Jingqing Ruan
Papers
2
Total Citations
27
H-Index
2
About
Jingqing Ruan is a pioneering researcher at the intersection of multi-agent systems, reinforcement learning, and large language models (LLMs), with a focus on solving complex, large-scale decision-making challenges. Her work centers on developing intelligent frameworks that enable autonomous systems—from mobile robots to unmanned surface vehicles—to collaborate effectively in real-world industrial applications. In her highly cited 2024 paper (24 citations), Ruan introduced an end-to-end deep reinforcement learning-based modular task allocation framework, a breakthrough for coordinating multi-robot teams in tasks like warehouse inspection and hydrographic surveying. More recently, she has ventured into the frontier of LLM-based agents, proposing an actor-critic approach to mitigate hallucination and coordination issues in large-scale multi-agent systems (2023). This work is particularly notable for its potential to scale AI-driven decision-making to unprecedented levels. With her innovative fusion of reinforcement learning and language models, Ruan is shaping the future of autonomous collaboration, earning recognition for her contributions to both foundational theory and practical deployment.
Research Focus
Key Achievements
Top Papers
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- 2