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uk Sunday 31 December 2017 Advertisement Google network learns to play Space Invaders in breakthrough for artificial intelligence The DQN network learned how to play classic video games including Space Invaders and Breakout without programming The network mastered the Atari game Space Invaders after being given just minimal information The network mastered the Atari game Space Invaders after being given just minimal information Photo: ALAMY Sarah Knapton By Sarah Knapton, Science Editor 7:18PM GMT 25 Feb 2015 Follow Artificial intelligence has taken a major step forward after Google created a network which learned to play a range of computer games on its own without being pre-programmed.
 The Deep Q Network (DQN) was given just the basic data from one Atari game and an algorithm which learned by trying out different scenarios to come up with the best score.
 Without any further programming the network worked out how to play a further 48 classic video games including Space Invaders and Breakout.
 Demis Hassabis of Google’s artificial intelligence arm DeepMind said the ultimate goal was to create a computer which had the mental capabilities of a toddler.
 “This work is the first time that anyone has built a single general learning system that can learn directly from experience to learn a wide range of challenging tasks,” he said.
 Related Articles 20 Apr 2016 06 Feb 2015 25 Feb 2015 billions and achieving little 19 Feb 2015 “In this case a set of Atari games and perform at better or human level on those fames “DQN can learn to play dozens of the games straight out of the box.
 We don’t preprogramme it between its games.
 “It has minimal sets of assumptions and all it gets access to are the raw pixel inputs and the game score and from there it has to figure out what it controls in the game world and how to get points and master the game just by playing the game directly.
 “It’s the first artificial agent that is capable of learning to excel over a diverse array of challenging tasks.
” Mr Hassabis said the network was far superior to the computer Deep Blue which became the first machine to surpass humans when it beat chess grand master Garry Kasparov in 1997.
 “With Deep Blue it was the team of chess grand masters which instilled the chess knowledge into a programme and that programme effectively executed that without adapting or learning anything," he said.
 “What we’ve done is build algorithms which learn from the ground up, so you give them perceptual experience and they learn how to do thinks directly.
 “The idea is that these types of systems are more human like in the way they learn because that is how humans learn, by learning from, the world around us, using our senses, to allow us to make decisions and plans.
” Google programmers said they had been amazed with some of the solutions that the network had come up with for winning the game, such as keeping the submarine just below water level in SeaQuest to stay alive and creating a tunnel in Breakout so that the ball passed through and could hit more bricks.
 “One of the things we’re trying to do we’re trying to build the ability of two or three year toddler, pre-linguistic toddler and we aren’t anywhere close to that," Mr Hassabis said.
 “But this is as good as a professional human game tester.
” The research was published in the journal Nature.
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