Embed to Control Partially Observed Systems: Representation Learning with Provable Sample Efficiency. (arXiv:2205.13476v1 [cs.LG])

Reinforcement learning in partially observed Markov decision processes
(POMDPs) faces two challenges. (i) It often takes the full history to predict
the future, which induces a sample complexity that scales exponentially with
the horizon. (ii) The observation and state spaces are often continuous, which
induces a sample complexity that scales exponentially with the extrinsic
dimension. Addressing such challenges requires learning a minimal but
sufficient representation of the observation and state histories by exploiting
the structure of the POMDP.

To this …

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