I think I'm having a bit of trouble wrapping my head around how a transposition table functions:
As I understand it you can store a value (simulation result?) for a given game state in this (hash) table and use it instead of a simulation when that game state is encountered again. However, since the simulation is (or at least in my case is) completely random play, I would think that you need to build up a sample size before the result value of the simulation starts meaning anything.
It seems to me that with the way I understand it now, these two principles can't both be achieved simultaneously, so what am I missing?
As a side note I have come across some information that suggests changing the valuation algorithm to account for the transposition table and have done so (hopefully correctly).