Mastering the game of Go
The complexity of Go means it has long been viewed as the most challenging of classical games for artificial intelligence. Despite decades of work, the strongest computer Go programs were only able to play at the level of human amateurs.rjX快充网络
Traditional AI methods, which construct a search tree over all possible positions, don’t have a chance in Go. This is because of the sheer number of possible moves and the difficulty of evaluating the strength of each possible board position.rjX快充网络
In order to capture the intuitive aspect of the game, we knew that we would need to take a novel approach. AlphaGo therefore combines an advanced tree search with deep neural networks. These neural networks take a description of the Go board as an input and process it through a number of different network layers containing millions of neuron-like connections. One neural network, the “policy network”, selects the next move to play. The other neural network, the “value network”, predicts the winner of the game.rjX快充网络
We showed AlphaGo a large number of strong amateur games to help it develop its own understanding of what reasonable human play looks like. Then we had it play against different versions of itself thousands of times, each time learning from its mistakes and incrementally improving until it became immensely strong, through a process known as reinforcement learning.rjX快充网络
Our Nature paper, published on 28th January 2016, describes the technical details behind this original approach in greater detail.rjX快充网络
Read more about how AlphaGo uses machine learning to master the game of Go in our blog post.rjX快充网络