INSUBCONTINENT EXCLUSIVE:
Losing to the computer in StarCraft has been a tradition of mine since the first game came out in 1998
Of course, the built-in &AI& is trivial for serious players to beat, and for years researchers have attempted to replicate human strategy
and skill in the latest version of the game
They&ve just made a huge leap with AlphaStar, which recently beat two leading pros 5-0.
The new system was created by DeepMind, and in many
ways it very unlike what you might call a &traditional& StarCraft AI
The computer opponents you can select in the game are really pretty dumb — they have basic built-in strategies, and know in general how to
attack and defend and how to progress down the tech tree
But they lack everything that makes a human player strong: adaptability, improvisation and imagination.
AlphaStar is different
It learned from watching humans play at first, but soon honed its skills by playing against facets of itself.
The first iterations watched
replays of games to learn the basics of µ& (i.e
controlling units effectively) and ¯o& (i.e
game economy and long-term goals) strategy
With this knowledge it was able to beat the in-game computer opponents on their hardest setting 95 percent of the time
But as any pro will tell you, that child play
So the real work started here.
Hundreds of agents were spawned and pitted against each other.
Because StarCraft is such a complex game, it
would be silly to think that there a single optimal strategy that works in all situations
So the machine learning agent was essentially split into hundreds of versions of itself, each given a slightly different task or strategy
One might attempt to achieve air superiority at all costs; another to focus on teching up; another to try various &cheese& attempts like
worker rushes and the like
Some were even given strong agents as targets, caring about nothing else but beating an already successful strategy.
This family of agents
fought and fought for hundreds of years of in-game time (undertaken in parallel, of course)
Over time the various agents learned (and of course reported back) various stratagems, from simple things such as how to scatter units under
an area-of-effect attack to complex multi-pronged offenses
Putting them all together produced the highly robust AlphaStar agent, with some 200 years of gameplay under its belt.
Most StarCraft II pros
are well younger than 200, so that a bit of an unfair advantage
There also the fact that AlphaStar, in its original incarnation anyway, has two other major benefits.
First, it gets its information
directly from the game engine, rather than having to observe the game screen — so it knows instantly that a unit is down to 20 HP without
Second, it can (though it doesn&t always) perform far more &actions per minute& than a human, because it isn&t limited by fleshy hands and
APM is just one measure among many that determines the outcome of a match, but it can&t hurt to be able to command a guy 20 times in a
second rather than two or three.
It worth noting here that AIs for micro control have existed for years, having demonstrated their prowess
in the original StarCraft
It incredibly useful to be able to perfectly cycle out units in a firefight so none takes lethal damage, or to perfectly time movements so
no attacker is idle, but the truth is good strategy beats good tactics pretty much every time
A good player can counter the perfect micro of an AI and take that valuable tool out of play.
AlphaStar was matched up against two pro
players, MaNa and TLO of the highly competitive Team Liquid
It beat them both handily, and the pros seemed excited rather than depressed by the machine learning system skill
Here game 2 against MaNa:
In comments after the game series, MaNa said:
I was impressed to see AlphaStar pull off advanced moves and
different strategies across almost every game, using a very human style of gameplay I wouldn&t have expected
I&ve realised how much my gameplay relies on forcing mistakes and being able to exploit human reactions, so this has put the game in a whole
We&re all excited to see what comes next.
And TLO, who actually is a Zerg main but gamely played Protoss for the experiment:
I was surprised
by how strong the agent was
AlphaStar takes well-known strategies and turns them on their head
The agent demonstrated strategies I hadn&t thought of before, which means there may still be new ways of playing the game that we haven&t
fully explored yet.
You can get the replays of the matches here.
AlphaStar is inarguably a strong player, but there are some important
First, when they handicapped the agent by making it play like a human, in that it had to move the camera around, could only click on visible
units, had a human-like delay on perception and so on, it was far less strong and in fact was beaten by MaNa
But that version, which perhaps may become the benchmark rather than its untethered cousin, is still under development, so for that and
other reasons it was never going to be as strong.
AlphaStar only plays Protoss, and the most successful versions of itself used very
micro-heavy units.
Most importantly, though, AlphaStar is still an extreme specialist
It only plays Protoss versus Protoss — probably has no idea what a Zerg looks like — with a single opponent, on a single map
As anyone who has played the game can tell you, the map and the races produce all kinds of variations, which massively complicate gameplay
In essence, AlphaStar is playing only a tiny fraction of the game — though admittedly many players also specialize like this.
That said,
the groundwork of designing a self-training agent is the hard part — the actual training is a matter of time and computing power
If it 1v1v1 on Bloodbath maybe it stalker/zealot time, while if it 2v2 on a big map with lots of elevation, out come the air units
(Is it obvious I&m not up on my SC2 strats)
The project continues and AlphaStar will grow stronger, naturally, but the team at DeepMind
thinks that some of the basics of the system, for instance how it efficiently visualizes the rest of the game as a result of every move it
makes, could be applied in many other areas where AIs must repeatedly make decisions that affect a complex and long-term series of
