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Abstract:
We address the problem of estimating the inputs of a dynamical system from measurements of the system's outputs. To this end, we introduce a novel estimation algorithm that explicitly trades off bias and variance to optimally reduce the overall estimation error. This optimal trade-off is done efficiently and adaptively in every time step. Experimentally, we show that our method often produces estimates with substantially lower error compared to the state-of-the-art. Finally, we consider the more complex Learning-from-Observations framework, where an agent should learn a controller from the outputs of an expert's demonstration. We incorporate our estimation algorithm as a building block inside this framework and show that it enables learning controllers successfully.
Reference:
Adaptive Input Estimation in Linear Dynamical Systems with Applications to Learning-from-Observations S. Curi, K. Y. Levy, A. KrauseIn 2019 IEEE 58th Conference on Decision and Control (CDC), 2019
Bibtex Entry:
@inproceedings{curi2019adaptive,
	Author = {Curi, Sebastian and Levy, Kfir Y and Krause, Andreas},
	Booktitle = {2019 IEEE 58th Conference on Decision and Control (CDC)},
	Organization = {IEEE},
	Pages = {4115--4120},
	Title = {Adaptive Input Estimation in Linear Dynamical Systems with Applications to Learning-from-Observations},
	Year = {2019}}