Recent developments in machine learning techniques have allowed automatic generation of video game levels that are stylistically similar to human-designed examples. While the output of machine learning models such as generative adversarial networks (GANs) is notoriously hard to control, the recently proposed latent variable evolution (LVE) technique searches the space of GAN parameters to generate outputs that optimize some objective performance metric, such as level playability. However, the question remains on how to automatically generate a diverse range of high-quality solutions based on a prespecified set of desired characteristics. We introduce a new method called latent space illumination (LSI), which uses state-of-the-art quality diversity algorithms designed to optimize in continuous spaces, i.e., MAP-Elites with a directional variation operator and Covariance Matrix Adaptation MAP-Elites, to effectively search the parameter space of theGAN along a set of multiple level mechanics. We show the performance of LSI algorithms in three experiments in SuperMario Bros., a benchmark domain for procedural content generation. Results suggest that LSI generates sets of Mario levels that are reliably mechanically diverse as well as playable.

中文翻译：

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在生成对抗网络的潜在空间中照亮马里奥场景

机器学习技术的最新发展允许自动生成在风格上类似于人类设计示例的视频游戏关卡。众所周知，生成对抗网络 (GAN) 等机器学习模型的输出难以控制，但最近提出的潜在变量进化 (LVE) 技术搜索 GAN 参数空间以生成优化某些客观性能指标的输出，例如级别可玩性。然而，问题仍然是如何根据一组预先指定的所需特征自动生成各种高质量的解决方案。我们引入了一种称为潜在空间照明 (LSI) 的新方法，该方法使用旨在在连续空间中进行优化的最先进的质量分集算法，即，具有方向变化算子和协方差矩阵自适应 MAP-Elites 的 MAP-Elites，可以沿着一组多级机制有效地搜索 GAN 的参数空间。我们在 SuperMario Bros. 的三个实验中展示了 LSI 算法的性能，这是一个程序内容生成的基准域。结果表明，LSI 生成了一组可靠的机械多样性和可玩性的马里奥关卡。