 shared paper)Pablo Sprechmann (3 shared papers)Adrià Puigdomènech Badia (3 shared papers)Charles Blundell (3 shared papers)Bilal Piot (1 shared paper)
- Journals
- arXiv (Cornell University) (2 papers)Neural Information Processing Systems (1 paper)International Conference on Learning Representations (1 paper)International Conference on Machine Learning (1 paper)
- Partner nations
- United StatesUnited KingdomCanada
In The Last Decade
Steven Kapturowski
5 papers receiving 189 citations
Peers
Comparison fields: 5 of 43
- Artificial Intelligence 147
- Computer Vision and Pattern Recognition 34
- Computational Theory and Mathematics 23
- Control and Systems Engineering 31
- Automotive Engineering 15
Countries citing papers authored by Steven Kapturowski
This map shows the geographic impact of Steven Kapturowski'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 Steven Kapturowski with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Steven Kapturowski more than expected).
Fields of papers citing papers by Steven Kapturowski
This network shows the impact of papers produced by Steven Kapturowski. 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 Steven Kapturowski. The network helps show where Steven Kapturowski may publish in the future.
Co-authors
The 25 scholars most cited alongside Steven Kapturowski, 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 | Recurrent Experience Replay in Distributed Reinforcement Learning. | 2018 | 96 |
| 2 | Agent57: Outperforming the Atari Human Benchmark | 2020 | 71 |
| 3 | 2020 | 28 | |
| 4 | Value-driven Hindsight Modelling | 2020 | 1 |
| 5 | Coverage as a Principle for Discovering Transferable Behavior in Reinforcement Learning | 2021 | 1 |
| 6 | 2024 | 0 |
About Steven Kapturowski
Steven Kapturowski is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Management Science and Operations Research, Economics and Econometrics and Electrical and Electronic Engineering, having authored 6 papers that have together received 197 indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (4 papers), Artificial Intelligence in Games (1 paper), Smart Grid Energy Management (1 paper), Sports Analytics and Performance (1 paper), Advanced Bandit Algorithms Research (1 paper), Human Pose and Action Recognition (1 paper), Data Stream Mining Techniques (1 paper) and Explainable Artificial Intelligence (XAI) (1 paper). The work is most often cited by research in Artificial Intelligence (147 citations), Computer Vision and Pattern Recognition (34 citations), Computational Theory and Mathematics (23 citations), Control and Systems Engineering (31 citations) and Automotive Engineering (15 citations). Steven Kapturowski has collaborated with scholars based in United States, United Kingdom and Canada. Frequent co-authors include John Quan, Rémi Munos, Will Dabney, Georg Ostrovski, Pablo Sprechmann, Adrià Puigdomènech Badia, Charles Blundell, Bilal Piot, Zhaohan Daniel Guo and Daniel Guo. Their work appears in journals such as arXiv (Cornell University), Neural Information Processing Systems, International Conference on Learning Representations and International Conference on Machine Learning.
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.