Provably efficient RL with Rich Observations via Latent State Decoding

25 Jan 2019Simon S. DuAkshay KrishnamurthyNan JiangAlekh AgarwalMiroslav DudíkJohn Langford

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... (read more)

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