讲述alpha zero的原文，发表在nature。 A long-standing goal of artificial intelligence is an algorithm that learns, tabula rasa, superhuman proficiency in challenging domains. Recently, AlphaGo became the first program to defeat a world champion in the game of Go. The tree search in AlphaGo evaluated positions and selected moves using deep neural networks. These neural networks were trained by supervised learning from human expert moves, and by reinforcement learning from self-play. Here we introduce an algorithm based solely on reinforcement learning, without human data, guidance or domain knowledge beyond game rules. AlphaGo becomes its own teacher: a neural network is trained to predict AlphaGo’s own move selections and also the winner of AlphaGo’s games. This neural network improves the strength of the tree search, resulting in higher quality move selection and stronger self-play in the next iteration. Starting tabula rasa, our new program AlphaGo Zero achieved superhuman performance, winning 100–0 against the previously published, champion-defeating AlphaGo.

DeepMind公布了AlphaGo的最新升级版本AlphaGo Zero，并于最新一期的《自然》杂志上，对其使用的相应技术做出详解。 DeepMind称，“AlphaGo Zero与AlphaGo最大的不同是做到了真正的自我学习，经过3天的训练，就以100：0的战绩完胜前代AlphaGo。”