PaintBot: A Reinforcement Learning Approach for Natural Media Painting

Understand

We propose a new automated digital painting framework, based on a painting agent trained through reinforcement learning.

• To synthesize an image, the agent selects a sequence of continuous-valued actions representing primitive painting strokes, which are accumulated on a digital canvas.
• Action selection is guided by a given reference image, which the agent attempts to replicate subject to the limitations of the action space and the agent's learned policy.
• The painting agent policy is determined using a variant of proximal policy optimization reinforcement learning.