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

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!

I will be attending @neuripsconf.bsky.social and am on the job market. Hit me up to chat about topics in RL (Zero-shot RL, Imitation Learning, Offline RL, Deep RL) or Alignment! Learn more about my research interests: hari-sikchi.github.io/research/

This is just a bad year for ICLR authors and reviewers 😥