DeepAI AI Chat
Log In Sign Up

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

by   Yonghyun Jeong, et al.

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.


page 1

page 3

page 6


Clear the Fog: Combat Value Assessment in Incomplete Information Games with Convolutional Encoder-Decoders

StarCraft, one of the most popular real-time strategy games, is a compel...

PGD: A Large-scale Professional Go Dataset for Data-driven Analytics

Lee Sedol is on a winning streak–does this legend rise again after the c...

Neuroevolution for RTS Micro

This paper uses neuroevolution of augmenting topologies to evolve contro...

Esports Athletes and Players: a Comparative Study

We present a comparative study of the players' and professional athletes...

Inferring Strategies from Limited Reconnaissance in Real-time Strategy Games

In typical real-time strategy (RTS) games, enemy units are visible only ...

A Dataset for StarCraft AI & an Example of Armies Clustering

This paper advocates the exploration of the full state of recorded real-...

Hidden Footprints: Learning Contextual Walkability from 3D Human Trails

Predicting where people can walk in a scene is important for many tasks,...