Automated community layout generation via deep reinforcement learning: towards sustainable residential planning

In the context of the increasing demand for quality living and the rapid modernization of urban areas, residential district layouts face many challenges, such as optimizing functionality within constrained spaces and accommodating the diverse requirements of residents. This study investigates intelligent residential district layout planning methods using deep reinforcement learning to address these difficulties. An agent can learn and make decisions by creating models and structuring residential area components and limits. The deep reinforcement learning method can improve residential layout design schemes by optimizing space utilization, resident convenience, and ecological sustainability. Effective and autonomous data-driven learning is its advantage. Investigating the deep reinforcement learning model and technical techniques for building layout design could improve layout design efficiency and proposal quality. Enhancing urban living quality requires rational, human-centric, and sustainable residential development.