OpenRacer

OpenRacer is an open-source platform that empowers you to train machine-learning models for autonomous racing. Built with Python and powered by a Unity-based 3D environment, OpenRacer offers a dynamic and accessible way to develop and test AI models in a simulated racing environment.

🚀 Features

• Python-Powered Training: Use Python to develop, train, and fine-tune your machine learning models.
• 3D Unity Environment: Experience immersive simulations with our Unity-based 3D racing environment, which connects to Python via WebSocket.
• Custom Track Creation: Generate your own tracks using coordinates provided by the server, or use premade tracks.
• Real-Time Progress Monitoring: Visualize your model's training progress and racing performance through an integrated web interface with live updates and detailed graphs.
• Hyperparameter Tuning: Easily adjust hyperparameters to optimize your model's performance.
• Open-Source Flexibility: Customize and extend the platform to suit your unique needs, thanks to its open-source codebase.

🎯 Impact

"Democratizing Autonomous Racing: Empowering Innovators and Learners to Shape the Future of AI."

OpenRacer is designed to lower the barrier to entry for autonomous racing and machine learning. Whether you're a hobbyist, a student, or a professional, our platform provides the tools you need to experiment, learn, and innovate in the field of AI.

📚 Getting Started

A stable version for wider use will be released soon

Nightly Installation

Prerequisites :

• Python 3.7+
• Unity 2022.3
• npm 9.5.0

Steps :

1. Clone the repository:

   git clone https://github.com/Loony4Logic/OpenRacer
   cd openracer

2. Install Python dependencies:

   cd "Python server"
   pip install -r requirements.txt

3. Open the Unity project:

   • Open Unity Hub, click on "Add Project," and select the OpenRacer folder within the project directory.

4. Start the web interface:

   cd React UI
   npm install
   npm start

🤝 Contributing

Contributions are welcome! Please create an issue or contact me through LinkedIn. I will add structured issues and guidelines after a while.

🛡️ License

This project is licensed under the MIT License - see the LICENSE file for details.

🙌 Acknowledgments

• Inspired by AWS DeepRacer.
• Special thanks to Tejas Nair for 3D model and Palak Agarwal.

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