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1. Safey-aware Adaptive Reinforcement Learning with Applications to Brushbot Navigation
University essay from KTH/ReglerteknikAbstract : This thesis presents a safety-aware learning framework that employs an adaptivemodel learning method together with barrier certificates for systems withpossibly nonstationary agent dynamics. To extract the dynamic structure ofthe model, we use a sparse optimization technique, and the resulting modelwill be used in combination with control barrier certificates which constrainfeedback controllers only when safety is about to be violated. READ MORE
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