Off-Policy Evaluation in Doubly Inhomogeneous Environments

This work aims to study off-policy evaluation (OPE) under scenarios where two key reinforcement learning (RL) assumptions—temporal stationarity and individual homogeneity are both violated. To handle the “double inhomogeneities”, we propose a class of latent factor models for the reward and transition functions, under which we develop a general OPE framework that consists of both model-based and model-free approaches. To our knowledge, this is the first article that develops statistically sound OPE methods in offline RL with double inhomogeneities. It contributes to a deeper understanding of OPE in environments, where standard RL assumptions are not met, and provides several practical approaches in these settings. We establish the theoretical properties of the proposed value estimators and empirically show that our approach outperforms state-of-the-art methods. Finally, we illustrate our method on a dataset from the Medical Information Mart for Intensive Care. An R implementation of the proposed procedure is available at https://github.com/ZeyuBian/2FEOPE. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.