# DefogGAN: Predicting Hidden Information in the StarCraft Fog of War with Generative Adversarial Nets

arXiv: https://arxiv.org/abs/2003.01927 · HTML: https://mlsanity.com/paper/2003.01927/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Mar 2020.

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

- [Unsupervised Hebbian Learning on Point Sets in StarCraft II](https://mlsanity.com/paper/2207.12323/) (2022)

## Abstract (arXiv)

We propose DefogGAN, a generative approach to the problem of inferring state information hidden in the fog of war for real-time strategy (RTS) games. Given a partially observed state, DefogGAN generates defogged images of a game as predictive information. Such information can lead to create a strategic agent for the game. DefogGAN is a conditional GAN variant featuring pyramidal reconstruction loss to optimize on multiple feature resolution scales.We have validated DefogGAN empirically using a large dataset of professional StarCraft replays. Our results indicate that DefogGAN can predict the enemy buildings and combat units as accurately as professional players do and achieves a superior performance among state-of-the-art defoggers.