A New Framework for Event-Triggered Consensus Control with Reinforcement Learning

In this paper, we propose a novel framework which can minimize event-triggered frequency while maintaining consensus control performance in multi-agent systems. Distributed event-triggering condition is learned automatically through re-inforcement learning techniques, integrating with existing consensus controllers. Different from conventional event-triggered consensus control methods that rely on Lyapunov-based stability analysis, the proposed data-driven framework reduces design conservatism through adaptive learning mechanisms. Experimental results demonstrate the effectiveness of our method in achieving substantial reductions in communication cost while preserving system stability and consensus performance.