SQT-ITD3 Experimental Dataset

The proposed method is rigorously evaluated against several state-of-the-art algorithms, including ISAC, ITD3, IPPO, and IDDPG, to ensure a comprehensive performance analysis. The experimental data, which is publicly available [here], provides detailed insights into the training and evaluation processes of each algorithm. The dataset encompasses a wide range of performance metrics, such as reward values, success rates, number of collisions, number of timeouts, and completion times, among others. These metrics are critical for assessing the robustness, efficiency, and reliability of the algorithms in various scenarios.

By systematically comparing the experimental data, the proposed method demonstrates significant improvements in key performance indicators. For instance, it achieves higher reward values and success rates while minimizing collisions and timeouts, indicating superior learning and decision-making capabilities. Additionally, the completion times are notably reduced, highlighting the algorithm's efficiency in achieving objectives. These results collectively validate the effectiveness and superiority of the proposed method over the existing state-of-the-art algorithms. Furthermore, the transparency of the experimental data allows for reproducibility and facilitates further research and development in the field.