1 citations · 2 across the 15 of their papers we have counts for
15 papers
Towards Scaling Reinforcement Learning to Massive Populations: Learning Mean-Field Representations
Aditya Makkar, Benjamin Unger, Jeongyeol Kwon +3
Modern multi-agent systems are increasingly deployed at scale over large populations of agents in settings such as ad-auctions, traffic routing, and recommendation systems. The dom…
MINDGAMES: A Live Arena for Evaluating Social and Strategic Reasoning in Multi-Agent LLMs
Kevin Wang, Anna Thöni, Benjamin Kempinski +50
Large language models (LLMs) are increasingly deployed as interactive agents, yet their capacity for social and strategic reasoning over extended interaction remains poorly underst…
BAGEL: Benchmarking Animal Knowledge Expertise in Language Models
Jiacheng Shen, Masato Hagiwara, Milad Alizadeh +9
Large language models have shown strong performance on broad-domain knowledge and reasoning benchmarks, but it remains unclear how well language models handle specialized animal-re…
Bench-MFG: A Benchmark Suite for Learning in Stationary Mean Field Games
Lorenzo Magnino, Jiacheng Shen, Matthieu Geist +2
The intersection of Mean Field Games (MFGs) and Reinforcement Learning (RL) has fostered a growing family of algorithms designed to solve large-scale multi-agent systems. However,…
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…
Solving Continuous Mean Field Games: Deep Reinforcement Learning for Non-Stationary Dynamics
Lorenzo Magnino, Kai Shao, Zida Wu +2
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…
Population-aware Online Mirror Descent for Mean-Field Games with Common Noise by Deep Reinforcement Learning
Zida Wu, Mathieu Lauriere, Matthieu Geist +2
Mean Field Games (MFGs) offer a powerful framework for studying large-scale multi-agent systems. Yet, learning Nash equilibria in MFGs remains a challenging problem, particularly w…
Finite-Sample Convergence Bounds for Trust Region Policy Optimization in Mean-Field Games
Antonio Ocello, Daniil Tiapkin, Lorenzo Mancini +2
We introduce Mean-Field Trust Region Policy Optimization (MF-TRPO), a novel algorithm designed to compute approximate Nash equilibria for ergodic Mean-Field Games (MFG) in finite s…
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…