6 papers · 1 filter
Decision-Making in Digital Twins: Optimization, Control, and Learning
Enlu Zhou
A digital twin is a virtual representation of a physical system, continuously updated with data from the physical system, enabling dynamic information exchange and decision-making.…
Quantum Monte Carlo Tree Search with Fixed Confidence
Mingjie Hu, Jian-Qiang Hu, Enlu Zhou
Recent advances in quantum computing are opening new opportunities for computationally intensive decision problems. This paper studies how quantum computing can improve Monte Carlo…
Evolving Robustness--Exploration Trade-off in Online Reinforcement Learning via Quantile Bayesian Risk MDPs
Meichen Song, Yuhao Wang, Enlu Zhou
In online reinforcement learning, data scarcity creates epistemic uncertainty that makes robustness important early in learning, whereas sufficient exploration is needed to learn t…
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…
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…
Adaptive Distributionally Robust Optimal Control with Bayesian Ambiguity Sets
Wentao Ma, Zhiping Chen, Huifu Xu +1
In stochastic optimal control (SOC), uncertainty may arise from incomplete knowledge of the true probability distribution of the underlying environment, which is known as Knightian…