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

2020/03/04 by Yonghyun Jeong, Jeong, Yonghyun, Hyun‐Jin Choi +5
Computer Science · Social Sciences · #Artificial Intelligence in Games #Digital Games and Media #FOS: Computer and information sciences #Generative Adversarial Networks and Image Synthesis #Machine Learning (cs.LG) #Machine Learning (stat.ML)

paper · pdf · doi:10.48550/arxiv.2003.01927

openalex publication_date 2020/03/04 · openalex created_date 2020/03/13 · openalex updated_date 2026/07/28

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.

Citations

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Energy-based Generative Adversarial Network
• Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm

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