Generative Adversarial Networks (GANs) are capable of generating convincing imitations of elements from a training set, but the distribution of elements in the training set affects to difficulty of properly training the GAN and the quality of the outputs it produces. This paper looks at six different GANs trained on different subsets of data from the game Lode Runner. The quality diversity algorithm MAP-Elites was used to explore the set of quality levels that could be produced by each GAN, where quality was defined as being beatable and having the longest solution path possible. Interestingly, a GAN trained on only 20 levels generated the largest set of diverse beatable levels while a GAN trained on 150 levels generated the smallest set of diverse beatable levels, thus challenging the notion that more is always better when training GANs.

中文翻译：

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启发各种生成对抗网络产生的可击败的Lode赛跑者水平的空间

生成对抗网络（GAN）能够从训练集中生成令人信服的元素模仿，但是训练集中元素的分布会影响正确训练GAN的难度及其产生的输出质量。本文着眼于对来自Lode Runner游戏的不同数据子集进行训练的六个不同GAN。使用质量多样性算法MAP-Elites来探索每个GAN可以产生的一组质量水平，其中质量被定义为可击败且具有最长的求解路径。有趣的是，仅训练20个级别的GAN生成了最大的一组多样的可击败级别，而训练了150个级别的GAN生成了最小的一组各种可击败级别，因此挑战了这样一种观念，即训练GAN时总是越多越好。