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

• URL: http://arxiv.org/abs/2601.21212v1
• Date: Thu, 29 Jan 2026 03:23:40 GMT
• Title: Intelli-Planner: Towards Customized Urban Planning via Large Language Model Empowered Reinforcement Learning
• Authors: Xixian Yong, Peilin Sun, Zihe Wang, Xiao Zhou,
• Abstract summary: We propose a novel framework integrating Deep Reinforcement Learning (DRL) with large language models (LLMs) to facilitate participatory and customized planning scheme generation.<n>Intelli-Planner utilizes demographic, geographic data, and planning preferences to determine high-level planning requirements and demands for each functional type.<n> 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.
• Score: 10.737909416707902
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Personalized Learning Path Planning with Goal-Driven Learner State Modeling [68.96987567845824]
  We introduce Pxplore, a novel framework for personalized learning paths.<n>We design a structured learner state model and an automated reward function that transforms abstract objectives into computable signals.<n>Experiments validate Pxplore's effectiveness in producing coherent, personalized, and goal-driven learning paths.
  arXiv  Detail & Related papers  (2025-10-15T06:59:49Z)
• DeepPlanner: Scaling Planning Capability for Deep Research Agents via Advantage Shaping [74.34061104176554]
  We propose DeepPlanner, an end-to-end RL framework that effectively enhances the planning capabilities of deep research agents.<n>Our approach shapes token-level advantage with an entropy-based term to allocate larger updates to high entropy tokens, and selectively upweights sample-level advantages for planning-intensive rollouts.
  arXiv  Detail & Related papers  (2025-10-14T20:47:05Z)
• A Goal Without a Plan Is Just a Wish: Efficient and Effective Global Planner Training for Long-Horizon Agent Tasks [66.86312354478478]
  Agents based on large language models (LLMs) struggle with brainless trial-and-error and generating hallucinatory actions due to a lack of global planning in long-horizon tasks.<n>We introduce a plan-and-execute framework and propose a planner training method to enhance the executor agent's planning abilities without human effort.<n>Experiments show that executor agents equipped with our planner outperform existing methods, achieving new state-of-the-art performance.
  arXiv  Detail & Related papers  (2025-10-07T06:10:53Z)
• Learning When to Plan: Efficiently Allocating Test-Time Compute for LLM Agents [35.79575378215309]
  Training large language models (LLMs) to reason via reinforcement learning (RL) significantly improves their problem-solving capabilities.<n>We introduce a conceptual framework formalizing dynamic planning for LLM agents, enabling them to flexibly decide when to allocate test-time compute for planning.<n>Experiments on the Crafter environment show that dynamic planning agents trained with this approach are more sample-efficient and consistently achieve more complex objectives.
  arXiv  Detail & Related papers  (2025-09-03T18:00:13Z)
• UrbanPlanBench: A Comprehensive Urban Planning Benchmark for Evaluating Large Language Models [26.94010977379045]
  We introduce a benchmark, UrbanPlanBench, to evaluate the efficacy of Large Language Models (LLMs) in urban planning.<n>We reveal a significant imbalance in the acquisition of planning knowledge among LLMs, with even the most proficient models falling short of meeting professional standards.<n>We present the largest-ever supervised fine-tuning dataset, UrbanPlanText, comprising over 30,000 instruction pairs sourced from urban planning exams and textbooks.
  arXiv  Detail & Related papers  (2025-04-23T13:53:59Z)
• LASP: Surveying the State-of-the-Art in Large Language Model-Assisted AI Planning [7.36760703426119]
  This survey aims to highlight the existing challenges in planning with language models. It focuses on key areas such as embodied environments, optimal scheduling, competitive and cooperative games, task decomposition, reasoning, and planning.
  arXiv  Detail & Related papers  (2024-09-03T11:39:52Z)
• Exploring and Benchmarking the Planning Capabilities of Large Language Models [57.23454975238014]
  This work lays the foundations for improving planning capabilities of large language models (LLMs) We construct a comprehensive benchmark suite encompassing both classical planning benchmarks and natural language scenarios. We investigate the use of many-shot in-context learning to enhance LLM planning, exploring the relationship between increased context length and improved planning performance.
  arXiv  Detail & Related papers  (2024-06-18T22:57:06Z)
• Large Language Models are Learnable Planners for Long-Term Recommendation [59.167795967630305]
  Planning for both immediate and long-term benefits becomes increasingly important in recommendation. Existing methods apply Reinforcement Learning to learn planning capacity by maximizing cumulative reward for long-term recommendation. We propose to leverage the remarkable planning capabilities over sparse data of Large Language Models for long-term recommendation.
  arXiv  Detail & Related papers  (2024-02-29T13:49:56Z)
• Large language model empowered participatory urban planning [5.402147437950729]
  This research introduces an innovative urban planning approach integrating Large Language Models (LLMs) within the participatory process. The framework, based on the crafted LLM agent, consists of role-play, collaborative generation, and feedback, solving a community-level land-use task catering to 1000 distinct interests.
  arXiv  Detail & Related papers  (2024-01-24T10:50:01Z)
• On the Prospects of Incorporating Large Language Models (LLMs) in Automated Planning and Scheduling (APS) [23.024862968785147]
  This paper investigates eight categories based on the unique applications of LLMs in addressing various aspects of planning problems. A critical insight resulting from our review is that the true potential of LLMs unfolds when they are integrated with traditional symbolic planners.
  arXiv  Detail & Related papers  (2024-01-04T19:22:09Z)
• LLM-Assist: Enhancing Closed-Loop Planning with Language-Based Reasoning [65.86754998249224]
  We develop a novel hybrid planner that leverages a conventional rule-based planner in conjunction with an LLM-based planner. Our approach navigates complex scenarios which existing planners struggle with, produces well-reasoned outputs while also remaining grounded through working alongside the rule-based approach.
  arXiv  Detail & Related papers  (2023-12-30T02:53:45Z)
• Towards Goal-oriented Intelligent Tutoring Systems in Online Education [65.17234980710386]
  We propose a new task, named Goal-oriented Intelligent Tutoring Systems (GITS)<n>GITS aims to enable the student's mastery of a designated concept by strategically planning a customized sequence of exercises and assessment.<n>We propose a novel graph-based reinforcement learning framework, named Planning-Assessment-Interaction (PAI)
  arXiv  Detail & Related papers  (2023-12-03T12:37:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.