Various methods to construct saliency maps are evaluated quantitatively with regards to their correctness. This is done in a reinforcement learning setting with DQN and Atari Breakout. The considered saliency map methods include multiple gradient-based and perturbation-based approaches. As means to evaluate them quantitatively, deletion is applied to measure how quickly the performance dwindles.