Deep Learning and Mean-Field Games: A Stochastic Optimal Control Perspective

Deep Learning and Mean-Field Games: A Stochastic Optimal Control Perspective

We provide a rigorous mathematical formulation of Deep Learning (DL) methodologies through an in-depth analysis of the learning procedures characterizing Neural Network (NN) models within the theoretical frameworks of Stochastic Optimal Control (SOC) and Mean-Field Games (MFGs). In particular, we sh...

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Bibliographic Details
Main Authors: Luca Di Persio, Matteo Garbelli
Format: Article
Language:English
Published: MDPI AG 2020-12-01
Series:Symmetry
Subjects:
deep learning
neural networks
stochastic optimal control
mean-field games
Hamilton–Jacobi–Bellman equation
Pontryagin maximum principle
Online Access:https://www.mdpi.com/2073-8994/13/1/14

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https://www.mdpi.com/2073-8994/13/1/14

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