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 offers a structured approach to decompose complex tasks into simpler sub-tasks%2C which is promising for multi-agent settings. This paper advances the field by introducing a hierarchical architecture that autonomously generates effective subgoals without explicit constraints%2C enhancing both flexibility and stability in training. We propose a dynamic goal generation strategy that adapts based on environmental changes. This method significantly improves the adaptability and sample efficiency of the learning process. Furthermore%2C we address the critical issue of credit assignment in multi-agent systems by synergizing our hierarchical architecture with a modified QMIX network%2C thus improving overall strategy coordination and efficiency. Comparative experiments with mainstream reinforcement learning algorithms demonstrate the superior convergence speed and performance of our approach in both single-agent and multi-agent environments%2C confirming its effectiveness and flexibility in complex scenarios. Our code is open-sourced at: url{https:%2F%2Fgithub.com%2FSICC-Group%2FGMAH}.?quality=80&w=800)
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