Automatic Design Method of Building Pipeline Layout Based on Deep
  Reinforcement Learning
Chen Yang Zhe Zheng Jia-Rui Lin
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.