Robust Bellman Updates after Offline Values Are Aligned
Abstract
Aligning values from heterogeneous dynamics and computing a robust update address different uncertainties. Evaluation should preserve behavior support and the ambiguity radius, checking their effects separately. Independent environment outcomes can assess the resulting policy. A computationally accurate robust update cannot compensate for values whose meaning was incompatible before the update was applied. This focused synthesis develops an empirical comparison that preserves the terminal action, observation process, and total verification cost. Its proposed tests are prospective; no new simulation run, model-training result, or intervention effect is reported.
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