Metadata-Version: 2.1
Name: tactic
Version: 0.0.1
Summary: Occpupy this library name for future development of 3D simulator related to tactics2d.
Home-page: https://github.com/WoodOxen/tactics
Author: WoodOxen
Author-email: rowena.academic@gmail.com
Keywords: simulator,autonomous driving
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: GNU Affero General Public License v3
Classifier: Operating System :: POSIX :: Linux
Requires-Python: >=3.7.0
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: fastparquet >=0.8.1
Requires-Dist: geopandas >=0.10.2
Requires-Dist: gymnasium >=0.28.0
Requires-Dist: joblib >=1.3.2
Requires-Dist: numpy >=1.21.5
Requires-Dist: pandas >=1.3.5
Requires-Dist: protobuf <=3.20.3
Requires-Dist: pygame >=2.5.2
Requires-Dist: pyproj >=3.2.1
Requires-Dist: scikit-learn >=1.0.0
Requires-Dist: tensorflow ==2.11.0
Requires-Dist: shapely ==2.0.2

# Tactics2D

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[![Read the Docs](https://img.shields.io/readthedocs/tactics2d)](https://tactics2d.readthedocs.io/en/latest/)
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[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
[![Github license](https://img.shields.io/github/license/WoodOxen/tactics2d)](https://github.com/WoodOxen/tactics2d/blob/dev/LICENSE)

## About

Tactics2D is a Python library that provides diverse traffic environments, facilitating the development of driving decision-making models.

## Quick Start

### Install from PyPI

You can simply install Tactics2D from PyPI with the following command

```shell
pip install tactics2d
```

### Install from Github

You can also install Tactics2D from from its source on GitHub.

[TODO]

If no errors occurs, you should have installed Tactics2D successfully.

### Samples

[TODO: The command lines to run sample codes.]

## Citation

If you find Tactics2D useful, please cite this in your publication.

```bibtex
@article{li2023tactics2d,
  title={Tactics2D: A Multi-agent Reinforcement Learning Environment for Driving Decision-making},
  author={Li, Yueyuan and Zhang, Songan and Jiang, Mingyang and Chen, Xingyuan and Yang, Ming},
  journal={arXiv preprint arXiv:2311.11058},
  year={2023}
}
```
