Raphael Kim

When deployment lacks key variables that training logs record in full, runtime confounding can push a reinforcement-learning policy off course over time. The work places the runtime restriction only in the numerator of an importance ratio and estimates nuisance functions from the full data, yielding an asymptotically normal off-policy estimator that attains the semiparametric efficiency bound, a policy-learning regret bound that differs from the full-information optimum by an irreducible runtime gap, and a sample-splitting test for that gap.

When deployment lacks key variables that training logs record in full, runtime confounding can push a reinforcement-learning policy off course over time. The work places the runtime restriction only in the numerator of an importance ratio and estimates nuisance functions from the full data, yielding an asymptotically normal off-policy estimator that attains the semiparametric efficiency bound, a policy-learning regret bound that differs from the full-information optimum by an irreducible runtime gap, and a sample-splitting test for that gap.