tactic


Nametactic JSON
Version 0.0.1 PyPI version JSON
download
home_pagehttps://github.com/WoodOxen/tactics
SummaryOccpupy this library name for future development of 3D simulator related to tactics2d.
upload_time2024-02-05 04:25:56
maintainer
docs_urlNone
authorWoodOxen
requires_python>=3.7.0
license
keywords simulator autonomous driving
VCS
bugtrack_url
requirements No requirements were recorded.
Travis-CI No Travis.
coveralls test coverage No coveralls.
            # Tactics2D

[![Codacy](https://app.codacy.com/project/badge/Grade/2bb48186b56d4e3ab963121a5923d6b5)](https://app.codacy.com/gh/WoodOxen/tactics2d/dashboard?utm_source=gh&utm_medium=referral&utm_content=&utm_campaign=Badge_grade)
[![Codecov](https://codecov.io/gh/WoodOxen/tactics2d/graph/badge.svg?token=X81Z6AOIMV)](https://codecov.io/gh/WoodOxen/tactics2d)
![Test Modules](https://github.com/WoodOxen/tactics2d/actions/workflows/test_modules.yml/badge.svg?)
[![Read the Docs](https://img.shields.io/readthedocs/tactics2d)](https://tactics2d.readthedocs.io/en/latest/)
[![Downloads](https://img.shields.io/pypi/dm/tactics2d)](https://pypi.org/project/tactics2d/)

[![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}
}
```

            

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