# Automatic Design Method of Building Pipeline Layout Based on Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2305.10760 · HTML: https://mlsanity.com/paper/2305.10760/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 20 references that are in the map.
- First posted on arXiv 18 May 2023.

## Builds on (most cited first, 4 of 4)

- [A Brief Survey of Deep Reinforcement Learning](https://mlsanity.com/paper/1708.05866/) (2017)
- [A Closer Look at Invalid Action Masking in Policy Gradient Algorithms](https://mlsanity.com/paper/2006.14171/) (2020)
- [Deep Reinforcement Learning Attitude Control of Fixed-Wing UAVs Using Proximal Policy Optimization](https://mlsanity.com/paper/1911.05478/) (2019)
- [Deep Reinforcement Learning for UAV Navigation Through Massive MIMO Technique](https://mlsanity.com/paper/1901.10832/) (2019)

## Abstract (arXiv)

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.