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Optimization Frameworks & Learning Algorithms for Discrete-Time Mean Field Games — arXiv:2611.01754

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.01754
License (as archived): CC0-1.0
Archived: 2026-11-03T11:25:10.618Z
SHA-256 of the archived text: fe19dff327d72861d715dbb7737f7b789483c39b6b1e4f7a9dbf718a67601175
Stable URL of this page: https://metraipolis.com/documento/dc_ea29fa92fb90

How to cite

Optimization Frameworks & Learning Algorithms for Discrete-Time Mean Field Games — arXiv:2611.01754 — arXiv (en), https://arxiv.org/abs/2611.01754 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_ea29fa92fb90 (Metraipolis archive dc_ea29fa92fb90, sha256 fe19dff327d72861d715dbb7737f7b789483c39b6b1e4f7a9dbf718a67601175)

BibTeX

@misc{dcea29fa92fb90,
  title = {Optimization Frameworks & Learning Algorithms for Discrete-Time Mean Field Games — arXiv:2611.01754},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.01754},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_ea29fa92fb90; sha256 fe19dff327d72861d715dbb7737f7b789483c39b6b1e4f7a9dbf718a67601175},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.01754. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

This monograph introduces discrete-time mean field games with finite state and action spaces, emphasizing optimization formulations and learning algorithms. Starting from Markov decision processes and finite-player stochastic games, it develops the notion of mean field Nash equilibrium, establishes existence and uniqueness results under suitable assumptions, and explains how mean field equilibria yield approximate equilibria in large finite-player games. Building on the linear programming formulation of Markov decision processes, occupation measures and primal-dual formulations connect equilibrium computation with mathematical optimization. The monograph presents fixed-point iterations, fictitious play, online mirror descent, and optimization-based methods, discussing their convergence properties and limitations. Numerical examples illustrate crowd motion, traffic routing, epidemic management, and the computation of multiple equilibria. It combines examples, theoretical results, and implementable algorithms, with bibliographic notes providing connections to the broader literature.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.LG, nuovi lavori del giorno).
Categorie arXiv: Optimization and Control (math.OC) ; Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2611.01754 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-03T11:02:37.832Z dalla pagina https://arxiv.org/list/cs.LG/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).