Deep Q-Learning Aircraft Landing Control
Machine Learning
Deep Q-Learning
Python
Gymnasium
PyTorch
This project explores how different hyperparameters affect deep
q-learning performance through systematic experimentation. Using a
custom gymnasium environment simulating aircraft descent, I
investigated the complex relationships between batch size,
learning rate, discount factor, and exploration strategies.
The neural network architecture consisted of an input layer, 3
hidden layers (256 neurons each), and an output layer, utilizing
ReLu activation functions. This setup enabled the agent to learn
control policies for managing descent rate, angle, and positioning
while maintaining stability throughout the landing sequence.
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Key Achievements
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Custom Environment: Developed a gymnasium
environment for aircraft descent simulation with physics-based
reward shaping
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Parameter Optimization: Conducted comprehensive
studies across batch sizes, learning rates, discount factors,
and exploration rates
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Performance Improvement: Achieved 51% above
average performance (397.9 vs 263.5) through optimal parameter
selection
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Stability Analysis: Utilized surface plots and
statistical analysis to evaluate parameter influence on model
stability
Key Learnings
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Deep Q-Learning Implementation: Gained hands-on
experience in implementing and optimizing deep q-learning
algorithms
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Reward Engineering: Mastered the art of reward
shaping for complex control tasks, learning through multiple
iterations
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Parameter Sensitivity: Understood the delicate
balance between learning stability and performance through
hyperparameter tuning
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Data Analysis: Developed skills in analyzing
experimental results and creating meaningful visualizations
