  The idea of style transfer has largely only been explored in image-based
tasks, which we attribute in part to the specific nature of loss functions used
for style transfer. We propose a general formulation of style transfer as an
extension of generative adversarial networks, by using a discriminator to
regularize a generator with an otherwise separate loss function. We apply our
approach to the task of learning to play chess in the style of a specific
player, and present empirical evidence for the viability of our approach.
