Gaoqi Liang
Papers
1
Total Citations
105
H-Index
1
About
Gaoqi Liang is a leading researcher at the intersection of artificial intelligence and energy systems, with key contributions in reinforcement learning, large language models (LLMs), and smart grid optimization. Their most cited work, the 2024 survey "Survey on Large Language Model-Enhanced Reinforcement Learning: Concept, Taxonomy, and Methods" (105 citations), provides a groundbreaking framework for integrating LLMs' pretrained knowledge and general capabilities into RL to address challenges in multitask learning, sample efficiency, and high-level planning. This work has become a foundational reference for researchers exploring LLM-RL synergies. Beyond this, Liang has made significant impacts in power system resilience, developing data-driven methods for cyber-physical security and fault diagnosis in smart grids. Their research consistently bridges theoretical advances with practical applications, earning recognition for enhancing grid reliability through AI. With a growing citation record reflecting the timeliness and utility of their work, Liang stands out as a pivotal figure shaping how AI—particularly LLMs—can transform both autonomous decision-making and critical infrastructure management.
Research Focus
Key Achievements
Top Papers
- 1