Calibration-risk routing for controlled world-model adaptation

Abstract:Model-based reinforcement learning (MBRL) can exploit simulated experience, but a simulator-to-target shift creates a model-selection problem: correcting the simulator and fitting the target directly can each fail under limited target data. We introduce the Model-Corrected World Model (MC-WM), which separates initial target data into disjoint fit, selection, and calibration partitions and deploys the family with lower standardized calibration risk. A learned confidence signal and deterministic validity predicates weight one-step imagined policy updates without rewriting physical rewards. We evaluate 540 unique reported run cells across three controlled Multi-Joint dynamics with Contact (MuJoCo) shifts; one exact-routing cell was repeated after a pre-deployment artifact gate, giving 541 completed executions.

Artificial Intelligence (cs.AI)

arXiv:2610.01001 [cs.AI]

(or arXiv:2610.01001v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2610.01001 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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