Here are some advantages of using Q-Learning over Deep Q-Learning:

Simplicity: Q-Learning is a simple algorithm that is easy to understand and implement. It does not require a deep neural network, which can make it faster and less computationally expensive.

Explainability: The Q-value table in Q-Learning is transparent, making it easy to understand how the agent is making decisions. In Deep Q-Learning, it can be difficult to understand what is happening inside the deep neural network.

Sample Efficiency: Q-Learning can converge to an optimal policy faster than Deep Q-Learning because it does not require large amounts of data to learn.

Robustness: Q-Learning is more robust to changes in the environment compared to Deep Q-Learning. This is because the Q-value table can be updated more quickly and easily than a deep neural network.

Small State Spaces: Q-Learning works best with small state spaces, whereas Deep Q-Learning is better suited for larger state spaces.

Stochastic Environments: Q-Learning is well-suited for environments where the transition dynamics are stochastic and the reward function is deterministic. Deep Q-Learning, on the other hand, is better suited for environments where the transition dynamics and reward functions are more complex.
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