Computer Science > Machine Learning
arXiv:2603.09053 (cs)
[Submitted on 10 Mar 2026]
Title:Sim2Act: Robust Simulation-to-Decision Learning via Adversarial Calibration and Group-Relative Perturbation
Authors:Hongyu Cao, Jinghan Zhang, Kunpeng Liu, Dongjie Wang, Feng Xia, Haifeng Chen, Xiaohua Hu, Yanjie Fu
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Abstract:Simulation-to-decision learning enables safe policy training in digital environments without risking real-world deployment, and has become essential in mission-critical domains such as supply chains and industrial systems. However, simulators learned from noisy or biased real-world data often exhibit prediction errors in decision-critical regions, leading to unstable action ranking and unreliable policies. Existing approaches either focus on improving average simulation fidelity or adopt conservative regularization, which may cause policy collapse by discarding high-risk high-reward actions.
We propose Sim2Act, a robust simulation-to-decision framework that addresses both simulator and policy robustness. First, we introduce an adversarial calibration mechanism that re-weights simulation errors in decision-critical state-action pairs to align surrogate fidelity with downstream decision impact. Second, we develop a group-relative perturbation strategy that stabilizes policy learning under simulator uncertainty without enforcing overly pessimistic constraints. Extensive experiments on multiple supply chain benchmarks demonstrate improved simulation robustness and more stable decision performance under structured and unstructured perturbations.
| Comments: | |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2603.09053 [cs.LG] |
| (or arXiv:2603.09053v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2603.09053
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View a PDF of the paper titled Sim2Act: Robust Simulation-to-Decision Learning via Adversarial Calibration and Group-Relative Perturbation, by Hongyu Cao and 6 other authors
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