## 4 Computational Experiments
### Ablation Experiments  
DRLLA consists of two main parts: the neural network and the training algorithm. In the proposed neural network, a multi-head self-attention mechanism is introduced to the static encoder for extracting task features, and the local attention mechanism is applied to decoding for constructing the scheduling solutions. In the training algorithm, the adaptive learning rate strategy is adopted to improve the training effect. In this subsection, ablation experiments are performed to further validate their effectiveness.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Datasets** & **Scales** & **Indicators** & **DRLLA** & **TRFM** & **PtrNet** & **RLPT** & **RLGA** & **ISA** & **IACO** \\ \hline \multirow{8}{*}{_Testing \(\_\_\)R2_} & 50-task & MPR & **1.0** & 0.983 & 0.959 & 0.491 & 0.792 & 0.835 & 0.532 \\  & & MCT (s) & 0.195 & 0.210 & 0.357 & **0.071** & 32.954 & 12.461 & 4.468 \\  & & MPR & **0.946** & 0.819 & 0.735 & 0.290 & 0.581 & 0.702 & 0.378 \\  & 100-task & MCT (s) & 0.429 & 0.446 & 0.376 & **0.149** & 57.148 & 34.163 & 13.647 \\  & & MPR & **0.783** & 0.668 & 0.558 & 0.224 & 0.475 & 0.547 & 0.310 \\  & 150-task & MCT (s) & 0.513 & 0.496 & 0.692 & **0.233** & 79.459 & 53.917 & 28.781 \\  & 200-task & MPR & **0.605** & 0.547 & 0.370 & 0.173 & 0.409 & 0.398 & 0.272 \\  & 250-task & MCT (s) & 0.605 & 0.714 & 0.859 & **0.337** & 99.515 & 71.086 & 49.661 \\  & 250-task & MPR & **0.513** & 0.476 & 0.274 & 0.143 & 0.367 & 0.307 & 0.245 \\  & 300-task & MCT (s) & 0.773 & 0.963 & 1.104 & **0.466** & 103.148 & 89.937 & 76.245 \\ \hline \multirow{8}{*}{_Testing \(\_\)R3_} & 50-task & MPR & **1.0** & 0.950 & 0.942 & 0.497 & 0.790 & 0.825 & 0.536 \\  & 100-task & MCT (s) & 0.192 & 0.219 & 0.324 & **0.071** & 33.821 & 12.998 & 4.207 \\  & 100-task & MPR & **0.927** & 0.790 & 0.757 & 0.298 & 0.575 & 0.697 & 0.376 \\  & 150-task & MCT (s) & 0.412 & 0.464 & 0.509 & **0.148** & 58.458 & 35.867 & 13.647 \\  & 150-task & MPR & **0.759** & 0.646 & 0.574 & 0.219 & 0.480 & 0.527 & 0.311 \\  & 150-task & MCT (s) & 0.482 & 0.521 & 0.692 & **0.234** & 73.412 & 54.157 & 26.482 \\  & 200-task & MPR & **0.652** & 0.533 & 0.343 & 0.172 & 0.408 & 0.417 & 0.271 \\  & 200-task & MCT (s) & 0.586 & 0.740 & 0.810 & **0.343** & 93.024 & 69.022 & 48.329 \\  & 250-task & MPR & **0.560** & 0.412 & 0.291 & 0.170 & 0.362 & 0.373 & 0.246 \\  & 300-task & MCT (s) & 0.750 & 0.958 & 1.065 & **0.464** & 105.293 & 81.362 & 79.694 \\ \hline \multirow{8}{*}{_Testing \(\_\)G_} & 50-task & MPR & **0.941** & 0.619 & 0.467 & 0.310 & 0.627 & 0.787 & 0.495 \\  & MCT (s) & 0.182 & 0.174 & 0.229 & **0.074** & 31.890 & 11.736 & 4.207 \\ \cline{1-1}  & 100-task & MPR & **0.738** & 0.526 & 0.308 & 0.261 & 0.505 & 0.648 & 0.328 \\ \cline{1-1}  & 150-task & MCT (s) & 0.239 & 0.451 & 0.441 & **0.179** & 47.403 & 30.683 & 14.383 \\ \cline{1-1}  & 150-task & MPR & **0.668** & 0.460 & 0.263 & 0.195 & 0.406 & 0.587 & 0.265 \\ \cline{1-1}  & 150-task & MCT (s) & 0.388 & 0.694 & 0.935 & **0.290** & 66.627 & 47.226 & 30.895 \\ \cline{1-1}  & 200-task & MPR & **0.615** & 0.420 & 0.243 & 0.151 & 0.352 & 0.445 & 0.232 \\ \cline{1-1}  & 200-task & MCT (s) & 0.491 & 0.825 & 1.116 & **0.395** & 87.145 & 58.114 & 49.884 \\ \cline{1-1}  & 250-task & MPR & **0.572** & 0.390 & 0.232 & 0.125 & 0.311 & 0.312 & 0.211 \\ \cline{1-1}  & 150-task & MCT (s) & 0.722 & 1.002 & 1.236 & **0.574** & 112.460 & 71.429 & 70.982 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Testing results on the datasets _Testing_R2_, _Testing_R3_, and _Testing_G_.