DeepMind Blog

AlphaGo Zero: Learning from scratch

DeepMind and Blizzard to release StarCraft II as an AI research environment

Today at BlizzCon 2016 in Anaheim, California, we announced our collaboration with Blizzard Entertainment to open up StarCraft II to AI and Machine Learning researchers around the world.

AlphaStar: Mastering the Real-Time Strategy Game StarCraft II

Games have been used for decades as an important way to test and evaluate the performance of artificial intelligence systems. As capabilities have increased, the research community has sought games with increasing complexity that capture different elements of intelligence required to solve scientific and real-world…

DeepMind and Blizzard Open StarCraft II as an AI Research Environment

DeepMind's scientific mission is to push the boundaries of AI by developing systems that can learn to solve complex problems. To do this, we design agents and test their ability in a wide range of environments from the purpose-built DeepMind Lab to established games, such as Atari and Go.

Neural scene representation and rendering

There is more than meets the eye when it comes to how we understand a visual scene: our brains draw on prior knowledge to reason and to make inferences that go far beyond the patterns of light that hit our retinas. For example, when entering a room for the first time, you instantly recognise the items it contains and…

Grandmaster level in StarCraft II using multi-agent reinforcement learning

AlphaStar is the first AI to reach the top league of a widely popular esport without any game restrictions. This January, a preliminary version of AlphaStar challenged two of the world's top players in StarCraft II, one of the most enduring and popular real-time strategy video games of all time. Since then, we have…

Mastering Stratego

Game-playing artificial intelligence (AI) systems have advanced to a new frontier. Stratego, the classic board game that’s more complex than chess and Go, and craftier than poker, has now been mastered. Published in Science, we present DeepNash, an AI agent that learned the game from scratch to a human expert level by…

AlphaGo's next move

With just three stones on the board, it was clear that this was going to be no ordinary game of Go.

Generally capable agents emerge from open-ended play

In recent years, artificial intelligence agents have succeeded in a range of complex game environments. For instance, AlphaZero beat world-champion programs in chess, shogi, and Go after starting out with knowing no more than the basic rules of how to play. Through reinforcement learning (RL), this single system…

MuZero: Mastering Go, chess, shogi and Atari without rules

In 2016, we introduced AlphaGo, the first artificial intelligence (AI) program to defeat humans at the ancient game of Go. Two years later, its successor - AlphaZero - learned from scratch to master Go, chess and shogi. Now, in a paper in the journal Nature, we describe MuZero, a significant step forward in the…

Learning by playing

Getting children (and adults) to tidy up after themselves can be a challenge, but we face an even greater challenge trying to get our AI agents to do the same. Success depends on the mastery of several core visuo-motor skills: approaching an object, grasping and lifting it, opening a box and putting things inside of…

Exploring the mysteries of Go with AlphaGo and China’s top players

Just over a year ago, we saw a major milestone in the field of artificial intelligence: DeepMind’s AlphaGo took on and defeated one of the world’s top Go players, the legendary Lee Sedol. Even then, we had no idea how this moment would affect the 3,000 year old game of Go and the growing global community of devotees…