Adversarial examples have been shown to exist for a variety of deep learning architectures. Deep reinforcement learning has shown promising results on training agent policies directly on raw inputs such as image pixels. In this paper we present a novel study into adversarial attacks on deep reinforcement learning polices. We compare the effectiveness of the attacks using adversarial examples vs. random noise. We present a novel method for reducing the number of times adversarial examples need to be injected for a successful attack, based on the value function. We further explore how re-training on random noise and FGSM perturbations affects the resilience against adversarial examples.
@article{arxiv.1705.06452,
title = {Delving into adversarial attacks on deep policies},
author = {Jernej Kos and Dawn Song},
journal= {arXiv preprint arXiv:1705.06452},
year = {2017}
}