Federated Learning Adaptive Dynamic Programming for Massive Multiagent Mean-Field Games-Based Optimal Consensus

Area of Science:

• Control Theory
• Artificial Intelligence
• Distributed Systems

Background:

• Massive multiagent systems face challenges in achieving real-time optimal consensus control due to numerous interactions and conflicts.
• Existing methods struggle with the complexity and scale of these systems, necessitating advanced control strategies.

Purpose of the Study:

• To develop a novel federated learning adaptive dynamic programming (FL-ADP) control scheme for optimal consensus in massive multiagent systems.
• To address the challenges posed by large-scale agent interactions and conflicts of interest.
• To solve mean-field games (MFGs)-based optimal consensus problems.

Main Methods:

• Approximation of individual agent interactions using mean-field games (MFGs).
• Development of a novel undiscounted performance index function incorporating mean-field coupling and tracking errors.
• Utilization of a critic-mass neural network to solve coupled Hamilton-Jacobi-Bellman and Fokker-Planck-Kolmogorov equations.
• Formulation of an event-triggered federated learning mechanism for algorithm convergence and communication efficiency.

Main Results:

• Derivation of an approximate optimal control policy and quantification of collective behavior probability density.
• Guarantee of uniform ultimate boundedness for tracking errors and weight estimation errors using Lyapunov's direct method.
• Validation of the FL-ADP scheme's effectiveness and rationality through simulations on massive multi-uncrewed aerial vehicle systems.

Conclusions:

• The proposed FL-ADP control scheme effectively solves the optimal consensus problem in massive multiagent systems.
• The method balances communication resource consumption with algorithm convergence, outperforming existing approaches.
• The developed technique provides a robust and efficient solution for real-time adaptive optimal consensus control in large-scale systems.