Explain the difference between Q-Learning and Deep Q-Learning
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Q-Learning is a type of reinforcement learning algorithm that uses a Q-value table to store the expected return for a given state-action pair. It is a model-free, offline learning algorithm that iteratively updates the Q-value table in order to find the optimal policy. Q-Learning does not require a deep neural network, and can be used with simpler, tabular-based representations.

Deep Q-Learning is a type of reinforcement learning algorithm that uses a deep neural network to approximate the Q-value function. It is an online learning algorithm that updates the weights of the neural network in order to find the optimal policy. Deep Q-Learning requires a deep neural network, and can be used with more complex representations, such as images or video frames.
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