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Summary

This study introduces an online reinforcement learning (RL) control method for unknown multiagent systems (MASs). The approach ensures stability and accelerates convergence using an event-triggered mechanism and neural networks.

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Area of Science:

Control Systems Engineering
Artificial Intelligence
Machine Learning

Background:

Multiagent systems (MASs) present complex control challenges, especially when system dynamics are unknown.
Optimal cooperative control is crucial for coordinated behavior in MASs.
Existing methods often struggle with stability guarantees under evolving control policies.

Purpose of the Study:

To develop an online reinforcement learning (RL) control method for unknown linear discrete-time multiagent systems (MASs).
To ensure the stability of MASs even with immature policies generated during learning.
To accelerate the convergence rate of the value iteration (VI) algorithm.

Main Methods:

An online learning scheme with evolving policies was designed, incorporating an event-triggered mechanism to filter admissible control policies.
A stability criterion was developed to guarantee system stability without requiring a monotonic value function sequence.
An acceleration mechanism for VI was introduced, analyzing the impact of relaxation factors on convergence speed.
Backpropagation (BP) neural networks (NNs) were utilized for practical implementation.

Main Results:

The proposed method successfully addresses the optimal cooperative control problem for unknown linear discrete-time MASs.
The event-triggered stability criterion effectively filters policies, ensuring system stability.
The acceleration mechanism significantly enhances the convergence rate of the VI algorithm.
Simulation results validate the effectiveness and performance of the developed control method.

Conclusions:

The developed online RL control method provides a robust solution for cooperative control in unknown MASs.
The integration of an event-triggered mechanism and acceleration techniques offers improved stability and efficiency.
The use of BP neural networks demonstrates the practical applicability of the proposed approach.