With data from BANDAI NAMCO Research, Kim and his collaborators at the NVIDIA AI Research Lab in Toronto used NVIDIA DGX systems to train the neural networks on the PAC-MAN episodes (a few million frames, in total) paired with data on the keystrokes of an AI agent playing the game. Game developers could use such a tool to automatically design new level layouts for existing games, using screenplay from the original levels as training data. No matter the game, the GAN can learn its rules simply by ingesting screen recordings and agent keystrokes from past gameplay. The AI keeps track of the virtual world, remembering what’s already been generated to maintain visual consistency from frame to frame. The GameGAN edition relies on neural networks, instead of a traditional game engine, to generate PAC-MAN’s environment. Game Changer: NVIDIA Researcher Seung-Wook Kim and his collaborators trained GameGAN on 50,000 episodes of PAC-MAN. Over the decades since, the hit game has seen versions for PCs, gaming consoles and cell phones. In 1981 alone, Americans inserted billions of quarters to play 75,000 hours of coin-operated games like PAC-MAN. Take a left at the pinball machine and continue straight past the air hockey, following the unmistakable soundtrack of PAC-MAN gobbling dots and avoiding ghosts Inky, Pinky, Blinky and Clyde. PAC-MAN enthusiasts once had to take their coins to the nearest arcade to play the classic maze chase. We’ll be making our AI tribute to the game available later this year on AI Playground, where anyone can experience our research demos firsthand. “This research presents exciting possibilities to help game developers accelerate the creative process of developing new level layouts, characters and even games.” “We were blown away when we saw the results, in disbelief that AI could recreate the iconic PAC-MAN experience without a game engine,” said Koichiro Tsutsumi from BANDAI NAMCO Research Inc., the research development company of the game’s publisher BANDAI NAMCO Entertainment Inc., which provided the PAC-MAN data to train GameGAN. 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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