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From Wikipedia:
Active learning is a special case of machine learning in which a learning algorithm can interactively query a user (or some other information source) to label new data points with the desired outputs.
Reinforcement learning (RL) is an area of machine learning concerned with how software agents ought to take actions in an environment in order to maximize the notion of cumulative reward.
How to distinguish them? What are the exact differences?
Active learning is based on the concept, if a learning algorithm can choose the data it wants to learn from, it can perform better than traditional methods with substantially less data for training. So it's kind of semi-supervised machine learning.
Reinforcement Learning is a type of machine learning technique that enables an agent to learn in an interactive environment by trial and error using feedback from its own actions and experiences. It is based on rewards and punishments mechanism which can be both active and passive.
In case of passive RL, the agent’s policy is fixed which means that it is told what to do. In contrast to this, in active RL, an agent needs to decide what to do as there’s no fixed policy that it can act on. Therefore, the goal of a passive RL agent is to execute a fixed policy (sequence of actions) and evaluate it while that of an active RL agent is to act and learn an optimal policy.
So, Active Learning is more like a concept and RL is a approach of solving problem as demonstrated in this paper
References:
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 "reinforcement signal" that tells us "how good" 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.
