Reimagining space layout design through deep reinforcement learning Article Swipe
Reza Kakooee
,
Benjamin Dillenburger
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1093/jcde/qwae025
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1093/jcde/qwae025
Space layout design is a critical aspect of architectural design, influencing functionality and aesthetics. The inherent combinatorial nature of layout design poses challenges for traditional planning approaches; thus, it demands the exploration of novel methods. This paper presents a novel framework that leverages the potential of deep reinforcement learning (RL) algorithms to optimize space layouts. RL has demonstrated remarkable success in addressing complex decision-making problems, yet its application in the design process remains relatively unexplored. We argue that RL is particularly well-suited for the design process due to its ability to accommodate offline tasks and seamless integration with existing computer-aided design software, effectively acting as a simulator for design exploration. Framing space layout design as an RL problem and employing RL methods allows for the automated exploration of the expansive design space, thereby enhancing the discovery of innovative solutions. This paper also elucidates the synergy between the design process and the RL problem, which opens new avenues for exploring the potential of RL algorithms in design. We aim to foster experimentation and collaboration within the RL and architecture communities. To facilitate our research, we have developed SpaceLayoutGym, an environment specifically designed for space layout design tasks. SpaceLayoutGym serves as a customizable environment that encapsulates the essential elements of the layout design process within an RL framework. To showcase the effectiveness of SpaceLayoutGym and the capabilities of RL as an artificial space layout designer, we employ the Proximal Policy Optimization (PPO) algorithm to train the RL agent in selected design scenarios with both geometrical constraints and topological objectives. The study further extends to contrast the effectiveness of PPO agents with that of genetic algorithms, and also includes a comparative analysis with existing layouts. Our results demonstrate the potential of RL to optimize space layouts, offering a promising direction for the future of artificial intelligence-aided design.
Related Topics
Concepts
Reinforcement learning
Computer science
Process (computing)
Engineering design process
Space (punctuation)
Expansive
Design process
Generative Design
Architecture
Software
Systems engineering
Human–computer interaction
Software engineering
Artificial intelligence
Engineering
Work in process
Programming language
Visual arts
Composite material
Art
Compressive strength
Materials science
Mechanical engineering
Operating system
Metric (unit)
Operations management
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1093/jcde/qwae025
- https://academic.oup.com/jcde/advance-article-pdf/doi/10.1093/jcde/qwae025/57105876/qwae025.pdf
- OA Status
- gold
- Cited By
- 19
- References
- 40
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4393276669
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4393276669Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1093/jcde/qwae025Digital Object Identifier
- Title
-
Reimagining space layout design through deep reinforcement learningWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-03-27Full publication date if available
- Authors
-
Reza Kakooee, Benjamin DillenburgerList of authors in order
- Landing page
-
https://doi.org/10.1093/jcde/qwae025Publisher landing page
- PDF URL
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https://academic.oup.com/jcde/advance-article-pdf/doi/10.1093/jcde/qwae025/57105876/qwae025.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://academic.oup.com/jcde/advance-article-pdf/doi/10.1093/jcde/qwae025/57105876/qwae025.pdfDirect OA link when available
- Concepts
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Reinforcement learning, Computer science, Process (computing), Engineering design process, Space (punctuation), Expansive, Design process, Generative Design, Architecture, Software, Systems engineering, Human–computer interaction, Software engineering, Artificial intelligence, Engineering, Work in process, Programming language, Visual arts, Composite material, Art, Compressive strength, Materials science, Mechanical engineering, Operating system, Metric (unit), Operations managementTop concepts (fields/topics) attached by OpenAlex
- Cited by
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19Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 10, 2024: 9Per-year citation counts (last 5 years)
- References (count)
-
40Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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