| Name | PyFlyt JSON |
| Version |
0.19.0
JSON |
| download |
| home_page | None |
| Summary | UAV Flight Simulator Gymnasium Environments for Reinforcement Learning Research. |
| upload_time | 2024-03-23 00:30:53 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.8 |
| license | MIT License Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. |
| keywords |
reinforcement learning
uavs
drones
quadcopter
ai
rl
gymnasium
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
[](https://github.com/astral-sh/ruff)
[](https://pepy.tech/project/pyflyt)
[](https://pepy.tech/project/pyflyt)
# PyFlyt - UAV Flight Simulator for Reinforcement Learning Research
Comes with Gymnasium and PettingZoo environments built in!
<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
```sh
pip3 install wheel numpy
pip3 install pyflyt
```
> `numpy` and `wheel` must be installed prior to `pyflyt` such that `pybullet` is built with `numpy` support.
## Usage
Usage is similar to any other Gymnasium and PettingZoo environment:
### Gymnasium
```python
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).
### PettingZoo
```python
from PyFlyt.pz_envs import MAFixedwingDogfightEnv
env = MAFixedwingDogfightEnv(render_mode="human")
observations, infos = env.reset()
while env.agents:
# this is where you would insert your policy
actions = {agent: env.action_space(agent).sample() for agent in env.agents}
observations, rewards, terminations, truncations, infos = env.step(actions)
env.close()
```
View the official documentation for pettingzoo environments [here](https://jjshoots.github.io/PyFlyt/documentation/pz_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}
}
```
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"description": "[](https://github.com/astral-sh/ruff)\n[](https://pepy.tech/project/pyflyt)\n[](https://pepy.tech/project/pyflyt)\n\n# PyFlyt - UAV Flight Simulator for Reinforcement Learning Research\n\nComes with Gymnasium and PettingZoo environments built in!\n\n<p align=\"center\">\n <img src=\"https://github.com/jjshoots/PyFlyt/blob/master/readme_assets/pyflyt_cover_photo.png?raw=true\" width=\"650px\"/>\n</p>\n\n#### [View the documentation here!](https://jjshoots.github.io/PyFlyt/documentation.html)\n\nThis is a library for testing reinforcement learning algorithms on UAVs.\nThis repo is still under development.\nWe are also actively looking for users and developers, if this sounds like you, don't hesitate to get in touch!\n\n## Installation\n\n```sh\npip3 install wheel numpy\npip3 install pyflyt\n```\n\n> `numpy` and `wheel` must be installed prior to `pyflyt` such that `pybullet` is built with `numpy` support.\n\n## Usage\n\nUsage is similar to any other Gymnasium and PettingZoo environment:\n\n### Gymnasium\n\n```python\nimport gymnasium\nimport PyFlyt.gym_envs # noqa\n\nenv = gymnasium.make(\"PyFlyt/QuadX-Hover-v0\", render_mode=\"human\")\nobs = env.reset()\n\ntermination = False\ntruncation = False\n\nwhile not termination or truncation:\n observation, reward, termination, truncation, info = env.step(env.action_space.sample())\n```\n\nView the official documentation for gymnasium environments [here](https://jjshoots.github.io/PyFlyt/documentation/gym_envs.html).\n\n### PettingZoo\n\n```python\nfrom PyFlyt.pz_envs import MAFixedwingDogfightEnv\n\nenv = MAFixedwingDogfightEnv(render_mode=\"human\")\nobservations, infos = env.reset()\n\nwhile env.agents:\n # this is where you would insert your policy\n actions = {agent: env.action_space(agent).sample() for agent in env.agents}\n\n observations, rewards, terminations, truncations, infos = env.step(actions)\nenv.close()\n```\n\nView the official documentation for pettingzoo environments [here](https://jjshoots.github.io/PyFlyt/documentation/pz_envs.html).\n\n## Citation\n\nIf you use our work in your research and would like to cite it, please use the following bibtex entry:\n\n```\n@article{tai2023pyflyt,\n title={PyFlyt--UAV Simulation Environments for Reinforcement Learning Research},\n author={Tai, Jun Jet and Wong, Jim and Innocente, Mauro and Horri, Nadjim and Brusey, James and Phang, Swee King},\n journal={arXiv preprint arXiv:2304.01305},\n year={2023}\n}\n```\n",
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