We applied Generative Adversarial Networks (GANs) to learn a model of DOOM
levels from human-designed content. Initially, we analysed the levels and
extracted several topological features. Then, for each level, we extracted a
set of images identifying the occupied area, the height map, the walls, and the
position of game objects. We trained two GANs: one using plain level images,
one using both the images and some of the features extracted during the
preliminary analysis. We used the two networks to generate new levels and
compared the results to assess whether the network trained using also the
topological features could generate levels more similar to human-designed ones.
Our results show that GANs can capture intrinsic structure of DOOM levels and
appears to be a promising approach to level generation in first person shooter
games.

使用生成对抗网络学习了 DOOM 地图的模型，并比较了用于生成新地图的两种不同类型的 GAN（一种是只使用地图的图像，另一种则是同时使用图像和部分拓扑特征），研究结果表明 GAN 可以很好地捕捉 DOOM 地图内在的结构，并且是一种有前途的第一人称射击游戏地图生成方法。