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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 · Romuald Elie · Olivier Pietquin · Matthieu Geist

Tue Jul 19 03:30 PM -- 05:30 PM (PDT) @ Hall E #817
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, \textit{e.g.} neural networks. We propose two methods to address this shortcoming. The first one learns a mixed strategy from distillation of historical data into a neural network and is applied to the Fictitious Play algorithm. The second one is an online mixing method based on regularization that does not require memorizing historical data or previous estimates. It is used to extend Online Mirror Descent. We demonstrate numerically that these methods efficiently enable the use of Deep RL algorithms to solve various MFGs. In addition, we show that these methods outperform SotA baselines from the literature.

Author Information

Mathieu Lauriere (Google Brain)
Sarah Perrin (Univ. Lille)
Sertan Girgin (Google Brain)
Paul Muller (Deepmind)
Ayush Jain (University of California, Berkeley)
Ayush Jain

Graduate student at Berkeley (2021-22), now working at Google Brain.

Theophile Cabannes (Google)
Georgios Piliouras (SUTD)
Julien Perolat (DeepMind)
Romuald Elie (Deepmind)
Olivier Pietquin (GOOGLE BRAIN)
Matthieu Geist (Google)

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