A stopping-time reward and chance-constrained SoC penalty embedded in an end-to-end learning framework improves battery reachability of target ranges, raises arbitrage profit, and lowers profit variance under volatile prices.
Reachability analysis and its application to the safety as- sessment of autonomous cars
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Set-based training of neural barrier certificates uses a loss function that encodes all safety properties so that zero loss formally proves the certificate is valid, collapsing iterative training and verification into one procedure.
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Learning Reachability of Energy Storage Arbitrage
A stopping-time reward and chance-constrained SoC penalty embedded in an end-to-end learning framework improves battery reachability of target ranges, raises arbitrage profit, and lowers profit variance under volatile prices.
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Set-Based Training of Neural Barrier Certificates for Safety Verification of Dynamical Systems
Set-based training of neural barrier certificates uses a loss function that encodes all safety properties so that zero loss formally proves the certificate is valid, collapsing iterative training and verification into one procedure.