Fictitious Play for Mean Field Games: Continuous Time Analysis and Applications
NIPS 2020, 2020.
In this paper we have shown that Fictitious Play can serve as a basis for building practical algorithms to solve a wide variety of Mean Field Games including finite horizon and γ-discounted MFGs as well as games perturbed by a common noise
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 c...More
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