## 1 Introduction  
The continuum manipulator is a bionic, slender super-redundant manipulator that provides superior reach and dexterity capabilities in cluttered environments. However, because of its high nonlinearity, the precise control of a continuum manipulator still remains a significant challenge. Reinforcement learning is a model-free control method and has been used for the control of continuum manipulators. [PERSON] [1] and [PERSON] [2] used the Q-Learning algorithm and DQN algorithm to train the control policy for a planar continuum manipulator, achieving the position control of the manipulator. [PERSON] [3] and [PERSON] [4] applied the Deep Deterministic Policy Gradient (DDPG) algorithm to the control of a continuum manipulator, achieving end-effector path tracking control. [PERSON] [5] used the Trust Region Policy Optimization (TRPO) algorithm to learn the controller policy of the continuum manipulator, achieving position control of the continuum manipulator. Although reinforcement learning algorithms have achieved great success in PCM control, they ignore the similarities between tasks, making them unable to effectively utilize training experiences from other tasks. Consequently, when faced with new tasks, the learning process often has to start from scratch, consuming substantial time and resources [6]. Therefore, how to use previous experience to improve the adaptability of agents to new tasks has become an important research direction. In order to enable the agent to have the ability to learn and adapt to new tasks quickly, researchers have proposed various solutions. Policy Distillation [7] extracts experience from multiple policies to form a more generalized comprehensive policy. This method can improve the adaptability of the strategy through self-distillation or cross-task distillation. Multi-task learning [8] improves the generalization ability of an agent by simultaneously learning on multiple related tasks. However, the success of the above methods usually depends on the correlation between tasks. If the differences between tasks are too great, the effect of experience sharing may not be obvious, and it may even interfere with the learning process. Some scholars have proposed the concept of meta-reinforcement learning, combining meta-learning algorithms with reinforcement learning algorithms based on the ability of meta-learning to rapidly learn with only a small number of samples [9]. Meta-reinforcement learning trains on multiple tasks, allowing agents to quickly adapt to new tasks with a small number of learning samples. Unlike traditional reinforcement learning, which optimizes control strategies for a single task, meta-reinforcement learning uses multiple tasks as its training samples. By training on multiple tasks, the agent acquires the ability to learn quickly. When faced with a new task, it is possible to rapidly learn the policy that adapts to the new task with only a small number of training samples by leveraging prior knowledge. Typical meta-reinforcement learning algorithms include the memory context-based \(RL^{2}\) algorithm [10], the SNAIL algorithm [11], and the CMRL algorithm [12]. These methods are easily combined with other learning algorithms and have a variety of network structures. However, complex learning tasks may require more complex models to process data and tasks, as well as more computational resources and training time. There are also task-inference-based algorithms, such as PEARL [13], MetaCURE [14], and MQL [15]. However, such methods may ignore some specific information related to the task during the task inference process, and their reliance on specific environmental conditions makes them difficult to generalize across different tasks [16]. Additionally, there are gradient descent-based algorithms, such as MAML meta-reinforcement learning [17]. As a novel meta-learning framework, MAML has spawned numerous improvements and extensions, including FO-MAML, ES-MAML [18], Reptile [19], MAESN [20], and Taming MAML [21].