  In many real-world decision problems there is partially observed, hidden or
latent information that remains fixed throughout an interaction. Such decision
problems can be modeled as Latent Markov Decision Processes (LMDPs), where a
latent variable is selected at the beginning of an interaction and is not
disclosed to the agent. In the last decade, there has been significant progress
in solving LMDPs under different structural assumptions. However, for general
LMDPs, there is no known learning algorithm that provably matches the existing
lower bound (Kwon et al., 2021). We introduce the first sample-efficient
algorithm for LMDPs without any additional structural assumptions. Our result
builds off a new perspective on the role of off-policy evaluation guarantees
and coverage coefficients in LMDPs, a perspective, that has been overlooked in
the context of exploration in partially observed environments. Specifically, we
establish a novel off-policy evaluation lemma and introduce a new coverage
coefficient for LMDPs. Then, we show how these can be used to derive
near-optimal guarantees of an optimistic exploration algorithm. These results,
we believe, can be valuable for a wide range of interactive learning problems
beyond LMDPs, and especially, for partially observed environments.
