What is the difference between imitation learning and classification done by experts?

In short, imitation learning means learning from the experts. Suppose I have a dataset with labels based on the actions of experts. I use a simple binary classifier algorithm to assess whether it is good expert action or bad expert action.

How is this binary classification different from imitation learning?

Imitation learning is associated with reinforcement learning, but, in this case, it looks more like a basic classification problem to me.

What is the difference between imitation learning and classification done by experts?

I am getting confused because imitation learning relates to reinforcement learning while classification relates to supervised learning.

This scenario is impractical in most settings because the table for the optimal policy will be too big. However, if you have enough entries from the table, you can use a general function approximator such as a neural network to find the optimal action. Again, you do not need to look at the rewards, but only at the state $$\rightarrow$$ action mappings. I do not know imitation learning in detail beyond this, but I suspect in the case of discrete actions (such as in Chess, Go), it would be trained with a cross-entropy objective as is typical of classification tasks.