Sunjie Zhang

University of Shanghai for Science and Technology

Papers

Total Citations

149

H-Index

About

Sunjie Zhang has made significant contributions to the field of robotics and artificial intelligence, with a primary focus on path planning and reinforcement learning. His most cited work, "Path Planning via an Improved DQN-Based Learning Policy" (2019, 149 citations), addresses a critical challenge in autonomous navigation—the core of robotics research. By enhancing Deep Q-Network (DQN) algorithms, Zhang pioneered methods that allow robots to learn optimal navigation strategies through experience, mimicking human skill acquisition. This work stands out for its innovative approach to improving learning efficiency and adaptability in complex environments, directly impacting the development of more intelligent and autonomous robotic systems. Zhang’s research bridges the gap between theoretical reinforcement learning and practical robotic applications, offering scalable solutions for real-world navigation tasks. His contributions have been widely recognized, with his work serving as a foundational reference for subsequent studies in intelligent path planning. Through his focus on experience-driven learning, Zhang continues to shape the future of autonomous systems, making his research essential reading for students and researchers exploring the intersection of AI and robotics.