A Distributed Reinforcement Learning Method for Output Consensus of Heterogeneous Multi-Agent Systems with Event-Triggered Mechanisms

This paper investigates the output consensus problem in heterogeneous multi-agent systems. To address the challenges of traditional analytical methods in handling unknown dynamics and disturbances, a control framework is proposed that combines known model structures with a data-driven adaptive mechanism. The framework uses a distributed internal model to compensate for system heterogeneity and incorporates an event-triggered mechanism to reduce communication burden. To improve transient tracking performance, a reinforcement learning strategy based on centralized training and decentralized execution is introduced to adaptively optimize local feedback gains. Simulation results show that the proposed method effectively bounds closed-loop signals, achieves relatively fast convergence, and demonstrates some robustness and communication efficiency under process noise.