assistant: For environments with a relatively small number of possible states, Q-Learning has multiple advantages over Deep Q-Learning:
1) Computational efficiency: Looking up the Q-values associated with state-action pairs in a table can be faster and less computationally expensive than approximating them with a deep neural network.
2) Interpretability: Q-value tables are easier to manually interpret than the weights and biases of a deep neural network. As a result, if an agent uses Q-Learning, it is oftentimes possible to manually inspect what it has learned and determine why it took a particular action.
3) Simplicity: Storing the Q-values associated with all possible state-action pairs in a table tends to be algorithmically simpler than approximating them with a model such as a deep neural network. This can reduce the amount of engineering effort required to develop and maintain systems with use Q-Learning.

However, it should be noted that for environments with a larger number of possible states, it is oftentimes necessary to approximate the Q-values with a model, rather than using a Q-value table. In other words, vanilla Q-Learning does not scale well to environments with a large number of possible states and the advantages above are not applicable to all domains.

prompter: What is the difference between an offline and an online learning algorithm?

assistant: An online learning algorithm trains on-, and interfaces with a dataset where newly generated input is added through a feedback loop. Whereas an offline learning algorithm trains on a static dataset that is ingested all at once. No new input is added to the dataset.

'Online' refers in this case to feedback loop by which the learning algorithm is iteratively exposed to new input to train on.

An example of an online learning algorithm:
Our conversation where I will learn from my interaction with you - through the feedback you can provide on the answer I give.

An example of an offline learning algorithm: 
A learning algorithm that is being trained to distinguish between cats and dogs, on a static dataset.

Online learning algorithms have an advantage if what they are setup to predict changes over time (for example: predicting fashion) or when feedback on the prediction output can be acquired over time (for example: our conversation). 

Offline learning algorithms tend to be better at detecting stable patterns (for example: what is a cat and what a dog) and when quality training data is available.

assistant: Offline learning and online learning are two different approaches in machine learning.

Offline learning refers to a learning process that takes place without any interactions with the environment. In offline learning, all of the training data is available to the algorithm beforehand, and the algorithm trains on this data to produce a model. This model can then be used to make predictions on new, unseen data. Examples of offline learning algorithms include linear regression, decision trees, and k-nearest neighbors.

Online learning, on the other hand, refers to a learning process that takes place in real-time and involves interactions with the environment. In online learning, the algorithm receives new data over time and updates its model based on this data. The algorithm is able to learn and adapt to changes in the data distribution as it receives new data. Examples of online learning algorithms include neural networks, support vector machines, and reinforcement learning.

In summary, the main difference between offline and online learning is that offline learning algorithms use all of the training data beforehand to train a model, while online learning algorithms receive new data over time and update their model based on this data.

assistant: Offline learning refers to using a static dataset for your learning algorithm, while online learning means that you are operating on an ever growing dataset and learning occurs as the data becomes available. 
If you were to train a comment filter, for example, you could either get a dataset of inappropriate comments an train on the entire set (that would be offline learning), or you could update the dataset as you find more inappropriate comments and train the filter on the new data (that would be online learning).

assistant: Online learning means that you are doing the learning as the data comes in. If you are doing it offline you will instead use an already collected, static, dataset.

Online learning can thus be used to get a continuously updated model while offline model would have to run an explicit learning pass to bring updated to the model that is being used.

assistant: 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.