# Enhancing Autonomous Driving Navigation Using Soft Actor-Critic
## 5 Experiments
### Training and Testing Results  
In this section, we present the performance of our model SAC compared to the DQN model. To show the learning performance in DRL, we often reward it as a standard criterion. Figure 5 shows the average reward of SAC, PPO, and DQN algorithms during training. We note that SAC converges faster than the PPO and DQN modes and achieves a high score of approximately 91.9. However, PPO achieves around 58.4, and its curve is not very similar to the curve of SAC. Furthermore, we notice that DQN is very low in terms of convergence and achieves only a 36.6 score, which shows the superiority of our model SAC compared to the two preceding models. We employ the average success rate as the main assessment metric in order to fairly assess the performance of the suggested framework in various scenarios, and each scenario depends on the number of AVs spawned in our environment. We have selected four specific densities for evaluation as follows: \(\textbf{AVs}=120\), \(\textbf{AVs}=140\), \(\textbf{AVs}=160\), and \(\textbf{AVs}=210\). Throughout the testing phase, we employ extra metrics, such as the collision rate, average time, average speed, average traveled distance, average steps, and average reward. * **Success rate (Succ %)**: During testing, we test our model for 50 episodes, and we compute the percentage of episodes in which the AV successfully reaches its destination. * **Collision rate ( Coll %)**: It computes the percentage of episodes when the AV collides with other vehicles. It is considered critical safety performance. * **Average time (Avg. time)**: It counts the time it takes for the AV to reach its destination in each episode. We computed the average time of 50 episodes. * **Average speed (Avg. Speed)**: It computes the average speed of the AV during episodes. Figure 4: (**a**) The AV gracefully enters the bustling roundabout, navigating through the presence of other vehicles, including motorcycles and bicycles, with precision. (**b**) The AV dutifully adheres to the meandering blue curve, faithfully following its path until it arrives at its intended destination. * **Average Traveled Distance (Avg. Dis)**: is the distance traveled by the AV from its start location to its destination. * **Average Steps (Avg. Steps)**: the number of steps that it takes for the AV to attain its destination. Table 3 illustrates the quantitative comparison between the SAC, PPO, and DQN models according to the previous metrics. We remark that the SAC model achieved the best results compared to the PPO and DQN models in all metrics. Despite the high density of AVs in the fourth scenario, we notice that SAC attains a 94% success rate, demonstrating the effectiveness of SAC compared to PPO and DQN, which reach 80% and 52% success rates, respectively. Moreover, SAC shows superior performance in controlling AVs to reach their destinations quickly, as shown by the average number of steps and average time spent in the four densities. Furthermore, the collision rate is very low, at 2% in the first three densities and 6% in the last density, indicating that SAC successfully avoids collisions with other vehicles, ensuring safety on the road. In contrast, PPO and DQN have a high number of crashes with vehicles, especially in the last density where the number of AVs is very high. Additionally, our model shows that the AV's traveled distance from its start location to its destination is around 32 m in all directions, which demonstrates that our approach is successful in finding the optimal path to the destination. However, PPO and DQN achieve around 33 m and 34 m in the traveled distance, respectively.