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Failing with Grace: Learning Neural Network Controllers that are Boundedly Unsafe
As a result, our method can learn a safe vector field for the closed-loop system and, at the same time, provide worst-case bounds on safety violation over the whole configuration space, defined by the overlap between the over-approximation of the forward reachable set of the closed-loop system and the set of unsafe states.
Formal Verification of Stochastic Systems with ReLU Neural Network Controllers
Using this abstraction, we propose a method to compute tight bounds on the safety probabilities of nodes in this graph, despite possible over-approximations of the transition probabilities between these nodes.
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