Supervised learning for a turn-based game? So I have 4GB of turn-by-turn data for many games of a particular strategy game. It appears that most people interested in using ML to build an AI for turn-based games use reinforcement learning to build a model on the fly.

Since I already have really good data, can I use supervised learning to solve this task? 

EDIT: I was considering using regression to assign a score to a given action based on its likelihood of eventually resulting in a win; is this the right way to think about it?
Maybe the correct way of addressing this is by making sub optimizations of every step, even though it could be done by regression, I would suggest decision trees.

You have and advantage: A game is made of discrete steps, so in every moment you can "stop" and decide the best move based on your (possibly comprehensive) history of moves.

Supervised learning vs reinforcement learning for a simple self driving rc car