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.

翻译：异步动作协调是多智能体系统(MAS)中普遍存在的挑战，可建模为Stackelberg博弈(SG)。然而，现有基于SG的多智能体强化学习(MARL)方法在可扩展性上严重受限于网络结构或环境约束。为解决此问题，我们提出Stackelberg决策变换器(STEER)这一启发式方法，以化解智能体间分层协调的困难。STEER通过融合SG的分层决策结构、自回归序列模型的建模能力以及MARL的探索性学习范式，高效管理时空双域的决策过程。本研究为开发一种可广泛适用于MAS中各类任务类型与环境配置的高效、自适应的异步动作协调方法做出了贡献。实验结果表明，该方法能收敛至Stackelberg均衡解，且在复杂场景中优于现有其他方法。