How to get better at video games, according to babies - Brian Christian
How to get better at video games, according to babies - Brian Christian with tags ai gaming, ai, artificial intelligence, gaming, atari, atari games, montezuma's revenge, deepmind, deep q network, dqn, breakout, boxing, video pinball, reinforcement learning, video games, machine learning, game points, model based design, model free reinforcement learning, babies, baby behavior, human intelligence, human mind, neuroscience, computer science, education, animation, brian christian, the alignment problem, gavin edwards, movult, ted, ted-ed, ted ed, teded, ted education
Explore what happened when the AI system Deep Q Networks (DQN) attempted to beat the Atari game Montezuma’s Revenge.
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In 2013, a group of researchers wanted to create an AI system that could beat every Atari game. They developed a system called Deep Q Networks (DQN) and less than two years later, it was superhuman. But there was one notable exception. When playing Montezuma’s Revenge, DQN couldn’t score a single point. What was it that made this game so vexingly difficult for AI? Brian Christian investigates.
Lesson by Brian Christian, directed by Gavin Edwards, Movult.
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