61 citations · 333 across the 51 of their papers we have counts for
60 papers
Picard-Based Acceleration of Newton Continuation for Mean Field Game PDE Systems
Mathieu Lauriere, Andrew Shi
We develop a method that uses Picard iterations to accelerate Newton continuation for the semi-implicit finite-difference discretization of forward-backward partial differential eq…
Learning Discrete-Time Major-Minor Mean Field Games
Kai Cui, Gökçe Dayanıklı, Mathieu Laurière +3
Recent techniques based on Mean Field Games (MFGs) allow the scalable analysis of multi-player games with many similar, rational agents. However, standard MFGs remain limited to ho…
On Imitation in Mean-field Games
Giorgia Ramponi, Pavel Kolev, Olivier Pietquin +3
We explore the problem of imitation learning (IL) in the context of mean-field games (MFGs), where the goal is to imitate the behavior of a population of agents following a Nash eq…
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…
The communication complexity of functions with large outputs
Lila Fontes, Sophie Laplante, Mathieu Lauriere +1
We study the two-party communication complexity of functions with large outputs, and show that the communication complexity can greatly vary depending on what output model is consi…
Actor-Critic learning for mean-field control in continuous time
Noufel Frikha, Maximilien Germain, Mathieu Laurière +2
We study policy gradient for mean-field control in continuous time in a reinforcement learning setting. By considering randomised policies with entropy regularisation, we derive a…
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…
Learning Correlated Equilibria in Mean-Field Games
Paul Muller, Romuald Elie, Mark Rowland +7
The designs of many large-scale systems today, from traffic routing environments to smart grids, rely on game-theoretic equilibrium concepts. However, as the size of an -player…
Reinforcement Learning for Intra-and-Inter-Bank Borrowing and Lending Mean Field Control Game
Andrea Angiuli, Nils Detering, Jean-Pierre Fouque +2
We propose a mean field control game model for the intra-and-inter-bank borrowing and lending problem. This framework allows to study the competitive game arising between groups of…
Learning in Mean Field Games: A Survey
Mathieu Laurière, Sarah Perrin, Julien Pérolat +5
Non-cooperative and cooperative games with a very large number of players have many applications but remain generally intractable when the number of players increases. Introduced b…
Reinforcement Learning Algorithm for Mixed Mean Field Control Games
Andrea Angiuli, Nils Detering, Jean-Pierre Fouque +2
We present a new combined \textit{mean field control game} (MFCG) problem which can be interpreted as a competitive game between collaborating groups and its solution as a Nash equ…
Machine Learning architectures for price formation models
Diogo Gomes, Julián Gutiérrez, Mathieu Laurière
Here, we study machine learning (ML) architectures to solve a mean-field games (MFGs) system arising in price formation models. We formulate a training process that relies on a min…
Scalable Deep Reinforcement Learning Algorithms for Mean Field Games
Mathieu Laurière, Sarah Perrin, Sertan Girgin +8
Mean Field Games (MFGs) have been introduced to efficiently approximate games with very large populations of strategic agents. Recently, the question of learning equilibria in MFGs…
Learning Equilibria in Mean-Field Games: Introducing Mean-Field PSRO
Paul Muller, Mark Rowland, Romuald Elie +6
Recent advances in multiagent learning have seen the introduction ofa family of algorithms that revolve around the population-based trainingmethod PSRO, showing convergence to Nash…
Solving N-player dynamic routing games with congestion: a mean field approach
Theophile Cabannes, Mathieu Lauriere, Julien Perolat +7
The recent emergence of navigational tools has changed traffic patterns and has now enabled new types of congestion-aware routing control like dynamic road pricing. Using the funda…
Policy iteration method for time-dependent Mean Field Games systems with non-separable Hamiltonians
Mathieu Laurière, Jiahao Song, Qing Tang
We introduce two algorithms based on a policy iteration method to numerically solve time-dependent Mean Field Game systems of partial differential equations with non-separable Hami…
Generalization in Mean Field Games by Learning Master Policies
Sarah Perrin, Mathieu Laurière, Julien Pérolat +3
Mean Field Games (MFGs) can potentially scale multi-agent systems to extremely large populations of agents. Yet, most of the literature assumes a single initial distribution for th…
Performance of a Markovian neural network versus dynamic programming on a fishing control problem
Mathieu Laurière, Gilles Pagès, Olivier Pironneau
Fishing quotas are unpleasant but efficient to control the productivity of a fishing site. A popular model has a stochastic differential equation for the biomass on which a stochas…
Deep Learning for Mean Field Games and Mean Field Control with Applications to Finance
René Carmona, Mathieu Laurière
Financial markets and more generally macro-economic models involve a large number of individuals interacting through variables such as prices resulting from the aggregate behavior…
Reinforcement Learning for Mean Field Games, with Applications to Economics
Andrea Angiuli, Jean-Pierre Fouque, Mathieu Lauriere
Mean field games (MFG) and mean field control problems (MFC) are frameworks to study Nash equilibria or social optima in games with a continuum of agents. These problems can be use…
Finite State Graphon Games with Applications to Epidemics
Alexander Aurell, Rene Carmona, Gokce Dayanikli +1
We consider a game for a continuum of non-identical players evolving on a finite state space. Their heterogeneous interactions are represented by a graphon, which can be viewed as…