In urban planning, land use readjustment plays a pivotal role in aligning
land use configurations with the current demands for sustainable urban
development. However, present-day urban planning practices face two main
issues. Firstly, land use decisions are predominantly dependent on human
experts. Besides, while resident engagement in urban planning can promote urban
sustainability and livability, it is challenging to reconcile the diverse
interests of stakeholders. To address these challenges, we introduce a
Consensus-based Multi-Agent Reinforcement Learning framework for real-world
land use readjustment. This framework serves participatory urban planning,
allowing diverse intelligent agents as stakeholder representatives to vote for
preferred land use types. Within this framework, we propose a novel consensus
mechanism in reward design to optimize land utilization through collective
decision making. To abstract the structure of the complex urban system, the
geographic information of cities is transformed into a spatial graph structure
and then processed by graph neural networks. Comprehensive experiments on both
traditional top-down planning and participatory planning methods from
real-world communities indicate that our computational framework enhances
global benefits and accommodates diverse interests, leading to improved
satisfaction across different demographic groups. By integrating Multi-Agent
Reinforcement Learning, our framework ensures that participatory urban planning
decisions are more dynamic and adaptive to evolving community needs and
provides a robust platform for automating complex real-world urban planning
processes.

城市规划中，土地利用调整在将土地配置与可持续城市发展的当前需求相一致方面起着至关重要的作用。然而，现今城市规划实践面临两个主要问题，即土地利用决策过于依赖人类专家，并且难以协调利益多样化的利益相关者。为了解决这些挑战，我们引入了基于共识的多智能体强化学习框架来进行现实世界的土地利用调整。该框架用于参与型城市规划，允许多样化的智能体作为利益相关者的代表为首选土地利用类型投票。在此框架中，我们提出了一种新的共识机制在奖励设计中，通过集体决策来优化土地利用。为了抽象复杂城市系统的结构，城市的地理信息被转化为空间图结构，然后通过图神经网络进行处理。对来自现实社区的传统自上而下规划和参与型规划方法的全面实验表明，我们的计算框架增强了全局效益并适应了多样化的利益，提高了不同人群的满意度。通过整合多智能体强化学习，我们的框架确保参与型城市规划决策更加动态和适应不断变化的社区需求，并提供了一个强大的平台来自动化处理复杂的现实世界城市规划过程。