Stability Via Adversarial Training of Neural Network Stochastic Control of Mean-Field Type

• URL: http://arxiv.org/abs/2210.00874v1
• Date: Tue, 27 Sep 2022 11:37:06 GMT
• Title: Stability Via Adversarial Training of Neural Network Stochastic Control of Mean-Field Type
• Authors: Julian Barreiro-Gomez and Salah Eddine Choutri and Boualem Djehiche
• Abstract summary: This is a class of data-driven mean-field-type control where the distribution of the variables such as the system states and control inputs are incorporated into the problem. We present a methodology to validate the feasibility of the approximations of the solutions via neural networks and evaluate their stability. We enhance the stability by enlarging the training set with adversarial inputs to obtain a more robust neural network.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In this paper, we present an approach to neural network mean-field-type control and its stochastic stability analysis by means of adversarial inputs (aka adversarial attacks). This is a class of data-driven mean-field-type control where the distribution of the variables such as the system states and control inputs are incorporated into the problem. Besides, we present a methodology to validate the feasibility of the approximations of the solutions via neural networks and evaluate their stability. Moreover, we enhance the stability by enlarging the training set with adversarial inputs to obtain a more robust neural network. Finally, a worked-out example based on the linear-quadratic mean-field type control problem (LQ-MTC) is presented to illustrate our methodology.

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