DefogGAN: Predicting Hidden Information in the StarCraft Fog of War with Generative Adversarial Nets
Abstract
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.
Cite
Text
Jeong et al. "DefogGAN: Predicting Hidden Information in the StarCraft Fog of War with Generative Adversarial Nets." AAAI Conference on Artificial Intelligence, 2020. doi:10.1609/AAAI.V34I04.5853Markdown
[Jeong et al. "DefogGAN: Predicting Hidden Information in the StarCraft Fog of War with Generative Adversarial Nets." AAAI Conference on Artificial Intelligence, 2020.](https://mlanthology.org/aaai/2020/jeong2020aaai-defoggan/) doi:10.1609/AAAI.V34I04.5853BibTeX
@inproceedings{jeong2020aaai-defoggan,
title = {{DefogGAN: Predicting Hidden Information in the StarCraft Fog of War with Generative Adversarial Nets}},
author = {Jeong, Yonghyun and Choi, Hyunjin and Kim, Byoungjip and Gwon, Youngjune},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2020},
pages = {4296-4303},
doi = {10.1609/AAAI.V34I04.5853},
url = {https://mlanthology.org/aaai/2020/jeong2020aaai-defoggan/}
}