LPaintB: Learning to Paint from Self-Supervision

Understand

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.