CROP: Certifying Robust Policies for Reinforcement Learning through Functional Smoothing


As reinforcement learning (RL) has achieved great success and been even adopted in safety-critical domains such as autonomous vehicles, a range of empirical studies have been conducted to improve its robustness against adversarial attacks. However, how to certify its robustness with theoretical guarantees still remains challenging. In this paper, we present the first unified framework CROP (Certifying Robust Policies for RL) to provide robustness certification on both action and reward levels. In particular, we propose two robustness certification criteria: robustness of per-state actions and lower bound of cumulative rewards. We then develop a local smoothing algorithm for policies derived from Q-functions to guarantee the robustness of actions taken along the trajectory; we also develop a global smoothing algorithm for certifying the lower bound of a finite-horizon cumulative reward, as well as a novel local smoothing algorithm to perform adaptive search in order to obtain tighter reward certification. Empirically, we apply CROP to evaluate several existing empirically robust RL algorithms, including adversarial training and different robust regularization, in four environments (two representative Atari games, Highway, and CartPole). Furthermore, by evaluating these algorithms against adversarial attacks, we demonstrate that our certifications are often tight. All experiment results are available at website

In ICLR 2022
Zijian Huang
Zijian Huang
Master of Science in Computer Science

Zijian Huang is a Master of Science in Computer Science student at University of Illinois at Urbana-Champaign(UIUC), supervised by Prof. Bo Li. His research interests include Machine Learning, Security and Computer Vision. Specifically, he is interested in the robustness of machine learning models. He is generally interested in the theoretical part of adversarial machine learning, like the certified robustness of reinforcement learning algorihtms, and also the pratical part, such as robustness of object detection models. Also, he has done some CV research projects, including 3D human pose detection and image/video synthesis.