How to structure and train an LSTM for real-time predictions in Keras?
The tough part about this problem is evaluating what moves are &quot;correct&quot; per se. In a fighting game sequence, there may be 2 or more moves which both work in theory in the current frame. If you are optimizing for knocking the other character out of frame, it would be useful to build a reward set which optimizes for this.
It may be worthwhile to build a data set that takes into account corresponding stick positional data and then labeling past and future time locations as advantageous/disadvantageous based on simulation, similar to a chess computer.
First you have to build something that can identify perfect combos- situations in which the enemy, regardless of their inputs, has no reliable way to escape the combo which results in high % or death. And then the problem becomes easier because the ai can just identify positions to set up this combo, and then executes them.
Train a cpu to combo via reinforcement, and then integrate with the identifier, and you'll have a pretty damn good bot.