Zeyuan Liu
Papers
1
Total Citations
23
H-Index
1
About
Zeyuan Liu is a researcher at the forefront of intelligent robotics and reinforcement learning, with a focus on developing efficient, adaptive path-planning algorithms for autonomous systems. His most influential work, "A modified dueling DQN algorithm for robot path planning incorporating priority experience replay and artificial potential fields," has already garnered 23 citations since its 2025 publication, signaling its rapid impact on the field. Liu’s key contribution lies in ingeniously fusing deep reinforcement learning with classical control methods: by integrating priority experience replay and artificial potential fields into a dueling deep Q-network (DQN) framework, he significantly improves both the learning efficiency and safety of robot navigation in complex environments. This hybrid approach addresses critical challenges in real-world deployment, such as obstacle avoidance and convergence speed, offering a more robust solution than traditional methods alone. Liu’s work is particularly notable for bridging the gap between theoretical algorithm design and practical robotic applications, making his research highly relevant for students and engineers seeking to advance autonomous navigation. His early-career citation record underscores the growing recognition of his innovative contributions to intelligent robotics.
Research Focus
Key Achievements
Top Papers
- 1