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Online Control with Adversarial Disturbances

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arxiv 1902.08721 v1 pith:B6QDIG76 submitted 2019-02-23 cs.LG cs.SYeess.SYmath.OCstat.ML

Naman Agarwal , Brian Bullins , Elad Hazan , Sham M. Kakade , Karan Singh This is my paper · ORCID
classification cs.LGcs.SYeess.SYmath.OCstat.ML
keywords adversarialcontroldisturbancesallowsmainnearlynoiseonline
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We study the control of a linear dynamical system with adversarial disturbances (as opposed to statistical noise). The objective we consider is one of regret: we desire an online control procedure that can do nearly as well as that of a procedure that has full knowledge of the disturbances in hindsight. Our main result is an efficient algorithm that provides nearly tight regret bounds for this problem. From a technical standpoint, this work generalizes upon previous work in two main aspects: our model allows for adversarial noise in the dynamics, and allows for general convex costs.

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