Stochastic Recursive Optimal Control of McKean-Vlasov Type: A Viscosity Solution Approach

TL;DR

This paper develops a viscosity solution framework for stochastic recursive optimal control problems of McKean-Vlasov type, utilizing dynamic programming and BSDE techniques in Wasserstein space, with applications to linear-quadratic models.

Contribution

It introduces a novel approach to characterize the value function as a viscosity solution of the HJB equation in Wasserstein space for McKean-Vlasov control problems.

Findings

Proved the value function is deterministic and law-invariant.

Established a dynamic programming principle in Wasserstein space.

Derived explicit optimal feedback controls using Riccati equations.

Abstract

In this paper, we study a kind of optimal control problem for forward-backward stochastic differential equations (FBSDEs for short) of McKean--Vlasov type via the dynamic programming principle (DPP for short) motivated by studying the infinite dimensional Hamilton--Jacobi--Bellman (HJB for short) equation derived from the decoupling field of the FBSDEs posed by Carmona and Delarue (\emph{Ann Probab}, 2015, \cite{cd15}). At the beginning, the value function is defined by the solution to the controlled BSDE alluded to earlier. On one hand, we can prove the value function is deterministic function with respect to the initial random variable; On the other hand, we can show that the value function is \emph{law-invariant}, i.e., depending on only via its distribution by virtue of BSDE property. Afterward, utilizing the notion of differentiability with respect to probability measures introduced by P.L. Lions \cite{Lions2012}, we are able to establish a DPP for the value function in the Wasserstein space of probability measures based on the application of BSDE approach, particularly, employing the notion of stochastic \emph{backward semigroups} associated with stochastic optimal control problems and It\^{o} formula along a flow property of the conditional law of the controlled forward state process. We prove that the value function is the unique viscosity solutions of the associated generalized HJB equations in some separable Hilbert space. Finally, as an application, we formulate an optimal control problem for linear stochastic differential equations with quadratic cost functionals of McKean-Vlasov type under nonlinear expectation, $g$-expectation introduced by Peng \cite{Peng04} and derive the optimal feedback control explicitly by means of several groups of Riccati equations.