I understand some recent chess engines (like alphazero or muzero) are based on neural networks. This question is not specific to chess, any other game (e.g. go) would do, but I keep chess for concreteness.
I am not interested how these engines are trained but how they "look like" after training. (For concreteness, it suffices for me that they give a "score" for a given board position.) Somewhat similar questions have been asked here but I can't find what I need.
Is it correct to view them as a neural network? Or, is there some other major component like search trees or other type of algorithm. I'm assuming that answer here is "yes" in an approriate sense. Note: The alpha zero paper talks about various forms of tree search. It is not clear to me how they are used to play, and from the appendix apparently some other engines use very limited search, like depth 2? That would be already interesting to me.
What is the depth of the network? Any reference to precise parameters?
What is the input and output format of these networks? Is the input just an array with pieces positions (excluding three-fold repetition rules and similar secondary issues). What is the output? Some info is here but I can't quite understand how it works specifically e.g. for chess.
What activation functions are used?
