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Related Experiment Video

Updated: Mar 7, 2026

WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control
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Neural Network-Based Solutions for Stochastic Optimal Control Using Path Integrals.

Karthikeyan Rajagopal, Sivasubramanya Nadar Balakrishnan, Jerome R Busemeyer

    IEEE Transactions on Neural Networks and Learning Systems
    |February 18, 2017
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel neural network approach for solving complex stochastic optimal control problems. The method effectively handles noise and dimensionality challenges, offering a more robust solution than existing techniques.

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    Area of Science:

    • Control Theory
    • Machine Learning
    • Optimization

    Background:

    • Existing methods for stochastic optimal control, such as the stochastic maximum principle (SMP) and Hamilton-Jacobi-Bellman (HJB) equations, face challenges with noise and dimensionality.
    • The SMP formalism becomes complex with noise, often leading to the neglect of noise effects in solutions.
    • While the HJB framework handles noise effectively, its path integral (PI) formulation can be limited by the curse of dimensionality.

    Purpose of the Study:

    • To propose an offline approximate dynamic programming approach using neural networks for finite horizon stochastic optimal control problems.
    • To address the limitations of existing methods in handling noise and the curse of dimensionality.
    • To develop a novel adaptive critic approach leveraging the path integral formulation.

    Main Methods:

    • Utilizing an offline approximate dynamic programming approach with neural networks.
    • Formulating the stochastic Hamilton-Jacobi-Bellman (HJB) equation as a path integral (PI) problem for control-affine nonlinear stochastic systems.
    • Employing the adaptive critic design paradigm, a neural network structure, to overcome the curse of dimensionality inherent in the PI formulation.

    Main Results:

    • A novel adaptive critic approach combined with the PI formulation was successfully developed.
    • The proposed algorithm demonstrated its potential in solving stochastic optimal control problems.
    • Simulation results on benchmark problems validated the effectiveness of the developed method.

    Conclusions:

    • The proposed offline approximate dynamic programming approach using neural networks offers a viable solution for finite horizon stochastic optimal control problems.
    • The integration of the PI formulation with adaptive critic designs effectively mitigates challenges posed by noise and dimensionality.
    • The demonstrated simulation results confirm the algorithm's capability and potential for practical applications in control systems.