  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.
