## 6 Simulation Results
### Experimental Configuration and Parameters  
In this section, we present the configuration of the experimental environment and platform. The experiments were conducted using an Intel Core i9-12900K processor and an NVIDIA RTX 3080 GPU with approximately 24 h of training time. The duration of each round is 10 s, discretized into 100 time steps (0.1 s per step). At each time step, each UAV executes a sequence of operations, including perception, localization, and decision-making. The experimental platform used UAVs with a conservative setting of maximum speed at 10 m/s (1 m/step) and maximum angular change of \(\pi\) rad/step, which are lower than the actual UAV capabilities to ensure algorithm reliability. These parameters were configured based on our discretized experimental design (1 m \(\times\) 1 m grid) to ensure both algorithm reliability and efficient performance evaluation. Each UAV was equipped with a directional radiation detector operating at 2.4 GHz band, with a conservative maximum effective radius of 200 m to ensure reliable detection in complex environments. The detector could either rotate in eight directions at \(\pi/4\) intervals or be turned off, providing 360\({}^{\circ}\) horizontal plane coverage while maintaining computational efficiency. The base station was positioned outside the \(1000\times 1000\) m\({}^{2}\) operation area, utilizing the UAVs' transmission range to ensure stable communication throughout the missions. For different rewards and penalties in the reward function, their weights are determined through systematic training process and adjusted based on specific mission scenarios to achieve a balance between task speed and accuracy. The simulation parameters are summarized in Table 3.  
This study develops a platform for multi-agent collaboration, deploying three red team UAVs and one blue team UAV. During each mission evolution, the system logs critical data. Figure 5a displays the global localization map, illustrating the distribution of red and blue UAVs within the defense area; blue rectangles indicate blue team device locations, while red rectangles represent red team UAV positions. Figure 5b shows the \begin{table}
\begin{tabular}{c c c} \hline \hline
**System Parameters** & **Numerical Settings** & **Description** \\ \hline \(X\times Y\) & \(1000\times 1000\) (m\({}^{2}\)) & map size \\ \(\Delta\phi_{\max}\) & \(\pi\) (rad/step) & the maximum angle change per step \\ \(\Delta v_{\max}\) & \(1\) (m/step) & the maximum speed change per step \\ \(m\) & \(3\) & number of red UAVs \\ \(n\) & \(1\) & number of blue UAVs \\ _episodes_ & \(1000\) & number of training episodes \\ \(T\) & \(100\) & time steps per round \\ \(e\) & \(1.0\) & greedy factor \\ \(\epsilon_{\min}\) & \(0.1\) & minimum greedy factor \\ \(\epsilon_{\mathrm{decay}}\) & \(0.99\) & greedy factor decay rate \\ \(\alpha\) & \(0.0001\) & actor learning rate \\ \(\beta\) & \(0.0005\) & critic learning rate \\ \(\epsilon_{\mathrm{clip}}\) & \(0.2\) & clip ratio \\ \(d_{max}\) & \(200\) (m) & maximum detection distance \\ \(\gamma\) & \(0.9\) & discount factor \\ \(r_{p}\) & \(0.5\) & perception reward \\ \(r_{fp}\) & \(0.5\) & first perception reward \\ \(r_{\mathrm{pos}}\) & \(0.5\) & three-point localization reward \\ \(p_{p}\) & \(-0.2\) & penalty for non-detection \\ \(p_{ff}\) & \(-0.2\) & penalty for inconsistent action \\ \(p_{c}\) & \(-0.2\) & after detection \\ \(D\) & \(1\) (m) & safe distance \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Hyperparameter setting. red team's perception and detection map, where UAVs achieve flexible detection through movement and orientation, with fan-shaped areas representing their sensing range. The red areas indicate where blue team UAVs are detected within the sensing range, while the orange areas show where blue team UAVs are not detected within the sensing range. Figure 5c provides an illustrative example of possible UAV trajectories in a multi-UAV scenario. The paths of four UAVs are shown, three red team UAVs and one blue team UAV.