A trio of AI researchers at Google’s Google DeepMind, working with a colleague from the University of Toronto, report that the AI algorithm Dreamer can learn to self-improve by mastering Minecraft in a short amount of time. In their study published in the journal Nature, Danijar Hafner, Jurgis Pasukonis, Timothy Lillicrap and Jimmy Ba programmed the AI app to play Minecraft without being trained and to achieve an expert level in just nine days.

Over the past several years, computer scientists have learned a lot about how deep learning can be used to train AI applications to conduct seemingly intelligent activities such as answering questions. Researchers have also found that AI apps can be trained to play games and perform better than humans. That research has extended into video game playing, which may seem to be redundant, because what could you get from a computer playing another computer?

In this new study, the researchers found that it can produce advances such as helping an AI app learn to improve its abilities over a short period of time, which could give robots the tools they need to perform well in the real world.

In this effort, the researchers programmed Dreamer to play the popular video game Minecraft by building a system of rewards, specifically rewards for finding diamonds. With this approach, the app did not need to be taught how to play the game; it just needed to know the parameters within which it could work, one of which included envisioning a virtual future world.

Once the algorithm had learned to play Minecraft, the researchers added a new twist—they only allowed it to play under a given scenario for 30 minutes at a time. At that point, the game would be restarted with a whole new virtual universe. Using this approach, the researchers found that the algorithm improved quickly, achieving expert status after playing the game for just nine days.

The research team suggests that the algorithm’s ability to imagine a future where all its goals have been achieved enabled it to remain focused on only those tasks that led to the desired goal, and then to use them in each new virtual world it encountered. This result could eventually be used to help robots teach themselves how to achieve predefined goals in the real world.

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