Deep Actor-Critics with Tight Risk Certificates

TL;DR

This paper develops tight risk certificates for deep actor-critic algorithms using minimal validation data and recursive PAC-Bayes bounds, enabling better generalization guarantees for real-world deployment.

Contribution

It introduces a novel method combining minimal evaluation data with recursive PAC-Bayes bounds to produce practical risk certificates for deep actor-critic algorithms.

Findings

Risk certificates are tight enough for practical use.

Minimal evaluation data suffices for accurate risk estimation.

Method applies across various tasks and policy levels.

Abstract

Deep actor-critic algorithms have reached a level where they influence everyday life. They are a driving force behind continual improvement of large language models through user feedback. However, their deployment in physical systems is not yet widely adopted, mainly because no validation scheme fully quantifies their risk of malfunction. We demonstrate that it is possible to develop tight risk certificates for deep actor-critic algorithms that predict generalization performance from validation-time observations. Our key insight centers on the effectiveness of minimal evaluation data. A small feasible set of evaluation roll-outs collected from a pretrained policy suffices to produce accurate risk certificates when combined with a simple adaptation of PAC-Bayes theory. Specifically, we adopt a recently introduced recursive PAC-Bayes approach, which splits validation data into portions and recursively builds PAC-Bayes bounds on the excess loss of each portion's predictor, using the predictor from the previous portion as a data-informed prior. Our empirical results across multiple locomotion tasks, actor-critic methods, and policy expertise levels demonstrate risk certificates tight enough to be considered for practical use.