Abstract: This letter presents a semi-parametric approach for learning safe data-driven control barrier functions (SDD-CBFs) for unknown continuous systems from noisy data. By leveraging optimization ...
Abstract: This letter designs and simulates an interference metasurface capable of generating range profiles of false targets, by manipulating the electromagnetic reflection properties of the surface ...
Overview: Reinforcement learning in 2025 is more practical than ever, with Python libraries evolving to support real-world simulations, robotics, and deci ...
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