Reinforcement Learning for Synchronization of Heterogeneous Multiagent Systems by Improved Q-Functions

Area of Science:

• Artificial Intelligence
• Control Systems
• Machine Learning

Background:

• Current reinforcement learning (RL) techniques struggle with data efficiency and adaptability to environmental changes in multiagent systems (MASs).
• Achieving optimal synchronization and minimizing individual performance in heterogeneous MASs remains a significant challenge.

Purpose of the Study:

• To develop a novel methodology for enhancing the adaptability and data efficiency of RL techniques in MASs.
• To enable followers in MASs to synchronize with a leader and minimize individual performance through a learned joint control protocol.

Main Methods:

• Formulation of an optimal synchronization problem for heterogeneous MASs.
• Development of an arbitration RL mechanism featuring an improved Q-function with an arbitration factor.
• Adaptive allocation of control over agent behaviors using on-policy and off-policy RL techniques.
• Proposal of an arbitration RL algorithm utilizing critic-only neural networks.

Main Results:

• The proposed arbitration RL mechanism effectively addresses challenges of insufficient data and environmental changes.
• Theoretical analysis and proofs confirm synchronization and performance optimality.
• Simulation results demonstrate the effectiveness of the developed method.

Conclusions:

• The arbitration RL approach significantly enhances the adaptability and data efficiency of multiagent synchronization.
• The method provides a robust solution for optimal control in dynamic and complex multiagent environments.