Today's post will introduce you to the model-free methods of Reinforcement Learning (RL). To have a model of the environment we need to store all the states and actions. To do so, we are limited to the infrastructure limits, that means we need to find another approach when the environment is too big and with too many variables. Therefore, model-free approaches can handle the problems where the world is too big to fit our infrastructures. To better comprehension, the Q-Learning algorithm will be presented and explained. Robot infinite environment - Photo by Dominik Scythe on Unsplash Terminologies Figure 1 - Agent-environment interaction Agent — The learner and the one that makes actions. The agent's goal is to maximise the cumulative reward across a set of actions and states. Action — A set of actions which the agent can perform. Different environments allow the agent to perform distinct kinds of actions. The set of all valid actions in a given environment is usually denomi