Lorenzo Magnino — accepted AI-conference papers

2025

Learning to Stop: Deep Learning for Mean Field Optimal Stopping

Lorenzo Magnino, Yuchen Zhu, Mathieu Lauriere

ICML 2025poster

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-agent setting, named multi-agent optimal stopping (MAOS), where agents cooperate to make optimal stopping decisions in…

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2025

Solving Continuous Mean Field Games: Deep Reinforcement Learning for Non-Stationary Dynamics

Lorenzo Magnino, Kai Shao, Zida Wu, Jiacheng Shen, Mathieu Lauriere

NeurIPS 2025poster

Mean field games (MFGs) have emerged as a powerful framework for modeling interactions in large-scale multi-agent systems. Despite recent advancements in reinforcement learning (RL) for MFGs, existing methods are typically limited to finite spaces or stationary models, hindering their applicability…

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