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Fictitious Play for Mean Field Games: Continuous Time Analysis and Applications

Sarah Perrin
Julien Perolat
Mathieu Laurière
Matthieu Geist
Romuald Elie
Olivier Pietquin
NeurIPS (2020)


In this paper, we deepen the analysis of continuous time Fictitious Play learning algorithm to the consideration of various finite state Mean Field Game settings (finite horizon, $\gamma$ discounted), allowing in particular for the introduction of an additional common noise. We first present a theoretical convergence analysis of the continuous time Fictitious Play process and prove that the induced exploitability decreases at a rate $O(\frac{1}{t})$. Such analysis emphasizes the use of exploitability as a relevant metric for evaluating the convergence towards a Nash equilibrium in the context of Mean Field Games. These theoretical contributions are supported by numerical experiments provided in either model-based or model-free settings. We provide hereby for the first time converging learning dynamic for Mean Field Games in the presence of common noise.

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