Illuminating Spaces: Deep Reinforcement Learning and Laser-Wall Partitioning for Architectural Layout Generation Article Swipe

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Reza Kakooee , Benjamin Dillenburger · YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2502.04407

Space layout design (SLD), occurring in the early stages of the design process, nonetheless influences both the functionality and aesthetics of the ultimate architectural outcome. The complexity of SLD necessitates innovative approaches to efficiently explore vast solution spaces. While image-based generative AI has emerged as a potential solution, they often rely on pixel-based space composition methods that lack intuitive representation of architectural processes. This paper leverages deep Reinforcement Learning (RL), as it offers a procedural approach that intuitively mimics the process of human designers. Effectively using RL for SLD requires an explorative space composing method to generate desirable design solutions. We introduce "laser-wall", a novel space partitioning method that conceptualizes walls as emitters of imaginary light beams to partition spaces. This approach bridges vector-based and pixel-based partitioning methods, offering both flexibility and exploratory power in generating diverse layouts. We present two planning strategies: one-shot planning, which generates entire layouts in a single pass, and dynamic planning, which allows for adaptive refinement by continuously transforming laser-walls. Additionally, we introduce on-light and off-light wall transformations for smooth and fast layout refinement, as well as identity-less and identity-full walls for versatile room assignment. We developed SpaceLayoutGym, an open-source OpenAI Gym compatible simulator for generating and evaluating space layouts. The RL agent processes the input design scenarios and generates solutions following a reward function that balances geometrical and topological requirements. Our results demonstrate that the RL-based laser-wall approach can generate diverse and functional space layouts that satisfy both geometric constraints and topological requirements and is architecturally intuitive.

Type

preprint

Language

en

Landing Page

http://arxiv.org/abs/2502.04407

PDF

https://arxiv.org/pdf/2502.04407

OA Status

green

Related Works

10

OpenAlex ID

https://openalex.org/W4407308595

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OpenAlex ID

https://openalex.org/W4407308595

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DOI

https://doi.org/10.48550/arxiv.2502.04407

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Title

Illuminating Spaces: Deep Reinforcement Learning and Laser-Wall Partitioning for Architectural Layout Generation

Work title

Type

preprint

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Language

en

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Publication year

2025

Year of publication

Publication date

2025-02-06

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Authors

Reza Kakooee, Benjamin Dillenburger

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Landing page

https://arxiv.org/abs/2502.04407

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PDF URL

https://arxiv.org/pdf/2502.04407

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Open access

Yes

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OA status

green

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OA URL

https://arxiv.org/pdf/2502.04407

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Concepts

Reinforcement learning, Reinforcement, Computer science, Artificial intelligence, Architectural engineering, Engineering, Structural engineering

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Cited by

0

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Related works (count)

10

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