5 papers
Self-fictitious-play for Potential Monotone Ergodic Mean-field Games
Yupeng Bai, Mathieu Laurière, Zhenjie Ren +1
We investigate long-time learning in ergodic, potential, monotone mean-field games (MFGs) via a self-fictitious-play (SFP) dynamics coupling an optimally controlled diffusion with…
Neural Parameter Calibration for Finite-State Mean Field Games
Anna C. M. Thöni, Grégoire Lambrecht, Gökçe Dayanıklı +3
Mean field games efficiently approximate a very large population of strategic agents. While these games can aid the understanding of complex systems, their deployment in real-world…
Robust -learning for mean-field control under Wasserstein uncertainty in common noise
Mathieu Laurière, Ariel Neufeld, Kyunghyun Park
In this article, we present a robust -learning algorithm for discrete-time mean-field control problems under Wasserstein uncertainty in the common noise law. The algorithm combi…
Population-Aware Imitation Learning in Mean-field Games with Common Noise
Grégoire Lambrecht, Mathieu Laurière
Mean Field Games (MFGs) provide a powerful framework for modeling the collective behavior of large populations of interacting agents. In this paper, we address the problem of Imita…
Iterative Schemes for Markov Perfect Equilibria
Felix Höfer, Mathieu Laurière, H. Mete Soner +1
We study Markov perfect equilibria in continuous-time dynamic games with finitely many symmetric players. The corresponding Nash system reduces to the Nash-Lasry-Lions equation for…