Hongyao Tang
- Artificial Intelligence top 10%
- Control and Systems Engineering
- Electrical and Electronic Engineering
- Computer Networks and Communications
- Computer Vision and Pattern Recognition
- Topics
- Reinforcement Learning in Robotics (13 papers)Adaptive Dynamic Programming Control (3 papers)Metaheuristic Optimization Algorithms Research (2 papers)
In The Last Decade
Hongyao Tang
17 papers receiving 207 citations
Peers
Comparison fields: 5 of 53
- Artificial Intelligence 113
- Control and Systems Engineering 50
- Electrical and Electronic Engineering 38
- Computer Networks and Communications 34
- Computer Vision and Pattern Recognition 29
Countries citing papers authored by Hongyao Tang
This map shows the geographic impact of Hongyao Tang'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 Hongyao Tang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hongyao Tang more than expected).
Fields of papers citing papers by Hongyao Tang
This network shows the impact of papers produced by Hongyao Tang. 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 Hongyao Tang. The network helps show where Hongyao Tang may publish in the future.
Co-authorship network of co-authors of Hongyao Tang
This figure shows the co-authorship network connecting the top 25 collaborators of Hongyao Tang. A scholar is included among the top collaborators of Hongyao Tang based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Hongyao Tang. Hongyao Tang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 12 | |
| 3 | 1 | |
| 4 | 81 | |
| 5 | 5 | |
| 6 | 0 | |
| 7 | Transfer among Agents: An Efficient Multiagent Transfer Learning Framework | 3 |
| 8 | 8 | |
| 9 | 14 | |
| 10 | 1 | |
| 11 | 5 | |
| 12 | 3 | |
| 13 | Q-value Path Decomposition for Deep Multiagent Reinforcement Learning | 3 |
| 14 | 18 | |
| 15 | Efficient meta reinforcement learning via meta goal generation | 1 |
| 16 | 41 | |
| 17 | Hierarchical Deep Multiagent Reinforcement Learning | 9 |
| 18 | 5 |
About Hongyao Tang
Hongyao Tang is a scholar working on Artificial Intelligence, Computational Theory and Mathematics and Automotive Engineering, having authored 18 papers that have together received 211 indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (13 papers), Adaptive Dynamic Programming Control (3 papers) and Metaheuristic Optimization Algorithms Research (2 papers). The work is most often cited by research in Artificial Intelligence (113 citations), Control and Systems Engineering (50 citations) and Automotive Engineering (19 citations). Hongyao Tang has collaborated with scholars based in China, Sweden and Canada. Frequent co-authors include Jianye Hao, Zhaopeng Meng, Tianpei Yang, Chenjia Bai, Zhen Wang, Peng Liu, Changjie Fan, Yingfeng Chen, Wulong Liu and Chen Chen. Their work appears in journals such as IEEE Transactions on Evolutionary Computation, IEEE Transactions on Neural Networks and Learning Systems and Electronics.
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.