\section{Conclusion}
In this paper, we give a sample efficient algorithm for reinforcement
learning in undercomplete POMDPs. Our results leverage a connection to
the observable operator model and employ a refined error analysis. To
our knowledge, this gives the first provably efficient algorithm for
strategic exploration in partially observable environments.
{"config": "arxiv", "file": "2006.12484/conclu.tex"}