Efficient Online Linear Control with Stochastic Convex Costs and Unknown Dynamics

Asaf Cassel; Alon Cohen; Tomer Koren

Efficient Online Linear Control with Stochastic Convex Costs and Unknown Dynamics

Asaf Cassel, Alon Cohen, Tomer Koren

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Abstract

We consider the problem of controlling an unknown linear dynamical system under a stochastic convex cost and full feedback of both the state and cost function. We present a computationally efficient algorithm that attains an optimal ^√T regret-rate compared to the best stabilizing linear controller in hindsight. In contrast to previous work, our algorithm is based on the Optimism in the Face of Uncertainty paradigm. This results in a substantially improved computational complexity and a simpler analysis.

Original language	English
Pages (from-to)	3589-3604
Number of pages	16
Journal	Proceedings of Machine Learning Research
Volume	178
State	Published - 2022
Event	35th Conference on Learning Theory, COLT 2022 - London, United Kingdom Duration: 2 Jul 2022 → 5 Jul 2022

Funding

Funders	Funder number
Deutsch Foundation
Yandex Initiative in Machine Learning
Blavatnik Family Foundation
Israel Science Foundation	2549/19

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https://proceedings.mlr.press/v178/cassel22a/cassel22a.pdf

Cite this

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title = "Efficient Online Linear Control with Stochastic Convex Costs and Unknown Dynamics",

abstract = "We consider the problem of controlling an unknown linear dynamical system under a stochastic convex cost and full feedback of both the state and cost function. We present a computationally efficient algorithm that attains an optimal √T regret-rate compared to the best stabilizing linear controller in hindsight. In contrast to previous work, our algorithm is based on the Optimism in the Face of Uncertainty paradigm. This results in a substantially improved computational complexity and a simpler analysis.",

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AU - Koren, Tomer

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AB - We consider the problem of controlling an unknown linear dynamical system under a stochastic convex cost and full feedback of both the state and cost function. We present a computationally efficient algorithm that attains an optimal √T regret-rate compared to the best stabilizing linear controller in hindsight. In contrast to previous work, our algorithm is based on the Optimism in the Face of Uncertainty paradigm. This results in a substantially improved computational complexity and a simpler analysis.

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ER -

Efficient Online Linear Control with Stochastic Convex Costs and Unknown Dynamics

Abstract

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