Distributed Stackelberg Strategies in State-Based Potential Games for Autonomous Decentralized Learning Manufacturing Systems

This article presents a novel game-theoretical (GT) framework, distributed Stackelberg strategies in state-based potential games (DS2-SbPGs), for autonomous multiobjective optimization in decentralized manufacturing systems. Existing approaches, including multiagent reinforcement learning (MARL) and native SbPG, struggle with scalability, coordination inefficiencies, and the complexity of tuning combined objective functions in real-world settings. DS2-SbPG integrates potential games and Stackelberg games, which improves the cooperative tradeoff capabilities of potential games and the multiobjective optimization handling by Stackelberg games. Notably, all training procedures are conducted in a fully distributed manner. DS2-SbPG offers a promising solution to finding optimal tradeoffs between objectives by eliminating the complexities of setting up combined objective optimization functions for individual players in self-learning domains, particularly in real-world industrial settings with diverse and numerous objectives between the subsystems. We formally prove that DS2-SbPG constitutes a dynamic potential game with guaranteed convergence. Experimental validation on a laboratory-scale testbed demonstrates the effectiveness of DS2-SbPG and its two variants: one with a single-leader–follower structure and another (Stack DS2-SbPG) for multileader–follower scenarios. Both variants significantly outperform native SbPG, achieving up to 10.61% reduction in power consumption while enhancing overall system performance, which signals the potential of DS2-SbPG in real-world applications.