# GAN Path Finder: Preliminary results

arXiv: https://arxiv.org/abs/1908.01499 · HTML: https://mlsanity.com/paper/1908.01499/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Aug 2019.

## Built on by (most cited first, 1 of 1)

- [TransPath: Learning Heuristics For Grid-Based Pathfinding via Transformers](https://mlsanity.com/paper/2212.11730/) (2022)

## Abstract (arXiv)

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.