Entropy Regularization in Mean-Field Games of Optimal Stopping

Jodi Dianetti, Roxana Dumitrescu, Giorgio Ferrari and Renyuan Xu
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Jodi Dianetti: Center for Mathematical Economics, Bielefeld University
Roxana Dumitrescu: Center for Mathematical Economics, Bielefeld University
Giorgio Ferrari: Center for Mathematical Economics, Bielefeld University
Renyuan Xu: Center for Mathematical Economics, Bielefeld University

No 755, Center for Mathematical Economics Working Papers from Center for Mathematical Economics, Bielefeld University

Abstract: We study mean-field games of optimal stopping (OS-MFGs) and introduce an entropyregularized framework to enable learning-based solution methods. By utilizing randomized stopping times, we reformulate the OS-MFG as a mean-field game of singular stochastic controls (SC-MFG) with entropy regularization. We establish the existence of equilibria and prove their stability as the entropy parameter vanishes. Fictitious play algorithms tailored for the regularized setting are introduced, and we show their convergence under both Lasry–Lions monotonicity and supermodular assumptions on the reward functional. Our work lays the theoretical foundation for model-free learning approaches to OS-MFGs.

Keywords: Mean-field game of optimal stopping; singular stochastic control; entropy regularization; randomized stopping times; fictitious play algorithm (search for similar items in EconPapers)
Pages: 29
Date: 2025-10-21
New Economics Papers: this item is included in nep-gth
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Persistent link: https://EconPapers.repec.org/RePEc:bie:wpaper:755

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