I'm currently in the process of developing an AI for a popular card game here in Germany (called "Schafkopf"). Obviously, one could try to find a perfect strategy with the help of some game theory, but I tried the path with ML. Now after implementing the game and going down the line with a deep q-learning (reinforcement learning) approach, I faced the following issue:
I ran the agent for about five hours and my q-table grow to a size of ~49k rows. Therefore concluding that a q-table is ineffective for a game with a tremendous amount of states (i.e. cards dealt to you, cards left in the stack for each given round ("card counting"), what cards are considered to be trump cards and so forth).
Now my question arises: Is there a more efficient way / approach to such card games? Genetic algorithms? Supervised learning?