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A single feedforward network learns to predict safety values and amortize minimal-intervention filtering, enabling safe RL in high-dimensional systems without analytical safety functions or competing policy objectives.
A single learned policy can safely coordinate multiple drones in dynamic environments, outperforming existing methods in safety and scalability.
Forget hand-engineering initial conditions for robust RL: this method *learns* which conditions are feasible while simultaneously training a safe policy.
