Ruiyi Wang

Papers

Total Citations

H-Index

About

Ruiyi Wang is a researcher whose work lies at the intersection of robotics, reinforcement learning, and deep learning, with a particular focus on intelligent path planning for mobile robots. In their most-cited work, "Path Planning for Mobile Robots Based on TPR-DDPG" (2021, 14 citations), Wang addresses a critical challenge in robotics: enabling autonomous navigation through complex environments. Their major contribution involves enhancing the Deep Deterministic Policy Gradient (DDPG) algorithm—a cornerstone of deep reinforcement learning—by introducing a novel approach that leverages the self-learning and generalization capabilities of RL combined with the representational power of deep learning. This work has garnered attention for its potential to improve the efficiency and adaptability of mobile robot navigation systems. Wang's research is particularly notable for bridging theoretical advances in deep RL with practical robotic applications, offering a pathway toward more autonomous and intelligent systems. With 14 citations on this key paper, Wang's contributions are gaining traction among researchers exploring reinforcement learning-based control, positioning them as an emerging voice in the field of intelligent robotics and autonomous systems.