Artist Agent: A Reinforcement Learning Approach to Automatic Stroke   Generation in Oriental Ink Painting
  Oriental ink painting, called Sumi-e, is one of the most appealing painting styles that has attracted artists around the world. Major challenges in computer-based Sumi-e simulation are to abstract complex scene information and draw smooth and natural brush strokes. To automatically find such strokes, we propose to model the brush as a reinforcement learning agent, and learn desired brush-trajectories by maximizing the sum of rewards in the policy search framework. We also provide elaborate design of actions, states, and rewards tailored for a Sumi-e agent. The effectiveness of our proposed approach is demonstrated through simulated Sumi-e experiments. 
Ning Xie (Tokyo Institute of Technology), Hirotaka Hachiya (Tokyo   Institute of Technology), Masashi Sugiyama (Tokyo Instit...
cs.LG cs.GR stat.ML