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