###### Abstract
[PERSON] and others proposed the Adaptive Distributed Guided Policy Search (ADGPS) [16], enabling multiple robotic arms to train independently and share experiences, thereby reducing trial-and-error and finding optimal paths more efficiently. Additionally, [PERSON] et al. employed the Twin Delayed Deep Deterministic Policy Gradient (TD3) method [17] to study the operation of picking up objects from a table using a mobile manipulator. [PERSON] and colleagues enhanced training outcomes through domain randomization and the introduction of noise during the reinforcement learning process [18]. These methods share a core principle of incorporating probabilistic approaches to significantly reduce the impact of ineffective actions. However, they still necessitate an extensive exploration time and can result in unproductive actions. In pursuit of solutions to mitigate ineffective exploration in reinforcement learning, a cohort of researchers has turned to transfer learning paradigms. Notably, [PERSON] et al. formulated the guided cost learning (GCL) algorithm [19], predicated on the MaxEnt IOC framework, to enforce trajectory constraints on robotic arms with an innovative twist: utilizing human-demonstrated paths as optimal guides for training. Expanding this concept, [PERSON] and team introduced the Generative Adversarial Imitation Learning (GAIL) algorithm [20], adeptly selecting trajectories closely mirroring human demonstrations, thereby curtailing inefficient maneuvers and enhancing the training process's speed. In a similar vein, [PERSON] et al. ingeniously melded DQN with behavioral cloning to develop the D3 QN algorithm [21], markedly diminishing exploration randomness in initial training phases. Furthermore, [PERSON] et al. devised the deep mimic approach [22], ingeniously segmenting the reward function into an aggregate of imitation-based exponential components, thereby refining the reinforcement learning process. Lastly, [PERSON] and collaborators unveiled the AMP algorithm [23], which deftly dissects the composite reward function into separate components of imitation and objective, consequently boosting the practicality of action generation. These methodologies shine in utilizing viable trajectory optimization solutions as constraints in reinforcement learning; although, their generalizability tends to lag behind that of the primary class of methodologies in this field. In an effort to tackle the inherent issues of learning efficiency and the efficacy of path planning in DRL algorithms applied to end-to-end control models, this research innovatively proposes a reward architecture grounded in fuzzy decision making. This framework is meticulously crafted to augment both the efficiency of the learning process and the effectiveness of exploration pathways. Critically, the integration of a cascaded FRS significantly bolsters the precision and resilience of path planning, marking a notable advancement in the domain of DRL. This research makes significant contributions in the field of robotic control, which are enumerated as follows:  
(1) It innovates a multifaceted FRS that intricately considers aspects such as positional accuracy, energy efficiency, and operational safety, thereby enabling a more nuanced representation of a robot's endpoint dynamics. (2) It pioneers the application of this FRS in a cascaded format for specific operational tasks, culminating in the development of a groundbreaking cascaded fuzzy reward architecture. (3) It applies this novel cascaded fuzzy reward system (CFRS) within an end-to-end control paradigm, where its practical effectiveness in facilitating end-to-end planning is rigorously demonstrated. This manuscript is systematically structured as follows: The second section sets the stage by elucidating the research backdrop, focusing on the intricate details of FRSs and the core tenets of end-to-end DRL. The third section rigorously outlines the architecture and theoretical underpinnings of the CFRS.