Tenglong Liu
Papers
1
Total Citations
2
H-Index
1
About
Tenglong Liu is an emerging researcher in the field of artificial intelligence, with a primary focus on reinforcement learning and its intersection with transfer learning. His most notable work, the comprehensive survey "A Survey on Transfer Reinforcement Learning" (2025), has already garnered 2 citations shortly after publication, signaling its growing influence. In this survey, Liu systematically explores how the reinforcement learning paradigm—which enables machines to autonomously complete tasks through continuous trial and error—can be enhanced by transfer learning techniques. He highlights how deep learning has propelled RL from breakthroughs in gaming to widespread applications in robotics, autonomous driving, and industrial automation. By synthesizing key methodologies and identifying open challenges, Liu provides a valuable roadmap for researchers aiming to improve sample efficiency and generalization in RL agents. His work is particularly significant for students and practitioners seeking to understand how prior knowledge can accelerate learning in complex, real-world environments. As a rising voice in AI, Tenglong Liu’s contributions are helping to shape the next generation of adaptive, intelligent systems.
Research Focus
Key Achievements
Top Papers
- 1A Survey on Transfer Reinforcement Learning2 citations · 2025