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

Authors

  • Yonghyun Jeong Samsung SDS
  • Hyunjin Choi Samsung SDS
  • Byoungjip Kim Samsung SDS
  • Youngjune Gwon Samsung SDS

DOI:

https://doi.org/10.1609/aaai.v34i04.5853

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.

Downloads

Published

2020-04-03

How to Cite

Jeong, Y., Choi, H., Kim, B., & Gwon, Y. (2020). DefogGAN: Predicting Hidden Information in the StarCraft Fog of War with Generative Adversarial Nets. Proceedings of the AAAI Conference on Artificial Intelligence, 34(04), 4296-4303. https://doi.org/10.1609/aaai.v34i04.5853

Issue

Section

AAAI Technical Track: Machine Learning