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Hierarchical Multiagent Formation Control Scheme via Actor-Critic Learning.

Chaoxu Mu, Jiangwen Peng, Changyin Sun

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    |March 18, 2022
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    Summary
    This summary is machine-generated.

    This study introduces a hierarchical leader-following formation control for large-scale multiagent systems (MAS). A novel multistep generalized policy iteration (MsGPI) algorithm enhances convergence speed and reduces runtime for optimal cooperative control.

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    Area of Science:

    • Robotics
    • Control Theory
    • Artificial Intelligence

    Background:

    • Large-scale multiagent systems (MAS) face challenges in cooperative formation control, particularly with subgroup consensus.
    • Existing multigroup techniques often lack inter-subgroup coordination.

    Purpose of the Study:

    • To develop a nearly optimal solution for cooperative formation control in large-scale MAS.
    • To address the consensus issue in multigroup decomposition using a hierarchical structure.

    Main Methods:

    • A hierarchical leader-following formation control structure with multigroup technique was designed, featuring two layers and three agent types.
    • Adaptive dynamic programming and a novel multistep generalized policy iteration (MsGPI) algorithm were employed for optimal control.
    • A neural network-based actor-critic structure was utilized for approximating control policies and value functions.

    Main Results:

    • The proposed MsGPI algorithm accelerates convergence and reduces runtime compared to traditional generalized policy iteration.
    • Stability, convergence, and optimality analyses were provided for the MsGPI algorithm.
    • A large-scale formation control problem demonstrated the effectiveness of the hierarchical structure and MsGPI algorithm.

    Conclusions:

    • The hierarchical leader-following formation control structure effectively addresses cooperative formation control in large-scale MAS.
    • The MsGPI algorithm offers improved performance in terms of speed and efficiency for optimal control problems.
    • The developed framework provides a robust solution for complex multiagent coordination tasks.