###### Abstract  
With the rapid advancement of industrial automation and artificial intelligence technologies, particularly in the textile industry, robotic technology is increasingly challenged with intelligent path planning and executing high-precision tasks. This study focuses on the automatic path planning and yarn-spool-assembly tasks of textile robotic arms, proposing an end-to-end planning and control model that integrates deep reinforcement learning. The innovation of this paper lies in the introduction of a cascaded fuzzy reward system, which is integrated into the end-to-end model to enhance learning efficiency and reduce ineffective exploration, thereby accelerating the convergence of the model. A series of experiments conducted in a simulated environment demonstrate the model  
terms of learning efficiency and path-planning effectiveness in sparse reward environments. For instance, [PERSON] et al. employed the DQN-NAF algorithm to control an industrial robotic arm model within the V-REP virtual environment [9]. By crafting a well-structured dense reward function, they successfully enabled the robotic arm to perform tasks effectively while avoiding obstacles in the environment. Similarly, [PERSON] and colleagues utilized the trust region policy optimization (TRPO) algorithm to adeptly control a UR5 robotic arm to reach target points [10]. Their research emphasized the challenges and issues encountered when applying reinforcement learning algorithms to the control of real robotic arms. [PERSON] and others delved deeper into the application of the deep deterministic policy gradient (DDPG) algorithm for robotic arm motion planning [11]. They not only examined motion planning in environments with and without obstacles but also proposed improvements to the DDPG algorithm, incorporating transfer learning [12] to accelerate the algorithm's convergence rate. In a notable advancement, [PERSON] et al. developed a model-driven DDPG algorithm [13] that replaced explicit reward functions with a fuzzy system (FS), enabling the successful accomplishment of pin insertion tasks using a six-degree-of-freedom robotic arm. In this study, the states in the DRL algorithm included observations of interactive forces and momentum during task execution, and a corresponding fuzzy reward system (FRS) was designed to achieve continuous control over the action space. This method was validated in both simulation and real-world tests, achieving a 100% success rate. Advanced algorithms such as proximal policy optimization (PPO) and DDPG have undeniably made strides in improving the efficiency of robotic arm control. Yet, they grapple with prolonged training cycles and languid convergence rates in real-world applications [14]. Additionally, the utility of these model-based DRL methods is somewhat hampered by their limited adaptability across varied environments and tasks, a critical aspect in diverse industrial scenarios like textile manufacturing. Robotic arms in these settings encounter a spectrum of complexities and uncertainties. In contrast, the wide applicability of data-driven reinforcement learning, particularly end-to-end control mechanisms for robotic arms, garners significant attention. Nonetheless, the application of reinforcement learning to high-degree-of-freedom industrial robotic arms is not without its challenges. Chief among these is the inefficacy in action selection, contributing to extended training durations and delayed convergence. This core challenge is attributed to reliance on uncertain exploration methods dependent on reward-function modeling, which becomes particularly pronounced in environments with sparse reward structures. To address the challenges posed by sparse rewards in robotic arm path planning, researchers have focused on enhancing the versatility of reinforcement learning algorithms. For instance, [PERSON] and colleagues introduced the QT-Opt algorithm [15], which involved training a neural network to act as a robotic arm controller using data from 580,000 grasping attempts made by seven robotic arms.