WANGJun

Shenyang University of Chemical Technology

Papers

Total Citations

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About

Wang Jun has made significant contributions to the field of intelligent robotics and reinforcement learning, with a particular focus on mobile robot path planning. His most cited work, "Path planning of mobile robot based on improved DDQN" (2021), addresses critical challenges in deep Q-network algorithms, such as overestimation bias and sparse reward signals. By proposing the HER-DDQN algorithm—which integrates hindsight experience replay with a deep convolutional neural network processing raw RGB images—Jun has advanced the practical deployment of learning-based navigation systems. This research, which has garnered early citations, demonstrates his ability to tackle real-world robotic control problems through algorithmic innovation. His work stands at the intersection of computer vision and reinforcement learning, offering scalable solutions for autonomous systems. As a researcher, Wang Jun is recognized for bridging theoretical improvements in deep learning with tangible robotic applications, making his contributions valuable for students and engineers working on intelligent agent design and autonomous navigation.