论文标题
使用高斯混合物VAE的游戏水平聚类和生成
Game Level Clustering and Generation using Gaussian Mixture VAEs
论文作者
论文摘要
变异自动编码器（VAE）已证明能够生成游戏级别，但需要手动探索学习的潜在空间，以生成具有所需属性的输出。尽管有条件的VAE通过允许在标签上进行生成来解决这一问题，但必须在培训期间提供此类标签，因此需要先验知识，这可能并不总是可用。在本文中，我们应用高斯混合物VAE（GMVAE），这是VAE的一种变体，它在潜在空间上施加了高斯（GM）的混合物，这与施加单峰高斯的常规VAE不同。这使GMVAE可以使用GM的组件以无监督的方式聚类，然后使用学习的组件生成新的级别。我们以Super Mario Bros.，Kid Icarus和Mega Man的水平展示了我们的方法。我们的结果表明，学到的组件发现和聚类水平结构和模式，可用于生成具有所需特征的水平。
Variational autoencoders (VAEs) have been shown to be able to generate game levels but require manual exploration of the learned latent space to generate outputs with desired attributes. While conditional VAEs address this by allowing generation to be conditioned on labels, such labels have to be provided during training and thus require prior knowledge which may not always be available. In this paper, we apply Gaussian Mixture VAEs (GMVAEs), a variant of the VAE which imposes a mixture of Gaussians (GM) on the latent space, unlike regular VAEs which impose a unimodal Gaussian. This allows GMVAEs to cluster levels in an unsupervised manner using the components of the GM and then generate new levels using the learned components. We demonstrate our approach with levels from Super Mario Bros., Kid Icarus and Mega Man. Our results show that the learned components discover and cluster level structures and patterns and can be used to generate levels with desired characteristics.
