1 citations · 2 across the 15 of their papers we have counts for
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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…
Reinforcement Learning for Finite Space Mean-Field Type Games
Kai Shao, Jiacheng Shen, Mathieu Laurière
Mean field type games (MFTGs) describe Nash equilibria between large coalitions: each coalition consists of a continuum of cooperative agents who maximize the average reward of the…
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…
Robust Cooperative Multi-Agent Reinforcement Learning:A Mean-Field Type Game Perspective
Muhammad Aneeq uz Zaman, Mathieu Laurière, Alec Koppel +1
In this paper, we study the problem of robust cooperative multi-agent reinforcement learning (RL) where a large number of cooperative agents with distributed information aim to lea…
A Deep Learning Method for Optimal Investment Under Relative Performance Criteria Among Heterogeneous Agents
Mathieu Laurière, Ludovic Tangpi, Xuchen Zhou
Graphon games have been introduced to study games with many players who interact through a weighted graph of interaction. By passing to the limit, a game with a continuum of player…
Population-aware Online Mirror Descent for Mean-Field Games by Deep Reinforcement Learning
Zida Wu, Mathieu Lauriere, Samuel Jia Cong Chua +3
Mean Field Games (MFGs) have the ability to handle large-scale multi-agent systems, but learning Nash equilibria in MFGs remains a challenging task. In this paper, we propose a dee…
Independent RL for Cooperative-Competitive Agents: A Mean-Field Perspective
Muhammad Aneeq uz Zaman, Alec Koppel, Mathieu Laurière +1
We address in this paper Reinforcement Learning (RL) among agents that are grouped into teams such that there is cooperation within each team but general-sum (non-zero sum) competi…