Scaling Mean Field Games by Online Mirror Descent

Julien Pérolat, Sarah Perrin, Romuald Elie, Mathieu Laurière, Georgios Piliouras, Matthieu Geist, Karl Tuyls, Olivier Pietquin
2022 International Joint Conference on Autonomous Agents & Multiagent Systems  
We address the scaling of equilibrium computation in Mean Field Games (MFGs) by using Online Mirror Descent (OMD). We show that continuous-time OMD provably converges to a Nash equilibrium under a natural and well-motivated set of monotonicity assumptions. A thorough experimental investigation on various single and multi-population MFGs shows that OMD outperforms traditional algorithms such as Fictitious Play. We empirically show that OMD scales and converges significantly faster than
more » ... Play by solving, for the first time to our knowledge, examples of MFGs with hundreds of billions states.
dblp:conf/atal/PerolatPELPGTP22 fatcat:pvyun7redfgirnhx3jltmewtuy