Intelli-Planner: Towards Customized Urban Planning via Large Language Model Empowered Reinforcement Learning

2026-01-29

AI Summary

This study addresses the inefficiency and limited public engagement of traditional urban planning approaches, which struggle to accommodate diverse stakeholder needs. To overcome these limitations, we propose a novel framework that integrates large language models (LLMs) with deep reinforcement learning (DRL) to generate customizable and participatory urban functional zoning layouts, leveraging demographic, geographic, and preference data from stakeholders. Our approach innovatively incorporates a knowledge-augmented module and a multidimensional evaluation system that enables semantic feedback, marking the first synergistic application of LLMs and DRL in urban planning. Experimental results across multiple urban scenarios demonstrate that the proposed method outperforms conventional baselines, achieves competitive performance with state-of-the-art DRL techniques on objective metrics, and significantly enhances user satisfaction and training convergence speed.

Technology Category

• Planning, Routing, and Scheduling: Planning with Language Models
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Search and Optimization: Learning to Search

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions

Abstract

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.

Problem

• urban planning
• stakeholder involvement
• customized planning
• large language models
• reinforcement learning

Innovation

• Large Language Model
• Deep Reinforcement Learning
• Urban Planning
• Knowledge Enhancement
• Stakeholder Satisfaction