## 5 Experimental Results
### Evaluation Results  
To validate the effectiveness of the proposed Agent-Weighted RLS Positioning Framework (AWR-PF), we conducted 97 independent positioning experiments using 28 h of real Iridium signal data. The intelligent agent in AWR-PF was the same as the one trained in Section 5.2. For comparative analysis, we selected two baseline algorithms: standard RLS (unweighted) and randomly weighted RLS (RWR), of which the weights sampled uniformly from (0, 2). The average cumulative reward obtained by the agent in the test set was recorded (as shown in Figure 13). In the initial testing phase, when encountering states not sufficiently covered during training, the agent demonstrated remarkable robustness, achieving a high average cumulative reward of 1100. This indicates that the agent successfully learned an appropriate weight-determination strategy through DDQN-EW. Subsequently, the accumulated average reward gradually increased to 1300, which is slightly lower than the final average cumulative reward attained during the training phase. Figure 13: Agent’s per-timestep average cumulative reward during testing. The dotted line indicates that the average cumulative rewards obtained by the agent after this step tend to stabilize. We compared the mean positioning accuracy of each step of the AWR-PF against both the standard RLS and RWR (Figure 14). Consistent with observations during training, all three approaches exhibited a sustained downward trend in 3D positioning error throughout the testing process. During the testing phase, the agent relies solely on acquired knowledge for autonomous decision-making without further exploration. This eliminates the need to recover from inaccurate initial positioning estimates caused by exploratory actions, resulting in significantly faster convergence of positioning errors compared to the training phase. During the initial testing steps, the agent immediately outperformed the RWR and approached the performance level of the standard RLS. This indicates that the trained agent successfully generalized knowledge acquired during training to the test set. As epoch weight determination progressed, the agent--benefiting from DDQN-EW's characteristic bias toward robust decision-making--demonstrated increasingly stable performance. Consequently, AWR-PF consistently outperformed both the standard RLS and RWR in subsequent iterations. To exclude the influence of absolute positioning error variations across iterations, we calculated the relative positioning errors of both the AWR-PF and RWR with respect to the standard RLS. Using the standard RLS as the baseline, we normalized the positioning errors at each timestep, enabling direct visual comparison of the three methods' performance throughout the iterative process (Figure 15). The results demonstrate that the RWR exhibited significant performance fluctuations in the initial phase, eventually converging to nearly identical performance with the standard RLS as iterations progressed. This convergence indicates the RWR's inability to substantially reduce the positioning error with the iterations. In contrast to the RWR, while the proposed AWR-PF still showed some initial performance fluctuations, these variations were markedly smoother and consistently superior to those of the RWR approach. After approximately 100 iterations, the AWR-PF achieved a stable 5% average reduction in positioning error compared to the standard RLS. This performance advantage was maintained consistently throughout the remaining iterations. Notably, this accuracy improvement was derived from the average results of 97 independent positioning trials, conclusively demonstrating that the proposed AWR-PF fundamentally addressed the core optimization challenge of epoch-characteristic-based weighting, rather than achieving incidental performance gains. Figure 14: Comparison of positioning performance with different positioning methods during testing: standard RLS, randomly weighted RLS (RWR), and proposed AWR-PF.