## 3 Hyperparameter Tuning and Performance Evaluation Metrics  
Previous studies have analyzed the parameter sensitivity in two ways. The first way is that the unsureness connected with the input parameter sensitivity has been propagated over the model. This process has a big impact on the general output change. Secondly, there has been the most correlation between the model output and the input parameter. In other words, the little changes in the input parameter led to noteworthy changes in the model output [41]. In this work, we used the first approach to analyze the parameter sensitivity for the DRL method. This means that PPO algorithm tries to address this by only making small updates to the model in an updated step through the back-propagation gradients, thereby stabilizing the DRL process. The sensitivity analysis in the DRL paradigm is a complex process through the black box.  
Figure 4: A typical observation in the urban network.  
PPO hyperparameter tuning is a principal means of selecting the proper variables for an optimal DRL method architecture in an urban network. In particular, the time horizon per training iteration is measured as a multiplication of the time horizon and the number of rollouts. The time horizon value is 1500 for a single rollout. The number of rollouts value is 4. Hence, the time horizon value is 6000 for a training iteration. The number of hidden layers affects the training accuracy and performance. The training accuracy is higher and the performance is lower with a higher hidden layer. The \"256 \(\times\) 256 \(\times\) 256\" indicates that we configured three hidden layers, and each layer consists of 256 neurons. GAE lambda (\(\lambda\)) defines the smoothing rate that estimates the weights of different bootstrap lengths to ensure a stable training process. The advantage function will be cut when the probability ratio between the old and updated policies is outside the variation between \((1-\epsilon)\) and \((1+\epsilon)\). The clip parameter value is 0.2. The step size is sensitive to obtain good results. The training process with a smaller value is short. The number of SGD iterations represents the number of SGD epochs per optimization round. In this work, a set of PPO hyperparameters is proposed for mixed-traffic conditions in the urban network shown in Table 2. Furthermore, the DRL agents outperformed the iteration value of 200.  
In our experiments, the performance of the DRL policy was proved by the average reward curve. A flattening of the average reward curve shows that the DRL policy totally converged. In the SUMO simulator, emissions and fuel consumption rely on the handbook emission factors for road transport (HBEFA). These values are designed as a timeline of speeds/accelerations for a single vehicle. Importantly, the measures of effectiveness (MOE), which can forecast and solve traffic problems, is applied to verify the performance of this method as follows:  
* Average speed: the mean velocity values of entire vehicles in the urban network.
* Fuel consumption: the mean fuel consumption values of entire vehicles in the urban network.
* Emissions: the mean emission values of entire vehicles in the urban network--namely, nitrogen oxide (Nox) and hydrocarbons (HC).