Deep learning for Mean Field Games with non-separable Hamiltonians-Reference-Cited by-同舟云学术

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Deep learning for Mean Field Games with non-separable Hamiltonians

Published:2023-09 Issue: Volume:174 Page:113802
ISSN:0960-0779
Container-title:Chaos, Solitons & Fractals
language:en
Short-container-title:Chaos, Solitons & Fractals

Author:

Assouli Mouhcine^ORCID,Missaoui Badr

Publisher

Elsevier BV

Subject

General Mathematics,General Physics and Astronomy,Statistical and Nonlinear Physics,Applied Mathematics

Reference45 articles.

1. A game-theoretic framework for autonomous vehicles velocity control: Bridging microscopic differential games and macroscopic mean field games;Huang,2019

2. Massive autonomous UAV path planning: A neural network based mean-field game theoretic approach;Shiri,2019

3. Mean field game of controls and an application to trade crowding;Cardaliaguet;Math Financial Econ,2018

4. Algorithmic trading in competitive markets with mean field games;Casgrain;SIAM News,2019

5. Income and wealth distribution in macroeconomics: A continuous-time approach;Achdou,2017

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1. Deep Policy Iteration for high-dimensional mean field games;Applied Mathematics and Computation;2024-11

2. Recent developments in machine learning methods for stochastic control and games;Numerical Algebra, Control and Optimization;2024

3. Challenges and Opportunities in Machine Learning for Geometry;Mathematics;2023-06-04

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