论文标题
基于连续段的级别生成和使用变量自动编码器混合
Sequential Segment-based Level Generation and Blending using Variational Autoencoders
论文作者
论文摘要
现有的使用潜在变量模型（例如VAE和GAN）在细分市场中生成的水平生成方法，并通过将这些单独生成的细分拼接在一起而产生最终水平。在本文中，我们通过训练VAE学习段生成的顺序模型，从而从逻辑上遵循以前的细分市场，从而构建了这些方法。通过将VAE与一个分类器相结合，该分类器确定是否将生成的段放在上一段的顶部，底部，左或右侧，我们获得了一条管道，该管道能够在这四个方向中的任何一个方向上进行任意长的较长级别，并且由逻辑上互相跟随的段组成。除了产生更连贯的非固定长度级别外，该方法还可以隐含地融合不同游戏的级别，这些级别没有相似的方向。我们使用Super Mario Bros.，Kid Icarus和Mega Man的水平来证明我们的方法，表明我们的方法产生的水平比以前的基于潜在变量的方法更连贯，并且能够在游戏中混合水平。
Existing methods of level generation using latent variable models such as VAEs and GANs do so in segments and produce the final level by stitching these separately generated segments together. In this paper, we build on these methods by training VAEs to learn a sequential model of segment generation such that generated segments logically follow from prior segments. By further combining the VAE with a classifier that determines whether to place the generated segment to the top, bottom, left or right of the previous segment, we obtain a pipeline that enables the generation of arbitrarily long levels that progress in any of these four directions and are composed of segments that logically follow one another. In addition to generating more coherent levels of non-fixed length, this method also enables implicit blending of levels from separate games that do not have similar orientation. We demonstrate our approach using levels from Super Mario Bros., Kid Icarus and Mega Man, showing that our method produces levels that are more coherent than previous latent variable-based approaches and are capable of blending levels across games.
