RL differs greatly from supervised learning. In supervised learning, each example is a pair consisting of an input object (typically a vector) and a desired output value (also called the supervisory signal). The supervised learning algorithm analyzes the training data and produces an inferred function, which can be used to map new examples.
RL does not provide an association between incoming data and the desired output values, so the learning structure is completely different. The main concept of RL is the presence of two components that interact with one another: an agent and an environment.
An RL agent learns to make decisions within an unfamiliar environment by performing a series of actions and obtaining the numerical rewards associated with them. By accumulating experience through a trial and error process, the agent learns which actions are the best to perform depending on the state it is in, defined by the environment and the set of previously performed actions. The agent...