  2D path planning in static environment is a well-known problem and one of the
common ways to solve it is to 1) represent the environment as a grid and 2)
perform a heuristic search for a path on it. At the same time 2D grid resembles
much a digital image, thus an appealing idea comes to being -- to treat the
problem as an image generation task and to solve it utilizing the recent
advances in deep learning. In this work we make an attempt to apply a
generative neural network as a path finder and report preliminary results,
convincing enough to claim that this direction of research is worth further
exploration.
