  We present a novel reinforcement learning-based natural media painting
algorithm. Our goal is to reproduce a reference image using brush strokes and
we encode the objective through observations. Our formulation takes into
account that the distribution of the reward in the action space is sparse and
training a reinforcement learning algorithm from scratch can be difficult. We
present an approach that combines self-supervised learning and reinforcement
learning to effectively transfer negative samples into positive ones and change
the reward distribution. We demonstrate the benefits of our painting agent to
reproduce reference images with brush strokes. The training phase takes about
one hour and the runtime algorithm takes about 30 seconds on a GTX1080 GPU
reproducing a 1000x800 image with 20,000 strokes.
