H∞ control for cooperative multi-agent systems: Event-triggered off-policy reinforcement learning approach

This paper investigates the H∞ synchronization control problem for cooperative multi-agent systems using an event-triggered off-policy reinforcement learning (OffRL) approach. Based on the connection graph from a global optimization perspective, a system model is first constructed to reformulate the problem as solving the Hamilton–Jacobi–Isaacs (HJI) equation. To address the computation and communication challenges associated with extensive information exchanges in multi-agent systems, an event-triggered scheme is introduced. A triggering condition is proposed, and its feasibility is rigorously analyzed in terms of system stability and the exclusion of Zeno behavior. To solve the HJI equation, a model-free OffRL algorithm is derived from the Bellman equation, leveraging system dataset collection to bypass potential inaccuracies in the system dynamic model. Finally, the feasibility and effectiveness of the proposed algorithm are rigorously demonstrated through theoretical analysis and a simulation example. © 2025 Elsevier B.V., All rights reserved.