Path-Planning DRL

A path planning and map coverage package based on Deep Reinforcement Learning

Table of Contents

1. About The Project
2. Getting Started
   • Prerequisites
   • Installation
3. Usage
4. Roadmap
5. Contributing
6. License
7. Contact
8. Acknowledgments

About The Project

The scope of the project is to create and research about Reinforcement Learning Map Coverage (and also Path Planning and Obstacle Avoidance) based methods. The model ideally should support both known and unknown obstacles that could be discovered using a sensor (e.g. LiDAR).

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Getting Started

This section contains useful information about how to setup the project and the environment.

Prerequisites

Create a new conda environment (optional but preferrable).

conda create -n <env_name> python==3.10

Installation

Below is an example of how you can instruct your audience on installing and setting up your app. This template doesn't rely on any external dependencies or services.

1. Clone the repo

 git clone https://github.com/your_username_/Project-Name.git

2. Install the requirements. (TODO)

    pip install -r requirements.txt

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Usage

Once you configured the environment you can procede to train the agent using the predefined commands. Use the run script:

    python run.py <flags>

Available flags:

• algo: "dqn", "a2c", "ppo", default="dqn"
• obs: "linear" or "image"
• sb: Use stablebaselines or not
• drone: Use drone environment (recommended)
• log: Log to W&B
• path: Print the path followed by the drone
• render: Show the environment
• easy: Use Easy Env (outdated)
• st: Use StEnv (outdated)

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Roadmap

• Simulate Lidar
• Add drone orientation
• Add Rover for Map Exploration
• Clean the code up.

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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

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

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Contact

Davide Buoso - davide.buoso@studenti.polito.it

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