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 …

We study high-confidence behavior-agnostic off-policy evaluation in reinforcement learning,
where the goal is to estimate a confidence interval on a target policy's value, given only …

We study the off-policy evaluation (OPE) problem in reinforcement learning with linear
function approximation, which aims to estimate the value function of a target policy based on …

To estimate the value functions of policies from exploratory data, most model-free off-policy
algorithms rely on importance sampling, where the use of importance sampling ratios often …

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 …

We develop a generic data-driven method for estimator selection in off-policy policy
evaluation settings. We establish a strong performance guarantee for the method, showing …

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 …

This article is concerned with constructing a confidence interval for a target policy's value
offline based on a pre-collected observational data in infinite horizon settings. Most of the …

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 …

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 …