In lots of games there are multiple phases or decision points that are not similar yet seem to have a dependency on one another when taking the perspective of the overall strategy of the player. A couple examples I thought up:
In a simple draw poker, you can have a strategy for discarding cards and a strategy for betting. They may not be mutually exclusive if you know your opponents betting will change with the number of cards you draw.
In Cribbage there are two phases, Discard to crib and the Play. The Play phase is definitely dependent on which cards are discarded in the discard phase. So it seems knowledge of Play strategy would be needed to make the Discard decision.
The intent is to learn how to set up an unsupervised learning algorithm to play a game with multiple types of decision making. Doesn't matter the game. I'm at a loss at the highest level in what ML models to learn to use for this scenario. I don't think a single NN would work because of the different decision types.
My question is how are these dependencies handled in ML? What are some known algorithms/models that can handle this?
I'm at a loss on what to even search for so feel free to dump some terminology and keywords on me. =)