Distributed FilterNet Reinforcement Learning for Achieving Output Consensus in Heterogeneous Multiplayer Multiagent...

Area of Science:

• Control Theory
• Multiagent Systems
• Game Theory

Background:

• Studying leader-follower consensus in complex multiagent systems presents challenges due to heterogeneous dynamics and internal agent objectives.
• Existing methods often struggle with decentralized control and data management for such systems.

Purpose of the Study:

• To develop a distributed control framework for achieving output consensus in multiagent systems with heterogeneous dynamics and multiple internal players.
• To design a reinforcement learning (RL) architecture that enables efficient, decentralized control without extensive data storage.

Main Methods:

• Formulated the problem as a multiplayer differential game for each agent, aiming for Nash equilibrium controls.
• Introduced a distributed control framework integrating feedforward (regulator) and feedback (game-theoretic Riccati) components.
• Developed a four-layer FilterNet RL architecture for policy identification, initialization, asynchronous updates, and real-time control.

Main Results:

• The FilterNet architecture effectively solves control solutions, reducing data requirements and accelerating convergence.
• Theoretical guarantees confirm the solvability and convergence of the proposed approach.
• Numerical simulations demonstrated the effectiveness and superiority of the FilterNet method compared to existing approaches.

Conclusions:

• The proposed FilterNet RL framework offers a robust and efficient solution for the leader-follower consensus problem in complex multiagent systems.
• This approach advances decentralized control strategies by integrating game theory and reinforcement learning effectively.