在生成对抗网络的潜在空间中照亮Mario场景
Illuminating Mario Scenes in the Latent Space of a Generative Adversarial Network
摘要：
生成对抗网络（GANs）正在迅速成为一种普遍存在的方法来程序化地生成视频游戏关卡。虽然GAN生成的级别在风格上类似于人类创作的示例，但人类设计师通常希望探索GANs的生成设计空间以提取有趣的级别。然而，人类设计师发现潜在向量不透明，宁愿沿着设计师指定的维度探索，例如敌人或障碍物的数量。我们建议使用最先进的质量多样性算法来优化连续空间，即具有方向变化算子和协方差矩阵自适应MAP-Elites的MAP-Elites，以有效地探索GAN的潜在空间，以提取在一组指定的游戏性措施中变化的水平。在《超级马里奥兄弟》的基准领域中，我们展示了设计师如何为我们的系统指定游戏性措施，并提取具有各种级别机制的高质量（可玩）级别，同时仍然保持与人类创作的示例的风格相似性。一项在线用户研究显示了自动生成的关卡的不同机制如何影响其感知难度和外观的主观评分。
英文摘要：
Generative adversarial networks (GANs) are quickly becoming a ubiquitous approach to procedurally generating video game levels. While GAN generated levels are stylistically similar to human-authored examples, human designers often want to explore the generative design space of GANs to extract interesting levels. However, human designers find latent vectors opaque and would rather explore along dimensions the designer specifies, such as number of enemies or obstacles. We propose using state-of-the-art quality diversity algorithms designed to optimize continuous spaces, i.e. MAP-Elites with a directional variation operator and Covariance Matrix Adaptation MAP-Elites, to efficiently explore the latent space of a GAN to extract levels that vary across a set of specified gameplay measures. In the benchmark domain of Super Mario Bros, we demonstrate how designers may specify gameplay measures to our system and extract high-quality (playable) levels with a diverse range of level mechanics, while still maintaining stylistic similarity to human authored examples. An online user study shows how the different mechanics of the automatically generated levels affect subjective ratings of their perceived difficulty and appearance.
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