A Stochastic Maximum Principle for Risk-Sensitive Mean-Field Type Control
KAUST DepartmentApplied Mathematics and Computational Science Program
Center for Uncertainty Quantification in Computational Science and Engineering (SRI-UQ)
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Online Publication Date2015-02-24
Print Publication Date2015-10
Permanent link to this recordhttp://hdl.handle.net/10754/622508
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AbstractIn this paper we study mean-field type control problems with risk-sensitive performance functionals. We establish a stochastic maximum principle (SMP) for optimal control of stochastic differential equations (SDEs) of mean-field type, in which the drift and the diffusion coefficients as well as the performance functional depend not only on the state and the control but also on the mean of the distribution of the state. Our result extends the risk-sensitive SMP (without mean-field coupling) of Lim and Zhou (2005), derived for feedback (or Markov) type optimal controls, to optimal control problems for non-Markovian dynamics which may be time-inconsistent in the sense that the Bellman optimality principle does not hold. In our approach to the risk-sensitive SMP, the smoothness assumption on the value-function imposed in Lim and Zhou (2005) needs not be satisfied. For a general action space a Peng's type SMP is derived, specifying the necessary conditions for optimality. Two examples are carried out to illustrate the proposed risk-sensitive mean-field type SMP under linear stochastic dynamics with exponential quadratic cost function. Explicit solutions are given for both mean-field free and mean-field models.
CitationDjehiche B, Tembine H, Tempone R (2015) A Stochastic Maximum Principle for Risk-Sensitive Mean-Field Type Control. IEEE Transactions on Automatic Control 60: 2640–2649. Available: http://dx.doi.org/10.1109/TAC.2015.2406973.