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Off-Policy Reinforcement Learning for Synchronization in Multiagent Graphical Games.

Jinna Li, Hamidreza Modares, Tianyou Chai

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    This study introduces a model-free reinforcement learning (RL) algorithm for multiagent system synchronization. The method achieves optimal synchronization without needing agent dynamics, ensuring agents reach a global Nash equilibrium.

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    Area of Science:

    • Control Systems Engineering
    • Artificial Intelligence
    • Game Theory

    Background:

    • Traditional control protocols for multiagent systems often require complete knowledge of agent dynamics.
    • Achieving optimal synchronization in multiagent systems presents significant control challenges.

    Purpose of the Study:

    • To develop a model-free, off-policy reinforcement learning (RL) algorithm for optimal synchronization of multiagent systems.
    • To solve the optimal synchronization problem without prior knowledge of agent dynamics, utilizing graphical games framework.

    Main Methods:

    • An off-policy reinforcement learning algorithm is developed using a behavior policy to collect data.
    • Off-policy Bellman equations are derived for agents to learn value functions and improve policies.
    • Actor-critic neural networks and least-square methods approximate control policies and value functions.

    Main Results:

    • The proposed off-policy RL algorithm enables real-time implementation using only measured data.
    • Optimal distributed policies are found, satisfying global Nash equilibrium and synchronizing all agents to a leader.
    • Simulation results demonstrate the effectiveness of the developed method.

    Conclusions:

    • The model-free, off-policy RL approach effectively solves the optimal synchronization problem for multiagent systems.
    • The algorithm achieves synchronization by finding policies that satisfy global Nash equilibrium.
    • This method offers a practical solution for real-time synchronization without requiring detailed system dynamics.