  Deep learning classifiers are known to be inherently vulnerable to
manipulation by intentionally perturbed inputs, named adversarial examples. In
this work, we establish that reinforcement learning techniques based on Deep
Q-Networks (DQNs) are also vulnerable to adversarial input perturbations, and
verify the transferability of adversarial examples across different DQN models.
Furthermore, we present a novel class of attacks based on this vulnerability
that enable policy manipulation and induction in the learning process of DQNs.
We propose an attack mechanism that exploits the transferability of adversarial
examples to implement policy induction attacks on DQNs, and demonstrate its
efficacy and impact through experimental study of a game-learning scenario.
