Hi,
i’m trying to build an effective AI for the Buraco card game (2 and 4 players).
I want to avoid the heuristic approach : i’m not an expert of the game and for the last games i’ve developed this way i obtained mediocre results with that path.
I know the montecarlo tree search algorithm, i’ve used it for a checkers game with discrete result but I’m really confused by the recent success of other Machine Learning options.
For example i found this answer in stack overflow that really puzzles me, it says :
"So again: build a bot which can play against itself. One common basis is a function Q(S,a) which assigns to any game state and possible action of the player a value -- this is called Q-learning. And this function is often implemented as a neural network ... although I would think it does not need to be that sophisticated here.”
I’m very new to Machine Learning (this should be Reinforcement Learning, right?) and i only know a little of Q-learning but it sounds like a great idea: i take my bot, making play against itself and then it learns from its results… the problem is that i have no idea how to start! (and neither if this approach could be good or not).
Could you help me to get the right direction?
Is the Q-learning strategy a good one for my domain?
Is the Montecarlo still the best option for me?
Would it work well in a 4 players game like Buraco (2 opponents and 1 team mate)?
Is there any other method that i’m ignoring?
PS: My goal is to develop an enjoyable AI for a casual application, i can even consider the possibility to make the AI cheating for example by looking at the players hands or deck. Even with this, ehm, permission i would not be able to build a good heuristic, i think
Thank you guys for your help!
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