## 6 Concluding Remarks and Future Directions  
The task offloading problem of IoT devices in complex edge environments for various applications was investigated in this paper, assuming backward and forward connections between tasks and using DAG to represent subtask offloading. We proposed MTD3 CO, a task offloading strategy based on meta reinforcement learning, in order to improve the algorithm's adaptability to the environment and new applications. First, we studied the system model for mobile applications with different DAG types and mobile devices with different DAG types. The data and energy transfer system models were built, as well as the system's latency model, energy consumption model, and MDP model. The concept of meta reinforcement learning was used to model the task offloading process in MEC as multiple MDPs based on the tasks, transform the unloading decision into a sequential prediction process based on the characteristics of subtask execution, design a seq2 seq-based parameter sharing network to fit the optimal unloading decision, use this sharing network to improve the TD3 algorithm, and propose a meta reinforcement learning algorithm. Inner-loop training and outer-loop training are the two primary types of algorithm training. The outer-loop training network creates meta-parameters that are used to initialize the parameters for the inner-loop training network. The inner-loop training network then fine-tunes the parameters to quickly adapt to new applications based on their specific applications. Lastly, comparison experiments were used to assess the algorithm's capability to adjust quickly in a variety of MEC environments and applications. The results show that the algorithm proposed in this paper could quickly adjust the strategy to adapt to the environment. Because the model in this paper ignores some signal effects and losses in the real world and uses a simplified problem model, future research will focus on how to design a more realistic problem model. Furthermore, the deep reinforcement learning algorithm employed is not cutting-edge; there are already some new algorithms under research, and how to apply more cutting-edge reinforcement learning algorithms will be a future focus of research. Lastly, achieving a unified system adaptation for heterogeneous IoT devices is a difficult future research area.  
Figure 7: Comparison of delay and energy consumption when the number of subtasks changes.  
**Author Contributions:** [PERSON] and [PERSON] conceptualized the original idea and completed the theoretical analysis; [PERSON] designed the technique road and supervised the research; [PERSON] and [PERSON] completed the numerical simulations and improved the system model and algorithm of the article and drafted the manuscript; [PERSON] designed and performed the experiments. All authors provided useful discussions and reviewed the manuscript. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was sponsored by the Shanghai Sailing Program (No. 20 YF1410900), the Shanghai Natural Science Foundation (23 ZR1414900), the National Natural Science Foundation (No. 61472139), the Shanghai Automobile Industry Science and Technology Development Foundation (No. 1915), and the Shanghai Science and Technology Innovation Action Plan (No. 20 dz1201400). Any opinions, findings, and conclusions are those of the authors, and do not necessarily reflect the views of the above agencies.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** No new data were created.  
**Acknowledgments:** The authors sincerely thank the School of Information Science and Engineering, East China University of Science and Technology for providing the research environment. The authors would like to thank all anonymous reviewers for their invaluable comments.  
**Conflicts of Interest:** The authors declare no conflict of interest.