Reinforcement learning (RL) is a subset of machine learning that allows an AI-driven system (sometimes referred to as an agent) to learn through trial and error using feedback from its actions. This feedback is either negative or positive, signaled as punishment or reward with, of course, the aim of maximizing the reward function.
In terms of learning methods, RL is similar to supervised learning only in that it uses mapping between input and output, but that is the only thing they have in common. Whereas in supervised learning, the feedback contains the correct set of actions for the agent to follow. In RL there is no such answer key. The agent decides what to do itself to perform the task correctly.
Compared with unsupervised learning, RL has different goals. The goal of unsupervised learning is to find similarities or differences between data points. RL's goal is to find the most suitable action model to maximize total cumulative reward for the RL agent. With no training dataset, the RL problem is solved by the agent's own actions with input from the environment.
