Jialing Zhou

Nanjing University of Science and Technology, Zhuhai Institute of Advanced Technology

Papers

Total Citations

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About

Jialing Zhou is a rising researcher at the forefront of multi-agent reinforcement learning and autonomous systems control. Their most impactful contribution is the development of **DTDE (Decentralized Training with Decentralized Execution)**, a novel cooperative multi-agent reinforcement learning framework published in 2021, which has garnered 44 citations and addresses critical limitations in single-agent-focused RL paradigms. Zhou’s work extends into practical autonomous driving applications, where they have pioneered a hybrid control strategy integrating deep reinforcement learning with real-time path following and collision avoidance mechanisms, ensuring safety guarantees for unmanned vehicles. More recently, Zhou has advanced the field of constrained control systems by introducing a **vector-type finite-time output-constrained control algorithm** for mobile robots, achieving stabilization within finite time while maintaining strict output boundaries. This work, published in 2025, demonstrates Zhou’s ability to bridge theoretical control theory with robotic implementation. With a growing citation impact and a clear trajectory from algorithmic foundations to real-world deployment, Zhou is establishing themselves as a key contributor to safe, cooperative, and constraint-aware intelligent systems.