ICML 2022spotlight70 citations
Scalable Deep Reinforcement Learning Algorithms for Mean Field Games
Mathieu Lauriere, Sarah Perrin, Sertan Girgin, Paul Muller, Ayush Jain, Theophile Cabannes, Georgios Piliouras, Julien Perolat
Abstract
Mean Field Games (MFGs) have been introduced to efficiently approximate games with very large populations of strategic agents. Recently, the question of learning equilibria in MFGs has gained momentum, particularly using model-free reinforcement learning (RL) methods. One limiting factor to further scale up using RL is that existing algorithms to solve MFGs require the mixing of approximated quantities such as strategies or $q$-values. This is far from being trivial in the case of non-linear function approximation that enjoy good generalization properties,
BibTeX
@InProceedings{pmlr-v162-lauriere22a,
title = {Scalable Deep Reinforcement Learning Algorithms for Mean Field Games},
author = {Lauriere, Mathieu and Perrin, Sarah and Girgin, Sertan and Muller, Paul and Jain, Ayush and Cabannes, Theophile and Piliouras, Georgios and Perolat, Julien and Elie, Romuald and Pietquin, Olivier and Geist, Matthieu},
booktitle = {Proceedings of the 39th International Conference on Machine Learning},
pages = {12078--12095},
year = {2022},
editor = {Chaudhuri, Kamalika and Jegelka, Stefanie and Song, Le and Szepesvari, Csaba and Niu, Gang and Sabato, Sivan},
volume = {162},
series = {Proceedings of Machine Learning Research},
month = {17--23 Jul},
publisher = {PMLR},
pdf = {https://proceedings.mlr.press/v162/lauriere22a/lauriere22a.pdf},
url = {https://proceedings.mlr.press/v162/lauriere22a.html},
abstract = {Mean Field Games (MFGs) have been introduced to efficiently approximate games with very large populations of strategic agents. Recently, the question of learning equilibria in MFGs has gained momentum, particularly using model-free reinforcement learning (RL) methods. One limiting factor to further scale up using RL is that existing algorithms to solve MFGs require the mixing of approximated quantities such as strategies or $q$-values. This is far from being trivial in the case of non-linear function approximation that enjoy good generalization properties,