11 papers
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…
Bayesian Risk-Sensitive Policy Optimization For MDPs With General Loss Functions
Xiaoshuang Wang, Yifan Lin, Enlu Zhou
Motivated by many application problems, we consider Markov decision processes (MDPs) with a general loss function and unknown parameters. To mitigate the epistemic uncertainty asso…
Online Bayesian Risk-Averse Reinforcement Learning
Yuhao Wang, Enlu Zhou
In this paper, we study the Bayesian risk-averse formulation in reinforcement learning (RL). To address the epistemic uncertainty due to a lack of data, we adopt the Bayesian Risk…
Ranking and Selection with Simultaneous Input Data Collection
Yuhao Wang, Enlu Zhou
In this paper, we propose a general and novel formulation of ranking and selection with the existence of streaming input data. The collection of multiple streams of such data may c…
Fixed Confidence and Fixed Tolerance Bi-level Optimization for Selecting the Best Optimized System
Yuhao Wang, Seong-Hee Kim, Enlu Zhou
In this paper, we study a fixed-confidence, fixed-tolerance formulation of a class of stochastic bi-level optimization problems, where the upper-level problem selects from a finite…
Reusing Historical Trajectories in Natural Policy Gradient via Importance Sampling: Convergence and Convergence Rate
Yifan Lin, Yuhao Wang, Enlu Zhou
Reinforcement learning provides a mathematical framework for learning-based control, whose success largely depends on the amount of data it can utilize. The efficient utilization o…