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Expert System-Based Multiagent Deep Deterministic Policy Gradient for Swarm Robot Decision Making.

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    An expert system-based multiagent deep deterministic policy gradient (ESB-MADDPG) enhances swarm robot decision-making by speeding up trajectory generation and ensuring smooth paths. Combining ESB-MADDPG with model predictive control (MPC) enables efficient and optimal trajectory tracking for swarm robots.

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    Area of Science:

    • Robotics
    • Artificial Intelligence
    • Control Systems

    Background:

    • Multiagent deep deterministic policy gradient (MADDPG) is a reinforcement learning algorithm for multiagent systems.
    • Traditional MADDPG faces challenges in swarm robotics due to time-consuming path planning and non-smooth trajectories.

    Purpose of the Study:

    • To develop a faster and smoother decision-making method for swarm robots.
    • To improve the efficiency and applicability of MADDPG in swarm robotics.

    Main Methods:

    • Proposed an expert system-based multiagent deep deterministic policy gradient (ESB-MADDPG) to accelerate trajectory gathering and ensure smooth paths.
    • Integrated an expert system for pre-trained offline trajectories, avoiding repeated retraining.
    • Employed model predictive control (MPC) for optimal tracking of generated offline trajectories.

    Main Results:

    • ESB-MADDPG significantly improves training speed compared to traditional MADDPG.
    • The method generates smooth, trackable trajectories suitable for swarm robots.
    • The combined ESB-MADDPG and MPC approach demonstrates efficient swarm robot decision-making.

    Conclusions:

    • The ESB-MADDPG method effectively addresses the limitations of traditional MADDPG in swarm robotics.
    • The integration with MPC allows for efficient and optimal trajectory tracking.
    • This approach offers a robust solution for swarm robot decision-making and control.