An evolutionary approach to passive learning in optimal control problems
Blueschke, D. (University of Klagenfurt)
Savin, Ivan (Universitat Autònoma de Barcelona. Institut de Ciència i Tecnologia Ambientals)
Blueschke-Nikolaeva, V. (University of Klagenfurt)

Date:	2020
Abstract:	We consider the optimal control problem of a small nonlinear econometric model under parameter uncertainty and passive learning (open-loop feedback). Traditionally, this type of problems has been approached by applying linear-quadratic optimization algorithms. However, the literature demonstrated that those methods are very sensitive to the choice of random seeds frequently producing very large objective function values (outliers). Furthermore, to apply those established methods, the original nonlinear problem must be linearized first, which runs the risk of solving already a different problem. Following Savin and Blueschke (Comput Econ 48(2):317-338, 2016) in explicitly addressing parameter uncertainty with a large Monte Carlo experiment of possible parameter realizations and optimizing it with the Differential Evolution algorithm, we extend this approach to the case of passive learning. Our approach provides more robust results demonstrating greater benefit from learning, while at the same time does not require to modify the original nonlinear problem at hand. This result opens new avenues for application of heuristic optimization methods to learning strategies in optimal control research.
Grants:	European Commission 741087
Note:	Unidad de excelencia María de Maeztu CEX2019-000940-M
Rights:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Language:	Anglès
Document:	Article ; recerca ; Versió publicada
Subject:	Optimal control ; Stochastic problems ; Differential Evolution ; Passive learning
Published in:	Computational Economics, Vol. 56, issue 3 (Oct. 2020) , p. 659-673, ISSN 1572-9974

DOI: 10.1007/s10614-019-09961-4
PMID: 33268916

15 p, 491.4 KB

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