Yali Du

Papers

Total Citations

139

H-Index

About

Yali Du is a prominent researcher specializing in reinforcement learning (RL), with a particular focus on safe RL, multi-agent systems, and goal-conditioned learning. Her work addresses one of the most pressing challenges in artificial intelligence: deploying RL algorithms responsibly and effectively in real-world environments such as autonomous driving and robotics. Du's most influential contribution, "A Review of Safe Reinforcement Learning: Methods, Theory and Applications" (2022), has garnered over 100 citations, establishing her as a leading voice in the safe RL community. This comprehensive survey systematically examines methods, theoretical foundations, and practical applications, serving as an essential reference for researchers navigating the complex intersection of safety and intelligent decision-making. Beyond safety, Du has made significant strides in multi-robot coordination, developing an end-to-end deep RL framework for modular task allocation in autonomous mobile systems, which has already attracted notable attention with 24 citations since 2024. Her work on goal-conditioned supervised learning further demonstrates her broad theoretical contributions, bridging offline RL and self-supervised paradigms. Collectively, Du's research shapes how intelligent systems can operate efficiently, collaboratively, and safely in demanding real-world deployments.