Artist Agent: A Reinforcement Learning Approach to Automatic Stroke Generation in Oriental Ink Painting

Understand

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.