Deep Reinforcement Learning for Prefab Assembly Planning in Robot-based Prefabricated Construction

Abstract

Smart construction has raised higher automation requirements of construction processes. The traditional construction planning does not match the demands of integrating smart construction with other technologies such as robotics, building information modelling (BIM), and internet of things (IoT). Therefore, more precise and meticulous construction planning is necessary. In this paper, leveraging recent advances in deep Reinforcement Learning (DRL), we design simulated construction environments for deep reinforcement learning and integrate these environments with deep Q-learning methods. We develop reliable controllers for assembly planning for prefabricated construction. For this, we first show that hand-designed rewards work well for these tasks; then we show deep neural policies can achieve good performance for some simple tasks.