Algorithms of Data Development For Deep Learning and Feedback Design
Loading...
Authors
Kang, Wei
Gong, Qi
Nakamura-Zimmerer, Tenavi
Subjects
Advisors
Date of Issue
2020-01-30
Date
Publisher
ArXiv
Language
Abstract
Recent research reveals that deep learning is an effective way of solving high dimensional Hamilton-Jacobi-Bellman equations. The resulting feedback control law in the form of a neural network is computationally efficient for real-time applications of optimal control. A critical part of this design method is to generate data for training the neural network and validating its accuracy. In this paper, we provide a survey of existing algorithms that can be used to generate data. All the algorithms surveyed in this paper are causality-free, i.e., the solution at a point is computed without using the value of the function at any other points. At the end of the paper, an illustrative example of optimal feedback design using deep learning is given.
Type
Preprint
Description
Series/Report No
Department
Applied Mathematics (MA)
Organization
Identifiers
NPS Report Number
Sponsors
Funder
Format
15 p.
Citation
Kang, Wei, Qi Gong, and Tenavi Nakamura-Zimmerer. "Algorithms of Data Development For Deep Learning and Feedback Design." arXiv preprint arXiv:1912.00492 (2019).
Distribution Statement
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.