Ruihong Zhou
Papers
1
Total Citations
2
H-Index
1
About
Ruihong Zhou is a researcher advancing intelligent robotics through deep reinforcement learning, with a focus on autonomous navigation and path planning. Her most cited work, "Research on the Local Path Planning for Mobile Robots based on PRO-Dueling Deep Q-Network (DQN) Algorithm" (2023), addresses critical limitations in traditional DQN approaches—namely slow convergence and inefficient use of effective experiences. By introducing a priority experience playback mechanism into the Dueling DQN architecture, Zhou’s algorithm significantly improves the speed and reliability of local path planning for mobile robots operating in dynamic environments. This contribution holds practical value for applications ranging from warehouse automation to autonomous vehicles. Though early in her career, her work has already garnered attention, with the paper cited twice in its first year—a promising indicator of growing impact. Zhou’s research sits at the intersection of reinforcement learning theory and real-world robotics, offering a more efficient framework for machines to navigate complex spaces. Her innovative integration of prioritized experience replay with dueling network structures marks a meaningful step forward in making autonomous navigation both faster and more robust.
Research Focus
Key Achievements
Top Papers
- 1