Simplicity: Q-Learning is a simpler algorithm compared to Deep Q-Learning, which requires more computational power. Q-Learning can be used with smaller state spaces and can be easily implemented on a variety of environments.

Stable convergence: Q-Learning has a guaranteed convergence to the optimal solution under certain conditions, while Deep Q-Learning can suffer from instability and may not converge to the optimal solution.

Interpreting results: Q-Learning can be more interpretable as it does not use deep neural networks, making it easier to understand why the agent is making certain decisions.

Less computational power required: Q-Learning requires less computational power compared to Deep Q-Learning, making it suitable for real-time applications with limited resources.

More sample efficient: Q-Learning can be more sample efficient, meaning it can converge to the optimal solution using fewer samples of experience compared to Deep Q-Learning.
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