## 3 Experimental Results
### Results  
In this section, the results of the DQN algorithm trained using three different scenarios are analyzed. Subsequently, a comparison will be made with the other three algorithms run using the same scenarios. Overall, successful results were obtained for all four methods compared in the study. The training was conducted over 2000 steps. Graphs depicting the average reward, fuel consumption and emission values obtained from the training sessions of the DQN algorithm based on route characteristics for the first scenario are shown in Figure 4.  
The graph presented in Figure 4 shows that the DQN algorithm achieved learning in a short period of time. Fuel consumption and emission values were obtained and collected at each step using the traci library in a simulation environment during training. The average reward swiftly reached its maximum, indicating the successful completion of learning and the minimization of fuel consumption. The emission values also reached their minimum levels in a short time. Although the average fuel consumption and average emission release are different in terms of their values, they appear identical graphically. This is due to the emission release being directly derived from the amount of fuel consumed. This result demonstrates that learning was accomplished in alignment with the objective. For the second scenario, the route used, the characteristics of the route and the graph of the values obtained as a result of the training study conducted using this route are shown in Figure 5.  
The route depicted in Figure 5 is a total of 8700 m in length and includes 10 traffic lights. Figure 5 shows that learning was successfully accomplished in alignment with the objective. The data obtained show that the average reward was maximized, while the average fuel consumption and average emission values were minimized in accordance with the desired goals. This indicates that positive results were achieved in terms of ensuring optimum speed on the determined route and reducing environmental impacts through the use of the DQN algorithm, a method of DRL. These data demonstrate the effectiveness of adjustments made on a specific route in optimizing traffic flow and reducing environmental  
Figure 4: For the first scenario, (**a**) route properties (**b**) DQN algorithm rewards, fuel consumption and emission values with respect to the number of steps. impacts. The third route used in the study, the parameters of this route and the graph obtained from the training study conducted using this route are shown in Figure 6.  
The graph presented in Figure 6 shows that the model successfully executed the learning process. This graph clearly demonstrates successful performance in maximizing the reward while simultaneously minimizing fuel consumption and emission release. Figure 5: For the second scenario, (**a**) route properties (**b**) DQN algorithm rewards, fuel consumption and emission values with respect to the number of steps. Figure 6: For the third scenario, (**a**) route properties (**b**) DQN algorithm rewards, fuel consumption and emission values with respect to the number of steps. During the training process, popular DRL methods such as POP, A2C and DDQN, along with the DQN method, were applied across three different scenarios, and the results obtained were examined. This evaluation, which includes comparative results of the average reward, average fuel consumption and emission values obtained at the end of the training, is presented in Figure 6. These graphs will be used to visually compare and analyze the performance of DRL methods used under different scenarios. In this way, a comprehensive evaluation of the efficiencies and performances of different methods is intended. An examination of the graphs labeled a, b and c in Figure 7 clearly shows that the DQN method is significantly more successful than the other methods. It achieves its maximum reward in fewer than 1000 steps.