Metadata-Version: 2.1
Name: PyFlyt
Version: 0.7.5
Summary: UAV Flight Simulator Gymnasium Environments for Reinforcement Learning Research.
Author-email: Jet <taijunjet@hotmail.com>
License: MIT License
        
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Project-URL: Repository, https://github.com/jjshoots/PyFlyt
Project-URL: Documentation, https://jjshoots.github.io/PyFlyt/documentation.html
Project-URL: Bug Report, https://github.com/jjshoots/PyFlyt/issues
Keywords: Reinforcement Learning,UAVs,drones,Quadcopter,AI,RL,Gymnasium
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE.txt

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# PyFlyt - UAV Flight Simulator Gymnasium Environments for Reinforcement Learning Research

<p align="center">
    <img src="https://github.com/jjshoots/PyFlyt/blob/master/readme_assets/pyflyt_cover_photo.png?raw=true" width="650px"/>
</p>

#### [View the documentation here!](https://jjshoots.github.io/PyFlyt/documentation.html)

This is a library for testing reinforcement learning algorithms on UAVs.
This repo is still under development.
We are also actively looking for users and developers, if this sounds like you, don't hesitate to get in touch!

## Installation

```
pip3 install pyflyt
```

## Usage

Usage is similar to any other Gymnasium and (soon) PettingZoo environment:

```py
import gymnasium
import PyFlyt.gym_envs # noqa

env = gymnasium.make("PyFlyt/QuadX-Hover-v0", render_mode="human")
obs = env.reset()

termination = False
truncation = False

while not termination or truncation:
    observation, reward, termination, truncation, info = env.step(env.action_space.sample())
```

View the official documentation for gymnasium environments [here](https://jjshoots.github.io/PyFlyt/documentation/gym_envs.html).

## Citation

If you use our work in your research and would like to cite it, please use the following bibtex entry:

```
@article{tai2023pyflyt,
  title={PyFlyt--UAV Simulation Environments for Reinforcement Learning Research},
  author={Tai, Jun Jet and Wong, Jim and Innocente, Mauro and Horri, Nadjim and Brusey, James and Phang, Swee King},
  journal={arXiv preprint arXiv:2304.01305},
  year={2023}
}
```
