# Predict Global Moves

This branch serves to test strategies to predict various global moves.

It seems that in the end it boils down to find an approximate model for the transition probability T(s, s') Given the current state s' we can either try to invert the equation for s or, just throw random configurations until we find a configuration where T is sufficiently large. For interpolating the function T and throwing random configurations s against it we can use a feed-forward network. If we want to go for inverting the function for s we can use a Deep Belief network or any other (and hopefully simpler) generative network.