  Deep neural networks have recently succeeded in digital halftoning using
vanilla convolutional layers with high parallelism. However, existing deep
methods fail to generate halftones with a satisfying blue-noise property and
require complex training schemes. In this paper, we propose a halftoning method
based on multi-agent deep reinforcement learning, called HALFTONERS, which
learns a shared policy to generate high-quality halftone images. Specifically,
we view the decision of each binary pixel value as an action of a virtual
agent, whose policy is trained by a low-variance policy gradient. Moreover, the
blue-noise property is achieved by a novel anisotropy suppressing loss
function. Experiments show that our halftoning method produces high-quality
halftones while staying relatively fast.
