Temporal-Difference Calibration of Black-Box Simulators
Read the original on arXiv Statistics ML →The Flow has not summarised this story yet — read it at arXiv Statistics ML.
The Flow has not summarised this story yet — read it at arXiv Statistics ML.
We develop a derivative-free temporal-difference (TD) framework for calibrating black-box simulators using one-step transitions from a target system and noisy simulator-generated continuation values....
arXiv:2606. 16923v1 Announce Type: new Abstract: Simulation-based inference (SBI) of latent parameters is often hindered by simulator misspecification, the mismatch between simulated and real-world observations caused by inherent modeling simplifications.
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The paper investigates how to collect data for recalibrating a misspecified simulator that models a partially observed real-world system as a fully observed Markovian process. It compares four data‑collection strategies: never recalibrating, passive on‑policy recalibration, continuous exploration across all units, and randomized experimentation on a sample of units. Theoretical analysis and a vending‑machine simulation show that experimentation can outperform passive adaptation when drift is invisible to on‑policy data or when recalibration lags behind ongoing drift, while exploration is preferable when trying worse actions is costly and dynamics may not have changed.
arXiv:2606. 18186v1 Announce Type: cross Abstract: Finite-dimensional (FD) diffusion policies exhibit temporal drift owing to discretization artifacts that degrade long-horizon performance (when deployed on physical systems).