(6/n) With RLZero, you can just pass in a YouTube video and ask an agent to mimic the behavior instantly. This brings us closer to true zero-shot cross-embodiment transfer.

(5/n) RLZero’s Prompt to Policy: Asking a humanoid agent to perform a headstand.

(4/n) Reward is an inconvenient and easily hackable form of task specification. Now, we can prompt and obtain behaviors zero-shot with language. Example: Asking a walker agent to perform a cartwheel.

🤖 Introducing RL Zero 🤖: a new approach to transform language into behavior zero-shot for embodied agents without labeled datasets!

(6/n) With RLZero, you can just pass in a YouTube video and ask an agent to mimic the behavior instantly. This brings us closer to true zero-shot cross-embodiment transfer.

(5/n) RLZero’s Prompt to Policy: Asking a humanoid agent to perform a headstand.

(4/n) Reward is an inconvenient and easily hackable form of task specification. Now, we can prompt and obtain behaviors zero-shot with language. Example: Asking a walker agent to perform a cartwheel.

What if I told you all solutions for RL lie on a (hyper) plane? Then, we can use that fact to learn a compressed representation for MDP that unlocks efficient policy inference for any reward fn. On this plane, solving RL is equivalent to solving a linear constrained optimization!