We wrote a batch-oriented VNC client in Go, which is loaded as a shared library in Python and incrementally updates a pair of buffers for each environment. Each screen buffer is 1024x768, so naively reading each frame from an external process would take 3GB/s of memory bandwidth. Our design goal for universe was to support a single Python process driving 20 environments in parallel at 60 frames per second. 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.
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