61 citations · 333 across the 51 of their papers we have counts for
5 papers · 2 filters
Deep Learning for Population-Dependent Controls in Mean Field Control Problems with Common Noise
Gokce Dayanikli, Mathieu Lauriere, Jiacheng Zhang
In this paper, we propose several approaches to learn the optimal population-dependent controls in order to solve mean field control problems (MFC). Such policies enable us to solv…
Machine Learning architectures for price formation models with common noise
Diogo Gomes, Julian Gutierrez, Mathieu Laurière
We propose a machine learning method to solve a mean-field game price formation model with common noise. This involves determining the price of a commodity traded among rational ag…
Recent Developments in Machine Learning Methods for Stochastic Control and Games
Ruimeng Hu, Mathieu Laurière
Stochastic optimal control and games have a wide range of applications, from finance and economics to social sciences, robotics, and energy management. Many real-world applications…
Deep Learning for Mean Field Optimal Transport
Sebastian Baudelet, Brieuc Frénais, Mathieu Laurière +2
Mean field control (MFC) problems have been introduced to study social optima in very large populations of strategic agents. The main idea is to consider an infinite population and…
A Machine Learning Method for Stackelberg Mean Field Games
Gokce Dayanikli, Mathieu Lauriere
We propose a single-level numerical approach to solve Stackelberg mean field game (MFG) problems. In Stackelberg MFG, an infinite population of agents play a non-cooperative game a…