Zhiqiang He

Zhiqiang He (何志强)

I am currently a Ph.D. student at University of Electro-Communications (Tokyo), focusing on Reinforcement Learning and it's application. I received M.S. from Northeastern University, where I was advised by Professor Jiao Wang.

I interned as a Research Engineer at Baidu Beijing from June to September 2021 (Received Super Special Offer), followed by a role as a Reinforcement Learning Algorithms Engineer at InspirAI from June 2022 to May 2023 (Received Top-Performing Team Prize).

Email / CV / Google Scholar / Github / Zhihu /

Academic Activities

Served as a peer reviewer for IEEE Transactions on Network Science and Engineering; IEEE Internet of Things Journal; IEEE Open Journal of the Computer Society.

Publication / Preprint

	Understanding World Models through Multi-Step Pruning Policy via Reinforcement Learning Zhiqiang He, Wen Qiu, Wei Zhao, Xun Shao, Zhi Liu Information Sciences, 2025. (IF=8.1, Q1) Source Code \| Download PDF Parallel Multi-Step Pruning Policies enhance diversity Sampling. (Analysis of convergence theory for MSPP and its PG Theorem.)
	A Survey on DRL based UAV Communications and Networking: DRL Fundamentals, Applications and Implementations Wei Zhao, Shaoxin Cui, Wen Qiu, Zhiqiang He, Zhi Liu, Xiao Zheng, Bomin Mao, Nei Kato IEEE Communications Surveys & Tutorials, 2025. (IF=42.8, Q1), * Corresponding author This survey outlines the evolution of fundamental reinforcement learning theory, highlighting how core challenges have driven the development of new methods.
	Erlang planning network: An iterative model-based reinforcement learning with multi-perspective Jiao Wang, Lemin Zhang, Zhiqiang He, Can Zhu, Zihui Zhao Pattern Recognition, 2022. (IF=8.5, Q1) Source Code \| Download PDF Bi-level reinforcement learning in Model-Based Reinforcement Learning.
	Control Strategy of Speed Servo Systems Based on Deep Reinforcement Learning Pengzhan Chen, Zhiqiang He, Chuanxi Chen, Jiahong Xu Algorithms, 2018 Source Code \| Download PDF (Cited 53 times) First paper applied Reinforcement Learning in Jump Speed Servo System.

Credits.