Provably efficient RL with Rich Observations via Latent State Decoding

Code

• github.com/Microsoft/StateDecoding
  OfficialIn papertorch★ 0

Abstract

We study the exploration problem in episodic MDPs with rich observations generated from a small number of latent states. Under certain identifiability assumptions, we demonstrate how to estimate a mapping from the observations to latent states inductively through a sequence of regression and clustering steps -- where previously decoded latent states provide labels for later regression problems -- and use it to construct good exploration policies. We provide finite-sample guarantees on the quality of the learned state decoding function and exploration policies, and complement our theory with an empirical evaluation on a class of hard exploration problems. Our method exponentially improves over Q-learning with na\"ive exploration, even when Q-learning has cheating access to latent states.

Tasks

Reproductions