A multilevel approach for stochastic nonlinear optimal control

Ajay Jasra, Jeremy Heng, Yaxian Xu, Adrian N. Bishop

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


We consider a class of finite-time horizon nonlinear stochastic optimal control problem. Although the optimal control admits a path integral representation for this class of control problems, efficient computation of the associated path integrals remains a challenging task. We propose a new Monte Carlo approach that significantly improves upon existing methodology. We tackle the issue of exponential growth in variance with the time horizon by casting optimal control estimation as a smoothing problem for a state-space model, and applying smoothing algorithms based on particle Markov chain Monte Carlo. To further reduce the cost, we then develop a multilevel Monte Carlo method which allows us to obtain an estimator of the optimal control with (Formula presented.) mean squared error with a cost of (Formula presented.). In contrast, a cost of (Formula presented.) is required for the existing methodology to achieve the same mean squared error. Our approach is illustrated on two numerical examples.
Original languageEnglish (US)
Pages (from-to)1-15
Number of pages15
JournalInternational Journal of Control
StatePublished - Dec 3 2020

Bibliographical note

KAUST Repository Item: Exported on 2020-12-16
Acknowledged KAUST grant number(s): CRG4 grant ref: 2584
Acknowledgements: A.J. and Y.X. were supported by an AcRF tier 2 [grant number R-155-000-161-112]. A.J. is affiliated with the Risk Management Institute, the Center for Quantitative Finance and the OR & Analytics cluster at NUS. A.J. was supported by a KAUST CRG4 grant ref: 2584


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