In deep reinforcement learning (DRL), software agents based on deep neural networks are used to explore environments in order to maximise a reward (e.g. score in a video game). Here, DRL was used to control a virtual MRI scanner and actively interpret acquired data. An environment was constructed in which correctly determining the shape of a phantom was rewarded with a high score, and penalised by increasing acquisition time. Following training, the algorithm had learnt to acquire sparse images, assigning TE, TR and flip angles that enabled it to act as an edge detector and deduce shape with 99.8% accuracy.