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    This study introduces a novel parallel multistep Q-learning algorithm for safe neural critic control, enhancing data utilization for improved safety and efficiency in learning control systems.

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    Area of Science:

    • * Optimal learning control
    • * Artificial intelligence in control systems
    • * Robotics and autonomous systems

    Background:

    • * Data-driven methods have advanced optimal learning control but often neglect systematic data utilization, including safety, efficiency, and error accumulation.
    • * Existing safe neural critic control approaches have limitations in comprehensive data handling.
    • * Need for enhanced data utilization in learning control to ensure safety and efficiency.

    Purpose of the Study:

    • * To introduce a parallel multistep evaluation mechanism for improved data utilization in safe neural critic control.
    • * To propose a novel parallel multistep Q-learning algorithm that enhances data efficiency and mitigates error accumulation.
    • * To formulate a novel control barrier function (CBF) for ensuring safety under asymmetric constraints.

    Main Methods:

    • * Development of a parallel multistep evaluation mechanism combining system interaction data and model-generated data.
    • * Proposal of a parallel multistep Q-learning algorithm leveraging the evaluation mechanism.
    • * Formulation of a novel control barrier function (CBF) for safety assurance with adjustable constraint strength.

    Main Results:

    • * The proposed algorithm enhances data utilization efficiency and mitigates error accumulation in learning control.
    • * The novel CBF effectively ensures safety during learning and control, handling asymmetric constraints.
    • * Analysis shows multistep information from data-driven models impacts actor-critic neural network performance.

    Conclusions:

    • * The parallel multistep Q-learning algorithm effectively utilizes data for safety, efficiency, and error bounds.
    • * The approach is validated in an orbital maneuver system, demonstrating practical applicability.
    • * This work advances safe learning control by addressing systematic data utilization challenges.