Uber has cracked two classic ’80s video games by giving an AI algorithm a new type of memory

have been immune to mastery via reinforcement learning, a technique that’s otherwise adept at learning to conquer video games..DeepMind, a subsidiary of Alphabet focused on artificial intelligence, famously used it to develop algorithms capable of learning how to play several classic video games at an expert level..Reinforcement-learning algorithms mesh well with most games, because they tweak their behavior in response to positive feedback—the score going up..Ordinary reinforcement-learning algorithms usually fail to get out of the first room in Montezuma’s Revenge, and in Pitfall!.The team demonstrated a fundamentally different approach to machine learning within an environment that offers few clues to show an algorithm how it is doing..AI researchers have typically tried to get around the issues posed by by Montezuma’s Revenge and Pitfall!.by instructing reinforcement-learning algorithms to explore randomly at times, while adding rewards for exploration—what’s known as “intrinsic motivation.” But the Uber researchers believe this fails to capture an important aspect of human curiosity..In October, a team at OpenAI, a nonprofit in San Francisco, demonstrated an algorithm capable of making significant progress in Montezuma’s Revenge..Now that AI algorithms can solve these video games, the challenge is to emerge from the arcade and solve real-world problems..Alex Irpan, a software engineer working on machine learning and robotics at Google, wrote a blog post the in which he questions why the Uber AI team had not provided a technical paper, alongside a press release, to give more details of their work.. More details

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