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    Area of Science:

    • Control Theory
    • Game Theory
    • Artificial Intelligence

    Background:

    • Multivehicle systems require robust control protocols for unknown communication environments.
    • Viability theory offers tools to analyze system controllability under constraints.
    • Reinforcement learning enables agents to learn optimal strategies through interaction.

    Purpose of the Study:

    • To propose an optimal-distributed control protocol for multivehicle systems.
    • To address challenges posed by unknown and switching communication graphs.
    • To extend the application of viability theory to multiagent and multiplayer differential games.

    Main Methods:

    • Formulating the control problem as a differential graphical game.
    • Utilizing viability theory to define system controllability and capture basins.
    • Employing reinforcement learning for online strategy optimization.
    • Combining offline capture basin approximation with online learning.

    Main Results:

    • The proposed distributed control law comprises offline capture basin approximation and online reinforcement learning.
    • Convergence properties of reinforcement learning estimation errors are proven.
    • Convergence of the control policy to the Pareto optimum of the differential graphical game is discussed.
    • Guaranteed approximation results for the capture basin are provided.

    Conclusions:

    • The developed protocol effectively manages multivehicle systems with dynamic communication.
    • The study successfully extends viability theory to complex multiagent scenarios.
    • Simulation results validate the proposed distributed control policy for differential graphical games.