Dynamic Garden Design Framework Integrating Generative Models and Reinforcement Learning

The rapid advancement of generative models and optimization algorithms has opened new opportunities for intelligent garden design systems. However, existing approaches often lack the ability to integrate aesthetic, functional and ecological considerations simultaneously and fail to adapt dynamically to complex design constraints. To address these challenges, this study proposes a dynamic garden design decision-making framework that integrates StyleGAN, Convolutional Neural Networks (CNNs) and the Soft Actor-Critic (SAC) reinforcement learning algorithm. The model combines StyleGAN’s capability to generate diverse and high-quality garden layouts, CNN’s feature extraction for spatial and visual representation and SAC’s optimization for balancing multiple objectives, including ecological benefit, resource efficiency and aesthetic quality. Experimental results on the Places Dataset and AID Dataset demonstrate that the proposed model significantly outperforms baseline methods in image quality, design diversity and optimization effectiveness. Specifically, it achieves superior performance in generating realistic and context-aware garden layouts while maintaining flexibility to adapt to environmental constraints. This research provides an adaptive framework for garden design and offers a foundation for extending dynamic design systems to fields like urban planning and interior design, promoting multi-objective optimization in diverse contexts.