So I'm training a classification task which takes as input some description of the state of a 2x2x2 rubix cube and outputs the optimal move to take. And a potential problem I noticed is that in many states more than one move is optimal. In particular, only about 46% of states have only one optimal move, and 24% have 2, 12% have three, etc. So I have a few choices.
The options I thought of were
- have each data point choose an optimal move at random
- Do cross-entropy minimization with the data point containing the same probability for each optimal move. i.e (0.5,0.5,0,0,0,0,0,0,0) if the first two are optimal
- Discard states with more than one optimal move (this seems really bad)
What is the standard practice? Also, is there a difference between 1 and 2?
If necessary you may assume that I'm using a neural network model