## 5 Simulation and Analysis
### Maneuvering Strategy Training and Testing
#### 5.2.3. PRED-RS-MADDPG Strategy vs. RS-MADDPG Strategy  
It can be proved that the PRED-RS-MADDPG strategy can outperform the script strategies with a great advantage, thereby indicating that the strategy obtained using the PRED-RS-MADDPG algorithm is highly effective. In this section, to test whether the intelligence of the strategy has improved after the introduction of target prediction, air combat confrontation experiments using the PRED-RS-MADDPG strategy vs. the RS-MADDPG strategy were designed and performed. To ensure that objectivity is maintained, the performance parameters of both the UCAVs were kept the same; both used the \"advantage\" parameters. Subsequently, two fair air combat situations were selected as the initial air combat situations: opposite situation and neutral situation. Figures 11 and 12 show the fly trajectory and the change in air combat situation of the 1-vs.-1 WVR air combat strategy obtained using the PRED-RS-MADDPG algorithm against the RS-MADDPG strategy in the opposite and the neutral initial situation, respectively. In Figures 11 and 12, the blue line is the fly trajectory of the UCAV using PRED-RS-MADDPG strategy, and the red line is the fly trajectory of the UCAV using RS-MADDPG strategy. In Figure 11, UCAV\({}_{1}\) took a left-turn action to perform a tactical evasion at the beginning of the air combat; UCAV\({}_{2}\) adopted a strategy of confronting the enemy at the beginning of the air combat. After 20 s, UCAV\({}_{1}\) was in a defensive situation. UCAV\({}_{1}\) kept turning left between 0 to 40 s. When UCAV\({}_{2}\) was beyond the 3 o'clock line of UCAV\({}_{1}\), UCAV\({}_{1}\) immediately turned right until it locked onto the target. During this period, UCAV\({}_{2}\) changed from attack to defense mode after 20 s.  
In Figure 12, UCAV\({}_{1}\) and UCAV\({}_{2}\), respectively, turn left and right to attack each other. At 20 s, if UCAV\({}_{1}\) continues to turn left, then UCAV\({}_{2}\) can track UCAV\({}_{1}\) steadily by accelerating the left turn, so that UCAV\({}_{2}\) will win. However, in 20 s, UCAV\({}_{1}\) predicted that UCAV\({}_{2}\) would turn left to put itself in a defensive situation, so it performed a large maneuver to turn right, forcing UCAV\({}_{2}\) to also turn right. Afterwards, at 40 s UCAV\({}_{1}\) entered UCAV\({}_{2}\)'s turning circle and successfully locked onto UCAV\({}_{2}\). Through the aforementioned analysis, we concluded that, even if UCAV\({}_{1}\) and UCAV\({}_{2}\) are in an absolutely equal air combat situation, UCAV\({}_{1}\) can still win, which shows that the introduction of the target position prediction can, in fact, effectively improve the intelligence of air combat strategies. An initial air combat situation was randomly generated and multiple simulations were used to determine the intercept time \(T_{I}\), defensive time, offensive time \(T_{O}\), and the probability of winning \(P_{W}\) in order to quantitatively analyze the degree of improvement. Consequently, initial opposite situations, initial offensive situations, initial defensive situations, and initial neutral situations, were obtained, each a 100 times. The intercept time obtained in the simulation of the winning air combat was noted, because, if the air combat fails, then the intercept time is infinite. A comparison of the intercept time \(T_{I}\), defensive time, and offensive time \(T_{O}\) of the air combat simulations between the PRED-RS-MADDPG strategy and the RS-MADDPG strategy is depicted in Figure 13, and the probabilities of winning \(P_{W}\) as per the results of the air combat simulations are presented in Table 3.  
Figure 13: Intercept time \(T_{I}\), defensive time, and offensive time \(T_{O}\) of the air combat simulations of PRED-RS-MADDPG strategy versus RS-MADDPG strategy.