Automated architectural spatial composition via multi-agent deep reinforcement learning for building renovation

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

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Author(s)

  • Zihuan Zhang
  • Zhe Guo
  • Hao ZHENG
  • Zao Li
  • Philip F. Yuan

Detail(s)

Original languageEnglish
Article number105702
Number of pages21
Journal / PublicationAutomation in Construction
Volume167
Online published19 Aug 2024
Publication statusPublished - Nov 2024

Abstract

This paper focuses on utilizing deep reinforcement learning technology to explore how to achieve automated architectural space composition under established built environment conditions to address the extensive demands of old building renovation. A reinforcement learning platform has been develop0065d, centered around the multi-agent deep deterministic policy gradient (MADDPG) algorithm. The paper leverages functional blocks as agents within Grasshopper to simulate existing architectural structures and their interactions, establishing a task-based reward system to guide the design process. Furthermore, data exchange between algorithmic and modeling software is facilitated through UDP communication. Training results using typical frame structure spaces indicate a significant increase in cumulative rewards around the 650,000th training step, effectively achieving the given design tasks. The paper demonstrates the potential of automated architectural space composition methods, based on deep reinforcement learning, to enhance design efficiency, optimize spatial arrangements, and promote human-machine collaborative design in building renovations.