Publications

Reinforcement Learning & Systems Intelligence @ UEC Tokyo

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Journal Articles & Preprints

This page currently lists works led or co-authored by Zhiqiang He.

	Plasticity-Aware Mixture of Experts for Learning Under QoE Shifts in Adaptive Video Streaming Zhiqiang He, Zhi Liu, IEEE Transactions on Multimedia (Accepted), 2026. (IF=9.7, Q1) Source Code \| Download PDF \| Response 1 PDF \| Response 2 PDF Mitigate plasticity loss in mixture-of-experts under shifting objectives, with theoretical justification.
	DiPerceiveNet: A bidirectional cross-scale perception network for vehicle re-identification Jihao Cai, Zhiqiang He, Zhi Liu, Yangjie Cao, Pattern Recognition, 2026. (IF=7.6, Q1) Download PDF A Dual Interaction Perception Network (DiPerceiveNet) is designed to establish a unified and iterative information flow.
	Scalable and Reliable Multi-agent Reinforcement Learning for Traffic Assignment Leizhen Wang, Peibo Duan, Cheng Lyu, Zewen Wang, Zhiqiang He, Nan Zheng, Zhenliang Ma Communications in Transportation Research, 2025. (IF=14.5, Q1) Source Code \| Download PDF A scalable multi-agent approach focusing on the action space policy.
	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, with convergence analysis for MSPP and its policy gradient 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 for 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 58 times) One of the first works applying reinforcement learning to jump speed servo systems.

If you are interested in any of these works or in collaborating on related topics, please feel free to contact me by email.

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