What youve described is the basic workings of the Neural Net system.

Usually the bigger effort is matching it to the game mechanism and the AI problem(s) you are seeking the NN to solve for you.

That fisrt includes processing/interpretting of the game state (or sequence of game states) to classify and encode it in a form that works with a NN (as its Input)

Next is what is to be decided by the AI, and what outputs (actions directives) it needs to produce. (decode what the NN produces)

Those things can get quite complex depending on the game specifics (like having inputs for 3+ turns back in a game so that the NN can detect a Trend instead of just a static single-situational analysis (if thats the type of game it is....)

Next, you may have your training system, but what about how you acquire all the training data for that game (which again in complex games can be a monumental task)

You may have to adjust (fiddle with/trial and error) the particular solution NNs (the neuron counts, the layers, even breaking into seperate NNS for different sub problems). You then need some method that PROVES the NN created does (sufficiently) what you want it to.

Then the packaging into an operating game - and testing it with real people (if they are the opponents) who dont always do what you expect .

You may have to revise the capabilities of the AI upward to compete with the opponents (and modify and restart the whole proceess)

One consideration may be optimization - HOW the NN AI is used in the game. Ex- does it need to be run every cycle or can its results be allowed to work for a while - and then part of the logic might be to determine HOW often/when the AI needs to be rerun .

Some optimization methods may make use of cached partial analysis (the encoding part) to cut down on the total AI processing the NN will make use of.