Considering the above, this paper conducts research around dynamic edge scenarios and proposes MTD3 CO, a meta reinforcement learning-based task offloading strategy that takes into account DAG task types, subtask decisions, device power, WPT service time, task completion time, and other factors to solve the problem that conventional reinforcement learning algorithms are inefficient and cannot adapt to various types of mobile applications. In this paper, we first describe the process of unloading scheduling decisions for different types of DAG tasks as each different MDP, then modify the network of the TD3 algorithm to design a strategy and action network suitable for task unloading problems with different numbers of subtasks, and finally transform the DAG subtask decision process into a sequential prediction process. Each RNN network included in the algorithm is trained on the basis of the TD3 algorithm, with its training process primarily divided into inner-looptraining and outer-loop training. Outer-loop training is primarily performed by the outer-loop learner of the edge server, which is uniformly trained for all applications in the system corresponding to the MDP environment and obtains the meta-strategy. The inner-loop learner on the edge device then downloads the meta-strategy parameters, initializes its own network, and performs a small number of training iterations to fine-tune the strategy according to its own task characteristics, achieving the goal of adapting to different tasks without requiring time-consuming retraining for general-purpose tasks. The following is a summary of this paper's primary contributions:  
* An edge system model, including a wireless charging module, is designed for the complicated edge computing environment for various types of mobile applications; on the basis of this architecture, a delay model, energy consumption model, and power model are built to quantify strategy performance. * To address the problem of new tasks not being adapted quickly enough, a meta reinforcement learning-based task offloading decision (MTD3 CO) is proposed. Among them, a Seq2 Seq-based deep network suitable for the task offloading process is created to change the offloading decision into a sequential prediction process in order to accomplish adaptation for applications with different numbers of subtasks. To optimize the second-order gradient present in the meta-strategy solution, a first-order approximate optimization algorithm is proposed to accelerate the solution. * Some simulation experiments were built on the basis of practical applications for tasks with different DAG topologies, including DAG topology, number of tasks, and transmission speed between MD and edge servers. After simulation experiments and comparing them to some baseline methods (e.g., improved DRL algorithm, HEFT-based heuristic algorithm, and greedy algorithm), it was shown that MTD3 CO produced the highest results after only a few training steps. The remainder of this paper is organized as follows: Section 2 introduces related academic research; Section 3 constructs the edge system model and models the task offloading problem; Section 4 describes the MTD3 CO algorithm's design and implementation; Section 5 presents simulated experiments and results; Section 6 concludes and reviews the paper's work.