Reinforcement learning aims to create algorithms that can learn and adapt to environmental changes. This programming technique is based on the concept of receiving external stimuli depending on the algorithm choices. A correct choice will involve a premium while an incorrect choice will lead to a penalty. The goal of system is to achieve the best possible result, of course.

In supervised learning, there is a teacher that tells the system which is the correct output (learning with a teacher). This is not always possible. Often we have only qualitative information (sometimes binary, right/wrong, or success/failure).

The information available is called reinforcement signals. But the system does not give any information on how to update the agent's behavior (that is, weights). You cannot define a cost function or a gradient. The goal of the system is to create the smart agents that have a machinery able to learn from their experience.

This flowchart shows reinforcement learning: