# GAN Path Finder: Preliminary results

## TL;DR

This paper explores using generative neural networks for 2D path planning in static environments, treating the environment as an image and leveraging deep learning advances, with preliminary promising results.

## Contribution

It introduces a novel approach of applying generative neural networks to 2D path planning, demonstrating initial feasibility and encouraging further research.

## Key findings

- Preliminary results show potential of neural networks for path finding.
- Treating environment maps as images enables deep learning application.
- Further exploration is justified based on initial success.

## Abstract

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.