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We study the exploration problem in episodic MDPs with rich observationsgenerated from a small number of latent states. Under certain identifiabilityassumptions, we demonstrate how to estimate a mapping from the observations tolatent states inductively through a sequence of regression and clustering steps-- where previously decoded latent states provide labels for later regressionproblems -- and use it to construct good exploration policies. We providefinite-sample guarantees on the quality of the learned state decoding functionand exploration policies, and complement our theory with an empiricalevaluation on a class of hard exploration problems. Our method exponentiallyimproves over $Q$-learning with na\"ive exploration, even when $Q$-learning hascheating access to latent states.

Provably efficient RL with Rich Observations via Latent State Decoding