PGN: A perturbation generation network against deep reinforcement
  learning
Xiangjuan Li Feifan Li Yang Li Quan Pan
Deep reinforcement learning has advanced greatly and applied in many areas.
In this paper, we explore the vulnerability of deep reinforcement learning by
proposing a novel generative model for creating effective adversarial examples
to attack the agent. Our proposed model can achieve both targeted attacks and
untargeted attacks. Considering the specificity of deep reinforcement learning,
we propose the action consistency ratio as a measure of stealthiness, and a new
measurement index of effectiveness and stealthiness. Experiment results show
that our method can ensure the effectiveness and stealthiness of attack
compared with other algorithms. Moreover, our methods are considerably faster
and thus can achieve rapid and efficient verification of the vulnerability of
deep reinforcement learning.