## 2 Related Research
Reasonable and effective marshalling can transform the problem of multiple mobile agents into a parallel problem of multiple single mobile agents. In CL-MIP, the gravity model in physics is imitated to calculate the density of source nodes, and full consideration of the geographic location relationship between source nodes is made. As a basis for grouping, the convergence rate of each mobile agent migration can be effectively improved. However, in the specific marshalling steps, there is still no perfect solution on how to choose the appropriate marshalling radius. Therefore, the effectiveness of grouping the core issues in this multi-mobile agent requires more verification. One research in the literature [8] considers the problem of learning cooperative policies in complex, partially observable domains without explicit communication. This work extends three classes of single-agent deep reinforcement learning algorithms based on policy gradient, temporal-difference error and actor-critic methods to cooperative multi-agent systems. Another research [9] defines group-based topologies and show how some wireless ad hoc sensor networks (WAHSN) routing protocols perform when the nodes are arranged in groups, connections between groups are established as a function of the proximity of the nodes and the neighbor's available capacity (based on the node's energy). The importance of evaluating localization algorithms in real nodes is proven in the literature [10]. Furthermore, it is used to avoid the overhead that resource-constrained nodes may not be able to bear. Mobile agent routing planning problem for single task is analyzed; node energy consumption and time consumption are two main factors in the process of mobile agent migration. The constraint conditions and objective function model of mobile agent routing problem between nodes for single task are established. An improved Dijkstra algorithm was designed to solve this model in the literature [11]. The simulation result shows that the proposed algorithm can get optimal mobile agent route between any two nodes in the wireless sensor network according to the current network environment. Research [12] has proposed that physical layer cooperative communication can significantly improve network capacity. Another researches [13] studied the cooperative communication between Long Term Evolution (LTE) cells, especially multimedia communication. One study [14] proved the importance of evaluating the location algorithm in the actual nodes to prevent the introduction of a large amount of overhead that the resource constrained nodes may not be able to bear. Single-mobile agent itinerary planning (SIP) and multi-mobile agent itinerary planning (MIP) are included in intelligent agent path planning algorithms. In a single intelligent proxy algorithm, based on the minimum distance of the node Sink. The scalability of the single agent algorithm is poor. The distance between source nodes that is suitable for scenarios is close, and the number of source nodes is small. For large-scale WSNs, many problems occur in single-agent data, such as large latency, unbalanced load and low reliability. The data in the sensing layer of the Internet of things is diverse. In the wireless multimedia sensor network, the data not only includes text, but also multimedia data such as audio and video data. Because of the limited resources, the sensor nodes cannot upload all the data; thus, it is of great significance to study the energy-saving and intelligent path algorithm. Cooperative communication refers to selecting multiple forwarding nodes for cooperative communication when forwarding data, which can improve the reliability of transmission; an intelligent mobile agent can collect data in the network, and integrate, compress and encrypt data at the same time, which provides additional flexibility and new functions for the Internet of things network layer.