论文标题
破坏道路：使用小数据集学习迭代级别的生成器
Path of Destruction: Learning an Iterative Level Generator Using a Small Dataset
论文作者
论文摘要
我们提出了一种新的程序内容生成方法，该方法从现有级别的数据集中学习迭代级别的生成器。我们称之为破坏方法的道路，将水平生成视为维修；级别是通过从随机起始级别进行迭代修复而创建的。第一步是通过将许多不同的突变序列引入现有级别，从而从原始级别集生成人工数据集。在生成的数据集中，特征是对破坏水平的观察，目标是修复观测值中间的突变瓷砖的特定动作。使用此数据集，训练了一个卷积网络，可以从观察值到各自的适当维修操作。然后，训练有素的网络用于从随机起始地图中迭代产生水平。我们通过将其应用于为多个2D游戏（Zelda，Danger Dave和Sokoban）和不同关键的超级参数生成独特且可播放的基于瓷砖的水平来证明这种方法。
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.
