BSDE Representation and Randomized Dynamic Programming Principle for Stochastic Control Problems of Infinite-Dimensional Jump-Diffusions
Elena Bandini, Fulvia Confortola, Andrea Cosso

TL;DR
This paper develops a probabilistic representation for stochastic control problems in infinite-dimensional spaces with jumps, using backward stochastic differential equations and randomization, extending existing results and linking to Hamilton-Jacobi-Bellman equations.
Contribution
It introduces a novel probabilistic representation for infinite-dimensional jump-diffusion control problems via backward SDEs and randomization, broadening applicability and theoretical understanding.
Findings
Probabilistic representation formula for control problem value function
Extension of non-linear Feynman-Kac formula to infinite dimensions
Value function as viscosity solution of HJB equation in Hilbert space
Abstract
We consider a general class of stochastic optimal control problems, where the state process lives in a real separable Hilbert space and is driven by a cylindrical Brownian motion and a Poisson random measure; no special structure is imposed on the coefficients, which are also allowed to be path-dependent; in addition, the diffusion coefficient can be degenerate. For such a class of stochastic control problems, we prove, by means of purely probabilistic techniques based on the so-called randomization method, that the value of the control problem admits a probabilistic representation formula (known as non-linear Feynman-Kac formula) in terms of a suitable backward stochastic differential equation. This probabilistic representation considerably extends current results in the literature on the infinite-dimensional case, and it is also relevant in finite dimension. Such a representation…
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