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math.OC2025
Convergence of Actor-Critic Learning for Mean Field Games and Mean Field Control in Continuous Spaces
Jean-Pierre Fouque, Mathieu Laurière, Mengrui Zhang
We establish the convergence of the deep actor-critic reinforcement learning algorithm presented in [Angiuli et al., 2023a] in the setting of continuous state and action spaces wit…
math.OC2025
Learning to Stop: Deep Learning for Mean Field Optimal Stopping
Lorenzo Magnino, Yuchen Zhu, Mathieu Laurière
Optimal stopping is a fundamental problem in optimization with applications in risk management, finance, robotics, and machine learning. We extend the standard framework to a multi…
math.OC2024
Analysis of Multiscale Reinforcement Q-Learning Algorithms for Mean Field Control Games
Andrea Angiuli, Jean-Pierre Fouque, Mathieu Laurière +1
Mean Field Control Games (MFCG), introduced in [Angiuli et al., 2022a], represent competitive games between a large number of large collaborative groups of agents in the infinite l…