Gradient-based meta-learning over neural state-space models adapts a model to a new dynamical system with little target data and few gradient steps, with physical constraints embedded in the architecture.
Meta-Learning Guarantees for Online Receding Horizon Learning Control
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abstract
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 for the controller cost that is $\tilde{O}((1+1/\sqrt{N})T^{3/4})$ with the number of iterations $N$. Thus, we show that the worst regret for learning within an iteration improves with experience of more iterations, with guarantee on rate of improvement.
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Meta-Learning for Physically-Constrained Neural System Identification
Gradient-based meta-learning over neural state-space models adapts a model to a new dynamical system with little target data and few gradient steps, with physical constraints embedded in the architecture.