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Learning Provably Stabilizing Neural Controllers for Discrete-Time Stochastic Systems
We show that this procedure can also be adapted to formally verifying that, under a given Lipschitz continuous control policy, the stochastic system stabilizes within some stabilizing region with probability~$1$.
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