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DefogGAN: Predicting Hidden Information in the StarCraft Fog of War with Generative Adversarial Nets
lib:d6898b7fce5324a4 (v1.0.0)
Authors: Yonghyun Jeong,Hyunjin Choi,Byoungjip Kim,Youngjune Gwon
ArXiv: 2003.01927
Document: PDF DOI
Artifact development version: GitHub
Abstract URL: https://arxiv.org/abs/2003.01927v2
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.
ArXiv: 2003.01927
Document: PDF DOI
Artifact development version: GitHub
Abstract URL: https://arxiv.org/abs/2003.01927v2
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.
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