Hsien‐Wei Tseng
Papers
1
Total Citations
12
H-Index
1
About
Hsien-Wei Tseng is a leading researcher in intelligent robotics and autonomous systems, with a primary focus on advancing robot navigation through deep reinforcement learning. His most cited work, "Enhanced Autonomous Navigation of Robots by Deep Reinforcement Learning Algorithm with Multistep Method" (2021, 12 citations), introduces the MS-DDQN—a novel algorithm that integrates multistep updates with a double deep Q-network. This innovation significantly improves the learning efficiency and stability of mobile robots in complex environments, enabling more reliable and adaptive autonomous navigation. Tseng’s contributions bridge the gap between theoretical reinforcement learning and practical robotic applications, offering a robust framework for real-world deployment. His work is highly regarded for its clarity and impact, serving as a key reference for researchers developing intelligent navigation systems. With a growing citation record, Tseng continues to shape the future of autonomous robotics, inspiring students and engineers to explore the potential of deep learning in dynamic, real-time decision-making.
Research Focus
Key Achievements
Top Papers
- 1