Learning To Paint with RL

This problem aims at training an agent to generate paintings using stroke based sequential actions. We plan to break down the given target image into a set number of strokes, using a RL agent, which can generate a painted image. Compare On Policy (PPO) and Off Policy (DDPG) methods for solving the problem. We also aim at incorporating style transfer while painting.

Applications

  • Learning to Paint is a complex process and often requires considerable time and effort to master the art
  • Making an AI agent learn how to reproduce an existing painting by decomposing it into a sequence of brush strokes across the canvas.
  • The training process of such an agent does not require any considerable vast experience.
  • The problem statement has multiple applications in real life such as handwriting reproduction, painting restoration etc.
  • Additionally using concepts from neural style transfer, the reward for the agent can be adjusted in order to incorporate style elements from a reference image.

For results, and presentation see this

Quickstart

For training and testing on MNIST

Open In Colab

For training and testing on celeb

Open In Colab

For training Style transfer RL on MNIST

Open In Colab

Style transfer

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Demo

Style Transfer on MNIST

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Using DDPG on CelebA

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Using PPO on MNIST

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Version Info

Version : 1.0.0

Authors

Acknowledgments