## 2 Related Work  
Video surveillance coverage optimization is an interesting topic which draws increasing attestations from both academic and industry departments along with the popularity of CCTV systems. By optimizing camera deployment, surveillance systems can enhance their ability to detect and track objects, monitor activities, and improve overall security and safety. This involves determining the placement of cameras to minimize blind spots, maximize coverage overlap, and ensure comprehensive monitoring of the area of interest. [PERSON] et al. [1] applied the convex optimization approach to improve multicamera deployment for visual coverage of a 3D object surface. [PERSON] et al. [2] proposed a visual distance approach for a multicamera deployment through parallel coverage optimization. View angle is introduced to enhance the accuracy of the visual coverage, and visual Voronoi partition is used for the optimization. [PERSON] et al. [3] improved the constrained two-archive evolutionary algorithm to deal with camera network deployment with Gaussian process regression. [PERSON] et al. [4] considered a deformable object for camera network deployment with the aim of visual coverage using the wolf pack algorithm to solve the optimization problem. [PERSON] et al. [5] applied abnormal detection to an elevator video system. However, these existing methods mainly focus on visual coverage, which considers the space equally. In fact, it is costly and not necessary to make a video surveillance system with full coverage of the target space. Therefore, it is essential to analyze the semantic value of the space, based on which camera deployment optimization can be achieved. Space syntax is a series of theories and techniques related to spatial configuration analysis. It was first introduced between the 1970s and 1980s by [PERSON], [PERSON], and their colleagues at University College London [6]. The main idea is that space can be considered as being composed of elements that can undergo network analysis, ultimately presented as maps and graphs. Since space syntax treats space as self-arranged according to certain rules and as divisible, it is often applied in large libraries, museums, road traffic systems, and urban planning. [PERSON] et al. [7] applied space syntax to the planning of rapid transit systems (RTSs), demonstrating the effectiveness of space syntax in analyzing urban topology. [PERSON] [8] proposed scaling the space syntax field to include other parameters, such as lighting, and integrated relevant performance measures into the space syntax analysis of mosque types to assist in studying overall spatial cognition based on comfort and environmental parameters. This approach used a case study of a typical mosque layout in multi-objective optimization, offering insights into architectural design for glare management and lighting potential. [PERSON] [9] used space syntax to analyze human interaction and social exchange patterns in cities. These results were utilized to develop comprehensive tourist activity spaces and revitalize neglected urban corners, improving land-use efficiency. [PERSON] [10] analyzed street networks using space syntax to evaluate the characteristics of students' school routes. [PERSON] [11] conducted spatial analysis of shopping centers, comparing different planning schemes using space syntax. [PERSON] [12] employed space syntax for disaster prevention analysis in underground battlefield spaces, showing the effectiveness of space syntax in small-scale spatial semantic analysis. Currently, space syntax analysis methods have been applied in various scenarios, but in video surveillance system optimization [13], they are mainly limited to qualitative analysis or only involve visibility coverage analysis, lacking a quantitative optimization plan based on spatial semantic attributes. Reinforcement learning (RL) is one of the main methods in machine learning.