The AlphaGo Zero method
In this section, we will discuss the structure of the method. The whole system contains several parts that need to be understood before we can implement them.
Overview
At a high level, the method consists of three components, all of which will be explained in detail later, so don't worry if something is not completely clear from this section:
- We constantly traverse the game tree using the Monte Carlo tree search (MCTS) algorithm, the core idea of which is to semi-randomly walk down the game states, expanding them and gathering statistics about the frequency of moves and underlying game outcomes. As the game tree is huge, both in terms of the depth and width, we don't try to build the full tree; we just randomly sample its most promising paths (that's the source of the method's name).
- At every moment, we have a best player, which is the model used to generate the data via self-play. Initially, this model has random weights...
