Title: Sim2Act: Robust Simulation-to-Decision Learning via Adversarial Calibration and Group-relative Perturbation
Primary area: other topics in machine learning (i.e., none of the above)
Keywords: Offline Policy Learning, Simulator Calibration, Robust Decision-Making, Supply Chain Optimization, Group Relative Preturbation

Abstract: In digital twins, simulation-to-decision has become a cornerstone in mission-critical domains like supply chains and power systems, because it enables safe decision learning in digital worlds without risking real-world deployments. 
However, the complex and noisy nature of real-world data calls for robust solutions against distribution shifts and uncertainty. 
Existing methods often fall short: 1) surrogate simulation models tend to be biased in decision-critical regions;  2) policies derived from surrogate models are highly sensitive to perturbations, thus, result in brittle performance. 
To address these challenges, we propose a novel two-step framework that advances 1) simulation fidelity by adversarial calibration and 2) policy robustness with group-relative perturbations. Our solution enables non-disruptive robustness that stable under perturbation while preserving decision performance.
We present intensive experiments on both synthetic and real-world domain datasets, including DataCo, GlobalStore, and OAS, to demonstrate the simulation and decision robustness of our method even in noisy and biased settings.

TL;DR: We propose a two-stage framework that calibrates predictive simulators and learns robust policies via group-relative perturbations, enabling reliable decision-making under distribution shift in offline supply chain scenarios.