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# Robots to Humans: You Lose. We Just Finally Conquered Ms. Pac-Man

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![Raisa Bruner](https://static.time.com/v3/assets/bltea6093859af6183b/blt7115d86fab01279f/698938c17cdbacff0cd5dc5b/r_bruner-web-resized.jpg?branch=production&width=1200&quality=75&auto=webp&crop=1:1)

by 

[Raisa Bruner](https://time.com/author/raisa-bruner/)


![Raisa Bruner](https://static.time.com/v3/assets/bltea6093859af6183b/blt7115d86fab01279f/698938c17cdbacff0cd5dc5b/r_bruner-web-resized.jpg?branch=production&width=96&quality=75&auto=webp)

## Raisa Bruner


Jun 14, 2017 9:53 PM UTC

![Raisa Bruner](https://static.time.com/v3/assets/bltea6093859af6183b/blt7115d86fab01279f/698938c17cdbacff0cd5dc5b/r_bruner-web-resized.jpg?branch=production&width=1200&quality=75&auto=webp&crop=1:1)

by 

[Raisa Bruner](https://time.com/author/raisa-bruner/)


![Raisa Bruner](https://static.time.com/v3/assets/bltea6093859af6183b/blt7115d86fab01279f/698938c17cdbacff0cd5dc5b/r_bruner-web-resized.jpg?branch=production&width=96&quality=75&auto=webp)

## Raisa Bruner


Jun 14, 2017 9:53 PM UTC

A group of tech superheroes — also known as researchers at recent [Microsoft](http://time.com/4818116/microsoft-surface-laptop-review/) acquisition Maluuba — have achieved the seeming impossible, and reached the maximum possible score on classic 1980s Atari game [Ms. Pac-Man.](http://time.com/4721103/april-fools-day-google-maps-ms-pac-man/)

“No human or AI has ever achieved this score because of the complexity of the game and availability of limited lives,” they state in voiceover on a video shared on YouTube Wednesday. Casual. Maluuba, a startup acquired by the tech giant, specializes in artificial intelligence, so perhaps it only makes sense that they would be capable of applying their technical expertise to this all-important gaming quandary. The key is their “advanced reinforcement learning mechanism,” as they explain in the video, which enables their AI to reach all the way to 999,999 — the maximum possible score, and four times the highest recorded reached by any human player.

“We performed well on the game by breaking it into many small problems, with a separate reinforcement learning agent for each problem,” they note. From ghosts to fruit, each “problem” has an “agent” with a “reward function,” meaning they’ve broken down the big decision-making of the overall game play into tiny decision-making tasks. That concept has legs way beyond arcade games, too, from financial models to sales funnels. Confused? Watch them explain how to dominate the game, above.

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