In this work, we consider the problem of estimating a behaviour policy for use in Off-Policy
Policy Evaluation (OPE) when the true behaviour policy is unknown. Via a series of …

Off-policy evaluation learns a target policy's value with a historical dataset generated by a
different behavior policy. In addition to a point estimate, many applications would benefit …

Off-policy evaluation (OPE) aims to estimate the return of a target policy using some pre-
collected observational data generated by a potentially different behavior policy. In many …

We consider off-policy evaluation (OPE), which evaluates the performance of a new policy
from observed data collected from previous experiments, without requiring the execution of …

Evaluating the value of a hypothetical target policy with only a logged dataset is important
but challenging. On the one hand, it brings opportunities for safe policy improvement under …

Abstract The Off-Policy Evaluation (OPE) problem consists in evaluating the performance of
new policies from the data collected by another one. OPE is crucial when evaluating a new …

Off-policy evaluation (OPE) is the task of estimating the expected reward of a given policy
based on offline data previously collected under different policies. Therefore, OPE is a key …

Off-policy evaluation (OPE) aims to accurately evaluate the performance of counterfactual
policies using only offline logged data. Although many estimators have been developed …

Off-policy policy estimators that use importance sampling (IS) can suffer from high variance
in long-horizon domains, and there has been particular excitement over new IS methods that …

We study high-confidence off-policy evaluation in the context of infinite-horizon Markov
decision processes, where the objective is to establish a confidence interval (CI) for the …