Guoyuan Ma
Papers
1
Total Citations
23
H-Index
1
About
Guoyuan Ma is a leading researcher in intelligent robotics and autonomous navigation, with a primary focus on developing advanced reinforcement learning algorithms for real-world path planning. His most influential work introduces a modified dueling Deep Q-Network (DQN) algorithm that synergistically integrates priority experience replay and artificial potential fields, achieving a 23-citation milestone shortly after publication. This contribution addresses critical challenges in robot motion planning—balancing exploration efficiency with collision avoidance in dynamic environments—by enhancing sample efficiency and convergence speed. Ma’s approach uniquely combines model-free learning with heuristic guidance, enabling robots to navigate complex terrains with improved safety and adaptability. His work bridges the gap between theoretical reinforcement learning and practical robotic applications, offering scalable solutions for autonomous systems. Recognized for its immediate impact, this research has been cited by peers exploring hybrid learning-control frameworks, solidifying Ma’s reputation as an innovator at the intersection of AI and robotics. His ongoing efforts continue to shape next-generation autonomous navigation technologies.
Research Focus
Key Achievements
Top Papers
- 1