## 4 Experiment Results
### Results and Analysis
#### 4.3.2 The Micro-Performance Evaluation  
Figure 6 shows the variation of average step inventory costs of the main manufacturer during the training process, which serves as a micro-performance evaluation index. Compared to the MAPPO method, the DualPolicy-IT (ours) and DualPolicy-w/oIT (ours)  
Figure 4: The comparison of four methods described about episode team reward. Figure 5: The comparison of four initial learning rates. methods, due to the dual policy mechanism, have already converged to a higher level around episode 400. However, the MAPPO method gradually stabilizes after episode 600. Compared to the DualPolicy-w/oIT (ours) method, the DualPolicy-IT (ours) method, thanks to the information transmission mechanism, achieves approximately a 25% improvement in the final convergence results. Due to the instability caused by treating the multi-agent problem as a single-agent problem, the SRL method is far inferior to the other three methods in terms of convergence speed and results. Figure 7 shows the variation of average step inventory costs of the two standard component suppliers during the training process, which serves as a micro-performance evaluation index. In this set of experiment result figures, compared to MAPPO and SRL, the DualPolicy-IT (ours) and DualPolicy-w/oIT (ours) methods, with the dual policy mechanism, can effectively explore the large state space and action space in the real-world environment, helping the model converge faster. Figures 8, 12 and 15 show the variation of average step inventory costs of the structural component supplier and the system component supplier of the central wing, the fuselage and the entire aircraft joining during training, serving as the micro-performance evaluation indexes. In this set of experiment result figures, it can be observed that due to treating the multi-agent problem as a single-agent problem, the SRL method leads to significant fluctuations  
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As the results are shown in Figure 8, 11, 14 and 15, the method DualPolicy-IT (ours) also displays some fluctuations. This may be due to small policy changes leading to significant variations when mapped to the evaluation index space. However, from the experimental results, we can observe that compared to other methods, DualPolicy-IT (ours) tends to better handle these fluctuations to avoid getting trapped in the local optimum and converges to superior results. Based on the comparison of results with different episode sizes in the experiments, we recommend setting the episode size to at least 700 or above to ensure the stability of the method training results. Figure 8: The comparison of four methods described about inventory cost of the center wing suppliers. Figure 9: The comparison of four methods described about inventory cost of the center fuselage suppliers. Figures 9, 10, 11, 14, 16 and 17 show the variation of average step inventory costs of the structural component supplier and the system component supplier of the center fuselage, the aft fuselage, the nose, the wing stabilizer, the system installing and the engine during training, serving as the micro-performance evaluation indexes. In this set of experiment result figures, compared to MAPPO and SRL, the DualPolicy-IT (ours) and DualPolicy-w/oIT (ours) methods, with the dual policy mechanism, can effectively explore the large state space and action space in the real-world environment, helping the model converge faster. Figure 13 also shows the variation of average step inventory costs of the structural component supplier and the system component supplier of the taifin during training, serving as the micro-performance evaluation index. In this set of experiment result figures, compared to the DualPolicy-w/oIT (ours) method, the DualPolicy-IT (ours) method, thanks to the information transmission mechanism, achieves evident performance improvement in the convergence level results. Figure 11: The comparison of four methods described about inventory cost of the nose suppliers.