61 citations · 333 across the 51 of their papers we have counts for
15 papers · 1 filter
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…
Numerical Methods for Mean Field Games and Mean Field Type Control
Mathieu Lauriere
Mean Field Games (MFG) have been introduced to tackle games with a large number of competing players. Considering the limit when the number of players is infinite, Nash equilibria…
Concave Utility Reinforcement Learning: the Mean-Field Game Viewpoint
Matthieu Geist, Julien Pérolat, Mathieu Laurière +5
Concave Utility Reinforcement Learning (CURL) extends RL from linear to concave utilities in the occupancy measure induced by the agent's policy. This encompasses not only RL but a…
Stochastic Graphon Games: II. The Linear-Quadratic Case
Alexander Aurell, Rene Carmona, Mathieu Lauriere
In this paper, we analyze linear-quadratic stochastic differential games with a continuum of players interacting through graphon aggregates, each state being subject to idiosyncrat…
Mean Field Games Flock! The Reinforcement Learning Way
Sarah Perrin, Mathieu Laurière, Julien Pérolat +3
We present a method enabling a large number of agents to learn how to flock, which is a natural behavior observed in large populations of animals. This problem has drawn a lot of i…
DeepSets and their derivative networks for solving symmetric PDEs
Maximilien Germain, Mathieu Laurière, Huyên Pham +1
Machine learning methods for solving nonlinear partial differential equations (PDEs) are hot topical issues, and different algorithms proposed in the literature show efficient nume…
Scaling up Mean Field Games with Online Mirror Descent
Julien Perolat, Sarah Perrin, Romuald Elie +5
We address scaling up equilibrium computation in Mean Field Games (MFGs) using Online Mirror Descent (OMD). We show that continuous-time OMD provably converges to a Nash equilibriu…
Mean Field Models to Regulate Carbon Emissions in Electricity Production
Rene Carmona, Gokce Dayanikli, Mathieu Lauriere
The most serious threat to ecosystems is the global climate change fueled by the uncontrolled increase in carbon emissions. In this project, we use mean field control and mean fiel…