Optimal adaptive control with separable drift uncertainty

We consider a problem of stochastic optimal control with separable drift uncertainty in strong formulation on a finite time horizon. The drift of the state Y u is multiplicatively influenced by an unknown random variable λ, while admissible controls u are required to be adapted to the observation fi...

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Main Authors: Cohen, SN, Knochenhauer, C, Merkel, A
Format: Journal article
Language:English
Published: Society for Industrial and Applied Mathematics 2025
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author Cohen, SN
Knochenhauer, C
Merkel, A
author_facet Cohen, SN
Knochenhauer, C
Merkel, A
author_sort Cohen, SN
collection OXFORD
description We consider a problem of stochastic optimal control with separable drift uncertainty in strong formulation on a finite time horizon. The drift of the state Y u is multiplicatively influenced by an unknown random variable λ, while admissible controls u are required to be adapted to the observation filtration. Choosing a control actively influences the state and information acquisition simultaneously and comes with a learning effect. The problem, initially non-Markovian, is embedded into a higher-dimensional Markovian, full information control problem with control-dependent filtration and noise. To that problem, we apply the stochastic Perron method to characterize the value function as the unique viscosity solution of the HJB equation, explicitly construct ε-optimal controls, and show that the values in the strong and weak formulation agree. Numerical illustrations show a significant difference between the adaptive control and the certainty equivalence control, highlighting a substantial learning effect.
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spelling oxford-uuid:ca85ed6f-2a3e-4e9b-89df-d77d135c15c72025-01-09T11:28:37ZOptimal adaptive control with separable drift uncertaintyJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:ca85ed6f-2a3e-4e9b-89df-d77d135c15c7EnglishSymplectic ElementsSociety for Industrial and Applied Mathematics2025Cohen, SNKnochenhauer, CMerkel, AWe consider a problem of stochastic optimal control with separable drift uncertainty in strong formulation on a finite time horizon. The drift of the state Y u is multiplicatively influenced by an unknown random variable λ, while admissible controls u are required to be adapted to the observation filtration. Choosing a control actively influences the state and information acquisition simultaneously and comes with a learning effect. The problem, initially non-Markovian, is embedded into a higher-dimensional Markovian, full information control problem with control-dependent filtration and noise. To that problem, we apply the stochastic Perron method to characterize the value function as the unique viscosity solution of the HJB equation, explicitly construct ε-optimal controls, and show that the values in the strong and weak formulation agree. Numerical illustrations show a significant difference between the adaptive control and the certainty equivalence control, highlighting a substantial learning effect.
spellingShingle Cohen, SN
Knochenhauer, C
Merkel, A
Optimal adaptive control with separable drift uncertainty
title Optimal adaptive control with separable drift uncertainty
title_full Optimal adaptive control with separable drift uncertainty
title_fullStr Optimal adaptive control with separable drift uncertainty
title_full_unstemmed Optimal adaptive control with separable drift uncertainty
title_short Optimal adaptive control with separable drift uncertainty
title_sort optimal adaptive control with separable drift uncertainty
work_keys_str_mv AT cohensn optimaladaptivecontrolwithseparabledriftuncertainty
AT knochenhauerc optimaladaptivecontrolwithseparabledriftuncertainty
AT merkela optimaladaptivecontrolwithseparabledriftuncertainty