Towards Robust Off-policy Learning for Runtime Uncertainty

TL;DR

This paper introduces a robust off-policy learning framework that accounts for runtime uncertainties by perturbing estimators adversarially, enhancing policy reliability during real-time deployment.

Contribution

It proposes a novel adversarial perturbation approach to improve off-policy estimators' robustness against runtime uncertainties, applicable to multiple existing methods.

Findings

The methods are theoretically justified for robustness.

Effective in simulation and real-world experiments.

Enhances policy stability under unexpected runtime conditions.

Abstract

Off-policy learning plays a pivotal role in optimizing and evaluating policies prior to the online deployment. However, during the real-time serving, we observe varieties of interventions and constraints that cause inconsistency between the online and offline settings, which we summarize and term as runtime uncertainty. Such uncertainty cannot be learned from the logged data due to its abnormality and rareness nature. To assert a certain level of robustness, we perturb the off-policy estimators along an adversarial direction in view of the runtime uncertainty. It allows the resulting estimators to be robust not only to observed but also unexpected runtime uncertainties. Leveraging this idea, we bring runtime-uncertainty robustness to three major off-policy learning methods: the inverse propensity score method, reward-model method, and doubly robust method. We theoretically justify the robustness of our methods to runtime uncertainty, and demonstrate their effectiveness using both the simulation and the real-world online experiments.