# IntersectionZoo

**Repository Path**: shawn2020/IntersectionZoo

## Basic Information

- **Project Name**: IntersectionZoo
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2025-12-16
- **Last Updated**: 2025-12-16

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

<p align="center">
  <img src="logo.png" width="600px" height="100px" alt="IntersectionZoo Logo">
</p>

[![Project Status: Active – The project has reached a stable, usable state and is being actively developed.](https://www.repostatus.org/badges/latest/active.svg)](https://www.repostatus.org/#active)
[![License](http://img.shields.io/badge/license-MIT-brightgreen.svg?style=flat)](https://github.com/mit-wu-lab/scenarioenv/blob/main/LICENSE)
![Static Badge](https://img.shields.io/badge/documentation-available-green)

# IntersectionZoo

[IntersectionZoo](https://intersectionzoo-docs.readthedocs.io/en/latest/) is a cooperative eco-driving-based multi-agent reinforcement learning environment for benchmarking contextual reinforcement learning algorithms to assess their generalization capabilities. Additionally, it also aims to advance eco-driving research through standardized environments and benchmarking. 

See our [documentation](https://intersectionzoo-docs.readthedocs.io/en/latest/) for more information on the application of IntersectionZoo. A comprehensive report of [benchmarking results](https://intersectionzoo-docs.readthedocs.io/en/latest/benchmarking.html) is available in the documentation.

# More information

- [Documentation](https://intersectionzoo-docs.readthedocs.io/en/latest/)
- [Installation instructions](https://intersectionzoo-docs.readthedocs.io/en/latest/usage.html#installation)
- [Tutorials](https://intersectionzoo-docs.readthedocs.io/en/latest/tutorial.html)
- [Intersection SUMO network files](https://drive.google.com/drive/folders/1y3W83MPfnt9mSFGbg8L9TLHTXElXvcHs?usp=sharing)
- [Benchmarking results](https://intersectionzoo-docs.readthedocs.io/en/latest/benchmarking.html)

# Technical questions

If you find a bug or are facing an issue, please open a new [issue](https://github.com/mit-wu-lab/IntersectionZoo/issues) in GitHub. The team can be reached through the contact details listed [here](https://intersectionzoo-docs.readthedocs.io/en/latest/contact.html).

# Getting involved

We welcome your contributions.

- Please report bugs and improvements by submitting [GitHub issue](https://github.com/mit-wu-lab/IntersectionZoo/issues).
- Submit your contributions using [pull requests](https://github.com/mit-wu-lab/IntersectionZoo/pulls).

# Citing IntersectionZoo

If you use IntersectionZoo in your work, you are highly encouraged to cite our paper:

V. Jayawardana, B. Freydt, A. Qu, C. Hickert, Z. Yan, C. Wu, "IntersectionZoo: Eco-driving for Benchmarking Multi-Agent Contextual Reinforcement Learning", International Conference on Learning Representations (ICLR) 2025. 

# Contributors

The Wu Lab at MIT actively maintains IntersectionZoo. The contributors are listed on the [IntersectionZoo Team Page](https://intersectionzoo-docs.readthedocs.io/en/latest/contact.html). The project was partially funded by the Utah Department of Transportation.