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A Game-Theoretic Lens for Robustness in Control.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	A Game-Theoretic Lens for Robustness in Control./
Author:	Ghai, Udaya.
Description:	1 online resource (267 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 85-08, Section: B.
Contained By:	Dissertations Abstracts International85-08B.
Subject:	Statistics. -
Online resource:	click for full text (PQDT)
ISBN:	9798381683295

A Game-Theoretic Lens for Robustness in Control.
Ghai, Udaya.

A Game-Theoretic Lens for Robustness in Control. - 1 online resource (267 pages)

Source: Dissertations Abstracts International, Volume: 85-08, Section: B.

Thesis (Ph.D.)--Princeton University, 2024.

Includes bibliographical references

The control of dynamical systems is a fundamental problem with a vast array of applications, from robotics to biological engineering. Recently, the game-theoretic primitive of regret minimization has been applied to control, yielding novel instance-optimal performance guarantees in more challenging non-stochastic control settings. This thesis further explores the benefits of a multi-agent perspective of control.Concretely, we begin with a new algorithm for generating disturbances for controller verification, which relies on recasting the players in the nonstochastic control game. Next, we provide a cooperative multi-agent extension of the nonstochastic control setting, involving a reduction from our multi-agent game to single agent regret minimization. Furthermore, we show new notions of robustness to failure can be attained through this perspective, even in a single-agent setting.While control is a powerful tool, it relies heavily on knowledge of the dynamics. The final chapters provide two very different approaches to robustness without such a model. The first approach extends the nonstochastic control methodology to model-free reinforcement learning. In an alternative approach, we consider unknown systems with dynamics that are approximately linear using tools from classical control theory.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798381683295Subjects--Topical Terms:

556824
Statistics.
Subjects--Index Terms:

Game-theoretic lensIndex Terms--Genre/Form:

554714
Electronic books.

A Game-Theoretic Lens for Robustness in Control.
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500 $a Advisor: Hazan, Elad.
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504 $a Includes bibliographical references
520 $a The control of dynamical systems is a fundamental problem with a vast array of applications, from robotics to biological engineering. Recently, the game-theoretic primitive of regret minimization has been applied to control, yielding novel instance-optimal performance guarantees in more challenging non-stochastic control settings. This thesis further explores the benefits of a multi-agent perspective of control.Concretely, we begin with a new algorithm for generating disturbances for controller verification, which relies on recasting the players in the nonstochastic control game. Next, we provide a cooperative multi-agent extension of the nonstochastic control setting, involving a reduction from our multi-agent game to single agent regret minimization. Furthermore, we show new notions of robustness to failure can be attained through this perspective, even in a single-agent setting.While control is a powerful tool, it relies heavily on knowledge of the dynamics. The final chapters provide two very different approaches to robustness without such a model. The first approach extends the nonstochastic control methodology to model-free reinforcement learning. In an alternative approach, we consider unknown systems with dynamics that are approximately linear using tools from classical control theory.
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538 $a Mode of access: World Wide Web
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653 $a Game-theoretic lens
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653 $a Non-stochastic control
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