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    This study introduces new inverse reinforcement learning (RL) algorithms for learning objective functions in control systems using only input-output data. These methods advance RL by not requiring full state information.

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    Area of Science:

    • Control Systems Engineering
    • Machine Learning
    • Optimization

    Background:

    • Traditional inverse reinforcement learning (RL) methods often require full state information and state-feedback control from expert demonstrations.
    • Static output-proportional-integral-derivative (OPFB) control systems present a challenge due to limited input-output measurements.

    Purpose of the Study:

    • To develop inverse RL algorithms for learning objective functions in linear discrete-time systems with static OPFB control.
    • To address the limitations of existing inverse RL methods by utilizing only input-output data.

    Main Methods:

    • A model-based inverse RL algorithm is proposed to reconstruct the input-output objective function using system dynamics and OPFB gain.
    • An input-output Q-function is developed based on state reconstruction techniques.
    • A data-driven inverse Q-learning algorithm is presented to learn the objective function from demonstrated inputs and outputs without prior system knowledge.

    Main Results:

    • The proposed algorithms successfully reconstruct the objective function and OPFB gain from input-output data.
    • The data-driven algorithm provides unbiased solutions despite exploration noise.
    • Convergence properties and the non-unique nature of solutions were analyzed.

    Conclusions:

    • The developed inverse RL algorithms effectively learn objective functions for systems with static OPFB control using limited data.
    • These methods offer a more general and practical approach to inverse RL in control engineering.
    • Numerical simulations confirm the efficacy of the proposed techniques.