  Recent adversarial attack developments have made reinforcement learning more
vulnerable, and different approaches exist to deploy attacks against it, where
the key is how to choose the right timing of the attack. Some work tries to
design an attack evaluation function to select critical points that will be
attacked if the value is greater than a certain threshold. This approach makes
it difficult to find the right place to deploy an attack without considering
the long-term impact. In addition, there is a lack of appropriate indicators of
assessment during attacks. To make the attacks more intelligent as well as to
remedy the existing problems, we propose the reinforcement learning-based
attacking framework by considering the effectiveness and stealthy
spontaneously, while we also propose a new metric to evaluate the performance
of the attack model in these two aspects. Experimental results show the
effectiveness of our proposed model and the goodness of our proposed evaluation
metric. Furthermore, we validate the transferability of the model, and also its
robustness under the adversarial training.

Machine Learning;Artificial Intelligence;Cryptography and Security;Computers and Society