What is the difference between active learning and reinforcement learning?
Active learning is a technique that is applied to Supervised Learning settings. In the supervised learning paradigm, you train a system by providing inputs and expected outputs (labels). The system learns to mimic the training data, ideally generalizing it to unseen but extrapolable cases. Active learning is applied normally in cases where obtaining labels is expensive so, we obtain new labels dynamically, defining an algorithmic strategy to maximize the usefulness of the new data points.
Reinforcement learning is a different paradigm, where we don't have labels, and therefore cannot use supervised learning. Instead of labels, we have a &quot;reinforcement signal&quot; that tells us &quot;how good&quot; the current outputs of the system being trained are.  Therefore, in reinforcement learning the system (ideally) learns a strategy to obtain as good rewards as possible.