Researchers at leading AI firm DeepMind developed a programme called AlphaStar capable of reaching the top eSport league for the popular video game, ranking among the top 0.2 per cent of all human players.
A paper detailing the achievement, published in the scientific journal Nature, reveals how a technique called reinforcement learning allowed the algorithm to essentially teach itself effective strategies and counter-strategies.
"The history of progress in artificial intelligence has been marked by milestone achievements in games. Ever since computers cracked Go, chess and poker, StarCraft has emerged by consensus as the next grand challenge," said David Silver, a principal research scientist at DeepMind.
"The game's complexity is much greater than chess, because players control hundreds of units; more complex than Go, because there are 1026 possible choices for every move; and players have less information about their opponents than in poker."
DeepMind, which was acquired by Google in 2014, was behind the first ever AI algorithm capable of beating a human champion at the ancient Chinese board game Go.
In 2016, the firm's AlphaGo program defeated grandmaster Lee Sedol, who is recognised as the best player in the world. The pioneering match finished 4-1, however improvements to the algorithm have seen new generations of AlphaGo defeat the original version.
A modified version of AlphaZero has since gone on to master other two player games like chess and shogi to a superhuman level.
"StarCraft has been a grand challenge for AI researchers for over 15 years," said DeepMind co-founder Demis Hassabis. "These impressive results mark an important step forward in our mission to create intelligent systems that will accelerate scientific discovery."
Professional StarCraft players described AlphaStar's technique as unusual but on a similar level to the very best human players of the game.
"AlphaStar is an intriguiing and unorthodox player - one with the reflexes and speed of the best pros but strategies and a styled that are entirely on its own," said Diego 'Kelazhur' Schwimmer, a professional StarCraft II player for eSports team Panda Global.
"The way AlphaStar was trained, with agents competing against each other in a league, has resulted in gameplay that's unimaginably unusual... Though some of AlphaStar's strategies may at first seem strange, I can't help but wonder if combining all the different play styles it demonstrated could actually be the best way to play the game."