Corpus ID: 220041530

Adaptive Control and Regret Minimization in Linear Quadratic Gaussian (LQG) Setting

@article{Lale2020AdaptiveCA,
  title={Adaptive Control and Regret Minimization in Linear Quadratic Gaussian (LQG) Setting},
  author={Sahin Lale and K. Azizzadenesheli and B. Hassibi and Anima Anandkumar},
  journal={arXiv: Learning},
  year={2020}
}

Sahin Lale, K. Azizzadenesheli, +1 author Anima Anandkumar

Published 2020

Computer Science, Mathematics

arXiv: Learning

We study the problem of adaptive control in partially observable linear quadratic Gaussian control systems, where the model dynamics are unknown a priori. We propose LqgOpt, a novel reinforcement learning algorithm based on the principle of optimism in the face of uncertainty, to effectively minimize the overall control cost. We employ the predictor state evolution representation of the system dynamics and deploy a recently proposed closed-loop system identification method, estimation, and… CONTINUE READING

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