Learning to Paint With Model-based Deep Reinforcement Learning [article]

Zhewei Huang, Wen Heng, Shuchang Zhou
2019 arXiv   pre-print
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
more » ... of human painters or stroke tracking data. The code is available at https://github.com/hzwer/ICCV2019-LearningToPaint.
arXiv:1903.04411v3 fatcat:j5dvpxwanzc4vda3aue3j4p6t4