Hierarchical Reinforced Urban Planning: Jointly Steering Region and Block Configurations

With the explosive accumulation of urban geographic, mobile, and IoT service data, AI-assisted automated urban planning, with a goal of configuring land-uses, has become an emerging interdisciplinary topic for smart cities. Existing literature mostly views urban planning as a generative task from the perspective of generating land-use configuration images. Such perspective is limited by two issues: 1) hierarchical planning dependency across multi scales: there are hierarchical dependencies between region-level urban function configurations and block-level building configurations. 2) sequential planning dependency within a scale: when planning the buildings of a place, planning a shopping mall can impose constraints on planning subsequent Points of Interest (POIs). In response, we propose a new perspective of formulating urban planning as a hierarchical decision process. That is, given a target region with many geographic blocks, a machine planner firstly selects the optimized urban function portfolios, thereafter, sequentially selects the most appropriate POI for each block based on its urban functions and previously-placed POIs over planning steps. We reformulate this decision process into a hierarchical reinforcement learning task and develop a novel hierarchical reinforced urban planning framework. This framework includes two components: 1) In region-level configuration, we present an actor-critic based method to overcome the challenge of weak reward feedback in planning the urban functions of regions. 2) In block-level configuration, we propose a single-agent iterative POI allocation strategy to model dependencies between POIs and urban functions, and between current and previous POIs. Finally, we present extensive experimental results on real-world urban data to demonstrate the enhanced performances of the “planning as hierarchical decision process” perspective and the reinforced planning model.