Dear Ktakuya332C,
I'm implementing the DeepCube in Python using TF/Keras.
I would like to ask about the convergence of ADI.
As I understand, the value of a state should reflect the minimum steps needed to solve the state.
My problem with AD is the following:
In the Vx = max(Vxi+A(x,i) ) rule xi can be a state, which is not in the training set. Thus Vxi can be anything, the v = max... rule doesn't apply to it.
Consider a chain with states getting further from the solution:
S0,S1, ..., S10
here: S0 is the solved state, Si needs minimum i rotation to solve it. Let S0,S1,...,S9 being in the training set, and S10 not being in the training set.
Given V(S0) = 0, V(S1) =1, V(S2)=2... V(S10) = 10 is a perfect solution with total loss being 0 for S0...S9. S10 has no neighbour with value 11, but it doesn't matter!
Similarly, V(S0)=0, V(S1) =1, V(S2)=0, V(S3)=-1, V(S4) = -2, V(S5) =-1, V(S6)=0, V(S7)=1 ...V(S10)=4 is a good solution.
In all the cases the value is not useful at all, since it increases instead of decreasing.
How to resolve this problem? My ADI - neural network converges to a solution like above mentioned.
Dear Ktakuya332C,
I'm implementing the DeepCube in Python using TF/Keras.
I would like to ask about the convergence of ADI.
As I understand, the value of a state should reflect the minimum steps needed to solve the state.
My problem with AD is the following:
In the Vx = max(Vxi+A(x,i) ) rule xi can be a state, which is not in the training set. Thus Vxi can be anything, the v = max... rule doesn't apply to it.
Consider a chain with states getting further from the solution:
S0,S1, ..., S10
here: S0 is the solved state, Si needs minimum i rotation to solve it. Let S0,S1,...,S9 being in the training set, and S10 not being in the training set.
Given V(S0) = 0, V(S1) =1, V(S2)=2... V(S10) = 10 is a perfect solution with total loss being 0 for S0...S9. S10 has no neighbour with value 11, but it doesn't matter!
Similarly, V(S0)=0, V(S1) =1, V(S2)=0, V(S3)=-1, V(S4) = -2, V(S5) =-1, V(S6)=0, V(S7)=1 ...V(S10)=4 is a good solution.
In all the cases the value is not useful at all, since it increases instead of decreasing.
How to resolve this problem? My ADI - neural network converges to a solution like above mentioned.