AlphaGo编辑历史

# AlphaGo

AlphaGo is a computer program developed by DeepMind Technologies (subsidiary of Alphabet Inc.) that plays the board game Go. Released in 2015, it became the first artificial intelligence (AI) to defeat a world champion Go player, Lee Sedol, in a five‑game match in March 2016. The system combines Monte Carlo tree search with deep neural networks trained through supervised learning from human expert games and reinforcement learning from self‑play. AlphaGo’s triumph represented a milestone in AI research, demonstrating that a combination of tree search and deep neural networks could tackle a game previously considered too complex for brute‑force approaches.

## 1 Background

### 1.1 The game of Go

Go is an ancient board game originating in China over 2,500 years ago. Two players alternately place black and white stones on a 19×19 grid, aiming to surround larger territories and capture opponent stones. The game’s branching factor (roughly 250 legal moves per turn, compared to about 35 in chess) and the difficulty of evaluating positions precisely long made it a grand challenge for artificial intelligence. Unlike chess, where brute‑force search engines reached world‑class level by the late 1990s, Go resisted such approaches because the search tree is too vast.

### 1.2 Prior computer Go approaches

Before AlphaGo, the best computer Go programs used Monte Carlo tree search (MCTS) with random playouts to estimate position values. Programs such as Zen, Crazy Stone, and Fuego achieved amateur dan (advanced) level but could not defeat professional human players. They relied on handcrafted heuristics and did not incorporate deep learning. The lack of accurate positional evaluation and move selection prevented them from reaching the professional tier.

## 2 Technical architecture

### 2.1 Overview of the system

AlphaGo is a hybrid system that integrates deep neural networks with Monte Carlo tree search. Two neural networks—a policy network and a va