Our design goal for universe was to support a single Python process driving 20 environments in parallel at 60 frames per second. The environment exposes a VNC server and the universe library turns the agent into a VNC client. Universe exposes a wide range of environments through a common interface: the agent operates a remote desktop by observing pixels of a screen and producing keyboard and mouse commands.
World of goo trial#
An AI agent starting from scratch and without any transfer from past experience is forced to discover the solution through a trial and error approach that may require millions of attempts. A human player can immediately see that they control the person, that the skull is probably bad to touch, or that it is probably a good idea to collect the key. The Atari 2600 game “Montezuma's Revenge,” which is notoriously difficult to learn with reinforcement learning. If we are to make progress towards generally intelligent agents, we must allow them to experience a wide repertoire of tasks so they can develop world knowledge and problem solving strategies that can be efficiently reused in a new task. In a standard training regime, we initialize agents from scratch and let them twitch randomly through tens of millions of trials as they learn to repeat actions that happen to lead to rewarding outcomes. One apparent challenge is that our agents don't carry their experience along with them to new tasks. Systems with general problem solving ability - something akin to human common sense, allowing an agent to rapidly solve a new hard task - remain out of reach. For instance, AlphaGo can easily defeat you at Go, but you can't explain the rules of a different board game to it and expect it to play with you. However, despite all of these advances, the systems we're building still fall into the category of “Narrow AI” - they can achieve super-human performance in a specific domain, but lack the ability to do anything sensible outside of it. A reinforcement learning system, AlphaGo, defeated the world champion at Go. They are also learning to generate images, sound, and text. Computers can now see, hear, and translate languages with unprecedented accuracies. The area of artificial intelligence has seen rapid progress over the last few years. We look forward to integrating these and many more. With support from EA, Microsoft Studios, Valve, Wolfram, and many others, we've already secured permission for Universe AI agents to freely access games and applications such as Portal, Fable Anniversary, World of Goo, RimWorld, Slime Rancher, Shovel Knight, SpaceChem, Wing Commander III, Command & Conquer: Red Alert 2, Syndicate, Magic Carpet, Mirror's Edge, Sid Meier's Alpha Centauri, and Wolfram Mathematica. There are many ways to help: giving us permission on your games, training agents across Universe tasks, (soon) integrating new games, or (soon) playing the games.
Our goal is to develop a single AI agent that can flexibly apply its past experience on Universe environments to quickly master unfamiliar, difficult environments, which would be a major step towards general intelligence. You'll need to have Docker and universe installed. Your AI will be given frames like the above 60 times per second.
World of goo code#
The sample code above will start your AI playing the Dusk Drive Flash game. Observation_n, reward_n, done_n, info = env.step(action_n) # agent which presses the Up arrow 60 times per secondĪction_n = for _ in observation_n] Import universe # register Universe environments into GymĮnv = gym.make('flashgames.DuskDrive-v0') # any Universe environment ID here Hundreds of these are ready for reinforcement learning, and almost all can be freely run with the universe Python library as follows: import gym Today's release consists of a thousand environments including Flash games, browser tasks, and games like slither.io and GTA V. Universe works by automatically launching the program behind a VNC remote desktop - it doesn't need special access to program internals, source code, or bot APIs. With Universe, any program can be turned into a Gym environment. In April, we launched Gym, a toolkit for developing and comparing reinforcement learning (RL) algorithms.
World of goo full#
We must train AI systems on the full range of tasks we expect them to solve, and Universe lets us train a single agent on any task a human can complete with a computer.Ī sample of Universe game environments played by human demonstrators.
Universe allows an AI agent to use a computer like a human does: by looking at screen pixels and operating a virtual keyboard and mouse.
World of goo software#
We're releasing Universe, a software platform for measuring and training an AI's general intelligence across the world's supply of games, websites and other applications.
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