Deep Reinforcement Learning: A New Approach to Automatic Planning of Highrise Buildings

SUN Chengyu;SONG Xiaodong;  

In the context of highrise and high-density development,automatic planning of building clusters is a promosing area.However,to satisfy the sunlight requirments is still full of difficulties.The latest deep reinforcement learning theories in artificial intelligence provides a new approach to the problem.This study proposes a two-stage approach.Firstly,an initial layout will be generated according to the land development indices including total floor area and building heights.The plan will then be optimized in terms of sunlight and requirements according to a framework of"one axis,dualistic model,and four essential elements",which is summarized from the theories.After five automation experiments of highrise building planning in Beijing,Shenyang,Zhengzhou,Shanghai,and Fuzhou,the new approach is proved efficient and reliable.

【Fund】： 国家重点研发计划“南方地区城镇居住建筑绿色设计新方法与技术协同优化”阶段成果(项目编号:2016YFC0700200)
【CateGory Index】： TU984

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