World-building, the process of developing both the narrative and physical world of a game, plays a vital role in the game's experience. Critically acclaimed independent and AAA video games are praised for strong world building, with game maps that masterfully intertwine with and elevate the narrative, captivating players and leaving a lasting impression. However, designing game maps that support a desired narrative is challenging, as it requires satisfying complex constraints from various considerations. Most existing map generation methods focus on considerations about gameplay mechanics or map topography, while the need to support the story is typically neglected. As a result, extensive manual adjustment is still required to design a game world that facilitates particular stories. In this work, we approach this problem by introducing an extra layer of plot facility layout design that is independent of the underlying map generation method in a world-building pipeline. Concretely, we present a system that leverages Reinforcement Learning (RL) to automatically assign concrete locations on a game map to abstract locations mentioned in a given story (plot facilities), following spatial constraints derived from the story. A decision-making agent moves the plot facilities around, considering their relationship to the map and each other, to locations on the map that best satisfy the constraints of the story. Our system considers input from multiple modalities: map images as pixels, facility locations as real values, and story constraints expressed in natural language. We develop a method of generating datasets of facility layout tasks, create an RL environment to train and evaluate RL models, and further analyze the behaviors of the agents through a group of comprehensive experiments and ablation studies, aiming to provide insights for RL-based plot facility layout design.

翻译：世界构建——即开发游戏的叙事与物理世界的过程——在游戏体验中扮演着至关重要的角色。广受好评的独立游戏与3A级视频游戏因其强大的世界构建而备受赞誉，其游戏地图巧妙地与叙事交织并提升叙事效果，令玩家沉浸其中并留下持久印象。然而，设计能够支撑预期叙事的游戏地图极具挑战性，因为这需要满足来自多方面考虑的复杂约束。现有的大多数地图生成方法侧重于游戏机制或地图地形的考量，而支持叙事的需要往往被忽视。因此，设计能够承载特定故事的游戏世界仍需大量手动调整。在本工作中，我们通过在世界构建管线中引入一个独立于底层地图生成方法的附加情节设施布局设计层来解决这一问题。具体而言，我们提出一个系统，利用强化学习根据从故事中推导出的空间约束，自动将给定故事中提到的抽象位置（情节设施）分配到游戏地图上的具体位置。一个决策智能体在考虑情节设施与地图及彼此之间关系的基础上，将其移动到最能满足故事约束的地图位置。我们的系统考虑多种模态的输入：以像素表示的地图图像、以实数值表示的设施位置以及以自然语言表达的故事约束。我们开发了一种生成设施布局任务数据集的方法，创建了一个用于训练和评估强化学习模型的强化学习环境，并通过一系列综合实验和消融研究进一步分析了智能体的行为，旨在为基于强化学习的情节设施布局设计提供见解。