## 1 Introduction  
In recent years, with the continuous maturation of artificial intelligence technology, machine learning has provided new approaches for solving scheduling problems in complex and uncertain environments. Moreover, integrating machine learning algorithms with job scheduling problems aligns more closely with the core principles of intelligent manufacturing. Particularly, with the ongoing development of deep reinforcement learning (DRL) [1, 2], intelligent agents learn optimal scheduling strategies through interaction with the environment guided by rewards. This approach has been widely applied to solve workshop scheduling problems. The panel block assembly line in shipbuilding is a specialized workshop, but its processing process will encounter bottlenecks such as block congestion [3]. To address these issues, we propose a DRL-based method to tackle the scheduling problem of the ship's panel block assembly line. The structural composition of a vessel primarily consists of various profiles and steel materials. The hull of a ship is typically streamlined, but near the midship, the shape tends to become flat [4]. In modern shipbuilding practices, the entire structure of a ship is generally divided into several small sections, referred to as blocks. These individual blocks are then combined to form larger sections or ring sections, which are further assembledto create the complete structure of the vessel. A block serves as the most fundamental intermediate unit in ship structure manufacturing [5]. After the hull is divided into blocks, based on their internal structural characteristics, blocks with complex structures and large outer panel curvatures are referred to as curved blocks, while those with flat or nearly flat profiles are known as panel blocks, as depicted in Figure 1[6]. For conventional vessels like bulk carriers and tankers, panel blocks can account for more than 60% of the total number of hull blocks. In some cases, the proportion of panel blocks can even reach around 80% for large and ultra-large oil tankers [7]. Furthermore, with the development of larger ships, such as large bulk carriers, oil tankers, and container ships, which commonly present the characteristics of longer midship, the demand for panel blocks has significantly increased. Due to the extensive processing required for a large number of panel blocks in the shipbuilding process, the manufacturing and assembly of panel blocks represent the primary bottleneck in the entire shipbuilding process. Furthermore, shipbuilding is an order-based industry, where the panel blocks of the ship for each order have different shapes and volumes, further increasing the complexity of scheduling for the panel block assembly line [8; 9]. As a result, the assembly process of the panel block assembly line possesses a high degree of variability and system complexity [10]. However, the production efficiency of most panel block assembly lines is not high [11]. This is primarily due to the continued use of traditional on-site scheduling methods, which make it difficult to obtain optimized scheduling solutions. For instance, some shipyards directly prioritize the production of simple blocks before complex ones without employing means of optimized scheduling to reduce labor hours. Consequently, optimizing the production plan of the panel block assembly line to enhance the overall shipbuilding process efficiency is of paramount importance [12; 13; 14; 15]. In light of these considerations, this paper proposes an end-to-end DRL approach to address the scheduling problem of panel block assembly lines in shipbuilding. Initially, we introduce a scheduling model based on the Markov Decision Process (MDP) [16] and creatively employ a disjunctive graph to represent the scheduling process of panel block assembly lines, thereby comprehensively and logically capturing the current state.