Towards Robust Off-policy Learning for Runtime Uncertainty
This addresses robustness issues in off-policy learning for real-time deployment, but it is incremental as it adapts existing methods rather than introducing a new paradigm.
The paper tackles the problem of runtime uncertainty causing inconsistency between offline and online settings in off-policy learning, proposing a method to perturb estimators along an adversarial direction to achieve robustness, with effectiveness demonstrated through simulation and real-world experiments.
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.