  Sketching is more fundamental to human cognition than speech. Deep Neural
Networks (DNNs) have achieved the state-of-the-art in speech-related tasks but
have not made significant development in generating stroke-based sketches a.k.a
sketches in vector format. Though there are Variational Auto Encoders (VAEs)
for generating sketches in vector format, there is no Generative Adversarial
Network (GAN) architecture for the same. In this paper, we propose a standalone
GAN architecture SkeGAN and a VAE-GAN architecture VASkeGAN, for sketch
generation in vector format. SkeGAN is a stochastic policy in Reinforcement
Learning (RL), capable of generating both multidimensional continuous and
discrete outputs. VASkeGAN hybridizes a VAE and a GAN, in order to couple the
efficient representation of data by VAE with the powerful generating
capabilities of a GAN, to produce visually appealing sketches. We also propose
a new metric called the Ske-score which quantifies the quality of vector
sketches. We have validated that SkeGAN and VASkeGAN generate visually
appealing sketches by using Human Turing Test and Ske-score.
