5 papers · 1 filter
Mean Field Reinforcement Learning
René Carmona, Mathieu Laurière
This monograph provides an introduction to mean field reinforcement learning through the lens of Markov decision processes arising from large-population stochastic control with mea…
Reconciling Discrete-Time Mixed Policies and Continuous-Time Relaxed Controls in Reinforcement Learning and Stochastic Control
Rene Carmona, Mathieu Lauriere
Reinforcement learning (RL) is currently one of the most prominent methods for optimizing dynamical systems, with breakthrough results across various fields. The framework is based…
Non-standard Stochastic Control with Nonlinear Feynman-Kac Costs
Rene Carmona, Mathieu Lauriere, Pierre-Louis Lions
We consider the conditional control problem introduced by P.L. Lions in his lectures at the Collège de France in November 2016. In his lectures, Lions emphasized some of the major…
Linear-Quadratic Zero-Sum Mean-Field Type Games: Optimality Conditions and Policy Optimization
René Carmona, Kenza Hamidouche, Mathieu Laurière +1
In this paper, zero-sum mean-field type games (ZSMFTG) with linear dynamics and quadratic cost are studied under infinite-horizon discounted utility function. ZSMFTG are a class of…
Convergence Analysis of Machine Learning Algorithms for the Numerical Solution of Mean Field Control and Games: I -- The Ergodic Case
René Carmona, Mathieu Laurière
We propose two algorithms for the solution of the optimal control of ergodic McKean-Vlasov dynamics. Both algorithms are based on approximations of the theoretical solutions by neu…