  We show how to teach machines to paint like human painters, who can use a
small number of strokes to create fantastic paintings. By employing a neural
renderer in model-based Deep Reinforcement Learning (DRL), our agents learn to
determine the position and color of each stroke and make long-term plans to
decompose texture-rich images into strokes. Experiments demonstrate that
excellent visual effects can be achieved using hundreds of strokes. The
training process does not require the experience of human painters or stroke
tracking data. The code is available at
https://github.com/hzwer/ICCV2019-LearningToPaint.

Computer Vision and Pattern Recognition;Artificial Intelligence