![]() Sure, that’s a funny quirk, but Nvidia believes that GameGAN could have all kinds of real-world applications that would help people like game developers. For instance, it might change the game environment. ![]() What that means in practice is that if you’re playing the GameGAN version of Pac-Man, and you make a move that would ordinarily result in Pac-Man’s death, the AI goes out of its way to avoid that outcome - sometimes breaking the rules of the game to do so. “So the learned GameGAN that reproduces this game has this bias of never killing Pac-Man.” That’s because the AI agent playing the game was too good at it: “The Pac-Man almost never dies,” explained Sanja Fidler, director of Nvidia’s Toronto research lab and a co-author on the GameGAN project, during the briefing. The sessions in question were themselves played by an AI agent, not by humans - which ultimately resulted in the GameGAN version of Pac-Man being a somewhat inaccurate representation of the real thing. It figured out that Pac-Man moves around the maze but can’t travel through walls it learned that the ghosts chase Pac-Man, and that the game ends if one touches him it understood that the ghosts turn blue when Pac-Man eats a power pellet, and that the pellet allows him to eat the ghosts. The training took place over four days on an Nvidia DGX system, one of the company’s AI workstations, using four Nvidia Quadro GV100 GPUs.īy observing the gameplay in the 50,000 “episodes” of Pac-Man, GameGAN learned how the game works. Nvidia’s researchers gave GameGAN only two inputs: the footage of the Pac-Man play sessions (which comprised a few million frames) paired with data on the keystrokes used to control the game. “We wanted to see whether the AI could learn the rules of an environment just by looking at the screenplay of an agent moving through the game. “This is the first research to emulate a game engine using GAN-based neural networks,” said Seung-Wook Kim, an Nvidia researcher and the project lead for GameGAN, in an Nvidia blog post. And GameGAN is the first GAN to be able to reproduce a video game on its own, according to Nvidia. It relies on generative adversarial networks (GAN), a common system in machine learning that pits two neural networks against each other for applications such as AI-generated images. ![]() The AI model in question is known as Nvidia GameGAN. Note that the graphics have the blurry look that is typical of AI-generated imagery. “It observed it just like a human might.”Ī clip of a person playing the GameGAN version of Pac-Man. “We trained this artificial intelligence on 50,000 episodes of Pac-Man being played, without the AI actually seeing any of the code or anything - just seeing pixels coming out of the game engine,” said Rev Lebaredian, vice president of simulation technology at Nvidia, in a media briefing earlier this week. ![]() With no innate understanding of Pac-Man’s gameplay, the AI “trained” by watching sessions of Pac-Man - the official version from Bandai Namco - to learn the game’s rules and mechanics. The company built an AI model that was able to create a fully functional, playable version of the seminal 8-bit arcade game without access to the underlying game engine. Nvidia Research announced Friday that it has produced a new iteration of Pac-Man that was generated entirely by AI. Now it’s understanding how video games work just by watching them being played. Artificial intelligence has advanced to the point of being able to drive cars and produce reasonably convincing “ deepfakes” in both audio and video. A lot has changed in the intervening four decades, including, of course, the capabilities of computers. Bandai Namco is celebrating Pac-Man’s 40th birthday all year long, but Friday is technically the big day: Namco began publicly testing Pac-Man in Tokyo arcades on May 22, 1980.
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