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New PAC-Bayes Framework for Controlling Unknown Linear Systems

By PulseAugur Editorial · [1 sources] ·

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

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]

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New PAC-Bayes Framework for Controlling Unknown Linear Systems

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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]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Jingge Zhu ·

    A PAC-Bayes Approach for Controlling Unknown Linear Discrete-time Systems

    This paper presents a PAC-Bayes framework for learning controllers for unknown stochastic linear discrete-time systems, where the system parameters are drawn from a fixed but unknown distribution. We derive a data-dependent high probability bound on the performance of any learned…

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