Early research into combining deep learning with reinforcement learning to develop an AI capable of mastering the game of Go, a complex strategy game previously considered too difficult for AI.

AlphaGo defeated Fan Hui, the European Go champion, marking the first time a computer program had beaten a professional Go player without handicap stones. The result was published in the journal "Nature".

AlphaGo defeated Lee Sedol, one of the world's top Go players, in a best-of-five match, winning 4-1. This was a landmark achievement that demonstrated the potential of AI in mastering complex strategic games.

An unidentified player with the handle “Master” (later confirmed to be a new version of AlphaGo) played a series of online Go games against top professional players, winning 60 out of 60 games.

AlphaGo defeated Ke Jie, then the world number one Go player, in a three-game match at the Future of Go Summit in Wuzhen, China.

DeepMind announced the retirement of AlphaGo from competitive play, shifting its focus to developing new algorithms based on the AlphaGo technology.

DeepMind introduced AlphaGo Zero, a version of AlphaGo that learned to play Go entirely from self-play, starting from random play and without any human data. It surpassed the performance of previous versions of AlphaGo in a relatively short period of time.