Real-Time Reinforcement Learning (RT-RL)

Introducing our AI system powering a new kind of agent for a new kind of media

Meet our AI agents. They’re nothing like what people think agents are today. They’re interactive, immersive, and physical. They’re also fast to train, cheap to run, and accessible from any browser. And they're only possible because of Real-Time Reinforcement Learning (RT-RL).

We started R&D on novel generative AI techniques five years ago in Google Research with the belief that agents will be a fundamentally new medium for AI. Just like cameras enabled photos, computers enabled video games, and the internet enabled social media, AI will enable agents.

But the agents we’re dreaming up are nothing like what people think agents are today. Ours are:

• 🤖 Interactive: perceiving and acting in milliseconds

• 🌐 Immersive: embodied and rendered in 3D

• 💪 Physical: simulated with real-world physics

In theory Reinforcement Learning is the ideal technique for these types of physically intelligent 3D agents. But in practice it's a slow, expensive, and offline process. Enter: Real-Time Reinforcement Learning to make our agents fast to train, cheap to run, and online. RT-RL unlocks:

• ⏱ Accelerated Learning: from hours and days of training time to seconds and minutes

• ⚡ Low-Latency Action: from offline inference on high-end servers to online inference in any consumer browser

• ➰ Human-In-The-Loop: from static 2D videos to interactive 3D agents

Today, RT-RL enables a new kind of agent for a new kind of media. Tomorrow, we make this a creative medium by empowering you to create agents in an experience that’s as joyful as play.

We like to say we’re building UGC gameplay for the AI generation. If that’s you, subscribe to stay tuned.