I'm an experienced programmer, but new to AI. I'm trying to learn ML techniques by building a bot to play a video game. I'm documenting my process as I go. The game I'm focusing on 1st is TFT (TeamFightTactics) which is in the AutoBattler genre - Essentially, you try to build the strongest army, but there are millions of choices & pros/cons to each. My current plan is to use a NeuralNetwork (TensorFlow) to learn the complexities of which armies are strong/weak against each other. I'm planning to hard-code the obvious rules/strategies to the game to make it easier on the NN. One of my big fears is the difficulty in gathering a large dataset of training data, so any advice on workarounds for that would be great. I'm thinking maybe split the game up into different types of decisions & use a different ML technique specifically targeted for each one. Hopefully that creates smaller NNs that can learn patterns easier with less data. Any advice on topics I should research? Thanks!
Make two bots (with the initial random actions) to play one against the other, and to learn during the play.