AlphaGo's main innovation is how it combines deep learning and Monte Carlo tree search to play Go. The AlphaGo architecture consists of four neural networks: a small supervised learning policy network, a large supervised-learning policy network, a reinforcement learning policy network, and a value network. We train all four of these networks plus the MCTS tree. The following sections will cover each training step.
AlphaGo
Supervised learning policy networks
The first step in training AlphaGo involves training policy networks on games played by two professionals (in board games such as chess and Go, it is common to keep records of historical games, the board state, and the moves made by each player at every turn)...
