Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
A deep reinforcement learning (DRL) based approach for spatial layout of land use and roads in urban communities. (Nature Computational Science)
| Date | Stars |
|---|---|
| 2026-07-31 | 306 |
| 2026-08-04 | 306 |
| 2026-08-05 | 306 |
| 2026-08-06 | 306 |
Today
— stars today
This week
— stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
# DRL urban planning
---
In this project, we propose a reinforcement-learning-based framework for assisting urban planners in the complex task of optimizing the spatial design of urban communities.
Our proposed model can generate land and road layout with superior spatial efficiency, and improve the productivity of human planners with a human-AI collaborative workflow.
This project was initially described in the [research article in *Nature Computational Science*](https://www.nature.com/articles/s43588-023-00503-5):
Yu Zheng, Yuming Lin, Liang Zhao, Tinghai Wu, Depeng Jin, Yong Li. **Spatial planning of urban communities via deep reinforcement learning**. Nat Comput Sci (2023). https://doi.org/10.1038/s43588-023-00503-5
Full text (PDF) is available at [this link](https://rdcu.be/dlRPZ).
## Installation
### Environment
* **Tested OS:** Linux
* Python >= 3.8
* PyTorch >= 1.8.1, <= 1.13.0
### Dependencies:
1. Install [PyTorch](https://pytorch.org/get-started/previous-versions/) with the correct CUDA version.
2. Set the following environment variable to avoid problems with multiprocess trajectory sampling:
```
export OMP_NUM_THREADS=1
```
## Data
The data used for training and evaluation can be found in [urban_planning/cfg/test_data](urban_planning/cfg/test_data).
We provide all the three scenarios used in our paper, including one synthetic grid community in [urban_planning/cfg/test_data/synthetic](urban_planning/cfg/test_data/synthetic), and two real-world communities, HLG and DHM, with and without planning concepts, in [urban_planning/cfg/test_data/real](urban_planning/cfg/test_data/real).
The data for the real-world communities are collected from the widely used [OpenStreetMap](https://www.openstreetmap.org/) (OSM) using [OSMnx](https://github.com/gboeing/osmnx).
For each case, we provide the following data:
* `init_plan.pickle`: the initial conditions of the community in [geopandas.GeoDataFrame](https://geopandas.org/en/stable/docs/reference/api/geopandas.GeoDataFrame.html) form, including the initial land blocks, roads, and junctions.
* `objectives.yaml`: the planning objectives (requirements) of the community in [yaml](https://yaml.org/) form, including the required number/area of different functionalities, and the minimum/maximum area of each land use type.
The figure below illustrates the initial conditions of the three scenarios.
With the initial conditions and planning objectives, the agent will generate millions of spatial plans for the community in real-time during training, which are stored in the replay buffer for training.
Codes for data generation (unorganized) can be found in [misc](misc).
## Training
You can train your own models using the provided config in [urban_planning/cfg/exp_cfg/real](urban_planning/cfg/exp_cfg/real).
For example, to train a model for the HLG community, run:
```
python3 -m urban_planning.train --cfg hlg --global_seed 111
```
You can replace `hlg` to `dhm` to train for the DHM community.
To train a model with planning concepts for the HLG community, run:
```
python3 -m urban_planning.train --cfg hlg_concept --global_seed 111
```
You can replace `hlg_concept` to `dhm_concept` to train for the DHM community.
You will see the following logs once you start training our model:
https://user-images.githubusercontent.com/27959377/242561690-c01480a2-cedf-4889-8506-14add002227a.mp4
## Visualization
You can visualize the generated spatial plans using the provided notebook in [demo](demo).
## Baselines
To evaluate the centralized heuristic, run:
```
python3 -m urban_planning.eval --cfg hlg --global_seed 111 --agent rule-centralized
```
To evaluate the decentralized heuristic, run:
```
python3 -m urban_planning.eval --cfg hlg --global_seed 111 --agent rule-decentralized
```
To evaluate the geometric set-coverage adapted baseliExcerpt of 39,694 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:df04c162e13f4f7c, topic:reinforcement-learning, desc:reinforcement learning