Kun Shao

926 total citations
16 papers, 378 citations indexed

About

Kun Shao is a scholar working on Artificial Intelligence, Sociology and Political Science and Control and Systems Engineering. According to data from OpenAlex, Kun Shao has authored 16 papers receiving a total of 378 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Artificial Intelligence, 5 papers in Sociology and Political Science and 3 papers in Control and Systems Engineering. Recurrent topics in Kun Shao's work include Reinforcement Learning in Robotics (9 papers), Artificial Intelligence in Games (8 papers) and Digital Games and Media (4 papers). Kun Shao is often cited by papers focused on Reinforcement Learning in Robotics (9 papers), Artificial Intelligence in Games (8 papers) and Digital Games and Media (4 papers). Kun Shao collaborates with scholars based in China, United States and Sweden. Kun Shao's co-authors include Dongbin Zhao, Yuanheng Zhu, Haitao Wang, Nannan Li, Tong Zhou, Li Dong, Derong Liu, Tingwen Huang, Roy Tamashiro and Le Lv and has published in prestigious journals such as Soft Computing, IEEE Transactions on Emerging Topics in Computational Intelligence and Interactive Technology and Smart Education.

In The Last Decade

Kun Shao

16 papers receiving 361 citations

Peers

Kun Shao
Shayegan Omidshafiei United States
Peter Sunehag Australia
Tengfei Shi China
Antonín Komenda Czechia
Landon Kraemer United States
Sanjay Jasola India
Abdel‐Illah Mouaddib France
Jilles Dibangoye France
Zhiyong Luo China
Jamil Ahmad Pakistan
Shayegan Omidshafiei United States
Kun Shao
Citations per year, relative to Kun Shao Kun Shao (= 1×) peers Shayegan Omidshafiei

Countries citing papers authored by Kun Shao

Since Specialization
Citations

This map shows the geographic impact of Kun Shao'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 Kun Shao with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kun Shao more than expected).

Fields of papers citing papers by Kun Shao

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Kun Shao. 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 Kun Shao. The network helps show where Kun Shao may publish in the future.

Co-authorship network of co-authors of Kun Shao

This figure shows the co-authorship network connecting the top 25 collaborators of Kun Shao. A scholar is included among the top collaborators of Kun Shao 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 Kun Shao. Kun Shao is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

16 of 16 papers shown
1.
Shao, Kun, Ming Zhou, Weinan Zhang, et al.. (2022). Promoting Quality and Diversity in Population-based Reinforcement Learning via Hierarchical Trajectory Space Exploration. 2022 International Conference on Robotics and Automation (ICRA). 30. 7544–7550. 1 indexed citations
2.
Shao, Kun, et al.. (2020). Cooperative Multi-Agent Deep Reinforcement Learning with Counterfactual Reward. 1–8. 2 indexed citations
3.
Shao, Kun, Yuanheng Zhu, & Dongbin Zhao. (2018). StarCraft Micromanagement With Reinforcement Learning and Curriculum Transfer Learning. IEEE Transactions on Emerging Topics in Computational Intelligence. 3(1). 73–84. 122 indexed citations
4.
Shao, Kun, et al.. (2018). Visual Navigation with Actor-Critic Deep Reinforcement Learning. 1–6. 7 indexed citations
5.
Shao, Kun, Dongbin Zhao, Nannan Li, & Yuanheng Zhu. (2018). Learning Battles in ViZDoom via Deep Reinforcement Learning. 1–4. 23 indexed citations
6.
Lv, Le, Dongbin Zhao, & Kun Shao. (2018). Deep sparse representation-based mid-level visual elements discovery in fine-grained classification. Soft Computing. 23(18). 8711–8722. 4 indexed citations
7.
Shao, Kun, et al.. (2018). A Review of Computational Intelligence for StarCraft AI. 1167–1173. 16 indexed citations
8.
Shao, Kun, Yuanheng Zhu, & Dongbin Zhao. (2017). Cooperative reinforcement learning for multiple units combat in starCraft. 4. 1–6. 17 indexed citations
9.
Shao, Kun, et al.. (2016). Review of deep reinforcement learning and discussions on the development of computer Go. 33(6). 701–717. 37 indexed citations
10.
Zhao, Dongbin, et al.. (2016). ADP with MCTS algorithm for Gomoku. 1–7. 8 indexed citations
11.
Zhao, Dongbin, Haitao Wang, Kun Shao, & Yuanheng Zhu. (2016). Deep reinforcement learning with experience replay based on SARSA. 1–6. 96 indexed citations
12.
Shao, Kun, et al.. (2016). Move prediction in Gomoku using deep learning. 292–297. 15 indexed citations
13.
Shao, Kun & Roy Tamashiro. (2013). Comparing Teacher Dispositions in China and the USA.. Research in higher education journal. 21. 3 indexed citations
14.
Shao, Kun, et al.. (2012). Making advanced computer science topics more accessible through interactive technologies. Interactive Technology and Smart Education. 9(2). 89–99. 1 indexed citations
15.
16.
Shao, Kun, et al.. (2007). Domain-Analysis in Software Reuse - Application in Warehouse Management. 505–510. 25 indexed citations

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.

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2026