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DeepMind struggles to overcome next milestone: ‘StarCraft II’

‘StarCraft’ is considered an important step in machine learning as the players cannot see the entire game environment and play simultaneously, rather than taking turns

By Jeremy Kahn  /  Bloomberg

Members of team Faze compete in the final match at the Intel Extreme Masters Expo in Katowice, Poland, on March 5. The expo was part of a world championship in which players competed in two games: Counter-Strike: Global Offensive and StarCraft II.

Photo: EPA

DeepMind, the Alphabet Inc-owned artificial intelligence (AI) company best known for creating software capable of beating the world’s best go players, has targeted StarCraft II as its next big research milestone, but so far, space is proving a difficult frontier for the company’s algorithms.

DeepMind’s algorithms, including those that performed with superhuman skill across a host of classic Atari titles, “cannot win a single game against the easiest built-in AI” in StarCraft II, let alone challenge skilled humans, the company said in a blog post on Wednesday.

The built-in agents, which are created by StarCraft publisher Activision Blizzard Inc, use hard-coded rules to determine their gameplay rather than the kinds of advanced machine learning techniques in which DeepMind specializes.

The company said new breakthroughs in machine learning would be required for its software agents to master the game.

The post did not reveal how close DeepMind might be to such breakthroughs.

The algorithm that mastered the Atari games was unveiled in June last year. Since then, DeepMind has published a number of research papers that hint it might be closing in on creating software capable of many of the tasks — such as prioritizing goals, long-term planning and memory — that any system would need to play StarCraft II successfully.

The company said that its algorithms performed well at learning some basic steps, such as moving around the game environment and selecting units, that would be critical to mastering the game.

StarCraft is considered an important target for machine-learning researchers because, unlike go, in which both players can see the entire board and take turns moving pieces, players in StarCraft cannot see what is happening in the entire game environment at one time and both players move their units simultaneously.

The game also requires players to carry out sub-tasks, such as building structures and mining resources, while also conducting reconnaissance, mounting attacks and defending territory.

To succeed, a player needs to have a good memory, prioritize among tasks and plan under conditions of uncertainty.

Because of these factors, StarCraft II comes much closer to approximating many real-world situations than games such as chess, go or even poker.

StarCraft II is also used in e-sports competitions, so there are highly skilled human opponents with which an AI can match wits.

To help computer scientists use StarCraft II as a testbed for AI, DeepMind has partnered with Blizzard to create an interface that allows outside software to access and play the game.

The two companies this week unveiled the interface, along with a set of tools to help other computer scientists train AI agents to play the game, at a machine learning conference in Australia.

Among the tools Blizzard is making public are a dataset of anonymized game replays — recordings of humans playing the game — that computer scientist would be able to use to help train their systems.

“One technique that we know allows our agents to learn stronger policies is imitation learning,” DeepMind said in its blog post. “This kind of training will soon be far easier thanks to Blizzard, which has committed to ongoing releases of hundreds of thousands of anonymized replays.”

DeepMind also said it was releasing a series of “mini-game” environments that would help researchers train their AI agents on basic components of the game.

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