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Gradient-Free Neural Hamilton-Jacobi Reachability for Scalable Safety-Critical Control

The authors propose a discrete-time neural reachability framework that learns backward reachable tubes (BRTs) and backward reach-avoid tubes (BRATs) through Bellman-Isaacs value propagation, and uses gradient-free value probes to construct approximate teacher actions, turning adversarial actor learning into supervised policy learning.

Part of the event: Gradient-Free Neural HJ Reachability Framework Proposed

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