Deep-Attack over the Deep Reinforcement Learning

TL;DR

This paper introduces a reinforcement learning-based framework for more effective and stealthy adversarial attacks on deep reinforcement learning systems, along with a new evaluation metric, demonstrating improved attack performance and robustness.

Contribution

It proposes a novel RL-based attack framework that balances effectiveness and stealth, and introduces a new metric for attack evaluation, addressing limitations of existing methods.

Findings

The proposed model effectively attacks deep RL systems.

The new metric accurately evaluates attack performance.

The attack model demonstrates transferability and robustness.

Abstract

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.