Papers
1
Total Citations
3
H-Index
1
About
Ze Cui is a rising researcher in the field of intelligent control systems and reinforcement learning, with a focus on nonlinear dynamics and autonomous decision-making. Their most notable contribution is the development of a deep neural network-based balance controller for the inverted pendulum system, a classic benchmark for complex, unstable nonlinear systems. In their 2024 paper, which has already garnered 3 citations, Cui introduced an innovative two-phase learning protocol combined with a detailed reward function, enabling end-to-end mapping of system states to control commands. This work demonstrates a significant advancement in applying deep reinforcement learning to real-time control challenges, offering a robust framework for asymmetry and instability management. While still early in their career, Cui’s research bridges theoretical machine learning and practical engineering, with potential applications in robotics, autonomous vehicles, and industrial automation. Their methodical approach to reward engineering and phased learning protocols marks them as an emerging voice in the quest for more reliable, adaptive control systems.
Research Focus
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Top Papers
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