Doodling is a useful and common intelligent skill that people can learn and
master. In this work, we propose a two-stage learning framework to teach a
machine to doodle in a simulated painting environment via Stroke Demonstration
and deep Q-learning (SDQ). The developed system, Doodle-SDQ, generates a
sequence of pen actions to reproduce a reference drawing and mimics the
behavior of human painters. In the first stage, it learns to draw simple
strokes by imitating in supervised fashion from a set of strokeaction pairs
collected from artist paintings. In the second stage, it is challenged to draw
real and more complex doodles without ground truth actions; thus, it is trained
with Qlearning. Our experiments confirm that (1) doodling can be learned
without direct stepby- step action supervision and (2) pretraining with stroke
demonstration via supervised learning is important to improve performance. We
further show that Doodle-SDQ is effective at producing plausible drawings in
different media types, including sketch and watercolor.

通过 Stroke Demonstration 和 deep Q-learning (SDQ) 提出了 Doodle-SDQ 两阶段学习框架，该系统可以产生一系列的笔触行为来复制参考图，并模仿人类画家的行为。实验结果证实了，(1) 在没有直接的逐步行动监督的情况下可以学会 doodling 的技能；(2) Stroke Demonstration 通过监督学习的预训练对于提高性能是很重要的。同时 Doodle-SDQ 还可以有效地在不同类型的媒介中产生逼真的图像，包括草图和水彩画。