Web: http://arxiv.org/abs/2010.11327

Jan. 14, 2022, 2:10 a.m. | Deepan Muthirayan, Pramod P. Khargonekar

cs.LG updates on arXiv.org arxiv.org

In this paper we provide provable regret guarantees for an online
meta-learning receding horizon control algorithm in an iterative control
setting. We consider the setting where, in each iteration the system to be
controlled is a linear deterministic system that is different and unknown, the
cost for the controller in an iteration is a general additive cost function and
there are affine control input constraints. By analysing conditions under which
sub-linear regret is achievable, we prove that the meta-learning online
receding horizon controller achieves an average of the dynamic regret …

arxiv for learning meta meta-learning online

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