A Probabilistic approach to classical solutions of the master equation for large population equilibria
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Abstract (arXiv)
We analyze a class of nonlinear partial differential equations (PDEs) defined on ℝd × P2(ℝd), where P2(ℝd) is the Wasserstein space of probability measures on ℝd with a finite second-order moment. We show that such equations admit a classical solutions for sufficiently small time intervals. Under additional constraints, we prove that their solution can be extended to arbitrary large intervals. These nonlinear PDEs arise in the recent developments in the theory of large population stochastic control. More precisely they are the so-called master equations corresponding to asymptotic equilibria for a large population of controlled players with mean-field interaction and subject to minimization constraints. The results in the paper are deduced by exploiting this connection. In particular, we study the differentiability with respect to the initial condition of the flow generated by a forward-backward stochastic system of McKean-Vlasov type. As a byproduct, we prove that the decoupling field generated by the forward-backward system is a classical solution of the corresponding master equation. Finally, we give several applications to mean-field games and to the control of McKean-Vlasov diffusion processes.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 11 Nov 2014.
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Citing papers per year: 2023: 2; 2024: 0; 2025: 0; 2026: 2.
4 papers.
- 2023Recent Developments in Machine Learning Methods for Stochastic Control and Gamescited by 24
- 2023Actor-Critic learning for mean-field control in continuous timecited by 22
- 2026Continuous-time q-learning for mean-field control with common noise, part-I: Theoretical foundationscited by 6
- 2026Stochastic Control Methods for Optimizationcited by 1
- 2026Continuous-time q-learning for mean-field control with common noise, part-I: Theoretical foundations30 Apr 2026
- 2026Stochastic Control Methods for Optimization3 Jan 2026
- 2023Recent Developments in Machine Learning Methods for Stochastic Control and Games17 Mar 2023
- 2023Actor-Critic learning for mean-field control in continuous time13 Mar 2023