Algorithms of data generation for deep learning and feedback design: A survey
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Authors
Kang, Wei
Gong, Qi
Nakamura-Zimmerer, Tenavi
Fahroo, Fariba
Subjects
data generation
HJB equation
optimal control
deep learning
HJB equation
optimal control
deep learning
Advisors
Date of Issue
2021-04-18
Date
Publisher
Elsevier
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. An illustrative example is given for the optimal feedback design using supervised learning in which the data is generated using causality-free algorithms.
Type
Article
Description
The article of record as published may be found at https://doi.org/10.1016/j.physd.2021.132955
Series/Report No
Department
Applied Mathematics (MA)
Organization
Identifiers
NPS Report Number
Sponsors
U.S. Naval Research Laboratory
Funding
Format
10 p.
Citation
Kang, Wei, et al. "Algorithms of data generation for deep learning and feedback design: A survey." Physica D: Nonlinear Phenomena 425 (2021): 132955.
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.