An Evolutionary Approach to Passive Learning in Optimal Control Problems

D. Blueschke*, I. Savin, V. Blueschke-Nikolaeva

*Corresponding author for this work

Research output: Contribution to journalArticleResearchpeer-review

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.

Original languageEnglish
Pages (from-to)659-673
Number of pages15
JournalComputational Economics
Volume56
Issue number3
DOIs
Publication statusPublished - Oct 2020

Keywords

  • Differential Evolution
  • Optimal control
  • Passive learning
  • Stochastic problems

Fingerprint

Dive into the research topics of 'An Evolutionary Approach to Passive Learning in Optimal Control Problems'. Together they form a unique fingerprint.

Cite this