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How did DeepMind use deep reinforcement learning to play Atari games without being taught the rules?
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using AI.
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artificial intelligence
Image generated by the Stable Diffusion model from the prompt “the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings,” which is the definition of artificial intelligence in the Encyclopædia Britannica article on the subject. Stable Diffusion is trained on a large set of images paired with textual descriptions and uses natural language processing to generate an image.
DeepMind achieved this breakthrough by utilizing deep reinforcement learning, a method that integrates deep learning with reinforcement learning.12
Instead of being programmed with specific rules or instructions, the system was designed to learn through a process of trial and error, receiving rewards and punishments based on its performance. By processing raw screen input directly from Atari 2600 games, the program utilized layered neural networks to analyze visual data and make decisions. This approach allowed the AI to autonomously develop strategies to play approximately 50 different games, effectively learning to optimize its actions to achieve the highest possible scores without any prior knowledge of the game mechanics.21