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Lunar-Lander

Implement reinforcement learning to train lunar lander to land on the moon safely. Assignment from Machine Learning Specialization by DeepLearning.AI and Standford

  • Write an unsupervised learning algorithm to Land the Lunar Lander Using Deep Q-Learning

    • The Rover was trained to land correctly on the surface, correctly between the flags as indicators after many unsuccessful attempts in learning how to do it.
    • The final landing after training the agent using appropriate parameters :

Instruction

Tested on Python 3.11

  1. Create virtual environment

    python -m venv lunar_lander

    Activate the virtual environment on Linux/macOS

    source lunar_lander/bin/activate

    Activate the virtual environment on Windows

    my_env\Scripts\activate.bat

  2. Install Xvfb to use pyvirtualdisplay for virtual display

    MacOS: Xvfb is not directly available on macOS. However, you can install XQuartz, which provides an X11 server that includes Xvfb functionality:

  3. Install swig for box2d-py and gym[box2d]

    MacOS:

    brew install swig

  4. Install dependencies

    pip install -r requirements.txt

  5. Start the program Run the .ipynb file on Jupyter Notebook or Jupyer Lab