<s>[INST] Explain the difference between Q-Learning and Deep Q-Learning [/INST] 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.</s>\ <s>[INST] What is the difference between an offline and an online learning algorithm? [/INST] Offline learning:
Able to be computed by user on their phones or desktop computers without the need for external databases or feedback mechanisms that need to access online resources.
Online learning:
The computing power is extremely high and needs to be performed online, in addition some of the algorithms need to access databases and/or other systems to be able to perform the task.</s>