4 papers
Optimal Data Acquisition for Reinforcement Learning: A Large Deviations Perspective
Mingjie Hu, Jian-Qiang Hu, Enlu Zhou
Data acquisition efficiency is a central challenge in deploying reinforcement learning in business and healthcare operations, where interactions are costly, slow, and often involve…
Quantum Grover Adaptive Search for Discrete Simulation Optimization
Mingjie Hu, Jian-qiang Hu, Enlu Zhou
Quantum computing has advanced rapidly in recent years and has shown advantages in a variety of domains. In this paper, we investigate its potential for discrete simulation optimiz…
Adaptive Simulation Experiment for LLM Policy Optimization
Mingjie Hu, Siyang Gao, Jian-qiang Hu +1
Large language models (LLMs) have significant potential to improve operational efficiency in operations management. Deploying these models requires specifying a policy that governs…
Long-Run Conditional Value-at-Risk Reinforcement Learning
Qixin Wang, Hao Cao, Jian-Qiang Hu +2
Conditional value-at-risk (CVaR) is a prominent risk measure in financial engineering, energy systems, and supply chain management. In these domains, Markov decision processes (MDP…