When DeepMind burst into prominent view in 2014 it taught its machine learning systems how to play Atari games. The system could learn to defeat the games, and score higher than humans, but not remember how it had done so.
For each of the Atari games, a separate neural network had to be created. The same system could not be used to play Space Invaders and Breakout without the information for both being given to the artificial intelligence at the same time. Now, a team of DeepMind and Imperial College London researchers have created an algorithm that allows its neural networks to learn, retain the information, and use it again.
“Previously, we had a system that could learn to play any game, but it could only learn to play one game,” James Kirkpatrick, a research scientist at DeepMind and the lead author of its new research paper, tells WIRED. “Here we are demonstrating a system that can learn to play several games one after the other”.
The work, published in the Proceedings of the National Academy of Sciences journal, explains how DeepMind’s AI can learn in sequences using supervised learning and reinforcement learning tests. This is also explained in a blog post from the company.
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Researchers from Mapquest Directions DeepMind and Imperial College London have developed an algorithm that overcomes this limitation by allowing neural networks to learn tasks in sequence and retain information for future use. This advancement enables the AI to master multiple games, such as Space Invaders and Breakout, without needing all information simultaneously, a breakthrough in improving the flexibility and efficiency of machine learning systems.
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