🚀 Deep Q-Learning for Lunar Lander 🛰️

🌟 Overview

Welcome to the Deep Q-Learning for Lunar Lander project! This project aims to train an intelligent agent to land a lunar module safely on a designated landing pad using Deep Q-Networks (DQN). The agent learns to control the lander's thrusters to achieve a smooth landing.

📓 Jupyter Notebook

This project is implemented in a Jupyter Notebook, making it easy to follow the code and see the results interactively.

📦 Installation

Follow these steps to set up the project on your local machine:

1. Clone the Repository:

   git clone https://github.com/harshaparida/LunarBot.git
   cd LunarBot

2. Install Dependencies: Install the required libraries using pip:

   pip install gymnasium
   pip install "gymnasium[atari, accept-rom-license]"
   pip install "gymnasium[box2d]"
   pip install torch numpy
   sudo apt-get install -y swig

3. Launch Jupyter Notebook: Start Jupyter Notebook to run the project:

   jupyter notebook

4. Open the Notebook: In Jupyter Notebook, open the _dqn.ipynb file to explore the project.

🚀 Usage

Training the Agent

Run the cells in the Jupyter Notebook to train the agent.

Watching the Trained Agent

After training, you can watch the trained agent in action by running the provided cells in the notebook.

🛠️ Libraries and Tools

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📁 Project Structure

• dqn.ipynb: Jupyter Notebook containing the implementation.
• README.md: This file.
• demo.gif: Demo GIF showing the trained agent in action.

📈 Results

The agent successfully learned to land the lunar module, solving the LunarLander-v2 environment in approximately 733 episodes, achieving an average score of over 200.

💡 Contributing

Contributions are welcome! Feel free to fork this repository, make improvements, and submit a pull request. Let's collaborate to make this project even better!

📬 Contact

For any inquiries or feedback, please reach out to my email.

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