## 7 Experiment
### Results and Analysis
The magnitude of the \(r_{collision}\) reward weight is a notable discrepancy. Our manually designed weights heavily penalize collisions to encourage safe driving policies. However, the weights learned through preference learning for avoiding collisions are significantly smaller. Our experiments revealed that increasing the collision penalty can lead to unintended behaviors, such as the vehicle rotating in place or becoming stationary, which do not align with our goals. Moreover, the weight assigned to \(r_{time}\) remains small in the active preference learning framework. The policy we derived through this method is significantly more effective than those designed by humans. While engineered rewards are intended to align with human cognitive processes, they often miss the complex interactions between incentives and may not accurately represent inherent human desires. Consequently, sub-incentives' emergence adds further intricacies, making it difficult for humans to create rewards that truly resonate with an agent's cognitive processes. We were organized into four groups to conduct experimental investigations into the effects of active preference learning on reinforcement learning algorithms' policies. The results of these experiments are depicted in Figure 8 and presented in Tables 3 and 4. The algorithms examined included DRLNDT + APRL, DRLNDT [3], SAC + APRL, and SAC. The DRLNDT algorithm's performance was compared against other methods using baseline comparisons and was found to be superior among the compared algorithms. The results suggest that policies not employing the reward function derived from active preference learning were notably less effective than those utilizing manually generated rewards. A comparative analysis between the SAC and DRLNDT algorithms suggests that these approaches have limited efficacy in policy learning, especially in environments with complex dynamics. Their utility seems to be confined primarily to simpler navigational tasks. Moreover, in a contemporary setting where both the initial and terminal positions undergo random transformations, it was observed that manually designed reward structures failed to provide the necessary reinforcement for cultivating a robust policy, as evidenced by an inadequate ratio of successful to unsuccessful trajectories. An enhanced strategy involved integrating SAC with APRL. Our findings indicate that active preference learning benefits stronger models and can uplift the performance of weaker ones. Occasionally, SAC would learn suboptimal trajectories due to its intrinsic limitations, leading to a higher \(mean\_length\) parameter than the DRLNDT + APRL findings. When assessing algorithmic performance, our primary metric was the \(success\_rate\), which offers insights into an algorithm's efficacy and reliability. Additionally, the \(mean\_reward\) was considered as a secondary measure for comparison. Upon reviewing Table 4, it was evident that DRLNDT + APRL exhibited the highest success rate. The data in the table corresponds to the reward evaluation of the model that yielded the highest rewards from our training iterations. As a pragmatic approach, we designated the model with the highest rewards as optimal. The experimental outcomes suggest that the reward function crafted through active preference is significantly more advantageous than its manually designed counterpart, aligning well with human intuition. However, as environmental complexity increases, the manually created incentives prove insufficient, leading to a degradation in policy performance. Furthermore, we analyzed the response times of the various algorithms. The unit for response time is in seconds (s), calculated using the Python framework. Response time indicates an algorithm's decision-making capabilities and ability to manage unexpected situations; shorter response times are critical for ensuring the safety of autonomous driving in emergency scenarios.