Machine (supervised) learning and board game

by vitor morev   Last Updated May 15, 2019 16:19 PM

Let's assume there's a board game like chess or go (in other words, perfect information game). We don't know how to write a good evaluation function for this type of board games, but we have a lot of played games with results. How can we use scikit-learn to predict a game result of a given position?

I tried classical minimax with alpha-beta pruning and greedy evaluation fuction, but it didn't work good when strategy (not only tactics) was required, so I decided to get an evaluation function by machine learning.



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