This study describes the AlphaZero algorithm, a general-purpose reinforcement learning algorithm that can achieve superhuman performance in challenging domains. It surpasses the capabilities of traditional game-playing programs in chess, shogi, and Go, demonstrating its ability to learn from scratch and master complex games without relying on handcrafted domain knowledge. AlphaZero utilizes deep neural networks and Monte-Carlo tree search (MCTS) to guide its gameplay, focusing on the most promising variations during its search. The study contrasts AlphaZero's approach to the widely used alpha-beta search, which focuses on evaluating large numbers of positions. Additionally, it analyzes AlphaZero's chess knowledge, finding that it independently discovered and frequently played common human openings, further solidifying its mastery of the game.