Reliable Off-policy Evaluation for Reinforcement Learning

In a sequential decision-making problem, off-policy evaluation (OPE) estimates the expected cumulative reward of a target policy using logged transition data generated from a different behavior policy, without execution of the target policy. Reinforcement learning in high-stake environments, such as healthcare and education, is often limited to off-policy settings due to safety or ethical concerns, or inability of exploration. Hence it is imperative to quantify the uncertainty of the off-policy estimate before deployment of the target policy. In this paper, we propose a novel framework that provides robust and optimistic cumulative reward estimates with statistical guarantees and develop non-asymptotic as well as asymptotic confidence intervals for OPE, leveraging methodologies from distributionally robust optimization. Our theoretical results are also supported by empirical analysis.