Si, N., Zhang, F., Zhou, Z. & Blanchet, J.. (2020). Distributionally Robust Policy Evaluation and Learning in Offline Contextual Bandits. Proceedings of the 37th International Conference on Machine Learning, in Proceedings of Machine Learning Research 119:8884-8894 Available from https://proceedings.mlr.press/v119/si20a.html.
Abstract
Policy learning using historical observational data is an important problem that has found widespread applications. However, existing literature rests on the crucial assumption that the future environment where the learned policy will be deployed is the same as the past environment that has generated the data {–} an assumption that is often false or too coarse an approximation. In this paper, we lift this assumption and aim to learn a distributionally robust policy with bandit observational data. We propose a novel learning algorithm that is able to learn a robust policy to adversarial perturbations and unknown covariate shifts. We first present a policy evaluation procedure in the ambiguous environment and also give a heuristic algorithm to solve the distributionally robust policy learning problems efficiently. Additionally, we provide extensive simulations to demonstrate the robustness of our policy.
Authors
Nian Si, Fan Zhang, Zhengyuan Zhou, Jose Blanchet
Publication date
2020/11/21
Conference
International Conference on Machine Learning
Pages
8884-8894
Publisher
PMLR
