Offline Policy Evaluation

Offline policy evaluation (OPE) aims to assess the performance of a reinforcement learning policy using only pre-collected data, avoiding costly or risky online experimentation. Current research focuses on improving the accuracy and robustness of OPE estimators, particularly addressing challenges like distributional shift and confounding factors, often employing techniques like importance sampling, ensemble methods, and generative models (e.g., diffusion models, normalizing flows) to enhance estimation. The development of reliable OPE methods is crucial for safe and efficient deployment of reinforcement learning in high-stakes applications such as healthcare and robotics, enabling informed policy selection and optimization without direct environmental interaction.