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

arXiv: https://arxiv.org/abs/2601.21212 · HTML: https://mlsanity.com/paper/2601.21212/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 76 references that are in the map.
- First posted on arXiv 29 Jan 2026.

## Builds on (most cited first, 20 of 20)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [Do As I Can, Not As I Say: Grounding Language in Robotic Affordances](https://mlsanity.com/paper/2204.01691/) (2022)
- [A Survey on Large Language Model based Autonomous Agents](https://mlsanity.com/paper/2308.11432/) (2023)
- [The Rise and Potential of Large Language Model Based Agents: A Survey](https://mlsanity.com/paper/2309.07864/) (2023)
- [ChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debate](https://mlsanity.com/paper/2308.07201/) (2023)
- [AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors](https://mlsanity.com/paper/2308.10848/) (2023)
- [Using cognitive psychology to understand GPT-3](https://mlsanity.com/paper/2206.14576/) (2022)
- [Do Embodied Agents Dream of Pixelated Sheep: Embodied Decision Making using Language Guided World Modelling](https://mlsanity.com/paper/2301.12050/) (2023)
- [Identifying and Manipulating the Personality Traits of Language Models](https://mlsanity.com/paper/2212.10276/) (2022)
- [ChatLLM Network: More brains, More intelligence](https://mlsanity.com/paper/2304.12998/) (2023)
- [Improving Grounded Language Understanding in a Collaborative Environment by Interacting with Agents Through Help Feedback](https://mlsanity.com/paper/2304.10750/) (2023)
- [Reimagining City Configuration: Automated Urban Planning via Adversarial Learning](https://mlsanity.com/paper/2008.09912/) (2020)
- [MuseCL: Predicting Urban Socioeconomic Indicators via Multi-Semantic Contrastive Learning](https://mlsanity.com/paper/2407.09523/) (2024)
- [Human-instructed Deep Hierarchical Generative Learning for Automated Urban Planning](https://mlsanity.com/paper/2212.00904/) (2022)
- [Modeling urbanization patterns with generative adversarial networks](https://mlsanity.com/paper/1801.02710/) (2018)
- [Spatial sensitivity analysis for urban land use prediction with physics-constrained conditional generative adversarial networks](https://mlsanity.com/paper/1907.09543/) (2019)
- [FAP-CD: Fairness-Driven Age-Friendly Community Planning via Conditional Diffusion Generation](https://mlsanity.com/paper/2412.16699/) (2024)

## Built on by (most cited first, 3 of 3)

- [HypeMed: Enhancing Medication Recommendations with Hypergraph-Based Patient Relationships](https://mlsanity.com/paper/2603.18459/) (2026)
- [Controllable Affective Generation via Latent Vector Steering](https://mlsanity.com/paper/2608.25569/) (2026)
- [From Static Personal Values to Contextualized Personalization: Bayesian Personalized Value Alignment for LLMs](https://mlsanity.com/paper/2609.28942/) (2026)

## Abstract (arXiv)

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.