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Numerical Quadrature for Probabilistic Policy Search.

Julia Vinogradska, Bastian Bischoff, Jan Achterhold

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    This study introduces numerical quadrature to improve multi-step predictions in learning control policies, enhancing data efficiency and policy quality. The method broadens optimization scope, reducing manual effort and minimizing system interaction time.

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    Area of Science:

    • Robotics
    • Machine Learning
    • Control Theory

    Background:

    • Learning control policies is an alternative to classic control theory.
    • Model-based approaches offer data efficiency but struggle with multi-step predictions.
    • Existing methods face challenges with larger planning horizons and approximations.

    Purpose of the Study:

    • To enhance multi-step-ahead predictions in learning control policies.
    • To improve data efficiency and policy quality in model-based approaches.
    • To extend policy optimization beyond single trajectories.

    Main Methods:

    • Utilizing numerical quadrature for accurate multi-step-ahead predictions.
    • Integrating probabilistic models to mitigate model bias.
    • Extending optimization to a region of starting states.

    Main Results:

    • Significantly more accurate multi-step-ahead predictions achieved.
    • Increased data efficiency and enhanced learned policy quality.
    • Reduced manual effort in policy optimization and minimized system interaction time.

    Conclusions:

    • Numerical quadrature effectively addresses limitations in multi-step predictions for control policies.
    • The proposed approach enhances learning robustness and efficiency.
    • Empirical evaluations validate the theoretical results on simulated benchmark problems.