Automated architectural spatial composition via multi-agent deep reinforcement learning for building renovation
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
Original language | English |
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Article number | 105702 |
Number of pages | 21 |
Journal / Publication | Automation in Construction |
Volume | 167 |
Online published | 19 Aug 2024 |
Publication status | Published - Nov 2024 |
Link(s)
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.
Citation Format(s)
Automated architectural spatial composition via multi-agent deep reinforcement learning for building renovation. / Zhang, Zihuan; Guo, Zhe; ZHENG, Hao et al.
In: Automation in Construction, Vol. 167, 105702, 11.2024.
In: Automation in Construction, Vol. 167, 105702, 11.2024.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review