I learn best through experimentation and example. I'm learning about neural networks and have (what I think) is a pretty good understanding of classification and regression and also supervised and unsupervised learning, but I've stumbled upon something I can't quiet figure out;
If I wanted to train an AI to play a complicated game; I'm thinking something like a RTS (eg. Age of Empires, Empire Earth etc.). In these types of games there is typically a number of entities controlled by the player (units, buildings) each with different capabilities. It seem like the problem of that the AI does would be classification (eg. choose that unit, and that action), however since the number of units is a variable how does one handle a classification problem in this way?
The only thing I can think of is multiple networks that do different stages (one for overall strategy, one for controlling this type of unit, one for that type of building etc.); but this seems like I'm making the problem to complicated.
Are there any good example of machine learning/neural networks learning complex games (not specifically RTS, but more complicated the Mario)?