In recent years, the real-time strategy game of StarCraft has emerged by consensus as an important challenge for AI research. It combines several major difficulties that are intractable for many existing algorithms: a large, structured action space; imperfect information about the opponent; a partially observed map; and cycles in the strategy space. Each of these challenges represents a major difficulty faced by real-world applications, for example those based on internet-scale action spaces, game theory in e.g. security, point-and-click interfaces, or robust AI in the presence of diverse and potentially exploitative user strategies. Here, we introduce AlphaStar: a novel combination of deep learning and reinforcement learning that mastered this challenging domain and defeated human professional players for the first time.