This is pretty interesting. DeepMind’s last triumph (beating the best human Go players with AlphaGo) is impressive, but Go is a great fit for neural networks as they stand today; it’s stateless, so you can fully evaluate your position based on the state of the board at a given turn. That’s not a good fit for most real-world problems, where you have to remember something that happened in the past. E.g. the ‘fog of war…
I think you're thinking about the difference between full information games vs partial information games, rather than stateless games (the state of a starcraft game is stored on a central server, the computer just has no access to it). There's already been excellent progress in poker AI based on traditional game theory, see http://poker.cs.ualberta.ca/ .
For instance, as any protoss player knows this all too well, a Zealot placed a mere pixel away from where it should could allow a stream of zerglings in, completely throwing any game plan you had up to that point out the window and enter crisis management mode. Usually this is a "mistake", which a computer may never make, but it needs to learn the relevance of such pixel-sized mistakes, which is unlikely to have as much of an impact in a poker game or Go game as it would in StarCraft.