Event-Triggered Data-Based Reinforcement Learning Control for Optimal Consensus of Heterogeneous Multi-Agent Systems in Graphical Games

Abstract

This study discusses the optimal consensus issue for heterogeneous multi-agent systems (MASs) defined by partially unknown dynamics within a graphical game framework. Data-driven reinforcement learning has shown efficacy in such systems; however, conventional implementations frequently depend on continuous time data transmission, which puts too much strain on computers and communication systems. This paper proposes a new event-triggered, data-based reinforcement learning control scheme to fix these problems. By adding an event-triggered mechanism (ETM) to the heterogeneous graphical game formulation, the control protocol is only updated when certain error thresholds are crossed. This uses much less resources than time-triggered methods. An off-policy integral reinforcement learning (IRL) algorithm is devised to ascertain the Nash equilibrium solution utilizing quantifiable system data, thereby obviating the necessity for precise knowledge of the internal system matrices. A theoretical analysis using Lyapunov stability theory shows that the suggested event-triggered strategy ensures asymptotic consensus and convergence to the best control weights. Finally, numerical simulation examples show that the proposed approach works well and is better than other methods. These examples show that the controller update frequency and communication load can be greatly reduced while keeping the system stable and optimal.