3 citations · 4 across the 2 of their papers we have counts for
3 papers
cs.CL2025
P3: Prompts Promote Prompting
Xinyu Zhang, Yuanquan Hu, Fangchao Liu +1
Current large language model (LLM) applications often employ multi-component prompts, comprising both system and user prompts, to guide model behaviors. While recent advancements h…
q-fin.TR2023★ 3 cited
Optimizing Trading Strategies in Quantitative Markets using Multi-Agent Reinforcement Learning
Hengxi Zhang, Zhendong Shi, Yuanquan Hu +3
Quantitative markets are characterized by swift dynamics and abundant uncertainties, making the pursuit of profit-driven stock trading actions inherently challenging. Within this c…
cs.MA2022★ 1 cited
Graphon Mean-Field Control for Cooperative Multi-Agent Reinforcement Learning
Yuanquan Hu, Xiaoli Wei, Junji Yan +1
The marriage between mean-field theory and reinforcement learning has shown a great capacity to solve large-scale control problems with homogeneous agents. To break the homogeneity…