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Statistical Model Checking (SMC) is a computationally very efficient verification technique based on selective system sampling. One well identified shortcoming of SMC is that, unlike probabilistic model checking, it cannot be applied to systems featuring nondeterminism, such as Markov Decision Processes (MDP). We address this limitation by developing an algorithm that resolves nondeterminism probabilistically, and then uses multiple rounds of sampling and Reinforcement Learning to provablydoi:10.1109/qest.2012.19 dblp:conf/qest/HenriquesMZPC12 fatcat:3b23dmoq7nfjzkmw6rxnhrxgk4