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    This study introduces a model-free reinforcement learning algorithm to optimize multiagent systems. The novel approach effectively solves complex consensus problems in real-time using measured data.

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    Area of Science:

    • Control Systems Engineering
    • Artificial Intelligence
    • Robotics

    Background:

    • Multiagent systems (MASs) present challenges in achieving coordinated behavior, particularly in nonlinear continuous-time scenarios.
    • Optimizing fully cooperative (FC) consensus in MASs requires solving complex coupled Hamilton-Jacobian-Bellman (HJB) equations.

    Purpose of the Study:

    • To develop an off-policy, model-free algorithm for optimizing the FC consensus problem in nonlinear continuous-time MASs.
    • To address the need for real-time optimization using only measured data, eliminating the requirement for system models.

    Main Methods:

    • The FC consensus problem was reformulated as solving coupled HJB equations.
    • A policy iteration (PI)-based algorithm was developed and validated for solving the HJB equations.
    • A model-free Bellman equation was derived to determine optimal value functions and control policies.
    • Actor-critic neural networks and a least-squares approach were employed for tuning weights in a model-free implementation.
    • An off-policy model-free integral reinforcement learning (IRL) algorithm was proposed.

    Main Results:

    • The proposed PI-based algorithm effectively solves the coupled HJB equation.
    • The model-free approach successfully approximates optimal policies and value functions using neural networks.
    • The off-policy model-free IRL algorithm demonstrates real-time optimization capabilities for FC consensus in MASs.
    • Simulation results verified the effectiveness and practical applicability of the developed IRL algorithm.

    Conclusions:

    • The presented off-policy model-free IRL algorithm provides an effective solution for optimizing FC consensus in nonlinear continuous-time MASs.
    • This method enables real-time system optimization without prior knowledge of system dynamics, utilizing only measured data.
    • The research contributes a novel approach to reinforcement learning applications in complex multiagent coordination problems.