$\text{DT}^2$: Decision-Targeted Digital Twins
A digital twin (DT) is a virtual model of a real-world system that can assist decision-making by simulating scenarios induced by different policies. However, typical machine learning-based DTs do not optimise for this use case. We prove that, when model capacity is limited, training DTs to minimise one-step transition errors can produce suboptimal models for ranking sets of policies according to a reward function. We further show that this holds empirically, even with expressive model classes. To address this, we introduce $\text{DT}^2$, a decision-targeted DT training paradigm. Firstly, $\text{DT}^2$ uses fitted Q-evaluation to estimate values of candidate policies from offline data. A D...
| Authors | Harry Amad, Mihaela van der Schaar |
|---|---|
| Primary category | cs.LG |
| Published | 24 June 2026 |
| Last updated at source | 24 June 2026 |
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