## 5 Experimental Results
### Evaluation Results
Experimental results from individual positioning trials demonstrate that the proposed AWR-PF achieved a maximum reduction of 45.15% in final error in single positioning trials compared to the standard RLS. As illustrated in Figure 16, representative positioning cases were selected to showcase the superior policy learned by the agent. The agent exhibited remarkable adaptability when processing complex epoch information, dynamically adjusting weighting parameters to optimize positioning accuracy across varying scenarios. The AWR-PF demonstrated adaptive weighting capability by dynamically reducing epoch weights when quality deteriorated (increasing positioning errors) to mitigate accuracy degradation (Figure 16e,f), while increasing weights for high-quality epochs (decreasing errors) to enhance their contribution to the positioning solution, thereby optimizing overall accuracy (Figure 16c,d). The AWR-PF's weighting strategy demonstrated exceptional robustness by nearly completely mitigating the adverse effects of severely degraded epochs (Figure 16a,b). Consequently, while both standard RLS and RWR exhibited increasing positioning errors under such conditions, the proposed AWR-PF maintained continuous error reduction. Moreover, in additional positioning experiments, the AWR-PF fully leveraged its temporal dependency-aware decision-making capability to dynamically modulate weighting strategies, resulting in comprehensively superior positioning accuracy throughout the entire RLS iterative process compared to both standard RLS and RWR approaches (Figure 16g-i). Furthermore, we analyzed the agent's inference latency and computational overhead to demonstrate AWR-PF's practical feasibility. First, we recorded average processing times for the AWR-PF and standard RLS during the testing phase (Figure 17). Results indicate that under current desktop computing configurations, the AWR-PF required approximately 0.4 ms for inference in each iteration step, while standard RLS consumed about 0.95 ms per iteration step. The agent's inference latency constituted roughly 30% of total computation time, demonstrating efficient processing enabled by our lightweight network architecture. Second, we estimated computational overhead based on network design. Given our fully connected layers, each neuronal connection involved one multiplication and one addition operation. The total number of floating point operations for each inference was \(2\times(9\times 256+256\times 256+256\times 20)\)\(=145\),920. This represents negligible computa  
Figure 15: Performance comparison of three positioning methods during testing from the standard RLS’s perspective. tional demand for modern processors. Collectively, these results confirm the technical viability of deploying our intelligent agent in practical engineering applications. Figure 16: Comparative analysis of typical single positioning trial errors reveals distinct performance patterns. The red box indicates that during these periods, the agent’s decision-making demonstrated particular advantages. (**a,b**) While positioning errors increase with the standard RLS, they demonstrate consistent reduction with the AVR-PF; (**c,d**) the AWR-PF approach achieves faster error reduction compared to the standard RLS; (**e,f**) the AWR-PF approach achieves slower error improvement compared to the standard RLS; (**g–i**) the AWR-PF maintains superior positioning accuracy throughout the entire iteration. Figure 17: Comparison of average processing time with standard RLS and proposed AWR-PF during testing.