To grasp these key concepts better, we can use the example of a teenager learning to drive. Let's assume that they have never been in a car, that this is the first time they are seeing one, and that they don't have any knowledge of how it works. There are three approaches to learning: 

1. They are given the keys and have to learn all by themselves, with no supervision at all.
2. Before being given the keys, they sit in the passenger seat for 100 hours and look at the expert driving in different weather conditions and on different roads.
3. They observe the expert driving but, most importantly, they have sessions where the expert provides feedback while driving. For example, the expert can give real-time instructions on how to park the car, and give direct feedback on how to stay in a lane.

As you may have guessed, the first case is a reinforcement learning approach where the agent has only sparse rewards from not breaking the car, pedestrians not yelling at them, and so on.

Regarding the second case, this is a passive IL approach with the competence that is acquired from the pure reproduction of the expert's actions. Overall, it's very close to a supervised learning approach.

The third and final case is an active IL approach that gives rise to a real imitation learning approach. In this case, it is required that, during the training phase, the expert instructs the learner on every move the learner makes.