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AlphaStar has to make a lot of decisions that may not have immediate effects. It has to make predictions of future moves that the player could make and adapt to said circumstances. This technology can have a lot of use outside of just SC2.

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The neural network architecture of AlphaStar is capable of modelling long sequences of actions based on imperfect information. The fundamental problem of making complex predictions over very long sequences of data appears in many real world challenges, such as weather prediction, language interpretation and more. Significant advances can be made in these domains using the developments from the AlphaStar project.

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StarCraft II is a real time strategy game, usually played between two players. Players can play 3 different races, the human race Terran, the parasitic alien race Zerg and the technologically advanced alien race Protoss. Players have to train units at the start of every game to collect resources, train armies and destroy the opponent's base.

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Massively multiplayer online role-playing game

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AlphaStar mastered SC2 using a deep learning network that was trained initially by observing human game replays, but the bulk of the training was done by playing a series of matches against itself and learning through a process similar to evolution in nature, where the best traits are passed onto the next generation.
The AlphaStar agent currently only plays Protoss vs Protoss matches, but in the future Deepmind is looking towards training an agent that is capable of handling all 9 match-ups.

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AlphaStar completed its training over a period of 2 weeks, and over those 2 weeks it played over 200 years worth of matches.
After those 2 weeks, this AI beat one of the best Protoss players MaNa in a Protoss vs. Protoss match.
The AI did not achieve this proficiency by being able to make more actions than the human players either. The average actions per minute (APM) of the AlphaStar agent was 280, which is significantly lower than the APM of professional players like MaNa who have an average APM of 400+.

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Unlike games like chess or Go where players see everything, crucial information is hidden from a StarCraft player and must be actively discovered by “scouting”.
So the AI is capable of adapting to human behavior and unpredictability, which can have several applications in solving problems outside of just StarCraft II

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This process of training deep learning networks to play video games can be crucial to play testing video games and help with finding bugs in the game development life cycle.

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For learning more about the AlphaStar project and other projects by Deepmind visit their official website.

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AlphaStar: Mastering the real-time strategy game StarCraft II

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Games have been used for decades as an important way to test and evaluate the performance of artificial intelligence systems. As capabilities have increased, the research community has sought games with increasing complexity that capture different elements of intelligence required to solve scientific and real-world problems. In recent years, StarCraft, considered to be one of the most challenging Real-Time Strategy (RTS) games and one of the longest-played esports of all time, has emerged by consensus as a “grand challenge” for AI research.