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arXiv · 1901.09018

Provably efficient RL with Rich Observations via Latent State Decoding

Simon S. Du·Akshay Krishnamurthy·Nan Jiang·Alekh Agarwal·Miroslav Dudík·John Langford
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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.

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Simon S. Du, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal, Miroslav Dudík, John Langford. 2019-01-25. Provably efficient RL with Rich Observations via Latent State Decoding. https://arxiv.org/abs/1901.09018

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