Hangyu Mao

671 total citations
24 papers, 232 citations indexed

About

Hangyu Mao is a scholar working on Artificial Intelligence, Electrical and Electronic Engineering and Computer Networks and Communications. According to data from OpenAlex, Hangyu Mao has authored 24 papers receiving a total of 232 indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Artificial Intelligence, 6 papers in Electrical and Electronic Engineering and 5 papers in Computer Networks and Communications. Recurrent topics in Hangyu Mao's work include Reinforcement Learning in Robotics (10 papers), Energy Efficiency and Management (2 papers) and Neural Networks and Reservoir Computing (2 papers). Hangyu Mao is often cited by papers focused on Reinforcement Learning in Robotics (10 papers), Energy Efficiency and Management (2 papers) and Neural Networks and Reservoir Computing (2 papers). Hangyu Mao collaborates with scholars based in China, United Kingdom and Sweden. Hangyu Mao's co-authors include Zhen Xiao, Ni Yan, Bin He, Jianye Hao, Wulong Liu, Dong Li, Jun Wang, Zhengchao Zhang, Jun Luo and Yu Liu and has published in prestigious journals such as IEEE Transactions on Intelligent Transportation Systems, IEEE Transactions on Cybernetics and Neurocomputing.

In The Last Decade

Hangyu Mao

21 papers receiving 224 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Hangyu Mao China 8 129 63 35 27 24 24 232
Johan Källström Sweden 5 83 0.6× 30 0.5× 18 0.5× 25 0.9× 39 1.6× 11 214
Mathieu Reymond Ireland 5 74 0.6× 29 0.5× 23 0.7× 33 1.2× 45 1.9× 7 198
Eugenio Bargiacchi Netherlands 4 71 0.6× 29 0.5× 18 0.5× 30 1.1× 33 1.4× 8 190
Conor F. Hayes Ireland 5 86 0.7× 29 0.5× 18 0.5× 28 1.0× 52 2.2× 11 209
Mark Liptrott United Kingdom 6 135 1.0× 49 0.8× 15 0.4× 16 0.6× 14 0.6× 14 276
Hitoshi Iima Japan 11 150 1.2× 71 1.1× 33 0.9× 21 0.8× 32 1.3× 77 349
Jingdong Wang China 5 132 1.0× 30 0.5× 24 0.7× 34 1.3× 13 0.5× 15 247
Patrick Riley United States 11 151 1.2× 53 0.8× 21 0.6× 13 0.5× 10 0.4× 26 292
Jean-Paul Jamont France 7 42 0.3× 77 1.2× 16 0.5× 17 0.6× 8 0.3× 23 170

Countries citing papers authored by Hangyu Mao

Since Specialization
Citations

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

Fields of papers citing papers by Hangyu Mao

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Hangyu Mao

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

All Works

20 of 20 papers shown
1.
2.
3.
Yuan, Lei, et al.. (2025). SkillTree: Explainable Skill-Based Deep Reinforcement Learning for Long-Horizon Control Tasks. Proceedings of the AAAI Conference on Artificial Intelligence. 39(20). 21491–21500.
4.
5.
Li, Ziyue, Zhishuai Li, Lei Bai, et al.. (2024). A General Scenario-Agnostic Reinforcement Learning for Traffic Signal Control. IEEE Transactions on Intelligent Transportation Systems. 25(9). 11330–11344. 11 indexed citations
6.
Chen, Yihong, Bin Zhang, Shiwei Shi, et al.. (2024). TPTU-v2: Boosting Task Planning and Tool Usage of Large Language Model-based Agents in Real-world Industry Systems. 371–385. 3 indexed citations
7.
Xie, Xin, et al.. (2024). Efficient Missing Key Tag Identification in Large-Scale RFID Systems: An Iterative Verification and Selection Method. IEEE Transactions on Mobile Computing. 24(3). 2253–2269. 2 indexed citations
8.
He, Bin, et al.. (2023). A closed-loop digital twin modeling method integrated with carbon footprint analysis. Computers & Industrial Engineering. 182. 109389–109389. 12 indexed citations
9.
Mao, Hangyu, et al.. (2023). WToE: Learning When to Explore in Multiagent Reinforcement Learning. IEEE Transactions on Cybernetics. 54(8). 4789–4801. 3 indexed citations
10.
Chen, Junjie, Hangyu Mao, Jiajun Jiang, et al.. (2023). Achieving Last-Mile Functional Coverage in Testing Chip Design Software Implementations. 1 indexed citations
11.
Hao, Jianye, Kai Li, Dong Li, et al.. (2023). Research and applications of game intelligence. Scientia Sinica Informationis. 53(10). 1892–1892. 4 indexed citations
12.
Tang, Hongyao, Zhaopeng Meng, Chen Chen, et al.. (2022). What about Inputting Policy in Value Function: Policy Representation and Policy-Extended Value Function Approximator. Proceedings of the AAAI Conference on Artificial Intelligence. 36(8). 8441–8449. 5 indexed citations
13.
Liu, Long, Bin He, Dong Zhang, & Hangyu Mao. (2022). Deep Belief Network-based Prediction for Gear Noise. 50–54. 1 indexed citations
14.
Mao, Hangyu, et al.. (2022). Fast and Fine-grained Autoscaler for Streaming Jobs with Reinforcement Learning. Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence. 564–570. 8 indexed citations
15.
16.
Mao, Hangyu, et al.. (2021). Learning State Representations via Temporal Cycle-Consistency Constraint in Model-Based Reinforcement Learning. International Conference on Learning Representations. 1 indexed citations
17.
Zhang, Xianjie, et al.. (2021). Structural relational inference actor-critic for multi-agent reinforcement learning. Neurocomputing. 459. 383–394. 13 indexed citations
18.
Yang, Tianpei, Jianye Hao, Weixun Wang, et al.. (2021). Transfer among Agents: An Efficient Multiagent Transfer Learning Framework. 3 indexed citations
19.
Mao, Hangyu, et al.. (2020). Learning Agent Communication under Limited Bandwidth by Message Pruning. Proceedings of the AAAI Conference on Artificial Intelligence. 34(4). 5142–5149. 48 indexed citations
20.
Mao, Hangyu, et al.. (2020). Learning multi-agent communication with double attentional deep reinforcement learning. Autonomous Agents and Multi-Agent Systems. 34(1). 28 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.

Explore authors with similar magnitude of impact

Breakdown of academic impact, for papers by Johan Källström Breakdown of academic impact, for papers by Mathieu Reymond Breakdown of academic impact, for papers by Eugenio Bargiacchi Breakdown of academic impact, for papers by Conor F. Hayes Breakdown of academic impact, for papers by Mark Liptrott Breakdown of academic impact, for papers by Hitoshi Iima Breakdown of academic impact, for papers by Jingdong Wang Breakdown of academic impact, for papers by Patrick Riley Breakdown of academic impact, for papers by Jean-Paul Jamont
Rankless by CCL
2026