This model uses "a state based on the statistics of the board after a potential action. All predictions would be compared but the action with the best state would be used".
So at each iteration, it's feeding a set of future states, computed based on the current state (future state made up of statistics of the game like nb of holes in the board, cleared rows, total height...) to a neural network and outputs one "value" per future state.
So at every step, you predict N "values" from the neural network for all N possible future states for the one you are currently in and choose the greatest one as your future state and thus associated action.
Now, my issue: the implementation says it's "deep Q-learning", but I do not see it that way. The action, nor some sort of current state is given as input of the network.
Since it is feeding the "future states", for me, it looks more like a value iteration algorithm with a neural network or at least something where you know the transition model?
Did I miss something and it is actually DQN? If not, do you have any references for this kind of RL model? Does this have a name?