Bei Peng
- Artificial Intelligence top 10%
- Reinforcement Learning in Robotics 10
- Data Stream Mining Techniques 3
- Machine Learning and Algorithms 2
- Topic Modeling 2
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- Robot Manipulation and Learning 3
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- Mobile Crowdsensing and Crowdsourcing 3
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- Software Engineering Research 3
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- Ethics and Social Impacts of AI 2
- Co-authors
- Matthew E. TaylorJames MacGlashanRobert LoftinDavid L. RobertsMichael L. LittmanJeff HuangMark K. HoGuan Wang
- Journals
- Applied Energy (1 paper)Journal of Machine Learning Research (1 paper)Autonomous Agents and Multi-Agent Systems (1 paper)
- Partner nations
- United StatesUnited KingdomNetherlands
In The Last Decade
Bei Peng
15 papers receiving 185 citations
Peers
Comparison fields: 5 of 46
- Artificial Intelligence 159
- Control and Systems Engineering 61
- Computer Science Applications 8
- Management Science and Operations Research 14
- Computer Vision and Pattern Recognition 21
Countries citing papers authored by Bei Peng
This map shows the geographic impact of Bei Peng'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 Bei Peng with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Bei Peng more than expected).
Fields of papers citing papers by Bei Peng
This network shows the impact of papers produced by Bei Peng. 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 Bei Peng. The network helps show where Bei Peng may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Bei Peng, 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 | 2025 | 0 | |
| 2 | 2024 | 0 | |
| 3 | 2024 | 7 | |
| 4 | 2024 | 0 | |
| 5 | 2022 | 5 | |
| 6 | Randomized Entity-wise Factorization for Multi-Agent Reinforcement Learning | 2021 | 7 |
| 7 | Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey | 2020 | 18 |
| 8 | 2018 | 5 | |
| 9 | 2017 | 3 | |
| 10 | 2017 | 40 | |
| 11 | 2016 | 16 | |
| 12 | Generating real-time crowd advice to improve reinforcement learning agents | 2015 | 2 |
| 13 | 2015 | 43 | |
| 14 | 2015 | 1 | |
| 15 | 2015 | 4 | |
| 16 | Training an Agent to Ground Commands with Reward and Punishment | 2014 | 5 |
| 17 | 2014 | 29 | |
| 18 | 2014 | 14 |
About Bei Peng
Bei Peng is a scholar working on Computer Science Applications, Artificial Intelligence and Music, having authored 18 papers that have together received 199 indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (10 papers), Software Engineering Research (3 papers), Robot Manipulation and Learning (3 papers), Data Stream Mining Techniques (3 papers), Mobile Crowdsensing and Crowdsourcing (3 papers), Machine Learning and Algorithms (2 papers), Topic Modeling (2 papers) and Ethics and Social Impacts of AI (2 papers). The work is most often cited by research in Artificial Intelligence (159 citations), Control and Systems Engineering (61 citations) and Computer Science Applications (8 citations). Bei Peng has collaborated with scholars based in United States, United Kingdom and Netherlands. Frequent co-authors include Matthew E. Taylor, James MacGlashan, Robert Loftin, David L. Roberts, Michael L. Littman, Jeff Huang, Mark K. Ho, Guan Wang, Jivko Sinapov and Matteo Leonetti. Their work appears in journals such as Applied Energy, Journal of Machine Learning Research and Autonomous Agents and Multi-Agent Systems.
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.