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Stochastic Online Optimization for Cyber-Physical and Robotic Systems
We propose a novel gradient-based online optimization framework for solving stochastic programming problems that frequently arise in the context of cyber-physical and robotic systems.
Bayesian model predictive control: Efficient model exploration and regret bounds using posterior sampling
The performance analysis of the method is based on posterior sampling theory and its practical efficiency is illustrated using a numerical example of a highly nonlinear dynamical car-trailer system.
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