Some advantages of using Q-Learning over Deep Q-Learning are:

Simplicity: Q-Learning is a simple algorithm and requires less computational resources compared to Deep Q-Learning.
Better understanding: Q-Learning is easier to understand and interpret compared to Deep Q-Learning, which uses deep neural networks.
Stable convergence: Q-Learning has a proven convergence rate and is more stable compared to Deep Q-Learning, which can suffer from instability issues such as oscillations or divergence.
Less data-intensive: Deep Q-Learning requires large amounts of training data, while Q-Learning can learn from a small amount of data.
Better performance in small state spaces: In smaller state spaces, Q-Learning can perform better than Deep Q-Learning, as the latter may overfit the data and struggle to generalize.

It's worth noting that while Deep Q-Learning is generally considered stronger than Q-Learning, the choice between the two ultimately depends on the problem being solved and the available computational resources.
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