They have a new reinforcement learning algorithm that should be generically applicable to anything where a long sequence of moves results in a specifically gradable outcome.

> The neural network in AlphaGo Zero is trained from games of selfplay by a novel reinforcement learning algorithm. In each position s, an MCTS search is executed, guided by the neural network fθ. The MCTS search outputs probabilities π of playing each move. These search probabilities usually select much stronger moves than the raw move probabilities p of the neural network fθ(s); MCTS may therefore be viewed as a powerful policy improvement operator. Self-play with search—using the improved MCTS-based policy to select each move, then using the game winner z as a sample of the value—may be viewed as a powerful policy evaluation operator. The main idea of our reinforcement learning algorithm is to use these search operators repeatedly in a policy iteration procedure: the neural network’s parameters are updated to make the move probabilities and value (p, v)= fθ(s) more closely match the improved search probabilities and selfplay winner (π, z); these new parameters are used in the next iteration of self-play to make the search even stronger.