They way most modern neural networks work is by defining training data and then building networks of decision trees. I've dabbled in some tensorflow network designs, and it takes a long time to get used to how it works, and get your mind wrapped around a lot of the concepts. However, someone with some experience could easily build a network in a few months time.
At it's most basic level, the neural network has a series of points to spend. Time Units are the currency of the game in Battlescape. Because of which, it actually becomes easier to define the network. You can spend those points by moving, attacking, throwing, or various other things, but it's still just a currency. Now, selecting the right thing to spend that currency on is the trick. First, you develop a hierarchy of networks. One for scouting(when no enemies are seen), another for attacking(for when an enemy is spotted), and maybe another one for retreating(for when forces are too strong), and perhaps a fourth one for regrouping. You train each of the sub-networks in isolation, then combine them for a more strategic AI that oversees it all.
For scouting, you define the success of the network by it's ability to find each enemy, and weigh that against defensive metric(have they ended up in a spot with a good defence ability, like behind a wall or in some smoke). For attacking, the success criteria is a lot easier, kill everything with minimal damage to your own units. For retreating, you have to get your units back to a point without killing them. And finally, for regrouping, the success depends on the ability to get everyone to a point with some defensive ability. Once you've trained each of the individual AIs, then you can easily train a strategic AI.
All of this is months and months of work. I won't have time to do this, but someone with some more interest in this could do it.
