In this master thesis we have acquainted the reader with AlphaZero, DeepMind’s algorithm capable of achieving, tabula rasa, superhuman performance in many challenging domains, such as chess, shogi (Japanese chess) and Go. Not only has AlphaZero managed to convincingly defeat the previous world-champions in all the aforementioned games, but its exceptional abilities have been described by chess grandmasters as those to be expected from a superhuman extraterrestrial species. The creation of an algorithm which would be able to achieve, tabula rasa, superhuman skills in various challenging domains has been a longstanding objective of artificial intelligence. AlphaZero, with its ability to adapt to diverse game rules, can be considered to be its realization and a major step towards the attainment of a general game playing system. In this thesis we have explained the basic theoretical concepts underpininng AlphaZero, thoroughly described the structure of the AlphaZero method and demonstrated its possibilities by implementing it for a game called Connect Four (or Four in a Row) using Python as the programming language and its additional libriaries. In chapter 1 we have introduced the pertinent concepts of game theory and artifical intelligence (with emphasis on reinforcement learning — a class of algorithms AlphaZero itself belongs to), studied both which charateristics are common to the problems successfully solved by AlphaZero and how to formally model such problems, as well as facilitated the understanding of the foundations of the AlphaZero method through making sense of reinforcement learning in general. The games AlphaZero has managed to master (such as chess, shogi and Go) have been formulated in the context of game theory as combinatorial games, which are deterministic, zero-sum, perfect information extensive games with two players. Moreover, we have desribed alternating Markov games, whose formalism AlphaZero follows and to which the AlphaZero method can be applied most directly, along with their special case, Markov decision processes (a formulation of the problems solved by reinforcement learning algorithms). In chapter 2 we have comprehensively described the structure of the AlphaZero method; with Monte Carlo tree search, self-play and supervised learning being its three main components, we have expounded on each one of them and elucidated on how they all come together to form a functional whole that constitues the AlphaZero algorithm. In chapter 3 we have discussed certain details of our AlphaZero implementation for the game of Connect Four, as well as presented the results: the progress in game-playing abilities over the course of training and the achieved level of play at the end of training. We have demonstrated our agents’ strengths through plays against other Connect Four algorithms, such as a minimax-based algorithm, as well as in matches against humans. Finally, as part of this master thesis, we have implemented and described a graphical user interface which allows human players to compete against agents obtained during the training process. By creating such a game implementation, we have “materialized” the obtained results and produced a ”tangible” final product of this master thesis.