## 4 Simulation Results and Analysis  
### Parameter Settings  
We set the parameters for the simulation as shown in Table 1.  
The number of communication nodes in the communication network is \(N=64\), distributed in a square site with the range of \(25\ \text{km}\times 25\ \text{km}\), and the nodes are randomly generated according to the Poisson distribution. It is assumed that the number of elements in the adjacent point set of the current node is \(M=4\), the learning factor is \(\alpha=0.9\), and the discount factor is \(\gamma=0.6\). In addition, to achieve a smooth transition from exploration to development, we set \(\varepsilon=1/\sqrt{t}\), where the step is the total hop number of the whole experiment. It is assumed that the jammer is in the middle position between the destination node and the source node and forms a circular jamming range. Because the communication nodes are in different spatial positions from the jammer, the jamming conditions of com  
\begin{table}
\begin{tabular}{c c} \hline \hline Parameters & Value \\ \hline Communication nodes \(N\) & 64 \\ Adjacent nodes \(M\) & 4 \\ Learning factor \(\alpha\) & 0.9 \\ Discount factor \(\gamma\) & 0.6 \\ Greedy index \(\varepsilon\) & \(1/\sqrt{t}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Settings of model-related parameters.  
munication nodes are different. The schematic diagram of simple communication nodes is shown in Figure 4.  
In the following simulation, we compared the performance of the proposed algorithm with that of the following methods:  
* MAB model optimal anti-jamming path selection algorithm: By using the UCBI algorithm to select the arm, in the first K round experiment, all available communication nodes were tested once for the same communication node, and the results of each experiment are independent identically distributed. In subsequent experiments, the selected nodes were determined by the average income of each communication node in the previous K round experiment and the number of times each communication node was selected.
* Classic \(Q\)-learning: The communication node constantly interacts with the environment and selects the communication node by the next hop through feedback and return. Successful transmission means that the source node transmits the information to the destination node, and the destination node successfully receives the information. When the experiment ends, the destination node is put in the end state, that is, in the state of the \"end\" of the application, and the agent and environment interactions can naturally be divided into a series of subsequences (the disturbance of each sequence from the source node to the node undisturbed by the normal communication jammer and the partial node that puts an end to the state). We call each subsequence an experiment, sometimes called a curtain. When the information is transmitted to the destination node, the agent returns to the source node to start a new experiment. \(\mathbf{model}(s,a)\) is used to learn the simulation process, and a cycle of n times becomes n-step planning. Planning takes an environment model as input and generates or improves the computational processes that interact with it.