>To get over this hurdle, the developers’ core idea was for AlphaGo to play the policy network against itself, to get an estimate of how likely a given board po

>To get over this hurdle, the developers’ core idea was for AlphaGo to play the policy network against itself, to get an estimate of how likely a given board position was to be a winning one. That probability of a win provided a rough valuation of the position. This part is confusing in my opinion. It makes it sounds like AlphaGo is not using Monte Carlo Tree Search, which is mentioned earlier. However, this is exactly what AlphaGo is doing, except with the policy network in place of previous methods for initializing move priors, and the value network in place of traditional methods for terminating and scoring simulations.