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.
- Programming Language: Python
- Frameworks and Libraries:
- Stable_baselines3
- Tensorflow
- Keras
- OpenAI Gym
- NumPy
- Matplotlib
- Tools:
- Jupyter Notebook
- 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.
- Clone the repository:
git clone https://github.com/TilakSanghvi/Self_Driving_Car.git
- Navigate to the project directory:
cd Self_Driving_Car
- Install the required dependencies:
pip install -r requirements.txt
- Open the Jupyter Notebook:
jupyter notebook Self_Driving_Car_RL.ipynb
- Run the cells in the notebook to start training the self-driving car model.
Contributions are welcome! Please follow these steps to contribute:
- Fork the repository.
- Create a new branch (
git checkout -b feature/your-feature-name
). - Make your changes and commit them (
git commit -m 'Add some feature'
). - Push to the branch (
git push origin feature/your-feature-name
). - Create a new Pull Request.
This project is licensed under the MIT License. See the LICENSE
file for more details.
For any inquiries, please contact Tilak Sanghvi at tilakcsanghvi@gmail.com.