Oracle-Efficient Pessimism: Offline Policy Optimization In Contextual Bandits
Lequn Wang, Akshay Krishnamurthy, Alex Slivkins
Abstract
We consider offline policy optimization (OPO) in contextual bandits, where one is given a fixed dataset of logged interactions. While pessimistic regularizers are typically used to mitigate distribution shift, prior implementations thereof are either specialized or computationally inefficient. We present the first \emph{general} oracle-efficient algorithm for pessimistic OPO: it reduces to supervised learning, leading to broad applicability. We obtain statistical guarantees analogous to those for prior pessimistic approaches. We instantiate our approach for both discrete and continuous actions and perform experiments in both settings, showing advantage over unregularized OPO across a wide range of configurations.
BibTeX
@InProceedings{pmlr-v238-wang24a,
title = {Oracle-Efficient Pessimism: Offline Policy Optimization In Contextual Bandits},
author = {Wang, Lequn and Krishnamurthy, Akshay and Slivkins, Alex},
booktitle = {Proceedings of The 27th International Conference on Artificial Intelligence and Statistics},
pages = {766--774},
year = {2024},
editor = {Dasgupta, Sanjoy and Mandt, Stephan and Li, Yingzhen},
volume = {238},
series = {Proceedings of Machine Learning Research},
month = {02--04 May},
publisher = {PMLR},
pdf = {https://proceedings.mlr.press/v238/wang24a/wang24a.pdf},
url = {https://proceedings.mlr.press/v238/wang24a.html},
abstract = {We consider offline policy optimization (OPO) in contextual bandits, where one is given a fixed dataset of logged interactions. While pessimistic regularizers are typically used to mitigate distribution shift, prior implementations thereof are either specialized or computationally inefficient. We present the first \emph{general} oracle-efficient algorithm for pessimistic OPO: it reduces to supervised learning, leading to broad applicability. We obtain statistical guarantees analogous to those for prior pessimistic approaches. We instantiate our approach for both discrete and continuous actions and perform experiments in both settings, showing advantage over unregularized OPO across a wide range of configurations.}
}