Intelligent Control in Asymmetric Decision-Making: An Event-Triggered RL Approach for Mismatched Uncertainties

Artificial intelligence (AI)-based multiplayer systems have attracted increasing attention across diverse fields. While most research focuses on simultaneous-move multiplayer games to achieve Nash equilibrium, there are complex applications that involve hierarchical decision-making, where certain players act before others. This power asymmetry increases the complexity of strategic interactions, especially in the presence of mismatched uncertainties that can compromise data reliability and decision-making. To this end, this article develops a novel event-triggered reinforcement learning (RL) approach for hierarchical multiplayer systems with mismatched uncertainties. Specifically, by establishing an auxiliary augment system and designing appropriate cost functions for the high-level leader and low-level followers, we reformulate the hierarchical robust control problem as an optimization task within the Stackelberg–Nash game framework. Furthermore, an event-triggered scheme is designed to reduce the computational overhead and a neural-RL-based method is developed to automatically learn the event-triggered control policies for hierarchical players. Theoretical analyses are conducted to 1) demonstrate the stability preservation of the designed robust-optimal transformation; 2) verify the achievement of Stackelberg–Nash equilibrium under the developed event-triggered policies; and 3) guarantee the boundedness of the impulsive closed-loop system. Finally, the simulation studies validate the effectiveness of the developed method.