Papers
2
Total Citations
8
H-Index
2
About
Wenbo Zheng is a researcher whose work bridges reinforcement learning, autonomous robotics, and intelligent automation. His key research areas include mobile robot path planning, reinforcement learning optimization, and robotic process automation (RPA) integrated with video technology. Zheng’s major contribution lies in advancing Q-learning-based reinforcement learning methods for mobile robot navigation, specifically by improving adaptive function parameter settings to solve complex path planning problems more effectively. His most cited work, "Mobile Robot Path Planning Based on Improved Reinforcement Learning Optimization" (2019), has garnered 6 citations, demonstrating its relevance in the growing field of autonomous robotics. In addition, Zheng explores the application of RPA and intelligent video technology in digital business transformation, as seen in his 2022 paper on power supply office management. This work highlights his ability to apply computational methods to real-world operational efficiency challenges. While his citation counts are modest, Zheng’s research contributes to the practical integration of AI-driven solutions in both robotics and enterprise automation, offering valuable insights for students and researchers interested in reinforcement learning applications and digital process innovation.
Research Focus
Key Achievements
Top Papers
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