  Adversarial examples have been shown to exist for a variety of deep learning
architectures. Deep reinforcement learning has shown promising results on
training agent policies directly on raw inputs such as image pixels. In this
paper we present a novel study into adversarial attacks on deep reinforcement
learning polices. We compare the effectiveness of the attacks using adversarial
examples vs. random noise. We present a novel method for reducing the number of
times adversarial examples need to be injected for a successful attack, based
on the value function. We further explore how re-training on random noise and
FGSM perturbations affects the resilience against adversarial examples.
