Machine learning for procedural content generation has recently become an active area of research. Levels vary in both form and function and are mostly unrelated to each other across games. This has made it difficult to assemble suitably large datasets to bring machine learning to level design in the same way as it's been used for image generation. Here we propose Generative Playing Networks which design levels for itself to play. The algorithm is built in two parts; an agent that learns to play game levels, and a generator that learns the distribution of playable levels. As the agent learns and improves its ability, the space of playable levels, as defined by the agent, grows. The generator targets the agent's playability estimates to then update its understanding of what constitutes a playable level. We call this process of learning the distribution of data found through self-discovery with an environment, self-supervised inductive learning. Unlike previous approaches to procedural content generation, Generative Playing Networks are end-to-end differentiable and do not require human-designed examples or domain knowledge. We demonstrate the capability of this framework by training an agent and level generator for a 2D dungeon crawler game.

翻译：程序内容生成的机床学习最近已成为一个活跃的研究领域。 级别在形式和功能上各不相同, 并且大多在游戏中互不相干。 这使得很难将合适的大型数据集组组装成合适的大型数据集, 使机器学习与图像生成使用相同的方式将机器学习提升到设计水平。 我们在这里建议创建设计自身玩耍水平的“ 创造游戏网络 ” 。 算法建于两个部分; 一个学会玩游戏水平的代理器, 以及一个学习可播放级别分布的生成器。 随着代理器学习和提高自身能力, 可播放级别空间( 由代理器定义) 不断增长。 生成器的可播放性估计以更新其对可播放级别的理解。 我们称之为此过程, 学习通过与环境的自我探索、 自我监督的感动学习等方法传播数据。 与以往的程序内容生成方法不同, 吉纳式播放网络是端到端的, 不需要人为设计的示例或域知识。 我们通过对2D 色棋游戏的代理器和水平进行训练, 展示这个框架的能力。