## 2 Materials and Methods
### Proposed Method
#### 2.3.4 Implementations
More notably, QMIX-GNN shows a winning rate improvement of 0.079 compared to QMIX and 0.052 compared to QMIX-HT, as well as faster convergence. These findings demonstrate that both the heterogeneous information processing structures and the GNN-based information fusion module play significant roles in enhancing inter-agent collaboration and contribute positively to model performance. ## 4 Discussion

In this section, we discuss the limitations of QMIX-GNN and some potential future work directions:

* In simpler scenarios such as 3m and 2s3z, both QMIX-GNN and QMIX achieve strong performance, indicating that the advantages of our approach are less pronounced in low-complexity settings. However, the benefits of QMIX-GNN become more evident as task complexity increases. In medium-difficulty scenarios such as 1c3s5z and 10m_vs_11m, our method converges faster than QMIX, demonstrating superior sample efficiency. In challenging scenarios such as MMM and MMM2, QMIX-GNN consistently outperforms QMIX, underscoring its effectiveness in handling complex coordination tasks. This performance trend can be attributed to our information infusion mechanism, which enhances agent coordination by leveraging structured relational data. While this mechanism significantly improves performance in complex environments, it introduces additional computational overhead that may not be necessary in simpler tasks. Additionally, our use of a \(k\)-nearest graph based on inherent SMAC features such as visual and attack radii imposes certain constraints on QMIX-GNN's applicability. Future work could explore adaptive strategies that dynamically adjust the degree of information infusion based on task complexity, which would enhance flexibility and reduce unnecessary learning overhead in less demanding scenarios. Figure 9: Training curves from the ablation study. * Our proposed QMIX-GNN is fundamentally reliant on the effective fusion of information across multiple agents to enable collaborative decision-making. Scenarios in which agents operate in complete isolation, such as decentralized systems lacking communication channels, are outside the primary scope of our work; nevertheless, this assumption imposes practical limits on the application of QMIX-GNN. In environments where robust inter-agent communication and information sharing cannot be guaranteed, the underlying information fusion mechanism may be ineffective, potentially leading to degraded performance. Future work could explore the design of lightweight or privacy-aware fusion strategies that would simplify the fusion architecture or incorporate privacy-preserving mechanisms while still enabling collaborative information sharing. This would help to extend the practical applicability of QMIX-GNN to a broader range of scenarios. * Although SMAC presents certain challenges, the current infusion mechanism in QMIX-GNN is specifically designed around the unique features of the SMAC, as mentioned above. As a result, while our experiments demonstrate promising performance in this context, the effectiveness of QMIX-GNN in more complex environments remains to be validated. Because the SMAC does not support macro mode, extending our evaluation to such settings is left for future work. In addition, future research could broaden the applicability of QMIX-GNN by focusing on development of a more general infusion mechanism that is adaptable to a wider range of environments. ## 5 Conclusions

To address the issue of agents in the Centralized Training and Decentralized Execution (CTDE) framework struggling to make correct decisions based solely on their own observations, this paper proposes a heterogeneous Multi-Agent Reinforcement Learning (MARL) model based on Graph Neural Networks (GNNs), which we name QMIX-GNN. QMIX-GNN effectively handles the inconsistent feature and action spaces of heterogeneous agents through the use of matrix projection and a tower-like structure, ensuring that diverse agent types can be processed appropriately.