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Robust $Q$-learning for mean-field control under Wasserst...
[Submitted on 18 Jun 2026] · 2026-06-19 · via cs.LG updates on arXiv.org

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Abstract:In this article, we present a robust $Q$-learning algorithm for discrete-time mean-field control problems under Wasserstein uncertainty in the common noise law. The algorithm combines a quantization-and-projection scheme with a Wasserstein dual reformulation on the common-noise space. We establish its convergence together with finite-time iteration bounds for both synchronous and asynchronous learning schemes. Numerical experiments on systemic risk and epidemic models compare the asynchronous implementation with an idealized Bellman iteration, illustrate the robustness-performance tradeoff under common-noise misspecification, and report the observed convergence behavior of the asynchronous $Q$-learning algorithm.

Submission history

From: Ariel Neufeld [view email]
[v1] Thu, 18 Jun 2026 15:20:00 UTC (115 KB)

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