This work finds strong adversarial examples for Deep Q Networks which are famous deep reinforcement learning models. We combine two subproblems of finding adversarial examples in deep reinforcement learning: finding states to perturb and determining how much to perturb. Therefore, the attack can jointly optimize this problem. Further, we trained Deep Q Networks to play Atari games: Breakout and Space Invader. Then, we used our attack to find adversarial examples on those games. As a result, we can achieve state-of-the-art results and showed that our attack is natural and stealthy. Paper: [ bib | pdf ]
![](https://baylor.ai/wp-content/uploads/2022/02/Screenshot-from-2022-02-17-12-51-00.png)