Effective urban planning is crucial for enhancing residents' quality of life and ensuring societal stability, playing a pivotal role in the sustainable development of cities. Current planning methods heavily rely on human experts, which are time-consuming and labor-intensive, or utilize deep learning algorithms, often limiting stakeholder involvement. To bridge these gaps, we propose Intelli-Planner, a novel framework integrating Deep Reinforcement Learning (DRL) with large language models (LLMs) to facilitate participatory and customized planning scheme generation. Intelli-Planner utilizes demographic, geographic data, and planning preferences to determine high-level planning requirements and demands for each functional type. During training, a knowledge enhancement module is employed to enhance the decision-making capability of the policy network. Additionally, we establish a multi-dimensional evaluation system and employ LLM-based stakeholders for satisfaction scoring. Experimental validation across diverse urban settings shows that Intelli-Planner surpasses traditional baselines and achieves comparable performance to state-of-the-art DRL-based methods in objective metrics, while enhancing stakeholder satisfaction and convergence speed. These findings underscore the effectiveness and superiority of our framework, highlighting the potential for integrating the latest advancements in LLMs with DRL approaches to revolutionize tasks related to functional areas planning.

翻译：有效的城市规划对于提升居民生活质量、保障社会稳定至关重要，在城市可持续发展中发挥着关键作用。当前规划方法严重依赖人类专家，耗时费力；或采用深度学习算法，往往限制了利益相关方的参与。为弥补这些不足，我们提出Intelli-Planner——一种融合深度强化学习与大语言模型的新型框架，以促进参与式、定制化规划方案的生成。Intelli-Planner利用人口统计数据、地理信息及规划偏好，确定各类功能分区的高层规划需求。训练过程中，通过知识增强模块提升策略网络的决策能力。此外，我们建立了多维评估体系，并采用基于大语言模型的利益相关方代理进行满意度评分。在不同城市环境下的实验验证表明，Intelli-Planner在客观指标上超越传统基线方法，与基于深度强化学习的最先进方法性能相当，同时显著提升了利益相关方满意度和收敛速度。这些发现印证了我们框架的有效性与优越性，彰显了将大语言模型最新进展与深度强化学习相结合、革新功能区规划相关任务的巨大潜力。