Great tutorial. I see active learning as a halfway house between supervised learning and reinforcement learning, because requesting labels is an action (as in RL), but of a very limited, predefined type.

A lot of problems which we initially model as supervised learning are in reality, in a live situation, more like active learning. Going the whole hog to RL may be unnecessary. But still, bandit-type models may be the right fully general setting.

These topics are very much in the air at the moment with a lot of new interest in bandit models and algorithms.