🏢 Washington University in St. Louis
Verified Safe Reinforcement Learning for Neural Network Dynamic Models
·1254 words·6 mins·
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Machine Learning
Reinforcement Learning
🏢 Washington University in St. Louis
Learning verified safe neural network controllers for complex nonlinear systems is now possible, achieving an order of magnitude longer safety horizons than state-of-the-art methods while maintaining …
SEEV: Synthesis with Efficient Exact Verification for ReLU Neural Barrier Functions
·1687 words·8 mins·
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AI Theory
Safety
🏢 Washington University in St. Louis
SEEV framework efficiently verifies ReLU neural barrier functions by reducing activation regions and using tight over-approximations, significantly improving verification efficiency without sacrificin…