61 citations · 333 across the 51 of their papers we have counts for
8 papers · 1 filter
Optimal control of conditioned processes with feedback controls
Yves Achdou, Mathieu Laurière, Pierre-Louis Lions
We consider a class of closed loop stochastic optimal control problems in finite time horizon, in which the cost is an expectation conditional on the event that the process has not…
Stochastic Graphon Games: I. The Static Case
Rene Carmona, Daniel Cooney, Christy Graves +1
We consider static finite-player network games and their continuum analogs, graphon games. Existence and uniqueness results are provided, as well as convergence of the finite-playe…
Backward propagation of chaos
Mathieu Laurière, Ludovic Tangpi
This paper develops a theory of propagation of chaos for a system of weakly interacting particles whose terminal configuration is fixed as opposed to the initial configuration as c…
Model-Free Mean-Field Reinforcement Learning: Mean-Field MDP and Mean-Field Q-Learning
René Carmona, Mathieu Laurière, Zongjun Tan
We study infinite horizon discounted Mean Field Control (MFC) problems with common noise through the lens of Mean Field Markov Decision Processes (MFMDP). We allow the agents to us…
Linear-Quadratic Mean-Field Reinforcement Learning: Convergence of Policy Gradient Methods
René Carmona, Mathieu Laurière, Zongjun Tan
We investigate reinforcement learning in the setting of Markov decision processes for a large number of exchangeable agents interacting in a mean field manner. Applications include…
A multipartite generalization of quantum discord
Chandrashekar Radhakrishnan, Mathieu Lauriere, Tim Byrnes
A generalization of quantum discord to multipartite systems is proposed. A key feature of our formulation is its consistency with the conventional definition of discord in bipartit…
Convergence Analysis of Machine Learning Algorithms for the Numerical Solution of Mean Field Control and Games: II -- The Finite Horizon Case
René Carmona, Mathieu Laurière
We propose two numerical methods for the optimal control of McKean-Vlasov dynamics in finite time horizon. Both methods are based on the introduction of a suitable loss function de…
On the Convergence of Model Free Learning in Mean Field Games
Romuald Elie, Julien Pérolat, Mathieu Laurière +2
Learning by experience in Multi-Agent Systems (MAS) is a difficult and exciting task, due to the lack of stationarity of the environment, whose dynamics evolves as the population l…