4 research outputs found
DefogGAN: Predicting Hidden Information in the StarCraft Fog of War with Generative Adversarial Nets
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