Online Reinforcement Learning Control Designs With Acceleration Mechanism for Unknown Multiagent Systems Through...

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.