## 5 Conclusions  
In this paper, we presented a multi-policy movement framework (MMF) that addressed the challenges of weakly supervised video moment localization. To emulate human behavior in localizing language descriptions, we developed a reinforcement learning-based framework featuring a multi-policy movement strategy that iteratively refined the position and duration of candidate windows to align with the ground truth. Our focus was on addressing unreasonable movement processes and inconsistencies in semantic alignment--issues often overlooked by current state-of-the-art methods.  
To overcome this problem, we first introduced the multi-policy movement strategy (MMS) to enhance the rationality of the movement process. We revised both the step size and method of movement to enable a more accurate approach towards the ground truth. Subsequently, we proposed a Gaussian-based Dual-Alignment Module (GDAM) that defined rewards by evaluating the semantic consistency of video-textual pairs. This module integrated the strengths of both scoring-based and reconstruction-based alignment methods, effectively addressing negative sample bias and language bias issues. Additionally, it enhanced network stability and facilitated a more focused approach to localization or boundary detection. Finally, our method effectively bridged the gap between weakly supervised techniques and reinforcement learning strategies. By combining efficient transformers with RL strategies, we could further enhance the network's semantic alignment capability, resulting in improved performance metrics.  
Our results improved the R@1 [EMAIL_ADDRESS] metric by 2.17 points and the R@1 [EMAIL_ADDRESS] metric by 1.03 points on the Charades-STA dataset. The same improvement was also seen on the ActivityNet dataset. Compared to other reinforcement learning methods, due to the improvement in the strategy, we only set the maximum step to 12 to quickly locate the target, which was 8 steps less than the usual 20 steps. Extensive experiments conducted on two benchmark datasets demonstrated the superior performance of our proposed approach.  
In the future, we plan to focus on developing a lightweight model for this task and reducing hyper-parameters to improve the method's generalization ability. Both datasets are related to people's lives, so this method can be used for surveillance and sports analytics.  
Figure 7: Failed test cases during testing.  
**Author Contributions:** Conceptualization, S.J.; investigation, S.J.; methodology, S.J.; project administration, B.Y.; software, S.J.; supervision, L.Z.; visualization, [PERSON]; writing--original draft, S.J.; writing--review and editing, Y.K. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the National Natural Science Foundation of China No. 62293543, No. U19B2039, 62276046, and the National Key Research and Development Program of China No. 2022 YFC3106101, No. 2018 AAA0102003.  
**Data Availability Statement:** Publicly available datasets were analyzed in this study. Charades-STA dataset is accessible at: [[https://prior.allenai.org/projects/charades](https://prior.allenai.org/projects/charades)]([https://prior.allenai.org/projects/charades](https://prior.allenai.org/projects/charades)) (accessed on 1 August 2024) while the ActivityNet Captions dataset can be found at: [[https://cs.stanford.edu/people/ranjaykrishna/densevid](https://cs.stanford.edu/people/ranjaykrishna/densevid)]([https://cs.stanford.edu/people/ranjaykrishna/densevid](https://cs.stanford.edu/people/ranjaykrishna/densevid)) (accessed on 1 August 2024).  
**Conflicts of Interest:** The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.