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Hidden Markov Models are used for modelling systems that are assumed to be Markov processes with hidden (i.e. unobserved) states.
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If the Markov assumption is wrong, will a learner still converge to a stable policy?
I'm trying to figure out what guarantees can be made if a learner wrongly assumes a problem obeys the Markov transition property. Assume I have a problem defined by a partially observable Markov decis …