This capability could be used by game developers to automatically generate layouts for new game levels, as well as by AI researchers to more easily develop simulator systems for training autonomous machines. GameGAN can even generate game layouts it’s never seen before, if trained on screenplays from games with multiple levels or versions. And it did.”Īs an artificial agent plays the GAN-generated game, GameGAN responds to the agent’s actions, generating new frames of the game environment in real time. “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 lead author on the project. Made up of two competing neural networks, a generator and a discriminator, GAN-based models learn to create new content that’s convincing enough to pass for the original. GameGAN is the first neural network model that mimics a computer game engine by harnessing generative adversarial networks, or GANs. That means that even without understanding a game’s fundamental rules, AI can recreate the game with convincing results. Trained on 50,000 episodes of the game, a powerful new AI model created by NVIDIA Research, called NVIDIA GameGAN, can generate a fully functional version of PAC-MAN - without an underlying game engine. Forty years to the day since PAC-MAN first hit arcades in Japan, and went on to munch a path to global stardom, the retro classic has been reborn, delivered courtesy of AI.
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