I am currently working on a project to build and train a neural network to play DotA 2 (at least be able to play like a really bad human).
The thing is all the neural network I have built before were made to play small games like 2048 or snake so the inputs were pretty straight-forward. (e.g for 2048 each input neuron was a tile of the game and the value was normalized from 2-2048 to 0-1).
But for DotA2 things get really more complicated because I have to encode a huge amount of possible objects: heros, creeps, towers, etc...
At the moment I am thinking about maybe dividing a simplified version of the map into tiles (about 3000 tiles) and have each tile related to 1 input but then again, how to encode such complex data ?
Hoping to read from some of you experts out here,