Asynchronous action coordination presents a pervasive challenge in
Multi-Agent Systems (MAS), which can be represented as a Stackelberg game (SG).
However, the scalability of existing Multi-Agent Reinforcement Learning (MARL)
methods based on SG is severely constrained by network structures or
environmental limitations. To address this issue, we propose the Stackelberg
Decision Transformer (STEER), a heuristic approach that resolves the
difficulties of hierarchical coordination among agents. STEER efficiently
manages decision-making processes in both spatial and temporal contexts by
incorporating the hierarchical decision structure of SG, the modeling
capability of autoregressive sequence models, and the exploratory learning
methodology of MARL. Our research contributes to the development of an
effective and adaptable asynchronous action coordination method that can be
widely applied to various task types and environmental configurations in MAS.
Experimental results demonstrate that our method can converge to Stackelberg
equilibrium solutions and outperforms other existing methods in complex
scenarios.Comment: 11pages, 7paper