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 this chapter, we dealt with the basics of reinforcement learning.
To begin with, we saw that the goal of learning with reinforcement is to create intelligent agents that are able to learn from their experience. So we analyzed the steps to follow to correctly apply a reinforcement learning algorithm. Later we explored the Agent-Environment interface. The entity that must achieve the goal is called an agent. The entity with which the agent must interact is called the environment, which corresponds...