  Collage is a creative art form that uses diverse material scraps as a base
unit to compose a single image. Although pixel-wise generation techniques can
reproduce a target image in collage style, it is not a suitable method due to
the solid stroke-by-stroke nature of the collage form. While some previous
works for stroke-based rendering produced decent sketches and paintings,
collages have received much less attention in research despite their popularity
as a style. In this paper, we propose a method for learning to make collages
via reinforcement learning without the need for demonstrations or collage
artwork data. We design the collage Markov Decision Process (MDP), which allows
the agent to handle various materials and propose a model-based soft
actor-critic to mitigate the agent's training burden derived from the
sophisticated dynamics of collage. Moreover, we devise additional techniques
such as active material selection and complexity-based multi-scale collage to
handle target images at any size and enhance the results' aesthetics by placing
relatively more scraps in areas of high complexity. Experimental results show
that the trained agent appropriately selected and pasted materials to
regenerate the target image into a collage and obtained a higher evaluation
score on content and style than pixel-wise generation methods. Code is
available at https://github.com/northadventure/CollageRL.
