The layout design of pipelines is a critical task in the construction industry. Currently, pipeline layout is designed manually by engineers, which is time-consuming and laborious. Automating and streamlining this process can reduce the burden on engineers and save time. In this paper, we propose a method for generating three-dimensional layout of pipelines based on deep reinforcement learning (DRL). Firstly, we abstract the geometric features of space to establish a training environment and define reward functions based on three constraints: pipeline length, elbow, and installation distance. Next, we collect data through interactions between the agent and the environment and train the DRL model. Finally, we use the well-trained DRL model to automatically design a single pipeline. Our results demonstrate that DRL models can complete the pipeline layout task in space in a much shorter time than traditional algorithms while ensuring high-quality layout outcomes.

翻译：管道布局设计是建筑行业中的一项关键任务。目前，管道布局由工程师手动设计，耗时且费力。自动化和简化这一过程可以减轻工程师的负担并节省时间。本文提出了一种基于深度强化学习（DRL）的三维管道布局生成方法。首先，我们抽象空间几何特征以建立训练环境，并根据三个约束条件（管道长度、弯头数量及安装距离）定义奖励函数。接着，通过智能体与环境的交互收集数据，并训练DRL模型。最后，利用训练好的DRL模型自动设计单根管道。结果表明，DRL模型在确保高质量布局结果的同时，完成空间管道布局任务所需的时间远短于传统算法。