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    Area of Science:

    • Control Engineering
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Optimal regulation and tracking are crucial for complex single and multiagent systems.
    • Traditional control methods face challenges with system complexity and adaptability.
    • Reinforcement learning (RL) offers a data-driven approach to control problems.

    Purpose of the Study:

    • To review the state-of-the-art in reinforcement learning (RL)-based feedback control.
    • To cover optimal regulation and tracking for single and multiagent systems.
    • To discuss existing RL solutions for control and graphical games.

    Main Methods:

    • Review of existing literature on RL for optimal control and games.
    • Discussion of Q-learning for discrete-time (DT) systems.
    • Discussion of integral RL algorithms for continuous-time (CT) systems.
    • Exploration of off-policy RL for both CT and DT systems.

    Main Results:

    • RL methods enable online learning of control solutions using system trajectory data.
    • Q-learning and integral RL are core algorithms for DT and CT systems, respectively.
    • Off-policy RL presents a promising new direction for both system types.

    Conclusions:

    • Reinforcement learning provides powerful, adaptive solutions for optimal control and game problems.
    • The reviewed algorithms and off-policy methods are key advancements in the field.
    • RL applications demonstrate practical utility in various domains.