Beyond Black-Box Advice: Learning-Augmented Algorithms for MDPs with Q-Value Predictions
Tongxin Li, Yiheng Lin, Shaolei Ren, Adam Wierman
Abstract
We study the tradeoff between consistency and robustness in the context of a single-trajectory time-varying Markov Decision Process (MDP) with untrusted machine-learned advice. Our work departs from the typical approach of treating advice as coming from black-box sources by instead considering a setting where additional information about how the advice is generated is available. We prove a first-of-its-kind consistency and robustness tradeoff given Q-value advice under a general MDP model that includes both continuous and discrete state/action spaces. Our results highlight that utilizing Q-value advice enables dynamic pursuit of the better of machine-learned advice and a robust baseline, thus result in near-optimal performance guarantees, which provably improves what can be obtained solely with black-box advice.
BibTeX
@inproceedings{
li2023beyond,
title={Beyond Black-Box Advice: Learning-Augmented Algorithms for {MDP}s with Q-Value Predictions},
author={Tongxin Li and Yiheng Lin and Shaolei Ren and Adam Wierman},
booktitle={Thirty-seventh Conference on Neural Information Processing Systems},
year={2023},
url={https://openreview.net/forum?id=RACcp8Zbr9}
}