Yanyu Geng
Papers
1
Total Citations
28
H-Index
1
About
Yanyu Geng is a researcher advancing the frontiers of intelligent robotics and reinforcement learning, with a particular focus on autonomous path planning. Their most notable contribution is a novel Q-learning algorithm enhanced by an improved whale optimization algorithm, published in 2022 and cited 28 times. This work addresses a critical bottleneck in mobile robotics: the slow convergence and inefficient exploration of traditional Q-learning when initializing Q-tables without a prior environmental model. By integrating bio-inspired optimization, Geng’s approach significantly accelerates learning and improves path planning efficiency, offering a practical solution for real-world autonomous navigation. This research bridges reinforcement learning and swarm intelligence, demonstrating how hybrid algorithms can overcome classical limitations. Geng’s work is particularly valuable for students and researchers in robotics and artificial intelligence, showcasing a clear path from theoretical improvement to applied impact. Their contributions underscore a commitment to making autonomous systems more adaptive and computationally efficient, with implications for everything from warehouse logistics to search-and-rescue operations.
Research Focus
Key Achievements
Top Papers
- 1