Constrained Best-Arm Learning with Uncertain Driving Inputs
Abstract
A constrained arm can appear admissible under a precisely estimated but inaccurate input distribution. Input-uncertain allocation distinguishes new evidence about inputs from new simulator replications. Evaluation should count both and independently assess the final joint requirement. A narrow conditional interval cannot make the driving model correct or guarantee performance outside represented inputs. 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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