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 …

Off-policy evaluation (OPE) in reinforcement learning is notoriously difficult in long-and
infinite-horizon settings due to diminishing overlap between behavior and target policies. In …

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 evaluation (OPE) in reinforcement learning is notoriously difficult in long-and
infinite-horizon settings due to diminishing overlap between behavior and target policies. In …

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 concerned with evaluating a new target policy using offline
data generated by a potentially different behavior policy. It is critical in a number 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 …

In reinforcement learning, off-policy evaluation (OPE) is the problem of estimating the
expected return of an evaluation policy given a fixed dataset that was collected by running …

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 off-policy evaluation (OPE) of contextual bandit policies for large discrete action
spaces where conventional importance-weighting approaches suffer from excessive …