I’m trying to create a machine that can play a certain dutch card game known as “Klaverjassen”. What I’m trying to do is generate randomly played games and have the algorithm learn good plays by looking at the outcome of these games. Then this improved bot will play games again, and keep learning, until it is sufficiently strong (AKA I can’t beat it anymore).

Each game is encoded as a feature/x-vector of 1’s and 0’s, with a single outcome y (which is the achieved score) between 0 and ~500. I was wondering what kind of regression algorithm would be good to learn this data. Simply using Linear regression isn’t going to be useful since there is a lot of interaction between the features, and manually adding in these interactions is infeasible since there is over 1,104 features.

I’m using MatLab, so algorithms/libraries in this language would be nice 🙂

For reference, this is what the data looks like: https://gofile.io/?c=kKs1rt (Matlab data file in zip)