This paper introduces a PAC-Bayes framework designed to learn controllers for unknown stochastic linear discrete-time systems. The research provides a data-dependent bound on controller performance and proposes new learning algorithms with theoretical guarantees. These algorithms are applicable to both finite and infinite controller spaces and offer performance comparable to LQG controllers in specific scenarios. AI
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IMPACT Introduces a novel theoretical framework for control systems, potentially impacting autonomous systems and robotics research.
RANK_REASON The cluster contains an academic paper detailing a new theoretical framework and algorithms for a specific control problem. [lever_c_demoted from research: ic=1 ai=0.7]