![]() ![]() The video-game examples prove that Genie could be used to generate such virtual playgrounds. Sandboxes like XLand will be crucial for training future bots on a range of different challenges before pitting them against real-world scenarios. In 2021, a different DeepMind team developed a virtual playground called XLand, in which bots learned how to cooperate on simple tasks such as moving obstacles. ![]() The team behind Genie works on open-ended learning, where AI-controlled bots are dropped into a virtual environment and left to solve various tasks by trial and error (a technique known as reinforcement learning). “I’m definitely interested to see what they build,” he says.īut the Google DeepMind researchers are interested in more than just game generation. While Genie is an in-house research project and won’t be released, Guzdial notes that the Google DeepMind team says it could one day be turned into a game-making tool-something he’s working on too. Genie often adds this effect to the games it generates. Many games of this type use parallax, where the foreground moves sideways faster than the background. Genie learned some common visual quirks found in platformers. “Genie uses many of the same technologies as contemporary large language models, where there has been significant progress in improving inference speed.” “There is no fundamental limitation that prevents us from reaching 30 frames per second,” says Tim Rocktäschel, a research scientist at Google DeepMind who leads the team behind the work. The game ticks along action by action, each new frame generated from scratch as the player plays.įuture versions of Genie could run faster. Press Jump, and Genie updates the current image to show the game character jumping press Left and the image changes to show the character moved to the left. It generates each new frame of the game on the fly depending on the action the player takes. Genie learned which of eight possible actions would cause the game character in a video to change its position. ![]()
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