学习从无到有生成级别
Learning to Generate Levels From Nothing
摘要：
用于过程内容生成的机器学习最近已成为一个活跃的研究领域。关卡的形式和功能各不相同，而且在游戏中彼此之间大多不相关。这使得很难组装合适的大数据集，以将机器学习引入关卡设计，就像它用于图像生成一样。在这里，我们提出了生成式游戏网络，它为自己设计了游戏级别。该算法由内置的两部分组成：一个学习玩游戏关卡的智能体和一个学习可玩关卡分布的生成器。随着智能体学习和提高其能力，由智能体定义的可玩级别的空间会增长。生成器以智能体的可玩性估计为目标，然后更新其对构成可玩级别的内容的理解。我们称这个学习过程为通过环境自我发现发现的数据分布，自我监督归纳学习。与以前的程序化内容生成方法不同，生成播放网络是端到端可区分的，不需要人类设计的示例或领域知识。我们证明了这个框架的能力，通过训练一个代理和水平生成器的2D地牢爬虫游戏。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
