Mean-field optimal control as Gamma-limit of finite agent controls
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Abstract (arXiv)
This paper focuses on the role of a government of a large population of interacting agents as a mean field optimal control problem derived from deterministic finite agent dynamics. The control problems are constrained by a PDE of continuity-type without diffusion, governing the dynamics of the probability distribution of the agent population. We derive existence of optimal controls in a measure-theoretical setting as natural limits of finite agent optimal controls without any assumption on the regularity of control competitors. In particular, we prove the consistency of mean-field optimal controls with corresponding underlying finite agent ones. The results follow from a Gamma-convergence argument constructed over the mean-field limit, which stems from leveraging the superposition principle.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 13 Mar 2018.
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Citing papers per year: 2023: 1; 2024: 1; 2025: 1; 2026: 1.
4 papers.
- 2023On kernel-based statistical learning in the mean field limitcited by 1
- 2024Data/moment-driven approaches for fast predictive control of collective dynamicscited by 0
- 2025Maximum Principle of Optimal Probability Density Controlcited by 0
- 2026Taming Epilepsy: Mean Field Control of Whole-Brain Dynamicscited by 0
- 2026Taming Epilepsy: Mean Field Control of Whole-Brain Dynamics11 Mar 2026
- 2025Maximum Principle of Optimal Probability Density Control23 May 2025
- 2024Data/moment-driven approaches for fast predictive control of collective dynamics23 Feb 2024
- 2023On kernel-based statistical learning in the mean field limit27 Oct 2023