[FrozenVoid]

>a ConvNet to process individual frames combined with a recurrent layer to track state over time. > are harder to apply Thats an understatement: Starcraft is immune to Monte-Carlo approach or anything based on analyzing pixel data: The tree state of actual battle has thousands of choices pet unit per second with minor variations in location, there is no discrete state of chessboard(at best millions of cells): viewing the game at low-level(pixels) creates gigantic amount of data. units constantly move/attack/die and get blocked by other units/terrain.

Predicting an enemy move(MC simulation) will be impossible and you can make several moves per second(even at 120-140 APM) easily. That means 1.you need real-time response, unlike Go there isn't a time buffer to decide 2.you always need to react at the current time(or allowing enemy advances) 3.there are very few "good moves" in starcraft(moving randomly on the "board" will just waste time) , so MC simulation will miss them more than 99% of time due randomness.

MC approach is vastly inferior in this case, i think they'll be forced to operate on higher level strategy rather than just microing every unit optimally(i.e. treating it like chess in real-time). Brute-forcing billions of potential moves simply won't work.

[kaden]

>Brute-forcing billions of potential moves simply won't work.

The problem is all AI/ML is essentially recorded, recursive, constrained brute forcing.

[FrozenVoid]

You can apply it on higher level like that guy who bruteforced the 7roach rush for Zerg in SC2. http://lbrandy.com/blog/2010/11/using-genetic-algorithms-to-... Problem is that build orders are just optimizing the opening economy and these unbalanced openings will be just patched out in the future.