Yongyan Wen
Papers
1
Total Citations
2
H-Index
1
About
Yongyan Wen is a leading researcher at the intersection of explainable artificial intelligence and deep reinforcement learning, with a primary focus on making complex autonomous systems both transparent and trustworthy. Their most notable contribution is the development of **SkillTree**, a groundbreaking framework introduced in their 2025 paper that integrates hierarchical skill-based learning with interpretable decision trees. This work directly addresses a critical limitation of deep reinforcement learning: its "black box" nature. By replacing opaque neural network policies with explainable tree structures, Wen enables long-horizon control tasks to be understood and verified by human operators, a vital step for deployment in safety-critical domains like robotics and autonomous driving. With 2 citations already, this early work signals strong potential for high impact. Wen’s research is pioneering a new paradigm where powerful AI agents are not only effective but also inherently explainable, bridging the gap between high-performance learning and human-centric accountability. Their work is essential reading for anyone interested in building AI systems that can be trusted and collaborated with in the real world.
Research Focus
Key Achievements
Top Papers
- 1