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Google's Deep Mind has learned how to play yet another game - this time because it had been 'incentivised' to want to win.

"Intrinsic rewards" meant the AI obtained "significantly improved exploration in a number of hard games, including the infamously difficult Montezuma's Revenge", wrote Google researchers in a paper.


Intrinsic motivation (IM) algorithms typically use signals to make the AI more 'curious' and are inspired by classic, human-based psychological ideas.

Montezuma's Revenge was a 1984 platform game for the Atari 2600 in which a character navigates a series of complex rooms in an underground Aztec temple.

The model, which had inbuilt rewards, explored 15 rooms out of a potential 24 – the old model, which was not incentivised, explored only two.


Deep Mind has already been trained to play Atari games, learning how to play 49 games by itself, and earlier this year beat Go champion of the world 4-1.

But games like Montezuma's Revenge are more challenging for the AI, said the researchers, because they require forward planning rather than just simple reaction.

The researchers now plan to develop a model to tackle even harder games like Starcraft, and to compete against humans in gaming contests.