LunarLander

 

About the Project

LunarLander is a classical problem in reinforcement learning, the goal is to control a lunar lander to safely land it on a designated landing pad. The environment provides continuous control over the lander's engines and rewards the agent based on its landing success and fuel usage.

 

 

States

• Lander Co-ordinates (x,y)
• Lander Velocity in x and y
• Angle (theta)
• Angular Velocity (theta velocity)
• Two booleans - left leg contact, right leg contact on ground or not

Actions

0: do nothing
1: fire left orientation engine
2: fire main engine
3: fire right orientation engine

Reward

• Increase if lander is closer to landing pad and decrease if its farther
• Increase if lander is moving slowly and decrease if its moving too fast
• Decrease the more lander tilts
• Increase 10 points when each leg lands the ground
• Decrease 0.3 points each frame the main engine is firing
• Decrease 0.03 points each frame the side engine is firing
• Grant 100 points for safe landing
• Deduct 100 points for crashing

 

Approaches used:

• DQN
• DDQN
• Advantage Actor-Critic (A2C)

 

Install library

pip install torch torchvision torchaudio gymnasium
pip install wheel setuptools pip --upgrade
pip install swig gymnasium[box2d] pygame

Train Model

python lunar_lander.py train 1000

Test Model

python lunar_lander.py model/[file_name]

 

Contact

Arcadio - @Arcadio Arcadio de Paula Fernandez
Hamna - @Hamna Ashraf
Kyle - @Kun Chen

Project Link: Lunar Lander