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    Area of Science:

    • Optimal Control
    • Distributed Computing
    • Artificial Intelligence

    Background:

    • Switch-based adaptive dynamic programming (ADP) is an optimal control problem requiring cost minimization across multiple dynamical modes.
    • High-dimensional systems pose significant computational challenges for traditional ADP due to exponentially increasing value function approximators and modes.

    Purpose of the Study:

    • To propose a distributed computational method for solving switch-based ADP in large-scale systems.
    • To address the computational complexity associated with high-dimensional systems and numerous dynamical modes in ADP.

    Main Methods:

    • System partitioning into agents, each managing a lower-dimensional state and local modes.
    • Agents minimize a local cost function while coordinating to avoid conflicts with neighboring agents' switching strategies.
    • A heuristic algorithm utilizing consensus dynamics and Nash equilibrium is employed for inter-agent coordination.

    Main Results:

    • The proposed distributed method effectively solves switch-based ADP problems in complex systems.
    • Demonstrated effectiveness through verification on traffic and building management test cases.

    Conclusions:

    • The distributed approach significantly enhances the scalability and feasibility of switch-based ADP for high-dimensional problems.
    • The consensus and Nash equilibrium-based algorithm provides a robust mechanism for decentralized optimal control.