## 1 Introduction
### Related Work  
In recent years, the integration of advanced computational techniques into manufacturing processes has attracted considerable interest [4]. Among these, the application of RL for multi-objective optimization has demonstrated early potential and remains an active area of research [5]. RL, a subfield of machine learning, enables agents to learn optimal strategies through interaction with an environment. This paradigm is particularly well-suited for complex decision-making problems, such as optimizing process parameters under temporal and operational constraints. The field of process parameter optimization has been extensively studied over the past two decades, with most research efforts centered on offline optimization techniques. A significant portion of these approaches employ evolutionary algorithms, such as Genetic Algorithms (GAs), Particle Swarm Optimization (PSO), and the Non-dominated Sorting Genetic Algorithm II (NSGA-II). While these methods are effective for both single- and multi-objective optimization problems, they are often computationally expensive. Their complexity tends to scale exponentially with the number of control parameters, rendering them less suitable for real-time or dynamically changing manufacturing environments. In recent years, RL has emerged as a promising alternative, offering improved efficiency, adaptability, and quality outcomes. Over the last five years, several studies have demonstrated RL's applicability to process optimization tasks. [PERSON] et al. [6] proposed an RL-based framework for process parameter optimization and benchmarked it against conventional methods such as GA and PSO. Their findings revealed that the RL-based approach achieved comparable performance to the best traditional algorithms while reducing response time by a factor of 100. These results were validated on both open-source and industrial datasets. The broader applicability of RL-based optimization has also been highlighted in other domains. For instance, [PERSON] [7] applied a multi-objective optimization framework to the design of composite structures for solar vehicles, illustrating the generalizability of such approaches beyond conventional manufacturing contexts. Additional evidence of RL's advantages in response time and robustness can be found across various application areas. [PERSON] et al. [8] showed that a Twin Delayed Deep Deterministic Policy Gradient (TD3) agent achieved faster convergence and lower execution time than a GA, while maintaining consistent performance across episodes demonstrating improved robustness in manufacturing optimization. In the field of chemical process control, [PERSON] et al. [9] employed Proximal Policy Optimization (PPO), reporting convergence speeds 10 to 10,000 times faster than other methods with stable outcomes under varying initial conditions. Similarly, [PERSON] et al. [10] proposed an RL-guided PSO variant that outperformed several traditional PSO algorithms on 28 benchmark functions, showing superior optimization performance and solution stability. Collectively, these studies underscore the potential of RL not only to deliver solution quality on par with traditional techniques but also to significantly enhance response time and robustness particularly in online and dynamic process parameter optimization scenarios. In addition to demonstrating superior performance in controlled evaluations, RL has been successfully applied across a range of manufacturing domains, delivering measurable improvements in quality, efficiency, and adaptability. In the field of injection molding, [PERSON] et al. [11] implemented an RL actor-critic architecture to enhance the process capability index (Cpk) for lens thickness in continuous production, achieving a notable improvement from 0.315 to 1.720. [PERSON] et al. [12] applied a convolutional neural network (CNN)-based actor-critic RL framework to optimize variable geometry in textile draping, leading to enhanced forming accuracy. [PERSON] et al.