This paper examines a subset of machine learning called reinforcement learning. In this paper we cover the basics of reinforcement learning and study one of reinforcement learning algorithms, Q-learning, and with the help of Python programming language, we apply this algorithm on a practical example. The first chapter of this paper contains an introduction in which we inform the reader how, nowadays, artificial intelligence and machine learning are increasingly being developed and applied in everyday life. We introduce some basic problems that machine learning deals with in order to get a basic perception of this area of computer science. In the second chapter of this paper we are introduced to the basic concepts of machine learning and the three types of machine learning: supervised, unsupervised and reinforcement learning. The third chapter is intended exclusively for the study of reinforcement learning. In this chapter we can see where all the reinforcement learning is applied and how it differs from other types of machine learning. After that, we study the characteristics of reinforcement learning and basic components. We learn about typical reinforcement learning scenario and study who the agent is, what the environment is, and what the states, actions and rewards are. After learning basics of reinforcement learning, in Chapter 4, we study about reinforcement learning algorithms. The fifth chapter is devoted to Markov Decision Process, where we can see how to mathematically formulate the problem of reinforcement learning. Finally, in Chapter Six, we study a certain reinforcement learning algorithm called Q-learning, and in one simple example, we can see how to implement that algorithm.