Learned Q-function safety filters are certified by verifying two sufficient conditions with a mixed-integer optimizer, using a multiplicative Q-network to prevent safe-set collapse during fine-tuning.
Real-time safe control of neural network dynamic models with sound approximation
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Verifiable Safety Q-Filters via Hamilton-Jacobi Reachability and Multiplicative Q-Networks
Learned Q-function safety filters are certified by verifying two sufficient conditions with a mixed-integer optimizer, using a multiplicative Q-network to prevent safe-set collapse during fine-tuning.