Magnetic resonance imaging (MRI) is an important but relatively slow imaging modality. MRI scan time can be reduced by undersampling the data and reconstructing the image using techniques such as compressed sensing or deep learning. However, the optimal undersampling pattern with respect to image quality and image reconstruction technique remains an open question. To approach this problem, our goal is to use reinforcement learning to train an agent to learn an optimal sampling policy. The image reconstruction technique is the environment and the reward is based upon an image metric.