I have recently been studying up on Machine Learning based chess engines and have begun to develop one of my own. I was wondering, realistically, doesn't the board technically contain all of the data used to predict any number of moves into the future? From what I can tell, models like AlphaGo use a search algorithm to pick which moves it should investigate further in order to make its move.
what if a RL model were to just be trained on the current position, not any other data besides what anyone could see from looking at the board. through training, would the model develop its own method of searching for future moves in order to maximize its reward potential in a game naturally?
I assume that if the model developed its own algo it would be significantly faster and more optimized at returning good moves with a depth level that would make it competent at the game.
Is there something I am missing?