Randomization method and backward SDEs for optimal control of partially observed path-dependent stochastic systems

Abstract : We consider a unifying framework for stochastic control problem including the following features: partial observation, path-dependence (both with respect to the state and the control), and without any non-degeneracy condition on the stochastic differential equation (SDE) for the controlled state process, driven by a Wiener process. In this context, we develop a general methodology, refereed to as the randomization method, studied in [23] for classical Markovian control under full observation, and consisting basically in replacing the control by an exogenous process independent of the driving noise of the SDE. Our first main result is to prove the equivalence between the primal control problem and the randomized control problem where optimization is performed over change of equivalent probability measures affecting the characteristics of the exogenous process. The randomized problem turns out to be associated by duality and separation argument to a backward SDE, which leads to the so-called randomized dynamic programming principle and randomized equation in terms of the path-dependent filter, and then characterizes the value function of the primal problem. In particular, classical optimal control problems with partial observation affected by non-degenerate Gaussian noise fall within the scope of our framework, and are treated by means of an associated backward SDE.
Type de document :
Pré-publication, Document de travail
48 pages. 2016
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https://hal.archives-ouvertes.fr/hal-01235335
Contributeur : Huyen Pham <>
Soumis le : lundi 12 septembre 2016 - 11:15:52
Dernière modification le : lundi 29 mai 2017 - 14:22:45
Document(s) archivé(s) le : mardi 13 décembre 2016 - 12:43:53

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  • HAL Id : hal-01235335, version 2
  • ARXIV : 1511.09274

Citation

Elena Bandini, Andrea Cosso, Marco Fuhrman, Huyên Pham. Randomization method and backward SDEs for optimal control of partially observed path-dependent stochastic systems. 48 pages. 2016. <hal-01235335v2>

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