Metadata-Version: 2.1
Name: rrt-ml
Version: 0.0.5
Summary: Rapidly exploring random trees with machine learning
Author-email: Joao Pedro Barros Ferreira <jpuerj@gmail.com>
Project-URL: Homepage, https://github.com/ferreirajoaouerj/RRT-ML
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE

# RRT-ML
Rapidly exploring random trees with machine learning - learned sampling distributions, local reinforcement learning controller and learned supervised distance function for car-like mobile robots


<!-- TABLE OF CONTENTS -->
<details>
  <summary>Table of Contents</summary>
  <ol>
    <li>
      <a href="#about-the-project">About The Project</a>
      <ul>
        <li><a href="#built-with">Built With</a></li>
      </ul>
    </li>
    <li>
      <a href="#getting-started">Getting Started</a>
      <ul>
        <li><a href="#prerequisites">Prerequisites</a></li>
        <li><a href="#installation">Installation</a></li>
      </ul>
    </li>
    <li><a href="#usage">Usage</a></li>
    <li><a href="#roadmap">Roadmap</a></li>
    <li><a href="#contributing">Contributing</a></li>
    <li><a href="#license">License</a></li>
    <li><a href="#contact">Contact</a></li>
    <li><a href="#acknowledgments">Acknowledgments</a></li>
  </ol>
</details>


<!-- ABOUT THE PROJECT -->
## About The Project

This project unites optimal rapidly exploring random trees (RRT*) with the following machine learning techniques:

* Learned distribution of samples (robot configurations) as proposed by [@cite itcher] 
* Reinforcement learning agent trained with MEGA [@cite spitis] as a local controller
* Supervised learning of a distance metric induced by the agent

The experiments are conducted in PyBullet with a car-like mobile robot in a narrow passage type of scenario. 
The code allows the training and testing of each machine learning modules individually, but also in the context of the broader RRT* approach.

<!-- GETTING STARTED -->
## Getting Started

Follow the instructions below to install the package.

### Prerequisites

You need to install [PyTorch](https://pytorch.org/) and [PyBullet](https://pybullet.org/wordpress/).

#### Torch

The easiest way is to use conda.

If you have a CUDA-enabled GPU:
```
conda install pytorch torchvision torchaudio cudatoolkit=11.3 -c pytorch
```

If you want to use CPU only:
```
conda install pytorch torchvision torchaudio cpuonly -c pytorch
```

#### PyBullet

Installing PyBullet with pip requires build tools. I recommend using conda:

```
conda install -c conda-forge pybullet
```

### Installation

Install the package with pip:

```
pip install rrt-ml
```

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<!-- USAGE EXAMPLES -->
## Usage

Use this space to show useful examples of how a project can be used. Additional screenshots, code examples and demos work well in this space. You may also link to more resources.

_For more examples, please refer to the [Documentation](https://example.com)_

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<!-- ROADMAP -->
## Roadmap

- [x] Add Changelog
- [x] Add back to top links
- [ ] Add Additional Templates w/ Examples
- [ ] Add "components" document to easily copy & paste sections of the readme
- [ ] Multi-language Support
    - [ ] Chinese
    - [ ] Spanish

See the [open issues](https://github.com/othneildrew/Best-README-Template/issues) for a full list of proposed features (and known issues).

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<!-- CONTRIBUTING -->
## Contributing

Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are **greatly appreciated**.

If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement".
Don't forget to give the project a star! Thanks again!

1. Fork the Project
2. Create your Feature Branch (`git checkout -b feature/AmazingFeature`)
3. Commit your Changes (`git commit -m 'Add some AmazingFeature'`)
4. Push to the Branch (`git push origin feature/AmazingFeature`)
5. Open a Pull Request

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<!-- LICENSE -->
## License

Distributed under the MIT License. See `LICENSE.txt` for more information.

<p align="right">(<a href="#readme-top">back to top</a>)</p>



<!-- CONTACT -->
## Contact

Your Name - [@your_twitter](https://twitter.com/your_username) - email@example.com

Project Link: [https://github.com/your_username/repo_name](https://github.com/your_username/repo_name)

<p align="right">(<a href="#readme-top">back to top</a>)</p>



<!-- ACKNOWLEDGMENTS -->
## Acknowledgments

Use this space to list resources you find helpful and would like to give credit to. I've included a few of my favorites to kick things off!

* [Choose an Open Source License](https://choosealicense.com)
* [GitHub Emoji Cheat Sheet](https://www.webpagefx.com/tools/emoji-cheat-sheet)
* [Malven's Flexbox Cheatsheet](https://flexbox.malven.co/)
* [Malven's Grid Cheatsheet](https://grid.malven.co/)
* [Img Shields](https://shields.io)
* [GitHub Pages](https://pages.github.com)
* [Font Awesome](https://fontawesome.com)
* [React Icons](https://react-icons.github.io/react-icons/search)

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