These in turn allow a player to harvest other resources, build more sophisticated bases and structures, and develop new capabilities that can be used to outwit the opponent. Each player starts with a number of worker units, which gather basic resources to build more units and structures and create new technologies. To start, a player must choose to play one of three different alien “races” - Zerg, Protoss or Terran, all of which have distinctive characteristics and abilities (although professional players tend to specialise in one race). There are several different ways to play the game, but in esports the most common is a 1v1 tournament played over five games.
In contrast, AlphaStar plays the full game of StarCraft II, using a deep neural network that is trained directly from raw game data by supervised learning and reinforcement learning.
Even with these modifications, no system has come anywhere close to rivalling the skill of professional players. The best results were made possible by hand-crafting major elements of the system, imposing significant restrictions on the game rules, giving systems superhuman capabilities, or by playing on simplified maps. The matches took place under professional match conditions on a competitive ladder map and without any game restrictions.Īlthough there have been significant successes in video games such as Atari, Mario, Quake III Arena Capture the Flag, and Dota 2, until now, AI techniques have struggled to cope with the complexity of StarCraft. In a series of test matches held on 19 December, AlphaStar decisively beat Team Liquid’s Grzegorz " MaNa" Komincz, one of the world’s strongest professional StarCraft players, 5-0, following a successful benchmark match against his team-mate Dario “ TLO” Wünsch. Now, we introduce our StarCraft II program AlphaStar, the first Artificial Intelligence to defeat a top professional player.
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