Self Driving Car

Overview

This project aims to develop a self-driving car using reinforcement learning techniques. The project includes a Jupyter Notebook that implements the necessary algorithms and models to simulate and train a self-driving car.

Tech Stack

• Programming Language: Python
• Frameworks and Libraries:
  • Stable_baselines3
  • Tensorflow
  • Keras
  • OpenAI Gym
  • NumPy
  • Matplotlib
• Tools:
  • Jupyter Notebook

Features

• Reinforcement Learning: Implement reinforcement learning algorithms to train the self-driving car.
• Simulation Environment: Use OpenAI Gym to create and manage the simulation environment.
• Data Visualization: Utilize Matplotlib for visualizing the car's learning process and performance metrics.
• Modular Code Structure: Organized code with clear modules for environment setup, model training, and evaluation.

Installation

1. Clone the repository:

   git clone https://github.com/TilakSanghvi/Self_Driving_Car.git

2. Navigate to the project directory:

   cd Self_Driving_Car

3. Install the required dependencies:

   pip install -r requirements.txt

Usage

1. Open the Jupyter Notebook:

   jupyter notebook Self_Driving_Car_RL.ipynb

2. Run the cells in the notebook to start training the self-driving car model.

Contributing

Contributions are welcome! Please follow these steps to contribute:

1. Fork the repository.
2. Create a new branch (git checkout -b feature/your-feature-name).
3. Make your changes and commit them (git commit -m 'Add some feature').
4. Push to the branch (git push origin feature/your-feature-name).
5. Create a new Pull Request.

License

This project is licensed under the MIT License. See the LICENSE file for more details.

Contact

For any inquiries, please contact Tilak Sanghvi at tilakcsanghvi@gmail.com.