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arXiv 2007.08746cs.LGstat.ML
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使用变分自编码器的基于序列段的电平生成和混合

Sequential Segment-based Level Generation and Blending using Variational Autoencoders

Northeastern University（东北大学）

Anurag Sarkar, Seth Cooper

备注：
8 pages, 9 figures, 11th Workshop on Procedural Content Generation (PCG 2020)
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摘要：

使用诸如VAE和GANs的潜在变量模型的现有级别生成方法在分段中这样做，并且通过将这些单独生成的分段拼接在一起来产生最终级别。在本文中，我们建立在这些方法的基础上，通过训练VAE来学习分段生成的顺序模型，使得生成的分段在逻辑上遵循先前的分段。通过进一步将VAE与分类器相结合，该分类器确定是否将所生成的段放置在前一段的顶部、底部、左侧或右侧，我们获得了一个流水线，该流水线能够生成任意长的级别，这些级别在这四个方向中的任何一个方向上前进，并且由逻辑上彼此跟随的段组成。除了生成更连贯的非固定长度的关卡之外，该方法还能够隐式地混合来自不具有相似方向的单独游戏的关卡。我们使用《超级马里奥兄弟》中的关卡来演示我们的方法，Kid Icarus和Mega Man，表明我们的方法产生的水平比以前基于潜在变量的方法更连贯，并且能够在游戏中混合水平。

英文摘要：

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.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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