We propose a new procedural content generation method which learns iterative
level generators from a dataset of existing levels. The Path of Destruction
method, as we call it, views level generation as repair; levels are created by
iteratively repairing from a random starting level. The first step is to
generate an artificial dataset from the original set of levels by introducing
many different sequences of mutations to existing levels. In the generated
dataset, features are observations of destroyed levels and targets are the
specific actions that repair the mutated tile in the middle of the
observations. Using this dataset, a convolutional network is trained to map
from observations to their respective appropriate repair actions. The trained
network is then used to iteratively produce levels from random starting maps.
We demonstrate this method by applying it to generate unique and playable
tile-based levels for several 2D games (Zelda, Danger Dave, and Sokoban) and
vary key hyperparameters.

本文介绍了一种新的程序化内容生成方法，通过从现有关卡数据集中学习迭代式关卡生成器来生成唯一可玩的瓷砖式关卡，并且提出了所谓的毁灭路径方法，将关卡生成视为修复过程，训练一个卷积网络将被广泛改变的关卡的特征作为输入和其修复操作作为输出，从而迭代地生成关卡。