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:

   • Download and install XQuartz from https://www.xquartz.org/.

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