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OPTCON3: An Active Learning Control Algorithm for Nonlinear Quadratic Stochastic Problems.

V Blueschke-Nikolaeva1, D Blueschke1, R Neck1

  • 1Department of Economics, University of Klagenfurt, Klagenfurt, Austria.

Computational Economics
|September 1, 2020
PubMed
Summary
This summary is machine-generated.

The new OPTCON3 algorithm finds optimal policies for complex control problems. It uses active learning to improve understanding of dynamic systems under uncertainty.

Keywords:
Active learningAlgorithmsDual controlStochastic optimal control

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Area of Science:

  • Control Theory
  • Operations Research
  • Economics

Background:

  • Stochastic control problems with nonlinear dynamics are challenging.
  • Existing methods may not fully capture the dual effect of policy optimization.

Purpose of the Study:

  • Introduce the OPTCON3 algorithm for approximately optimal policies.
  • Incorporate active learning and the dual effect in policy optimization.
  • Evaluate OPTCON3 on economic models.

Main Methods:

  • Approximates nonlinear models with time-varying linear models.
  • Applies a Kendrick-like procedure to linearized models.
  • Integrates active learning for system stochastics.

Main Results:

  • OPTCON3 calculates approximately optimal policies.
  • Demonstrates effectiveness on two economic models.
  • Compares favorably to previous solutions.

Conclusions:

  • OPTCON3 shows promise for adaptive economic policy under uncertainty.
  • Enhances understanding of optimizing policies with active learning.
  • Provides a novel approach to stochastic control.